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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/30242/genome-assembly-tools-and-software-part1</guid>
	<pubDate>Mon, 19 Dec 2016 18:09:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/30242/genome-assembly-tools-and-software-part1</link>
	<title><![CDATA[Genome Assembly Tools and Software - PART1 !!]]></title>
	<description><![CDATA[<p>The genome assemblers generally take a file of short sequence reads and a file of quality-value as the input. Since the quality-value file for the high throughput short reads is usually highly memory-intensive, only a few assemblers, best suited for your assembly. For the sake of computational memory saving and convenience of data inquiry, high-throughput short reads data is always initially formatted to specific data structure. Currently, existing data structure for this usage can be predominantly classified into two categories: string-based model and graph-based model.</p><p>We therefore list many genomle assembly tools here. We mainly reported for the assembly of genomes while the others are designed aiming at handling complex genomes.</p><ul>
<li><a href="http://microbiology.se/software/trimetass/" title="TriMetAss 1.2 &ndash; The Trinity-based Iterative Metagenomics Assembler">TriMetAss 1.2 &ndash; The Trinity-based Iterative Metagenomics Assembler</a>
<ul>
<li>TriMetAss is an extension to the Trinity software [1], which can assemble select regions surrounding interesting features in metagenomic data. The software is particularly useful for very common and well-conserved genes (and &ndash; in theory &ndash; non-coding regions) that can occur in multiple contexts in the microbial community under study. It uses Vmatch [2] to extend seed reads (or contigs generated by another assembler) into longer contigs, by iteratively calling Vmatch and Trinity, until some stop criteria are met. Currently, TriMetAss lacks a thorough documentation, but you can direct questions to me if the README.txt file and the &ldquo;-h&rdquo; option is not sufficient to understand the software.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/sharpa/OMWare" title="OMWare 1.0 &ndash; Efficient Assembly of Genome-wide Physical Maps">OMWare 1.0 &ndash; Efficient Assembly of Genome-wide Physical Maps</a>
<ul>
<li>
<p>The purpose of this Python module is help scientists use optical map data.<br />Once complete, it will encapsulate and abstractify optical maps and their most common manipulations as they exist in a variety of formats.</p>
</li>
</ul>
</li>
<li><a href="https://github.com/SaraEl-Metwally/LightAssembler" title="LightAssembler &ndash; Lightweight Resources Assembly Algorithm">LightAssembler &ndash; Lightweight Resources Assembly Algorithm</a>
<ul>
<li>
<p>Lightweight resources assembly algorithm for high-throughput sequencing reads.<br />System requirements<br />64-bit machine with g++ compiler or gcc in general, pthreads,and zlib libraries.</p>
</li>
</ul>
</li>
<li><a href="http://bioinf.spbau.ru/quast" title="QUAST 4.1 &ndash; Quality Assessment Tool for Genome Assemblies">QUAST 4.1 &ndash; Quality Assessment Tool for Genome Assemblies</a>
<ul>
<li>
<p>QUAST evaluates genome assemblies.<br />QUAST works both with and without a reference genome.&nbsp;<br />The tool accepts multiple assemblies, thus is suitable for comparison.</p>
</li>
</ul>
</li>
<li><a href="http://www.dnabaser.com/index.html" title="DNA Baser 4.36 &ndash; DNA Sequence Assembly &amp; Analysis">DNA Baser 4.36 &ndash; DNA Sequence Assembly &amp; Analysis</a>
<ul>
<li>DNA Sequence Assembler is revolutionary bioinformatics software for automatic DNA sequence assembly , DNA sequence analysis, contig editing, file format conversion and mutation detection.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/younglululu/COCACOLA" title="COCACOLA &ndash; Binning Metagenomic Contigs using Sequence COmposition, Read CoverAge, CO-alignment, and Paired-end Read LinkAge">COCACOLA &ndash; Binning Metagenomic Contigs using Sequence COmposition, Read CoverAge, CO-alignment, and Paired-end Read LinkAge<br /></a>
<ul>
<li>COCACOLA: a general framework for binning contigs in metagenomic studies incorporating read COverage, CorrelAtion, sequence COmposition and paired-end read LinkAge<br /><br /></li>
</ul>
</li>
<li><a href="http://downloads.jbei.org/data/microbial_communities/MaxBin/MaxBin.html" title="MaxBin 2.2 &ndash; Binning Assembled Metagenomic Sequences">MaxBin 2.2 &ndash; Binning Assembled Metagenomic Sequences</a>
<ul>
<li>MaxBin is software for binning assembled metagenomic sequences based on an Expectation-Maximization algorithm. Users can understand the underlying bins (genomes) of the microbes in their metagenomes by simply providing assembled metagenomic sequences and the reads coverage information or sequencing reads.&nbsp;<br /><br /></li>
</ul>
</li>
<li><a href="http://compbio.fmph.uniba.sk/gaml/" title="GAML 0.1 &ndash; Genome Assembly by Maximum Likelihood">GAML 0.1 &ndash; Genome Assembly by Maximum Likelihood<br /></a>
<ul>
<li>GAML is a prototype genome assembly tool based on maximizing likelihood of the assembly in a model encompaasing error rate, insert length and other features of indvidual sequencing technologies. It can combine datasets produced by different technologies (currently Illumina, 454 and Pacific Biosciences).<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/kkrizanovic/NanoMark" title="NanoMark &ndash; DNA Assembly Benchmark for Nanopore long reads">NanoMark &ndash; DNA Assembly Benchmark for Nanopore long reads</a>
<ul>
<li>
<p>DNA Assembly Benchmark for Nanopore long reads<br />A system for benchmarking DNA assembly tools, based on 3rd generation sequencers.</p>
</li>
</ul>
</li>
<li><a href="http://ibest.github.io/ARC/" title="ARC 1.1.4-beta &ndash; Assembly by Reduced Complexity">ARC 1.1.4-beta &ndash; Assembly by Reduced Complexity</a>
<ul>
<li>
<p>ARC is a pipeline which facilitates iterative, reference guided de novo assemblies with the intent of:&nbsp;<br />1.Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.<br />2.Reducing/removing reference bias as compared to mapping based approaches.</p>
</li>
</ul>
</li>
<li><a href="https://bioinformatics.cs.vt.edu/zhanglab/software/transps/" title="TransPS 1.1.0 &ndash; Transcriptome Post Scaffolding">TransPS 1.1.0 &ndash; Transcriptome Post Scaffolding</a>
<ul>
<li>TransPS is a pipeline for post-processing of pre-assembled transcriptomes using reference based method. It applies an align-layout-consensus structure, consisting of three major stages. First, query sequences are aligned with a reference genome. Second, query sequences are ordered based on the alignment to the reference. Third, non-redundant sequences matched to the same gene of reference genome are scaffolded into one contig.&nbsp;<br /><br /></li>
</ul>
</li>
<li><a href="http://andersonlab.qb3.berkeley.edu/#/software" title="assemblyManager &ndash; Computing the Robotic Commands for 2ab Assembly">assemblyManager &ndash; Computing the Robotic Commands for 2ab Assembly</a>
<ul>
<li>Clotho provides persistence to such objects through relational databases that at least partially correspond the Clotho data model. Beyond database access and data model API support, Clotho Apps provide more specific functionality to Clotho such as viewing and editing data, running simulations, and automating various tasks. When thinking about Clotho Apps, an appropriate analogy would be Apps running on the Android operating system rather than the add-ons that extend the functionality of Firefox<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/transcriptomeassembly/" title="BinPacker 1.1 &ndash; Packing-Based De Novo Transcriptome Assembly from RNA-seq Data">BinPacker 1.1 &ndash; Packing-Based De Novo Transcriptome Assembly from RNA-seq Data</a>
<ul>
<li>BinPacker is a novel de novo assembler by modeling the transcriptome assembly problem as tracking a set of trajectories of items with their sizes representing coverage of their corresponding isoforms by solving a series of bin-packing problems<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/lh3/fermikit" title="FermiKit 0.13 &ndash; De novo Assembly based Variant Calling pipeline for Illumina Short Reads">FermiKit 0.13 &ndash; De novo Assembly based Variant Calling pipeline for Illumina Short Reads</a>
<ul>
<li>FermiKit is a&nbsp;<em>de novo</em>&nbsp;assembly based variant calling pipeline for deep Illumina resequencing data. It assembles reads into unitigs, maps them to the reference genome and then calls variants from the alignment to an accuracy comparable to conventional mapping based pipelines (see evaluation in the&nbsp;<code>tex</code>&nbsp;directory). The assembly does not only encode SNPs and short INDELs, but also retains long deletions, novel sequence insertions, translocations and copy numbers<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/Reedwarbler/REPdenovo" title="REPdenovo &ndash; A tool to Construct Repeats directly from Raw Reads">REPdenovo &ndash; A tool to Construct Repeats directly from Raw Reads</a>
<ul>
<li>
<p>REPdenovo is designed for constructing repeats directly from sequence reads. It based on the idea of frequent k-mer assembly. REPdenovo provides many functionalities, and can generate much longer repeats than existing tools. The overall pipeline is shown in the mannual file. REPdenovo supports the following main functionalities.<br />1.Assembly. This step performs k-mer counting. Then we find frequent k-mers whose frequencies are over certain threshold. We then assemble these frequent k-mers into consensus repeats (in the form of contigs). Then we merge the constructed contigs to more completeness ones.<br />2.Scaffolding. We use paired-end reads to connect repeat contigs into scaffolds, also provide the average coverage (indicates the copy number) for each constructed repeats.</p>
</li>
</ul>
</li>
<li><a href="https://github.com/rdpstaff/Xander_assembler" title="Xander &ndash; Gene-targeted Metagenomic Assembler">Xander &ndash; Gene-targeted Metagenomic Assembler</a>
<ul>
<li>Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. We present a novel method for targeting assembly of specific protein-coding genes using a graph structure combining both de Bruijn graphs and protein HMMs. The inclusion of HMM information guides the assembly, with concomitant gene annotation.&nbsp;<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/swapassembler/" title="SWAP-Assembler 2 &ndash; A scalable and fully parallelized Genome Assembler">SWAP-Assembler 2 &ndash; A scalable and fully parallelized Genome Assembler</a>
<ul>
<li>There is a growing gap between the output of new generation massively parallel sequencing machines and the ability to process and analyze the sequencing data. We present SWAP-Assembler, a scalable and fully parallelized genome assembler designed for massive sequencing data. Intend of using traditional de Bruijn Graph, SWAP-Assembler adopts multi-step bi-directed graph (MSG). With MSG, the standard genome assembly (SGA) is equivalent to the edge merging operations in a semi-group. Then a computation model, SWAP, is designed to parallelize semi-group computation. Experimental results showed that SWAP-Assembler is the fastest and most efficient assemblers ever, it can generated contigs with highest accuracy over all five selected assemblers and longest contig N50 in all selected parallel assemblers. Specially, in the scalability test, SWAP-Assembler can scales up to 1024 cores when processing Fish and Yanhuang dataset, and finishes the assembly work in only 15 and 29 minutes respecitively<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/ksanao/TGNet" title="TGNet &ndash; Visualization and Quality Assessment of de novo Genome Assemblies">TGNet &ndash; Visualization and Quality Assessment of de novo Genome Assemblies</a>
<ul>
<li>TGNet is a Cytoscape-based tool for visualization and quality assessment of de novo genome assemblies. Specifically it facilitates rapid detection of inconsistencies between a genome assembly and an independently derived transcriptome assembly.<br /><br /></li>
</ul>
</li>
<li><a href="http://sanger-pathogens.github.io/circlator/" title="Circlator 1.1.3 &ndash; A tool to Circularize Genome Assemblies">Circlator 1.1.3 &ndash; A tool to Circularize Genome Assemblies</a>
<ul>
<li>A tool to circularize genome assemblies. The algorithm and benchmarks are described in the&nbsp;<a href="http://www.genomebiology.com/2015/16/1/294">Genome Biology manuscript</a>.&nbsp;<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/hitbio/misFinder" title="misFinder v0.4.05.05 &ndash; Identify Mis-assemblies in an unbiased manner using Reference and Paired-end Reads">misFinder v0.4.05.05 &ndash; Identify Mis-assemblies in an unbiased manner using Reference and Paired-end Reads</a>
<ul>
<li>misFinder is a tool that aims to identify the assembly errors with high accuracy in an unbiased way and correct these errors at their mis-assembled positions to improve the assembly accuracy for downstream analysis. It combines the information of reference (or close related reference) genome and aligned paired-end reads to the assembled sequence. Structure variation and mis-assembly can be detected by comparing the reference genome and assembled sequence.<br /><br /></li>
</ul>
</li>
<li><a href="http://edwards.sdsu.edu/scaffold_builder/" title="Scaffold_builder v2.2 &ndash; Order Contigs generated by draft sequencing along a Reference Sequence">Scaffold_builder v2.2 &ndash; Order Contigs generated by draft sequencing along a Reference Sequence</a>
<ul>
<li>The abundance of repeat elements in genomes can impede the assembly of a single sequence. The tool Scaffold_builder was designed to generate scaffolds (super contigs of sequences joined by N-bases) using the homology provided by a closely related reference sequence. Scaffold_builder is an advanced wrapper for Nucmer, written in Python that resolves several situations that may arise when mapping contigs to the reference genome.<br /><br /></li>
</ul>
</li>
<li><a href="https://sites.google.com/a/lbl.gov/rnnotator/" title="Rnnotator 3.5.0 &ndash; de novo Transcriptome Assembly pipeline from stranded RNA-Seq reads">Rnnotator 3.5.0 &ndash; de novo Transcriptome Assembly pipeline from stranded RNA-Seq reads</a>
<ul>
<li>Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. Rnnotator is an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. The contigs produced by Rnnotator are highly accurate and reconstruct full-length genes when transcripts are sequenced sufficiently deep, roughly 30X for a given transcript. Rnnotator was designed to assemble Illumina single or paired-end reads. Rnnotator is also able to incorporate strand-specific RNA-Seq reads into the assembly in order to further improve the assembly.<br /><br /></li>
</ul>
</li>
<li><a href="http://satrap.cribi.unipd.it/cgi-bin/satrap.pl" title="SATRAP 0.2 &ndash; SOLiD Assembler TRAnslation Program">SATRAP 0.2 &ndash; SOLiD Assembler TRAnslation Program</a>
<ul>
<li>
<p>A color space assembly must be translated into bases before applying bioinformatics analyses. SATRAP is designed to accomplish this important task adopting a very efficient strategy. The package integrates the Oases pipeline and several optimizations specifically designed for color space management. All steps of the pipeline allow to produce a SOLiD de novo transcriptome assembly and the subsequent color space translation. Alternatively, SATRAP can be used as a stand alone program to perform color space translation for either RNA-seq or DNA-seq SOLiD assemblies.</p>
</li>
</ul>
</li>
<li><a href="http://rrwick.github.io/Bandage/" title="Bandage v0.7.1 &ndash; Navigating De novo Assembly Graphs Easily">Bandage v0.7.1 &ndash; Navigating De novo Assembly Graphs Easily</a>
<ul>
<li>Bandage is a program for visualising de novo assembly graphs. By displaying connections which are not present in the contigs file, Bandage opens up new possibilities for analysing de novo assemblies.<br /><br /></li>
</ul>
</li>
<li><a href="http://hapcol.algolab.eu/" title="HapCol 1.1.1 &ndash; Haplotype Assembly from Long Gapless Reads">HapCol 1.1.1 &ndash; Haplotype Assembly from Long Gapless Reads</a>
<ul>
<li>A fast and memory-efficient method for haplotype assembly from long gapless reads, like those produced by SMRT sequencing technologies (PacBio RS II) and Oxford Nanopore flow cell technologies (MinION).<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/chengyuan/reago-1.1" title="REAGO 1.1 &ndash; REconstruct 16S ribosomal RNA Genes from MetagenOmic data">REAGO 1.1 &ndash; REconstruct 16S ribosomal RNA Genes from MetagenOmic data<br /></a>
<ul>
<li>an assembly tool for 16S ribosomal RNA recovery from metagenomic data<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bioinfo.ufpr.br/" title="FGAP 1.8.1 &ndash; Automated Gap Closing tool">FGAP 1.8.1 &ndash; Automated Gap Closing tool</a>
<ul>
<li>FGAP aims to improve genome sequences by merging alternative assemblies or incorporating alternative data, analyzing the gap region and indicating the best sequence to close the gap.<br /><br /></li>
</ul>
</li>
<li><a href="http://deweylab.biostat.wisc.edu/detonate/" title="DETONATE 1.10 &ndash; DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation">DETONATE 1.10 &ndash; DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation</a>
<ul>
<li>DETONATE consists of two component packages, RSEM-EVAL and REF-EVAL. Both packages are mainly intended to be used to evaluate de novo transcriptome assemblies, although REF-EVAL can be used to compare sets of any kinds of genomic sequences.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/trinityrnaseq/trinityrnaseq/wiki" title="Trinity 2.1.1 &ndash; RNA-Seq De novo Assembly">Trinity 2.1.1 &ndash; RNA-Seq De novo Assembly<br /></a>
<ul>
<li>Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/shenkers/isoscm" title="IsoSCM 2.0.11 &ndash; Transcript Assembly tool using Multiple Change-point Inference to improve 3&rsquo;UTR Annotation">IsoSCM 2.0.11 &ndash; Transcript Assembly tool using Multiple Change-point Inference to improve 3&rsquo;UTR Annotation</a>
<ul>
<li>IsoSCM (Isoform Structural Change Model) is a new method for transcript assembly &nbsp;that incorporates change-point analysis to improve the 3&prime; UTR annotation process.<br /><br /></li>
</ul>
</li>
<li><a href="http://sanger-pathogens.github.io/iva/" title="IVA 1.0.3 &ndash; Iterative Virus Assembler">IVA 1.0.3 &ndash; Iterative Virus Assembler</a>
<ul>
<li>IVA is a de novo assembler designed to assemble virus genomes that have no repeat sequences, using Illumina read pairs sequenced from mixed populations at extremely high and variable depth.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/spa-assembler/" title="SFA-SPA 0.2.1 &ndash; A Suffix Array based Short Peptide Assembler for Metagenomic Data">SFA-SPA 0.2.1 &ndash; A Suffix Array based Short Peptide Assembler for Metagenomic Data</a>
<ul>
<li>SFA-SPA is a suffix array based short peptide assembler for metagenomic data<br /><br /></li>
</ul>
</li>
<li><a href="http://www.earlham.ac.uk/rampart/" title="RAMPART 0.12.2 &ndash; A Workflow Management System for de novo Genome Assembly">RAMPART 0.12.2 &ndash; A Workflow Management System for de novo Genome Assembly</a>
<ul>
<li>RAMPART is a de novo assembly pipeline that makes use of third party-tools and High Performance Computing resources. It can be used as a single interface to several popular assemblers, and can perform automated comparison and analysis of any generated assemblies<br /><br /></li>
</ul>
</li>
<li><a href="http://wgs-assembler.sourceforge.net/wiki/index.php?title=Main_Page" title="Celera Assembler 8.3 &ndash; Whole Genome Shotgun Assembler">Celera Assembler 8.3 &ndash; Whole Genome Shotgun Assembler</a>
<ul>
<li>Celera Assembler (wgs-assembler) is scientific software for DNA research. It can reconstruct long sequences of genomic DNA given the fragmentary data produced by whole-genome shotgun sequencing. The Celera Assembler has enabled discovery in microbial genomes, large eukaryotic genomes, diploid genomes, and genomes from environmental samples. Celera Assembler contributed the first diploid sequence of an individual human, and metagenomics assemblies of the Global Ocean Sampling<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/ngopt/" title="A5-miseq 20150522 &ndash; de novo Assembly &amp; Analysis of Illumina Sequence data">A5-miseq 20150522 &ndash; de novo Assembly &amp; Analysis of Illumina Sequence data</a>
<ul>
<li>de novo assembly &amp; analysis of Illumina sequence data, including the A5 pipeline, A5-miseq, tools to evaluate assembly quality, and scripts to facilitate data submission to NCBI and the RAST annotation system<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bcgsc.ca/platform/bioinfo/software/trans-abyss" title="Trans-ABySS 1.5.3 &ndash; Analyze ABySS multi-k-assembled Shotgun Transcriptome Data.">Trans-ABySS 1.5.3 &ndash; Analyze ABySS multi-k-assembled Shotgun Transcriptome Data.</a>
<ul>
<li>Trans-ABySS is a software pipeline for analyzing ABySS-assembled contigs from shotgun transcriptome data. The pipeline accepts assemblies that were generated across a wide range of k values in order to address variable transcript expression levels. It first filters and merges the multi-k assemblies, generating a much smaller set of nonredundant contigs. It contains scripts that map assembled contigs to known transcripts, currently supporting Blat and Exonerate contig-to-genome aligners. It identifies novel splicing events like exon-skipping, novel exons, retained introns, novel introns, and alternative splicing. Its scripts can also estimate gene expression levels, identify candidate polyadenylation sites, and identify candidate gene-fusion events.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/sat-assembler/" title="SAT-Assembler 20160120 &ndash; Scalable and Accurate Targeted Gene Assembly Tool">SAT-Assembler 20160120 &ndash; Scalable and Accurate Targeted Gene Assembly Tool</a>
<ul>
<li>SAT-Assembler can perform targeted gene assembly for both RNA-Seq and metagenomic data. It addresses the above challenges of de novo assembly of large scale NGS data by conducting family-specic gene assembly, homology-guided overlap graph construction, and careful graph traversal.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/operasf/" title="Opera 2.0.2 &ndash; Sequence Assembly Program">Opera 2.0.2 &ndash; Sequence Assembly Program</a>
<ul>
<li>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program . It uses information from paired-end reads to optimally order and orient contigs assembled from shotgun-sequencing reads.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.genecodes.com/" title="Sequencher 5.4.1 &ndash; DNA Sequence Assembly and Analysis">Sequencher 5.4.1 &ndash; DNA Sequence Assembly and Analysis</a>
<ul>
<li>Sequencher is the industry standard software for DNA sequence analysis. It works with all automated sequencers and is widely known for its lightning-fast contig assembly, short learning curve, user-friendly editing tools, and superb technical support. First released almost 15 years ago, Sequencher is currently used for sequence analysis tasks in every major genomic and pharmaceutical company as well as numerous academic and government labs in over 40 countries around the world. Life Science researchers use Sequencher for many diverse DNA sequence analysis applications including de novo gene sequencing, mutation detection, forensic human identification, systematics, and more.<br /><br /></li>
</ul>
</li>
<li><a href="http://minia.genouest.org/" title="Minia 2.0.3 &ndash; Short-read Assembler based on a de Bruijn graph">Minia 2.0.3 &ndash; Short-read Assembler based on a de Bruijn graph</a>
<ul>
<li>Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day<br /><br /></li>
</ul>
</li>
<li><a href="http://www.genome.umd.edu/masurca.html" title="MaSuRCA 3.1.3 &ndash; Whole Genome Short Read Assembler">MaSuRCA 3.1.3 &ndash; Whole Genome Short Read Assembler</a>
<ul>
<li>MaSuRCA is whole genome assembly software. It combines the efficiency of the de Bruijn graph and Overlap-Layout-Consensus (OLC) approaches. MaSuRCA can assemble data sets containing only short reads from Illumina sequencing or a mixture of short reads and long reads (Sanger, 454).<br /><br /></li>
</ul>
</li>
<li><a href="http://kmergenie.bx.psu.edu/" title="KmerGenie 1.6982 &ndash; K-mer size Selection for Genome Assembly">KmerGenie 1.6982 &ndash; K-mer size Selection for Genome Assembly</a>
<ul>
<li>KmerGenie estimates the best k-mer length for genome de novo assembly. Given a set of reads, KmerGenie first computes the k-mer abundance histogram for many values of k. Then, for each value of k, it predicts the number of distinct genomic k-mers in the dataset, and returns the k-mer length which maximizes this number. Experiments show that KmerGenie&rsquo;s choices lead to assemblies that are close to the best possible over all k-mer lengths.<br /><br /></li>
</ul>
</li>
<li><a href="http://software.broadinstitute.org/software/pilon/" title="pilon v1.16 &ndash; Automated Assembly Improvement">pilon v1.16 &ndash; Automated Assembly Improvement</a>
<ul>
<li>pilon uses read alignment analysis to diagnose, report, and automatically improve de novo genome assemblies.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.phrap.org/phredphrapconsed.html#block_phrap" title="Phred/Phrap/Consed 29.0 &ndash; DNA Sequence Assembler &amp; Finishing Tools">Phred/Phrap/Consed 29.0 &ndash; DNA Sequence Assembler &amp; Finishing Tools<br /></a>
<ul>
<li>phrap is a program for assembling shotgun DNA sequence data. Among other features, it allows use of the entire read and not just the trimmed high quality part, it uses a combination of user-supplied and internally computed data quality information to improve assembly accuracy in the presence of repeats, it constructs the contig sequence as a mosaic of the highest quality read segments rather than a consensus, it provides extensive assembly information to assist in trouble-shooting assembly problems, and it handles large datasets.<br /><br /></li>
</ul>
</li>
<li><a href="https://www.qiagenbioinformatics.com/products/clc-genomics-workbench/" title="CLC Genomics Workbench 8.5.1 &ndash; Assembly &amp; Analysis of Sequencing Data">CLC Genomics Workbench 8.5.1 &ndash; Assembly &amp; Analysis of Sequencing Data</a>
<ul>
<li>CLC Genomics Workbench, for analyzing and visualizing Next Generation Sequencing data, incorporates cutting-edge technology and algorithms, while also supporting and integrating with the rest of your typical NGS workflow.<br /><br /></li>
</ul>
</li>
<li><a href="http://schatzlab.cshl.edu/research/metassembler/" title="Metassembler 1.5 &ndash; Combines multiple Whole Genome de novo Assemblies into a combined Consensus Assembly">Metassembler 1.5 &ndash; Combines multiple Whole Genome de novo Assemblies into a combined Consensus Assembly</a>
<ul>
<li>Metassembler is a software package for reconciling assemblies produced by de novo short-read assemblers such as SOAPdenovo and ALLPATHS-LG. The goal of assembly reconciliation, or &ldquo;metassembly,&rdquo; is to combine multiple assemblies into a single genome that is superior to all of its constituents<br /><br /></li>
</ul>
</li>
<li><a href="https://ics.hutton.ac.uk/tablet/" title="Tablet 1.15.09.01 &ndash; Next Generation Sequence Assembly Visualization">Tablet 1.15.09.01 &ndash; Next Generation Sequence Assembly Visualization</a>
<ul>
<li>Tablet is a lightweight, high-performance graphical viewer for next generation sequence assemblies and alignments.Supporting a range of input assembly formats, Tablet provides high-quality visualizations showing data in packed or stacked views, allowing instant access and navigation to any region of interest, and whole contig overviews and data summaries. Tablet is both multi-core aware and memory efficient, allowing it to handle assemblies containing millions of reads, even on a 32-bit desktop machine.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bcgsc.ca/platform/bioinfo/software/abyss" title="ABySS 1.9.0 &ndash; de novo, parallel, paired-end Sequence Assembler">ABySS 1.9.0 &ndash; de novo, parallel, paired-end Sequence Assembler</a>
<ul>
<li>ABySS (Assembly By Short Sequences) is a de novo, parallel, paired-end sequence assembler that is designed for short reads. The single-processor version is useful for assembling genomes up to 100 Mbases in size. The parallel version is implemented using MPI and is capable of assembling larger genomes.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bcgsc.ca/platform/bioinfo/software/cleat" title="CLEAT 2.0 &ndash; Identifies 3&prime; UTR Ends of Transcripts in de novo RNA-Seq Assemblies">CLEAT 2.0 &ndash; Identifies 3&prime; UTR Ends of Transcripts in de novo RNA-Seq Assemblies</a>
<ul>
<li>CLEAT is a post-processing tool for CLEavage site Analysis of Transcriptomes. CLEAT is designed to work on trans-ABySS output.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/ythuang0522/StriDe" title="StriDe &ndash; novel Assembler">StriDe &ndash; novel Assembler</a>
<ul>
<li>The StriDe Assembler integrates string and de Bruijn graph by decomposing reads within error-prone regions, while extending paire-end read into long reads for assembly through repetitive regions.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.sanger.ac.uk/science/tools/reapr" title="REAPR 1.0.18 &ndash; Genome Assembly Evaluation">REAPR 1.0.18 &ndash; Genome Assembly Evaluation</a>
<ul>
<li>REAPR (Recognising Errors in Assemblies using Paired Reads) is a tool that evaluates the accuracy of a genome assembly using mapped paired end reads, without the use of a reference genome for comparison. It can be used in any stage of an assembly pipeline to automatically break incorrect scaffolds and flag other errors in an assembly for manual inspection. It reports mis-assemblies and other warnings, and produces a new broken assembly based on the error calls.<br /><br /></li>
</ul>
</li>
<li><a href="https://www.baseclear.com/genomics/bioinformatics/basetools/gapfiller" title="GapFiller 1.10 &ndash; Close Gaps within Pre-assembled Scaffolds">GapFiller 1.10 &ndash; Close Gaps within Pre-assembled Scaffolds</a>
<ul>
<li>GapFiller is a stand-alone program for closing gaps within pre-assembled scaffolds. It is unique in offering the possibility to manually control the gapclosure process. By using the distance information of paired-read data, GapFiller seeks to close the gap from each edge in an iterative manner. From a good number of tests we see the program yields excellent results both on bacterial en eukaryotic &nbsp;datasets. The command-line Perl script and additional files van be downloaded below. The input data is given by pre-assembled scaffold sequences (FASTA) and NGS paired-read data (FASTA or FASTQ).<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bcgsc.ca/platform/bioinfo/software/ssake" title="SSAKE 3.8.4 &ndash; Assembling Millions of short DNA Sequences">SSAKE 3.8.4 &ndash; Assembling Millions of short DNA Sequences</a>
<ul>
<li>SSAKE is a genomics application for assembling millions of very short DNA sequences.SSAKE is designed to help leverage the information from short sequence reads by stringently assembling them into contiguous sequences that can be used to characterize novel sequencing targets.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/jts/sga" title="SGA 0.10.14 &ndash; String Graph Assembler">SGA 0.10.14 &ndash; String Graph Assembler</a>
<ul>
<li>SGA is a de novo assembler designed to assemble large genomes from high coverage short read data. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.<br /><br /></li>
</ul>
</li>
<li><a href="https://bibiserv.cebitec.uni-bielefeld.de/cgcat" title="r2cat &ndash; Synteny Plots &amp; Comparative Assembly">r2cat &ndash; Synteny Plots &amp; Comparative Assembly<br /></a>
<ul>
<li>r2cat (related reference based contig arrangement tool) can be used to order a set of contigs with respect to a single reference genome. This is done by mapping the contigs onto the reference using a q-gram filter. The mapping is visualized in a synteny plot.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.bcgsc.ca/platform/bioinfo/software/tasr" title="TASR 1.6 &ndash; Targeted Assembly of Sequence Reads">TASR 1.6 &ndash; Targeted Assembly of Sequence Reads</a>
<ul>
<li>TASR (Targeted Assembly of Sequence Reads) &nbsp;is a genomics application that allows hypothesis-based interrogation of genomic regions (sequence targets) of interest.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/bio-rainbow/" title="Rainbow v2.0.4 &ndash; Clustering and Assembling Short Reads, especially for RAD">Rainbow v2.0.4 &ndash; Clustering and Assembling Short Reads, especially for RAD</a>
<ul>
<li>Rainbow package consists of several programs used for RAD-seq related clustering and de novo assembly.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.sanger.ac.uk/science/tools/caf" title="CAFTOOLS 2.0.2 &ndash; Tools for the Common Assembly Format (CAF)">CAFTOOLS 2.0.2 &ndash; Tools for the Common Assembly Format (CAF)</a>
<ul>
<li>CAFTOOLS comprises a set of libraries and programs for manipulating DNA sequence assemblies using CAF files, a comprehensive representation of a sequence assembly as a text file.</li>
</ul>
</li>
<li>Gap Resolution &ndash; Improving Newbler Genome Assemblies. Gap Resolution was developed by DOE Joint Genome Institute to improve Newbler genome assemblies by automating the closure of sequence gaps caused by repetitive regions in the DNA.<br /><br /></li>
<li><a href="http://jgi.doe.gov/data-and-tools/meraculous/" title="Meraculous 2.0.5 &ndash; De novo Genome Assembler from Short Reads">Meraculous 2.0.5 &ndash; De novo Genome Assembler from Short Reads</a>
<ul>
<li>Meraculous is a new algorithm for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast Pichia stipitis.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/coperead/" title="COPE 1.2.5 &ndash; Pair-end Reads Connection tool to facilitate Genome Assembly">COPE 1.2.5 &ndash; Pair-end Reads Connection tool to facilitate Genome Assembly</a>
<ul>
<li>COPE (Connecting Overlapped Pair-End reads) is a method to align and connect the illumina sequenced Pair-End reads of which the insert size is smaller than the sum of the two read length.The connected reads can be used in genome assembly, resequencing and transcriptome research.<br /><br /></li>
</ul>
</li>
<li><a href="http://sco.h-its.org/exelixis/web/software/pear/index.html" title="PEAR 0.9.6 &ndash; Pair-End reads AssembleR">PEAR 0.9.6 &ndash; Pair-End reads AssembleR</a>
<ul>
<li>PEAR is an ultrafast, memory-efficient and highly accurate pair-end reads assembler. It is fully parallelized and can run with as low as just a few kilobytes of memory.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/ebardenovo/" title="EBARDenovo 2.0.1 &ndash; Highly-accurate de novo Assembler of Paired-end RNA-Seq">EBARDenovo 2.0.1 &ndash; Highly-accurate de novo Assembler of Paired-end RNA-Seq</a>
<ul>
<li>EBARDenovo is a highly-accurate search-based de novo assembler of paired-end RNA-Seq for advance transcriptomic study.<br /><br /></li>
</ul>
</li>
<li><a href="http://marthlab.org/" title="EagleView 2.2 &ndash; Genome Assembler Viewer">EagleView 2.2 &ndash; Genome Assembler Viewer</a>
<ul>
<li>EagleView is an information-rich genome assembler viewer with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations. It provides an easy way for inspecting visually the quality of a genome assembly and validating polymorphism candidate sites (e.g., SNPs) reported by polymorphism discovery tools. It can also facilitate data interpretation and hypothesis generation.<br /><br /></li>
</ul>
</li>
<li><a href="http://bioinformatics.tudelft.nl/" title="MAIA 0.5 &ndash; Integrating Genome Assemblies">MAIA 0.5 &ndash; Integrating Genome Assemblies</a>
<ul>
<li>
<p>MAIA (Multiple Assembly IntegrAtion) is an algorithm to integrate multiple genome assemblies. For example, assemblies originating from:<br />&ndash; Different runs of a de novo assembler<br />&ndash; Assemblies of different data types<br />&ndash; Comparative assemblies</p>
</li>
</ul>
</li>
<li><a href="http://cqb.pku.edu.cn/ZhuLab/InteMAP/index.html" title="InteMAP 1.0 &ndash; Integrated Metagenomic Assembly pipeline for NGS Short Reads">InteMAP 1.0 &ndash; Integrated Metagenomic Assembly pipeline for NGS Short Reads<br /></a>
<ul>
<li>InteMAP is a pipeline which integrates individual assemblers for assembling metagenomic short sequencing reads.<br /><br /></li>
</ul>
</li>
<li><a href="http://cqb.pku.edu.cn/ZhuLab/MAP/index.php" title="MAP 20121108 &ndash; A de novo Metagenomic Assembly program for Shotgun DNA reads">MAP 20121108 &ndash; A de novo Metagenomic Assembly program for Shotgun DNA reads</a>
<ul>
<li>MAP (Metagenomic Assembly program) is a de novo assembly approach and its implementation based on an improved Overlap/Layout/Consensus (OLC) strategy incorporated with several special algorithms.MAP uses the mate pair information, resulting in being more applicable to shotgun DNA reads (recommended as &gt; 200 bp) currently widely-used in metagenome projects. Results of extensive tests on simulated data show that MAP can be superior to both Celera and Phrap for typical longer reads by Sanger sequencing, as well as has an evident advantage over Celera, Newbler, and the newest Genovo, for typical shorter reads by 454 sequencing.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.sanger.ac.uk/science/tools/phusion" title="Phusion 2.1c &ndash; Assembly Genome Sequences from Whole Genome Shotgun(WGS) Reads">Phusion 2.1c &ndash; Assembly Genome Sequences from Whole Genome Shotgun(WGS) Reads</a>
<ul>
<li>Phusion is a software package for assembling genome sequences from whole genome shotgun(WGS) reads.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.codoncode.com/aligner/index.htm" title="CodonCode Aligner 6.0.2 &ndash; DNA Sequence Assembly &amp; Alignment">CodonCode Aligner 6.0.2 &ndash; DNA Sequence Assembly &amp; Alignment</a>
<ul>
<li>CodonCode Aligner is a program for sequence assembly, contig editing, and mutation detection, available for Windows and Mac OS X. Aligner is compatible with Phred-Phrap and fully supports sequence quality scores, while offering a familiar, easy-to-learn user interface.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/ceruleanassembler/" title="Cerulean 0.1.1 &ndash; Hybrid Genome Assembler">Cerulean 0.1.1 &ndash; Hybrid Genome Assembler</a>
<ul>
<li>Cerulean is a hybrid assembly using high throughput short and long reads<br /><br /></li>
</ul>
</li>
<li><a href="http://fenderglass.github.io/Ragout/" title="Ragout 1.2 &ndash; Tool for Reference-assisted Assembly">Ragout 1.2 &ndash; Tool for Reference-assisted Assembly</a>
<ul>
<li>Ragout (Reference-Assisted Genome Ordering UTility) is a tool for assisted assembly using multiple references. It takes a short read assembly (a set of contigs), a set of related references and a corresponding phylogenetic tree and then assembles the contigs into scaffolds.<br /><br /></li>
</ul>
</li>
<li><a href="https://zlab.umassmed.edu/~zhuangj/laSV/" title="laSV 1.0.2 &ndash; Local Assembly based Structural Variation Discovery tool">laSV 1.0.2 &ndash; Local Assembly based Structural Variation Discovery tool</a>
<ul>
<li>laSV is a software that employs a local de novo assembly based approach to detect genomic structural variations from whole-genome high-throughput sequencing datasets.<br /><br /></li>
</ul>
</li>
<li><a href="http://bioinf.spbau.ru/en/spades" title="SPAdes 3.6.2 &ndash; Single-cell Genome Assembler">SPAdes 3.6.2 &ndash; Single-cell Genome Assembler</a>
<ul>
<li>SPAdes (St. Petersburg genome assembler) is intended for both standard isolates and single-cell MDA bacteria assemblies.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/hitbio/PERGA" title="PERGA 0.5.03.02 &ndash; Paired End Reads Guided Assembler">PERGA 0.5.03.02 &ndash; Paired End Reads Guided Assembler</a>
<ul>
<li>PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/p/telescoper/wiki/Home/" title="Telescoper 0.2 &ndash; De novo Assembly Algorithm">Telescoper 0.2 &ndash; De novo Assembly Algorithm</a>
<ul>
<li>Telescoper is a local assembly algorithm designed for short-reads from NGS platforms such as Illumina. The reads must come from two libraries: one short insert, and one long insert.<br /><br /></li>
</ul>
</li>
<li><a href="http://metacompass.cbcb.umd.edu/" title="MetaCompass 1.0 &ndash; Comparative Assembly of Metagenomic Sequences">MetaCompass 1.0 &ndash; Comparative Assembly of Metagenomic Sequences</a>
<ul>
<li>MetaCompass is a software package for comparative assembly of metagenomic reads. MetaCompass achieves comparable assembly performance to the state of the art de novo assemblers, but these two different approaches complement each other a lot. So combining contigs between MetaCompass and other independent de novo assemblers give us the best overall metagenomic assembly.<br /><br /></li>
</ul>
</li>
<li><a href="http://evopipes.net/docs.html#scarf_section" title="SCARF &ndash; Scaffolded and Corrected Assembly of Roche 454">SCARF &ndash; Scaffolded and Corrected Assembly of Roche 454</a>
<ul>
<li>SCARF is a next-gen sequence assembly tool for evolutionary genomics. Designed especially for assembling 454 EST sequences against high quality reference sequences from related species.<br /><br /></li>
</ul>
</li>
<li><a href="http://metagenomics.atc.tcs.com/MetaCAA/" title="MetaCAA &ndash; Assembly of Metagenomic Datasets">MetaCAA &ndash; Assembly of Metagenomic Datasets</a>
<ul>
<li>MetaCAA is a sequence-assembly tool specifically intended for metagenomes.<br /><br /></li>
</ul>
</li>
<li><a href="http://mjsull.github.io/Contiguity/" title="Contiguity 1.0.4 &ndash; Contig Adjacency Graph Construction and Visualisation">Contiguity 1.0.4 &ndash; Contig Adjacency Graph Construction and Visualisation</a>
<ul>
<li>Contiguity is interactive software for the visualization and manipulation of de novo genome assemblies.<br /><br /></li>
</ul>
</li>
<li><a href="http://bioresearch.byu.edu/scaffoldscaffolder/" title="ScaffoldScaffolder 0.1 &ndash; Solving Contig Orientation via Bidirected to Directed Graph Reduction">ScaffoldScaffolder 0.1 &ndash; Solving Contig Orientation via Bidirected to Directed Graph Reduction</a>
<ul>
<li>ScaffoldScaffolder is a stand-alone scaffolding algorithm which was designed specifically for scaffolding diploid genomes.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/cbg-ethz/haploclique" title="HaploClique 0.1 &ndash; Viral Quasispecies Assembly from Paired-end data">HaploClique 0.1 &ndash; Viral Quasispecies Assembly from Paired-end data</a>
<ul>
<li>HaploClique is a computational approach to reconstruct the structure of a viral quasispecies from next-generation sequencing data as obtained from bulk sequencing of mixed virus samples.<br /><br /></li>
</ul>
</li>
<li><a href="http://omics.informatics.indiana.edu/TAG/" title="TAG 0.91 &ndash; Transcript Assembly by Mapping Reads to Graphs">TAG 0.91 &ndash; Transcript Assembly by Mapping Reads to Graphs</a>
<ul>
<li>TAG is a tool for metatranscriptome assembly using de Bruijn graph of matched metagenome as the reference<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/bioinfomaticsCSU/EPGA2" title="EPGA2 &ndash; De Novo Assembler">EPGA2 &ndash; De Novo Assembler</a>
<ul>
<li>EPGA2 updates some modules in EPGA which can improve memory efficiency in genome asssembly.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/gmcloser/" title="GMcloser 1.5.1 / GMvalue 1.3 &ndash; Closing the Gaps in Scaffolds with Preassembled Contigs">GMcloser 1.5.1 / GMvalue 1.3 &ndash; Closing the Gaps in Scaffolds with Preassembled Contigs</a>
<ul>
<li>GMcloser fills and closes the gaps present in scaffold assemblies, especially those generated by the de novo assembly of whole genomes with next-generation sequencing (NGS) reads.<br /><br /></li>
</ul>
</li>
<li><a href="https://sites.google.com/a/ucr.edu/slicembler/" title="SLICEMBLER &ndash; Meta-assembler Designed for Ultra-deep Sequencing data">SLICEMBLER &ndash; Meta-assembler Designed for Ultra-deep Sequencing data</a>
<ul>
<li>SLICEMBLER is a meta-assembler designed for ultra-deep sequencing data<br /><br /></li>
</ul>
</li>
<li><a href="http://www.cs.colostate.edu/seq/seqlandscape/" title="SEQLandscape v1 &ndash; Generation and Visualization of Sequence Landscape">SEQLandscape v1 &ndash; Generation and Visualization of Sequence Landscape</a>
<ul>
<li>
<p>SEQLandscape is an application allowing the generation and visualization of a sequence landscape.&nbsp;HyDA-Vista: Towards Optimal Guided Selection of k-mer Size for Sequence Assembly.</p>
</li>
</ul>
</li>
<li><a href="http://www.cs.colostate.edu/seq/missequel/" title="misSEQuel v1.0beta &ndash; Misassembly Detection in Draft Genomes">misSEQuel v1.0beta &ndash; Misassembly Detection in Draft Genomes</a>
<ul>
<li>misSEQuel is a software that enhances the quality of draft genomes by identifying misassembly errors and their breakpoints using paired-end sequence reads and optical mapping data.<br /><br /></li>
</ul>
</li>
<li><a href="http://scit.us/projects/dawg/" title="Dawg 1.2 &ndash; Simulating Sequence Evolution">Dawg 1.2 &ndash; Simulating Sequence Evolution</a>
<ul>
<li>Dawg (DNA Assembly with Gaps) is an application designed to simulate the evolution of recombinant DNA sequences in continuous time based on the robust general time reversible model with gamma and invariant rate heterogeneity and a novel length-dependent model of gap formation.<br /><br /></li>
</ul>
</li>
<li><a href="http://busco.ezlab.org/" title="BUSCO v1.1b1 &ndash; Assessing Genome Assembly and Annotation Completeness with Single-copy Orthologs">BUSCO v1.1b1 &ndash; Assessing Genome Assembly and Annotation Completeness with Single-copy Orthologs</a>
<ul>
<li>BUSCO completeness assessment employs sets of Benchmarking Universal Single-Copy Orthologs from OrthoDB to provide quantitative measures of the completeness of genome assemblies, annotated gene sets, and transcriptomes in terms of expected gene content.<br /><br /></li>
</ul>
</li>
<li><a href="http://kakitone.github.io/finishingTool/" title="FinisherSC 2.0 &ndash; A Repeat-aware tool for upgrading de-novo Assembly using Long Reads">FinisherSC 2.0 &ndash; A Repeat-aware tool for upgrading de-novo Assembly using Long Reads</a>
<ul>
<li>FinisherSC is a repeat-aware and scalable tool for upgrading de-novo assembly using long reads.<br /><br /></li>
</ul>
</li>
<li><a href="https://whatshap.readthedocs.io/en/latest/" title="WhatsHap &ndash; Haplotype Assembly for Future-Generation Sequencing Reads">WhatsHap &ndash; Haplotype Assembly for Future-Generation Sequencing Reads</a>
<ul>
<li>WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called haplotype assembly. It is especially suitable for long reads, but works also well with short reads.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.mscs.mu.edu/~bozdag/compartmentalized_assembler.html" title="Compartmentalized Assembler &ndash; Assembly of Physical Maps">Compartmentalized Assembler &ndash; Assembly of Physical Maps</a>
<ul>
<li>Compartmentalized assembler is a novel method for the assemlby of high quality physical maps from fingerprinted clones.<br /><br /></li>
</ul>
</li>
<li><a href="http://genome.jgi.doe.gov/viz/" title="Elviz &ndash; Exploration of Metagenomic Assemblies">Elviz &ndash; Exploration of Metagenomic Assemblies</a>
<ul>
<li>Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenome data and their complex metadata.<br /><br /></li>
</ul>
</li>
<li><a href="http://bs.ipm.ir/softwares/ssp/" title="SSP &ndash; de novo Transcriptome Assembler">SSP &ndash; de novo Transcriptome Assembler</a>
<ul>
<li>SSP is a de novo transcriptome assembler that assembles RNA-seq reads into transcripts. SSP aims to reconstructs all the alternatively spliced isoforms and estimates the expression level of them.<br /><br /></li>
</ul>
</li>
<li><a href="http://viramp.com/" title="VirAmp &ndash; Galaxy-based Viral Genome Assembly pipeline">VirAmp &ndash; Galaxy-based Viral Genome Assembly pipeline</a>
<ul>
<li>VirAmp is a web-based semi-de novo fast virus genome assembly pipeline designed for extremely high coverage NGS data. VirAmp is a collection of existing tools, combined into a single Galaxy interface. Users without further computational knowledge can easily operate the pipeline.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/juliema/aTRAM" title="aTRAM&nbsp;1.04 &ndash; automated Target Restricted Assembly Method">aTRAM&nbsp;1.04 &ndash; automated Target Restricted Assembly Method</a>
<ul>
<li>aTRAM performs targeted de novo assembly of loci from paired-end Illumina runs.<br /><br /></li>
</ul>
</li>
<li><a href="http://denovoassembler.sourceforge.net/" title="Ray 2.3.1 &ndash; Parallel Genome Assemblies for Parallel DNA sequencing">Ray 2.3.1 &ndash; Parallel Genome Assemblies for Parallel DNA sequencing</a>
<ul>
<li>Ray is a parallel software that computes de novo genome assemblies with next-generation sequencing data.<br /><br /></li>
</ul>
</li>
<li><a href="http://genome.cs.nthu.edu.tw/CAR/" title="CAR &ndash; Contig Assembly of Prokaryotic Draft Genomes Using Rearrangements">CAR &ndash; Contig Assembly of Prokaryotic Draft Genomes Using Rearrangements</a>
<ul>
<li>CAR is an efficient and more accurate tool for assembling contigs of a prokaryotic draft genome based on a reference genome.<br /><br /></li>
</ul>
</li>
<li><a href="http://www.lstmed.ac.uk/vtbuilder" title="VTBuilder &ndash; Assembly of Multi Isoform Transcriptomes">VTBuilder &ndash; Assembly of Multi Isoform Transcriptomes</a>
<ul>
<li>VTBuilder is a tool for the inference of non-chimeric contigs from read data that has been sequenced from complex multi-isoformic transcriptomes, such as snake venom glands, or rapidly evolving viral populations, such as HIV-1.<br /><br /></li>
</ul>
</li>
<li><a href="http://bioinfolab.uncc.edu/TruHmm_package/" title="TruHmm &ndash; TRanscription Unit Assembly by a Hidden Markov model">TruHmm &ndash; TRanscription Unit Assembly by a Hidden Markov model</a>
<ul>
<li>TruHmm is a reference based transcriptome assembler for prokaryotes, and is suitable for assembling transcripts for directional RNA-seq library.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/rnaseqassembly/?source=navbar" title="Bridger 20141201 &ndash; RNA-Seq Assembly">Bridger 20141201 &ndash; RNA-Seq Assembly</a>
<ul>
<li>Bridger is a new de novo transcriptome assembler which takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers.<br /><br /></li>
</ul>
</li>
<li><a href="https://sourceforge.net/projects/grasp-release/" title="GRASP 0.0.4 &ndash; Guided Reference-based Assembly of Short Peptides">GRASP 0.0.4 &ndash; Guided Reference-based Assembly of Short Peptides</a>
<ul>
<li>GRASP is a gene annotation tool for metagenomic studies. GRASP assembles the fragmented short-peptides, which are called from the NGS reads, and aligns the assembled contigs to the query reference protein. GRASP achieves much higher sensitivity than BLASTP for gene annotation purpose.<br /><br /></li>
</ul>
</li>
<li><a href="http://cortexassembler.sourceforge.net/index.html" title="Cortex 1.05.21 &ndash; Genome Assembly and Variation Analysis">Cortex 1.05.21 &ndash; Genome Assembly and Variation Analysis</a>
<ul>
<li>Cortex is an efficient and low-memory software framework for analysis of genomes using sequence data. There are two main executables, being developed in parallel streams: cortex_con (primary contact Mario Caccamo) is for consensus genome assembly, and cortex_var (primary contact Zamin Iqbal) is for variation and population assembly.<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/voutcn/megahit" title="MEGAHIT v0.1.4 &ndash; Large and Complex Metagenomics Assembly via Succinct de Bruijn graph">MEGAHIT v0.1.4 &ndash; Large and Complex Metagenomics Assembly via Succinct de Bruijn graph</a>
<ul>
<li>MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph to achieve low memory usage, whereas its goal is not to make memory usage as low as possible.<br /><br /></li>
</ul>
</li>
<li><a href="http://sb.nhri.org.tw/CISA/en/CISA" title="CISA 20140304 &ndash; Contig Integrator for Sequence Assembly">CISA 20140304 &ndash; Contig Integrator for Sequence Assembly</a>
<ul>
<li>CISA has been developed to integrate the assemblies into a hybrid set of contigs, resulting in assemblies of superior contiguity and accuracy, compared with the assemblies generated by the state-of-the-art assemblers and the hybrid assemblies merged by existing tools<br /><br /></li>
</ul>
</li>
<li><a href="http://cole-trapnell-lab.github.io/cufflinks/" title="Cufflinks 2.2.1 &ndash; Transcript Assembler &amp; Abundance Estimator for RNA-Seq">Cufflinks 2.2.1 &ndash; Transcript Assembler &amp; Abundance Estimator for RNA-Seq</a>
<ul>
<li>Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one.<br /><br /></li>
</ul>
</li>
<li><a href="http://colibread.inria.fr/software/mapsembler2/" title="mapsembler 2.2.4 &ndash; Targetted Assembly of Short Sequence Reads">mapsembler 2.2.4 &ndash; Targetted Assembly of Short Sequence Reads</a>
<ul>
<li>Mapsembler is a targeted assembly software. It takes as input a set of NGS raw reads and a set of input sequences (starters). It first determines if each starter is read-coherent, e.g. whether reads confirm the presence of each starter in the original sequence. Then for each read-coherent starter, Mapsembler outputs its sequence neighborhood as a linear sequence or as a graph, depending on the user choice.<br /><br /></li>
</ul>
</li>
<li><a href="https://urgi.versailles.inra.fr/Tools/Tedna" title="Tedna 1.2.2 &ndash; Transposable Element De Novo Assembler">Tedna 1.2.2 &ndash; Transposable Element De Novo Assembler</a>
<ul>
<li>Tedna is a lightweight de novo transposable element assembler. It assembles the transposable elements directly from the raw reads.<br /><br /></li>
</ul>
</li>
<li><a href="http://chitsazlab.org/software.html" title="HyDA 1.3.1 / Squeezambler 2.0.3 &ndash; Hybrid De Novo Assembler">HyDA 1.3.1 / Squeezambler 2.0.3 &ndash; Hybrid De Novo Assembler</a>
<ul>
<li>HyDA is a multipurpose assembler, particularly tested for single cell and normal multicell genome co-assembly<br /><br /></li>
</ul>
</li>
<li><a href="https://github.com/neufeld/pandaseq" title="PANDASEQ 2.8 / Pandaseq-sam 1.3 &ndash; PAired-eND Assembler for DNA sequences">PANDASEQ 2.8 / Pandaseq-sam 1.3 &ndash; PAired-eND Assembler for DNA sequences</a>
<ul>
<li>PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.<br /><br /></li>
</ul>
</li>
<li><a href="http://lge.ibi.unicamp.br/zorro/" title="ZORRO 2.2 &ndash; Hybrid Sequencing Technology Assembler">ZORRO 2.2 &ndash; Hybrid Sequencing Technology Assembler</a>
<ul>
<li>ZORRO is a hybrid sequencing technology assembler. It merges two sets of pre-assembled contigs into a more contiguous and consistent assembly.<br /><br /></li>
</ul>
</li>
<li><a href="http://ccb.jhu.edu/software/FLASH/" title="FLASH 1.2.11 &ndash; Fast Length Adjustment of SHort reads">FLASH 1.2.11 &ndash; Fast Length Adjustment of SHort reads</a>
<ul>
<li>FLASH (Fast Length Adjustment of SHort reads) is a very accurate fast tool to merge paired-end reads from fragments that are shorter than twice the length of reads. The extended length of reads has a significant positive impact on improvement of genome assemblies.<br /><br /></li>
</ul>
</li>
<li><a href="http://software.broadinstitute.org/allpaths-lg/blog/" title="ALLPATHS-LG 51750 &ndash; Whole Genome Shotgun Assembler">ALLPATHS-LG 51750 &ndash; Whole Genome Shotgun Assembler</a>
<ul>
<li>ALLPATHS-LG (Large Genome) is a whole genome shotgun assembler that can generate high quality assemblies from short reads. It works on both small and large (mammalian size) genomes. To use it, you should first generate ~100 base Illumina reads from two libraries: one from ~180 bp fragments, and one from ~3000 bp fragments, both at about 45x coverage. Sequence from longer fragments will enable longer-range continuity.<br /><br /></li>
</ul>
</li>
<li><a href="http://bioinformaticsonline.com/pages/view/30440/genome-assembly-tools-and-software-part2">More Tools</a> at&nbsp;http://bioinformaticsonline.com/pages/view/30440/genome-assembly-tools-and-software-part2</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29992/spines</guid>
	<pubDate>Mon, 28 Nov 2016 05:33:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29992/spines</link>
	<title><![CDATA[Spines]]></title>
	<description><![CDATA[<p><a href="https://www.broadinstitute.org/ftp/distribution/software/spines/"><em>Spines</em></a>&nbsp;is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages:&nbsp;<em>Satsuma,</em>&nbsp;a highly parallelized program for high-sensitivity, genome-wide synteny;&nbsp;<em>Papaya,</em>&nbsp;an all-purpose alignment tool for less diverged sequences; and&nbsp;<em>SLAP,</em>&nbsp;a context-sensitive local aligner for diverged sequences with large gaps.</p>
<p>Access&nbsp;<em>Spines</em>&nbsp;<a href="https://www.broadinstitute.org/ftp/distribution/software/spines/">here</a>.</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/genome-sequencing-and-analysis/spines" rel="nofollow">https://www.broadinstitute.org/genome-sequencing-and-analysis/spines</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</guid>
	<pubDate>Mon, 06 Feb 2017 04:26:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</link>
	<title><![CDATA[FSA: Fast Statistical Alignment]]></title>
	<description><![CDATA[<p><span>FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in&nbsp;</span><a href="http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-39.html">"Posterior Decoding Methods for Optimization and Control of Multiple Alignments</a><span>."</span></p>
<p>FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:</p>
<ul>
<li>FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.</li>
<li>FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.</li>
<li>FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.</li>
<li>The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and&nbsp;<a href="http://fsa.sourceforge.net/images/Suchard_SIV.fsa.mov">movie</a>&nbsp;at the top of the page).</li>
</ul>
<p><span>You can see more information on the&nbsp;</span><a href="http://fsa.sourceforge.net/FAQ.html">FAQ</a><span>.&nbsp;</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://fsa.sourceforge.net/" rel="nofollow">http://fsa.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/30440/genome-assembly-tools-and-software-part2</guid>
	<pubDate>Tue, 27 Dec 2016 16:14:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/30440/genome-assembly-tools-and-software-part2</link>
	<title><![CDATA[Genome Assembly Tools and Software - PART2 !!]]></title>
	<description><![CDATA[<p>The genome assemblers generally take a file of short sequence reads and a file of quality-value as the input. Since the quality-value file for the high throughput short reads is usually highly memory-intensive, only a few assemblers, best suited for your assembly. For the sake of computational memory saving and convenience of data inquiry, high-throughput short reads data is always initially formatted to specific data structure. Currently, existing data structure for this usage can be predominantly classified into two categories: string-based model and graph-based model.</p><p>We therefore list many genomle assembly tools here. We mainly reported for the assembly of genomes while the others are designed aiming at handling complex genomes.</p><ul>
<li><a href="http://smithlabresearch.org/software/rmap/" title="RMAP 2.1 &ndash; Short-read Mapping">RMAP 2.1 &ndash; Short-read Mapping<br /></a><a href="http://smithlabresearch.org/software/rmap/" target="_blank">RMAP</a>&nbsp;is aimed to map accurately reads from the next-generation sequencing technology. RMAP can map reads with or without error probability information (quality scores) and supports paired-end reads or bisulfite-treated reads mapping. There is no limitaions on read widths or number of mismatches. RMAP can now map more than 8 million reads in an hour at full sensitivity to 2 mismatches<br /><br /></li>
<li><a href="https://sourceforge.net/p/mira-assembler/wiki/Home/" title="MIRA 4.0.2 &ndash; Whole Genome Shotgun and EST Sequence Assembler">MIRA 4.0.2 &ndash; Whole Genome Shotgun and EST Sequence Assembler<br /></a><a href="http://sourceforge.net/p/mira-assembler/wiki/Home/" target="_blank">MIRA</a>&nbsp;(Mimicking Intelligent Read Assembly)is a whole genome shotgun and EST sequence assembler for Sanger, 454, Solexa (Illumina), IonTorrent data and PacBio (the later at the moment only CCS and error-corrected CLR reads). It can be seen as a Swiss army knife of sequence assembly developed and used in the past 12 years to get assembly jobs done efficiently &ndash; and especially accurately. That is, without actually putting too much manual work into finishing the assembly.<br /><br /></li>
<li><a href="http://www.brown.edu/Research/Istrail_Lab/hapcompass.php" title="HapCompass 0.7.7 &ndash; A Cycle-Basis Algorithm for Accurate Haplotype Assembly">HapCompass 0.7.7 &ndash; A Cycle-Basis Algorithm for Accurate Haplotype Assembly<br /></a><a href="http://www.brown.edu/Research/Istrail_Lab/hapcompass.php" target="_blank">HapCompass</a>&nbsp;for polyploid genomes can currently be used to create accurate pairwise SNP phasings.Given a set of aligned sequence reads in a SAM file and a set of variant calls in VCF format, HAPCOMPASS will assemble reads into haplotypes.<br /><br /></li>
<li><a href="http://www.csc.kth.se/~vezzi/software/" title="GAM-NGS 1.1b &ndash; Genome Assemblies Merger for Next Generation Sequencing">GAM-NGS 1.1b &ndash; Genome Assemblies Merger for Next Generation Sequencing<br /></a><a href="http://www.csc.kth.se/~vezzi/software/" target="_blank">GAM-NGS</a>&nbsp;is able to merge two or more assemblies and it rteturns an improved assembly (more contiguous and more correct). GAM-NGS shows its full potential with multi-library Illumina-based projects.<br /><br /></li>
<li><a href="http://omics.informatics.indiana.edu/GeneStitch/" title="GeneStitch 1.2.1 &ndash; Network Matching Algorithm to Gene Assembly">GeneStitch 1.2.1 &ndash; Network Matching Algorithm to Gene Assembly<br /></a><a href="http://omics.informatics.indiana.edu/GeneStitch/" target="_blank">GeneStitch</a>&nbsp;is a tool to assemble genes using network matching algorithm. Given an already-assembled dataset, it is capable of assembling contigs together to form more complete genes with the help of a reference gene set. Currently the assembly software that GeneStitch support is SOAPdenovo.<br /><br /></li>
<li><a href="http://bioen-compbio.bioen.illinois.edu/RACA/" title="RACA 0.9.1.1 &ndash; Reference-Assisted Chromosome Assembly">RACA 0.9.1.1 &ndash; Reference-Assisted Chromosome Assembly<br /></a><a href="http://bioen-compbio.bioen.illinois.edu/RACA/" target="_blank">RACA</a>&nbsp;is an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.<br /><br /></li>
<li><a href="https://software.broadinstitute.org/software/discovar/blog/" title="DISCOVAR 51750 &ndash; Genome Shotgun Assembler and Variant Caller">DISCOVAR 51750 &ndash; Genome Shotgun Assembler and Variant Caller<br /></a><a href="http://www.broadinstitute.org/software/discovar/blog/" target="_blank">DISCOVAR</a>&nbsp;is a whole genome shotgun assembler and variant caller that can generate high quality assemblies and variant calls from the latest 250 base Illumina PCR-free fragment reads.<br /><br /></li>
<li><a href="http://www.seqan.de/projects/seqcons/" title="SeqCons 1.0 &ndash; de novo and reference-guided Sequence Assembly">SeqCons 1.0 &ndash; de novo and reference-guided Sequence Assembly<br /></a><a href="http://www.seqan.de/projects/seqcons/" target="_blank">&nbsp;SeqCons</a>&nbsp;(Sequence consensus) is an open source consensus computation program for Linux and Windows. The algorithm can be used for de novo and reference-guided sequence assembly.<br /><br /></li>
<li><a href="http://www.personal.psu.edu/jhm10/Vera/SoftwareC.html" title="SimAssemblyStage1/2 0.2 &ndash; Assembly Alignment of Contigs">SimAssemblyStage1/2 0.2 &ndash; Assembly Alignment of Contigs<br /></a><a href="http://www.personal.psu.edu/jhm10/Vera/SoftwareC.html" target="_blank">SimAssemblyStage1</a>: Perfectly aligns TranscriptSimulator reads to their nucleotide templates using read title inforamation, creating ideal simulated assembly of super contigs.<br /><br /></li>
<li><a href="http://www.csc.kth.se/~vezzi/software/" title="GapFiller &ndash; Closing the Gap within Paired Reads">GapFiller &ndash; Closing the Gap within Paired Reads<br /></a><a href="http://www.csc.kth.se/~vezzi/software/" target="_blank">GapFiller</a>&nbsp;is not a standard de novo assembler. It aims &ldquo;only&rdquo; at closing the gap between pairs of reads as a first step of a large number of downstream analysis<br /><br /></li>
<li><a href="http://www.sanger.ac.uk/science/tools/pagit" title="PAGIT 1.01 &ndash; Post Assembly Genome Improvement Toolkit">PAGIT 1.01 &ndash; Post Assembly Genome Improvement Toolkit<br /></a><a href="http://www.sanger.ac.uk/resources/software/pagit/" target="_blank">PAGIT</a>&nbsp;(Post Assembly Genome Improvement Toolkit) is a tools to generate automatically high quality sequence by ordering contigs, closing gaps, correcting sequence errors and transferring annotation.<br /><br /></li>
<li><a href="https://www.bsse.ethz.ch/cbg/software.html" title="ShoRAH 0.8.2 &ndash; Short Reads Assembly into Haplotypes">ShoRAH 0.8.2 &ndash; Short Reads Assembly into Haplotypes<br /></a><a href="http://www.bsse.ethz.ch/cbg/software/shorah" target="_blank">ShoRAH</a>&nbsp;is a software package that allows for inference about the structure of a population from a set of short sequence reads as obtained from ultra-deep sequencing of a mixed sample. The package contains programs that support mapping of reads to a reference genome, correcting sequencing errors by locally clustering reads in small windows of the alignment, reconstructing a minimal set of global haplotypes that explain the reads, and estimating the frequencies of the inferred haplotypes.<br /><br /></li>
<li><a href="http://www.genomics.cn/en/navigation/show_navigation?nid=2732" title="RePS 2.0 &ndash; WGS Sequence Assembler">RePS 2.0 &ndash; WGS Sequence Assembler<br /></a><a href="http://www.genomics.cn/en/navigation/show_navigation?nid=2732" target="_blank">RePS</a>&nbsp;(Repeat-masked Phrap with scaffolding), a WGS sequence assembler, that explicitly identifies exact kmer repeats from the shotgun data and removes them prior to the assembly. The established software Phrap is used to compute meaningful error probabilities for each base. Clone-end-pairing information is used to construct scaffolds that order and orient the contigs. The updated version of RePS incorporates some of the ideas introduced by Phusion on clustering<br /><br /></li>
<li><a href="http://bibiserv2.cebitec.uni-bielefeld.de/sessionTimeout.jsf" title="treecat &ndash; Phylogenetic Comparative Assembly">treecat &ndash; Phylogenetic Comparative Assembly<br /></a><a href="http://bibiserv2.cebitec.uni-bielefeld.de/cgcat?id=cgcat_treecat" target="_blank">treecat</a>&nbsp;(phylogenetic tree based contig arrangement tool) takes several genomes and their relationships in a phylogenetic tree into account to estimate a possible ordering of the contigs.<br /><br /></li>
<li><a href="http://alumni.cs.ucr.edu/~liw/isolasso.html" title="IsoLasso 2.6.1 &ndash; A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly">IsoLasso 2.6.1 &ndash; A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly<br /></a><a href="http://alumni.cs.ucr.edu/~liw/isolasso.html" target="_blank">IsoLasso</a>&nbsp;is an algorithm to assemble transcripts and estimate their expression levels from RNA-Seq reads.<br /><br /></li>
<li><a href="http://alumni.cs.ucr.edu/~liw/cem.html" title="CEM 0.9.1 &ndash; Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads">CEM 0.9.1 &ndash; Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads<br /></a><a href="http://alumni.cs.ucr.edu/~liw/cem.html" target="_blank">CEM</a>&nbsp;is an algorithm to assemble transcripts and estimate their expression levels from RNA-Seq reads.<br /><br /></li>
<li><a href="http://alan.cs.gsu.edu/NGS/?q=malta" title="MaLTA &ndash; Transcriptome Assembly and Quantification from Ion Torrent RNA-Seq data">MaLTA &ndash; Transcriptome Assembly and Quantification from Ion Torrent RNA-Seq data<br /></a><a href="http://alan.cs.gsu.edu/NGS/?q=malta" target="_blank">MaLTA</a>&nbsp;is a method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data.<br /><br /></li>
<li><a href="http://amos.sourceforge.net/wiki/index.php/AMOS" title="AMOS 3.1.0 &ndash; Whole Genome Shotgun Assembler">AMOS 3.1.0 &ndash; Whole Genome Shotgun Assembler<br /></a><a href="http://amos.sourceforge.net/wiki/index.php/AMOS" target="_blank">AMOS</a>&nbsp;(<strong>A</strong><strong>M</strong>odular,&nbsp;<strong>O</strong>pen-<strong>S</strong>ource)&nbsp;consortium is committed to the development of open-source whole genome assembly software. The project acronym (AMOS) represents our primary goal &mdash; to produce A Modular, Open-Source whole genome assembler.Open-source so that everyone is welcome to contribute and help build outstanding assembly tools, and modular in nature so that new contributions can be easily inserted into an existing assembly pipeline. This modular design will foster the development of new assembly algorithms and allow the AMOS project to continually grow and improve in hopes of eventually becoming a widely accepted and deployed assembly infrastructure. In this sense, AMOS is both a design philosophy and a software system.<br /><br /></li>
<li><a href="http://amos.sourceforge.net/wiki/index.php/AutoEditor" title="AutoEditor 1.20 &ndash; Automated Correction of Genome Sequence Errors">AutoEditor 1.20 &ndash; Automated Correction of Genome Sequence Errors<br /></a><a href="http://amos.sourceforge.net/wiki/index.php/AutoEditor" target="_blank">AutoEditor</a>&nbsp;is a tool for correcting sequencing and basecaller errors using sequence assembly and chromatogram data. On average AutoEditor corrects 80% of erroneous base calls, with an accuracy of 99.99%.This in turn improves the overall accuracy of genome sequences and facilitates the use of these sequences for polymorphism discovery.<br /><br /></li>
<li><a href="http://www.csd.uwo.ca/~ilie/SAGE/" title="SAGE &ndash; String Graph Assembly of GEnomes">SAGE &ndash; String Graph Assembly of GEnomes<br /></a><a href="http://www.csd.uwo.ca/~ilie/SAGE/" target="_blank">SAGE</a>&nbsp;is a new string-overlap graph-based de novo genome assembler.<br /><br /></li>
<li><a href="http://omega.omicsbio.org/" title="Omega 1.0.2 &ndash; Overlap-graph de novo Assembler for Metagenomics">Omega 1.0.2 &ndash; Overlap-graph de novo Assembler for Metagenomics<br /></a><a href="http://omega.omicsbio.org/" target="_blank">Omega</a>&nbsp;is a software for assembling and scaffolding Illumina sequencing data of microbial communities.<br /><br /></li>
<li><a href="http://www.compgenome.org/TCGA-Assembler/" title="TCGA-Assembler 1.0.3 &ndash; Open-Source Software for Retrieving and Processing TCGA Data">TCGA-Assembler 1.0.3 &ndash; Open-Source Software for Retrieving and Processing TCGA Data<br /></a><a href="http://www.compgenome.org/TCGA-Assembler/" target="_blank">TCGA-Assembler</a>&nbsp;is an open-source, freely available tool that automatically downloads, assembles, and processes public The Cancer Genome Atlas (TCGA) data, to facilitate downstream data analysis by relieving investigators from the burdens of data preparation.<br /><br /></li>
<li><a href="http://sammate.sourceforge.net/" title="SAMMate 2.7.4 / assemblySAM 1.1 &ndash;  Processing Short Read Alignments in SAM/BAM format / RNA-Seq Assembly and Analysis">SAMMate 2.7.4 / assemblySAM 1.1 &ndash; Processing Short Read Alignments in SAM/BAM format / RNA-Seq Assembly and Analysis<br /></a>
<p><a href="http://sammate.sourceforge.net/" target="_blank">SAMMate</a>&nbsp;is an open source GUI software suite to process RNA-Seq data. It is composed of two modules: assemblySAM and SAMMate.</p>
<p>assemblySAM employs a novel method to localize and assemble RNA-seq reads into RNA transcript sequences.<br /><br /></p>
</li>
<li><a href="http://www.cs.tau.ac.il/~bchor/StringGraph/" title="StringGraph beta &ndash; String Graph Construction Using Incremental Hashing">StringGraph beta &ndash; String Graph Construction Using Incremental Hashing<br /></a><a href="http://www.cs.tau.ac.il/~bchor/StringGraph/" target="_blank">StringGraph</a>&nbsp;is a novel, hash based method for constructing the string graph.<br /><br /></li>
<li><a href="http://mindthegap.genouest.org/" title="MindTheGap 1.0.0 &ndash; Detection and Assembly of Insertion Variants">MindTheGap 1.0.0 &ndash; Detection and Assembly of Insertion Variants<br /></a><a href="http://mindthegap.genouest.org/" target="_blank">MindTheGap</a>&nbsp;is a software that performs detection and assembly of DNA insertion variants in NGS read datasets with respect to a reference genome.<br /><br /></li>
<li><a href="http://cbcb.umd.edu/software/metAMOS" title="MetAMOS 1.5rc3 &ndash; Metagenomic Assembly pipeline for AMOS">MetAMOS 1.5rc3 &ndash; Metagenomic Assembly pipeline for AMOS<br /></a><a href="http://cbcb.umd.edu/software/metAMOS" target="_blank">MetAMOS</a>&nbsp;is an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations.<br /><br /></li>
<li><a href="http://impact.crhc.illinois.edu/projects.aspx#tiger" title="TIGER &ndash; DNA Sequence Assembly">TIGER &ndash; DNA Sequence Assembly<br /></a><a href="http://impact.crhc.illinois.edu/projects.aspx#tiger" target="_blank">Tiger</a>&nbsp;is a novel de novo assembly framework &nbsp;which adapts to available computing resources by iteratively decomposing the assembly problem into sub-problems.<br /><br /></li>
<li><a href="https://github.com/baoe/AlignGraph" title="AlignGraph &ndash; Secondary de novo Genome Assembly guided by closely related References">AlignGraph &ndash; Secondary de novo Genome Assembly guided by closely related References<br /></a><a href="https://github.com/baoe/AlignGraph" target="_blank">AlignGraph</a>&nbsp;is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism.<br /><br /></li>
<li><a href="http://compbio.cs.toronto.edu/hapsembler/scarpa.html" title="scarpa 0.241 &ndash; Scaffolding Reads with Practical Algorithms">scarpa 0.241 &ndash; Scaffolding Reads with Practical Algorithms<br /></a><a href="http://compbio.cs.toronto.edu/hapsembler/scarpa.html" target="_blank">Scarpa</a>&nbsp;is a stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data.<br /><br /></li>
<li><a href="http://genetics.cs.ucla.edu/vga/" title="VGA v1 &ndash; Viral Genome Assembler">VGA v1 &ndash; Viral Genome Assembler<br /></a><a href="http://genetics.cs.ucla.edu/vga/" target="_blank">VGA</a>&nbsp;is a method for accurate assembly of a heterogeneous viral population consisting of individuals viral genomes (also known as quasi-species).<br /><br /></li>
<li><a href="https://cbcl.ics.uci.edu//doku.php/software#genomix" title="Genomix 0.2.11 &ndash; Parallel Genome Assembly using Hyracks">Genomix 0.2.11 &ndash; Parallel Genome Assembly using Hyracks<br /></a><a href="https://cbcl.ics.uci.edu//doku.php/software#genomix" target="_blank">Genomix</a>&nbsp;is a parallel genome assembly system built from the ground up with scalability in mind. It can assemble large and high-coverage genomes from fastq files in a short time and produces assemblies similar to Velvet or Ray in quality.<br /><br /></li>
<li><a href="http://shendurelab.github.io/LACHESIS/" title="LACHESIS &ndash; Genome Assembly with Contact Probability Maps">LACHESIS &ndash; Genome Assembly with Contact Probability Maps<br /></a><a href="http://shendurelab.github.io/LACHESIS/" target="_blank">LACHESIS</a>&nbsp;is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly.<br /><br /></li>
<li><a href="http://www.cmbb.arizona.edu/?page_id=312" title="KGBassembler 1.2 &ndash; Karyotype-based Genome Assembler for Brassicaceae Species">KGBassembler 1.2 &ndash; Karyotype-based Genome Assembler for Brassicaceae Species<br /></a><a href="http://www.cmbb.arizona.edu/?page_id=312" target="_blank">KGBassembler</a>&nbsp;(Brassicaceae genome assembler) is a C++ based tool for assembling contigs and/or scaffolds to full chromosomes based on the karyotype maps of Brassicaceae species and without the need of genetic and physical maps.<br /><br /></li>
<li><a href="https://sourceforge.net/projects/autoassemblyd/" title="AutoAssemblyD 0.1 &ndash; Graphical User Interface system for several Genome Assembler">AutoAssemblyD 0.1 &ndash; Graphical User Interface system for several Genome Assembler<br />The&nbsp;</a><a href="http://sourceforge.net/projects/autoassemblyd/" target="_blank">AssemblyD</a>&nbsp;is a software which performed the local and remote genome assembly by several assemblers based on an XML Template which can replace the large command lines required by most assemblers.<a href="http://www.mybiosoftware.com/autoassemblyd-0-1-graphical-user-interface-system-for-several-genome-assembler.html" title="AutoAssemblyD 0.1 &ndash; Graphical User Interface system for several Genome Assembler"><br /><br /></a></li>
<li><a href="http://bio.cs.put.poznan.pl/programs/519227629dfb89a7fa000001" title="SR-ASM &ndash; DNA Assembly of the Short Sequences coming from 454 sequencer">SR-ASM &ndash; DNA Assembly of the Short Sequences coming from 454 sequencer<br /></a><a href="http://bio.cs.put.poznan.pl/programs/519227629dfb89a7fa000001" target="_blank">SR-ASM</a>&nbsp;(Short Reads ASseMbly) algorithm is designed for DNA assembly of the short sequences coming from 454 sequencers.<a href="http://www.mybiosoftware.com/sr-asm-dna-assembly-short-sequences-coming-454-sequencer.html" title="SR-ASM &ndash; DNA Assembly of the Short Sequences coming from 454 sequencer"><br /><br /></a></li>
<li><a href="http://www.bx.psu.edu/miller_lab/" title="YASRA 2.33 &ndash; Yet Another Short Read Assembler">YASRA 2.33 &ndash; Yet Another Short Read Assembler<br /></a><a href="http://www.bx.psu.edu/miller_lab/" target="_blank">YASRA</a>&nbsp;performs comparative assembly of short reads using a reference genome, which can differ substantially from the genome being sequenced.<a href="http://www.mybiosoftware.com/yasra-2-32-short-read-assembler.html" title="YASRA 2.33 &ndash; Yet Another Short Read Assembler"><br /><br /></a></li>
<li><a href="http://derisilab.ucsf.edu/software/price/index.html" title="PRICE 1.2 &ndash; de novo Genome Assembler">PRICE 1.2 &ndash; de novo Genome Assembler<br /></a><a href="http://derisilab.ucsf.edu/software/price/index.html" target="_blank">PRICE</a>&nbsp;(Paired-Read Iterative Contig Extension) is a de novo genome assembler implemented in C++. Its name describes the strategy that it implements for genome assembly: PRICE uses paired-read information to iteratively increase the size of existing contigs. Initially, those contigs can be individual reads from a subset of the paired-read dataset, non-paired reads from sequencing technologies that provide non-paired data, or contigs that were output from a prior run of PRICE or any other&nbsp;<a href="http://www.mybiosoftware.com/price-0-18-de-novo-genome-assembler.html" title="PRICE 1.2 &ndash; de novo Genome Assembler"><br /><br /></a></li>
<li><a href="https://sc932.github.com/ALE/" title="ALE 20130717 &ndash; Assembly Likelihood Estimator">ALE 20130717 &ndash; Assembly Likelihood Estimator<br /></a><a href="http://sc932.github.com/ALE/" target="_blank">ALE</a>&nbsp;is a probabalistic framework for determining the likelihood of an assembly given the data (raw reads) used to assemble it. It allows for the rapid discovery of errors and comparisons between similar assemblies.<a href="http://www.mybiosoftware.com/ale-assembly-likelihood-estimator.html" title="ALE 20130717 &ndash; Assembly Likelihood Estimator"><br /><br /></a></li>
<li><a href="https://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE" title="SSPACE 3.0 &ndash; Scaffolding pre-assembled Contigs using Paired-read data">SSPACE 3.0 &ndash; Scaffolding pre-assembled Contigs using Paired-read data<br /></a><a href="http://www.baseclear.com/lab-products/bioinformatics-tools/sspace-standard/" target="_blank">SSPACE</a>&nbsp;(SSAKE-based Scaffolding of Pre-Assembled Contigs after Extension) is a stand-alone program for scaffolding pre-assembled contigs using paired-read data. It is unique in offering the possibility to manually control the scaffolding process. By using the distance information of paired-end and/or matepair data, SSPACE is able to assess the order, distance and orientation of your contigs and combine them into scaffolds. Currently we offer this as a command-line tool in Perl. The input data is given by pre-assembled contig sequences (FASTA) and NGS paired-read data (FASTA or FASTQ). The final scaffolds are provided in FASTA format.<a href="http://www.mybiosoftware.com/sspace-1-2-scaffolding-pre-assembled-contigs-paired-read-data.html" title="SSPACE 3.0 &ndash; Scaffolding pre-assembled Contigs using Paired-read data"><br /><br /></a></li>
<li><a href="http://www.sanger.ac.uk/science/tools/image" title="IMAGE 2.4.1 &ndash; Iterative Mapping and Assembly for Gap Elimination">IMAGE 2.4.1 &ndash; Iterative Mapping and Assembly for Gap Elimination<br /></a><a href="http://www.sanger.ac.uk/resources/software/pagit/#IMAGE" target="_blank">IMAGE</a>&nbsp;( Iterative Mapping and Assembly for Gap Elimination) is a software designed to close gaps in any draft assembly using Illumina paired end reads. IMAGE is best described in several stages: aligning of Illumina reads at contig ends; local assembly of reads into new contigs; reference contigs are extended or merged; iterating the whole process to extend and merge more contigs.<a href="http://www.mybiosoftware.com/image-2-3-iterative-mapping-assembly-gap-elimination.html" title="IMAGE 2.4.1 &ndash; Iterative Mapping and Assembly for Gap Elimination"><br /><br /></a></li>
<li><a href="https://www.hgsc.bcm.edu/software/atlas-gapfill" title="ATLAS GapFill 2.2 &ndash; Deals with the Repetitive Gap Assembly problem">ATLAS GapFill 2.2 &ndash; Deals with the Repetitive Gap Assembly problem<br /></a><a href="https://www.hgsc.bcm.edu/software/atlas-gapfill" target="_blank">ATLAS GapFill</a>&nbsp;deals with the repetitive gap assembly problem by using the unique gap-flanking sequences to group reads and convert the problem to a local assembly task. Localizing the assembly reduces the numbers of repeats in the assembly, allows more data to be incorporated, and allows for gaps to be filled.<a href="http://www.mybiosoftware.com/atlas-gapfill-2-2-deals-repetitive-gap-assembly-problem.html" title="ATLAS GapFill 2.2 &ndash; Deals with the Repetitive Gap Assembly problem"><br /><br /></a></li>
<li><a href="https://www.hgsc.bcm.edu/software/atlas-whole-genome-assembly-suite" title="Atlas 2005 &ndash; Whole Genome Assembly Suite">Atlas 2005 &ndash; Whole Genome Assembly Suite<br /></a><a href="https://www.hgsc.bcm.edu/software/atlas-whole-genome-assembly-suite" target="_blank">Atlas</a>&nbsp;is a collection of software tools to facilitate the assembly of large genomes from whole genome shotgun reads, or a combination of whole genome shotgun reads and BAC or other localized reads.<a href="http://www.mybiosoftware.com/atlas-2005-genome-assembly-suite.html" title="Atlas 2005 &ndash; Whole Genome Assembly Suite"><br /><br /></a></li>
<li><a href="http://bio.math.berkeley.edu/cgal/" title="CGAL 0.9.6b &ndash; Computing Genome Assembly Likelihoods">CGAL 0.9.6b &ndash; Computing Genome Assembly Likelihoods<br /></a><a href="http://bio.math.berkeley.edu/cgal/" target="_blank">CGAL</a>&nbsp;is a tool for computing genome assembly likelihoods. It computes the likelihood of reads with respect to the assembly and a statistical model which can be used as a metric for evaluating assemblies.<a href="http://www.mybiosoftware.com/cgal-0-9-6-computing-genome-assembly-likelihoods.html" title="CGAL 0.9.6b &ndash; Computing Genome Assembly Likelihoods"><br /><br /></a></li>
<li><a href="https://github.com/lh3/fermi" title="Fermi 1.1 &ndash; WGS de novo Assembler based on the FMD-index for large Genomes">Fermi 1.1 &ndash; WGS de novo Assembler based on the FMD-index for large Genomes<br /></a><a href="https://github.com/lh3/fermi" target="_blank">Fermi</a>&nbsp;is a de novo assembler for Illumina reads from whole-genome short-gun sequencing. It also provides tools for error correction, sequence-to-read alignment and comparison between read sets. It uses the FMD-index, a novel compressed data structure, as the key data&nbsp;<a href="http://www.mybiosoftware.com/fermi-1-1-wgs-de-novo-assembler-based-on-the-fmd-index-for-large-genomes.html" title="Fermi 1.1 &ndash; WGS de novo Assembler based on the FMD-index for large Genomes"><br /><br /></a></li>
<li><a href="http://pasha.sourceforge.net/homepage.htm#latest" title="PASHA 1.0.10 &ndash; Parallelized Short Read Assembly">PASHA 1.0.10 &ndash; Parallelized Short Read Assembly<br /></a><a href="http://pasha.sourceforge.net/" target="_blank">PASHA</a>&nbsp;is a parallel short read assembler for large genomes using de Bruijn graphs. Taking advantage of both shared-memory multi-core CPUs and distributed-memory compute clusters, PASHA has demonstrated its potential to perform high-quality de-novo assembly of large genomes in reasonable time with modest computing resources. Our evaluation using three small real paired-end datasets shows that PASHA is able to produce better assemblies with comparable genome coverage and mis-assembly rates compared to three leading assemblers: Velvet, ABySS and SOAPdenovo. Moreover, PASHA achieves the fastest speed for all three datasets on a single CPU.<a href="http://www.mybiosoftware.com/pasha-1-0-5-parallelized-short-read-assembly.html" title="PASHA 1.0.10 &ndash; Parallelized Short Read Assembly"><br /><br /></a></li>
<li><a href="http://xgenovo.dna.bio.keio.ac.jp/" title="XGenovo &ndash; Extended Genovo Metagenomic Assembler by Incorporating Paired-End Information">XGenovo &ndash; Extended Genovo Metagenomic Assembler by Incorporating Paired-End Information<br /></a><a href="http://xgenovo.dna.bio.keio.ac.jp/" target="_blank">XGenovo</a>&nbsp;(Extended Genovo) is an extended genovo metagenomic assembler by incorporating paired-end information<a href="http://www.mybiosoftware.com/xgenovo-extended-genovo-metagenomic-assembler-by-incorporating-paired-end-information.html" title="XGenovo &ndash; Extended Genovo Metagenomic Assembler by Incorporating Paired-End Information"><br /><br /></a></li>
<li><a href="http://metavelvet.dna.bio.keio.ac.jp/" title="MetaVelvet 1.2.01 / MetaVelvet-SL &ndash; An Extension of Velvet Assembler to de novo Metagenomic Assembly / utilizing Supervised Learning">MetaVelvet 1.2.01 / MetaVelvet-SL &ndash; An Extension of Velvet Assembler to de novo Metagenomic Assembly / utilizing Supervised Learning<br /></a><a href="http://metavelvet.dna.bio.keio.ac.jp/" target="_blank">MetaVelvet</a>&nbsp;is an extension of Velvet assembler to de novo metagenome assembly from short sequence reads<a href="http://www.mybiosoftware.com/metavelvet-1-2-01-metavelvet-sl-an-extension-of-velvet-assembler-to-de-novo-metagenomic-assembly-utilizing-supervised-learning.html" title="MetaVelvet 1.2.01 / MetaVelvet-SL &ndash; An Extension of Velvet Assembler to de novo Metagenomic Assembly / utilizing Supervised Learning"><br /><br /></a></li>
<li><a href="http://www.genomic.ch/edena.php" title="Edena v3.131028 &ndash; De Novo Short Reads Assembler">Edena v3.131028 &ndash; De Novo Short Reads Assembler<br /></a><a href="http://www.genomic.ch/edena.php" target="_blank">Edena</a>&nbsp;is an assembler dedicated to process the millions of very short reads produced by the Illumina Genome Analyzer<a href="http://www.mybiosoftware.com/edena-v3-dev110920-de-novo-short-reads-assembler.html" title="Edena v3.131028 &ndash; De Novo Short Reads Assembler"><br /><br /></a></li>
<li><a href="https://github.com/gramarga/ConPADE" title="ConPADE 1.00 &ndash; Contig Ploidy and Allele Dosage Estimation">ConPADE 1.00 &ndash; Contig Ploidy and Allele Dosage Estimation<br /></a><a href="http://research.microsoft.com/en-us/downloads/62815951-4b89-47a5-9e3d-7054182dafbb/default.aspx" target="_blank">ConPADE</a>&nbsp;is a tool used to estimate contig ploidy and allele dosage in polyploid genome assemblies.<a href="http://www.mybiosoftware.com/conpade-1-00-contig-ploidy-and-allele-dosage-estimation.html" title="ConPADE 1.00 &ndash; Contig Ploidy and Allele Dosage Estimation"><br /><br /></a></li>
<li><a href="https://sourceforge.net/projects/eloper/" title="ELOPER 1.2 &ndash; Elongation of Paired-end Reads for de novo Assembly">ELOPER 1.2 &ndash; Elongation of Paired-end Reads for de novo Assembly<br /></a><a href="http://sourceforge.net/projects/eloper/" target="_blank">ELOPER</a>&nbsp;is a pre-processing tool for pair-end sequences that produces a better read library for assembly programs.<a href="http://www.mybiosoftware.com/eloper-1-2-elongation-of-paired-end-reads-for-de-novo-assembly.html" title="ELOPER 1.2 &ndash; Elongation of Paired-end Reads for de novo Assembly"><br /><br /></a></li>
<li><a href="http://www.ebi.ac.uk/~zerbino/oases/" title="Oases 0.2.08 &ndash; De novo Transcriptome Assembler for very short reads">Oases 0.2.08 &ndash; De novo Transcriptome Assembler for very short reads<br /></a><a href="http://www.ebi.ac.uk/~zerbino/oases/" target="_blank">Oases</a>&nbsp;designed to heuristically assemble RNA-seq reads in the absence of a reference genome, across a broad spectrum of expression values and in presence of alternative isoforms. It achieves this by using an array of hash lengths, a dynamic filtering of noise, a robust resolution of alternative splicing events, and the efficient merging of multiple assemblies. It was tested on human and mouse RNA-seq data and is shown to improve significantly on the transABySS and Trinity de novo&nbsp;<a href="http://www.mybiosoftware.com/oases-0-2-06-de-novo-transcriptome-assembler-short-reads.html" title="Oases 0.2.08 &ndash; De novo Transcriptome Assembler for very short reads"><br /><br /></a></li>
<li><a href="http://www.physics.rutgers.edu/~anirvans/SOPRA/" title="SOPRA 1.4.6 &ndash; Statistical Optimization of Paired Read Assembly">SOPRA 1.4.6 &ndash; Statistical Optimization of Paired Read Assembly<br /></a><a href="http://www.physics.rutgers.edu/~anirvans/SOPRA/" target="_blank">SOPRA</a>&nbsp;is an assembler for mate pair/paired-end reads from high throughput sequencing platforms, e.g. Illumina and SOLiD.<a href="http://www.mybiosoftware.com/sopra-1-4-6-statistical-optimization-paired-read-assembly.html" title="SOPRA 1.4.6 &ndash; Statistical Optimization of Paired Read Assembly"><br /><br /></a></li>
<li><a href="http://rnc.r.dendai.ac.jp/hapAssembly.html" title="hapAssembly &ndash; Haplotype Assembly from Whole-Genome Sequence Data">hapAssembly &ndash; Haplotype Assembly from Whole-Genome Sequence Data<br /></a><a href="http://rnc.r.dendai.ac.jp/hapAssembly.html" target="_blank">hapAssembly</a>&nbsp;&nbsp;beats the previous best for the important Haplotype Assembly Problem. It is&nbsp;an approach to finding optimal solutions for the haplotype assembly problem under the minimum-error-correction (MEC) model.<a href="http://www.mybiosoftware.com/hapassembly-haplotype-assembly-whole-genome-sequence-data.html" title="hapAssembly &ndash; Haplotype Assembly from Whole-Genome Sequence Data"><br /><br /></a></li>
<li><a href="https://code.google.com/archive/p/pbsim/" title="PBSIM 1.0.3 &ndash; PacBio Reads Simulator">PBSIM 1.0.3 &ndash; PacBio Reads Simulator<br /></a>PacBio sequencers produced two types of characteristic reads: CCS (short and low error rate) and CLR (long and high error rate), both of which could be useful for de novo assembly of genomes.&nbsp;<a href="https://code.google.com/p/pbsim/" target="_blank">PBSIM</a>&nbsp;simulates those PacBio reads by using either a model-based or sampling-based simulation.<a href="http://www.mybiosoftware.com/pbsim-1-0-3-pacbio-reads-simulator.html" title="PBSIM 1.0.3 &ndash; PacBio Reads Simulator"><br /><br /></a></li>
<li><a href="http://marte.ic.unicamp.br:8747/" title="SIS &ndash; Generate Draft Genome Sequence Scaffolds for Prokaryotes">SIS &ndash; Generate Draft Genome Sequence Scaffolds for Prokaryotes<br /></a><a href="http://marte.ic.unicamp.br:8747/" target="_blank">SIS</a>&nbsp;(Scaffolds from Inversion Signatures)is a new easy-to-use tool to generate contig scaffolds<a href="http://www.mybiosoftware.com/sis-generate-draft-genome-sequence-scaffolds-prokaryotes.html" title="SIS &ndash; Generate Draft Genome Sequence Scaffolds for Prokaryotes"><br /><br /></a></li>
<li><a href="https://www.cs.helsinki.fi/group/scaffold/normalizedN50/" title="NN50-calculator 0.5 &ndash; Evaluate the Correctness of Genome Assemblies">NN50-calculator 0.5 &ndash; Evaluate the Correctness of Genome Assemblies<br /></a><a href="http://www.cs.helsinki.fi/group/scaffold/normalizedN50/" target="_blank">NN50-calculator</a>&nbsp;(Normalized N50 calculator) is a tool for evaluating the correctness of genome assemblies.<a href="http://www.mybiosoftware.com/nn50-calculator-0-5-evaluate-correctness-genome-assemblies.html" title="NN50-calculator 0.5 &ndash; Evaluate the Correctness of Genome Assemblies"><br /><br /></a></li>
<li><a href="http://josephryan.github.io/baa.pl/" title="Baa.pl 0.20 &ndash; use BLAT to ASSESS an ASSEMBLY">Baa.pl 0.20 &ndash; use BLAT to ASSESS an ASSEMBLY<br /></a><a href="http://josephryan.github.io/baa.pl/" target="_blank">Baa.pl</a>&nbsp;is a simple script that parses the output of a BLAT run of a transcriptome vs. a genome assembly.<a href="http://www.mybiosoftware.com/baa-pl-0-10-blat-assess-assembly.html" title="Baa.pl 0.20 &ndash; use BLAT to ASSESS an ASSEMBLY"><br /><br /></a></li>
<li><a href="http://compbio.cs.toronto.edu/hapsembler/index.html" title="hapsembler 2.21 &ndash; Haplotype-specific Genome Assembly Toolkit">hapsembler 2.21 &ndash; Haplotype-specific Genome Assembly Toolkit<br /></a><a href="http://compbio.cs.toronto.edu/hapsembler/index.html" target="_blank">Hapsembler</a>&nbsp;is a haplotype-specific genome assembly toolkit that is designed for genomes that are rich in SNPs and other types of polymorphism. Hapsembler can be used to assemble reads from a variety of platforms including Illumina and Roche/454.<a href="http://www.mybiosoftware.com/hapsembler-2-1-haplotype-specific-genome-assembly-toolkit.html" title="hapsembler 2.21 &ndash; Haplotype-specific Genome Assembly Toolkit"><br /><br /></a></li>
<li><a href="http://alan.cs.gsu.edu/NGS/?q=content/vispa" title="ViSpA 02 &ndash; Viral Spectrum Assembler">ViSpA 02 &ndash; Viral Spectrum Assembler<br /></a><a href="http://alan.cs.gsu.edu/NGS/?q=content/vispa" target="_blank">ViSpA</a>&nbsp;(Viral Spectrum Assembling) implements a novel viral assembling and frequency estimation methods. This software uses a simple error correction, viral variants assembling based on maximum-bandwidth paths in weighted read graphs and frequency estimation via Expectation Maximization on all reads.<a href="http://www.mybiosoftware.com/vispa-01-viral-spectrum-assembler.html" title="ViSpA 02 &ndash; Viral Spectrum Assembler"><br /><br /></a></li>
<li><a href="http://www.vicbioinformatics.com/software.velvetoptimiser.shtml" title="VelvetOptimiser 2.2.5 &ndash; Automatically Optimise Velvet Assembler Parameters">VelvetOptimiser 2.2.5 &ndash; Automatically Optimise Velvet Assembler Parameters<br /></a><a href="http://www.vicbioinformatics.com/software.velvetoptimiser.shtml" target="_blank">VelvetOptimiser</a>&nbsp;is a multi-threaded Perl script for automatically optimising the three primary parameter options (K, -exp_cov, -cov_cutoff) for the Velvet de novo sequence assembler.<a href="http://www.mybiosoftware.com/velvetoptimiser-2-2-5-automatically-optimise-velvet-assembler-parameters.html" title="VelvetOptimiser 2.2.5 &ndash; Automatically Optimise Velvet Assembler Parameters"><br /><br /></a></li>
<li><a href="http://www.vicbioinformatics.com/software.assemblet.shtml" title="Assemblet 0.1 &ndash; Antigenic Variation Assembler">Assemblet 0.1 &ndash; Antigenic Variation Assembler<br /></a><a href="http://www.vicbioinformatics.com/software.assemblet.shtml" target="_blank">Assemblet</a>&nbsp;is a short read assembler for assembling antigenic variant sequences in bacteria.<a href="http://www.mybiosoftware.com/assemblet-0-1-antigenic-variation-assembler.html" title="Assemblet 0.1 &ndash; Antigenic Variation Assembler"><br /><br /></a></li>
<li><a href="http://www.vicbioinformatics.com/software.velvetk.shtml" title="VelvetK 20120606 &ndash; Find a reasonable K-mer size to Assemble Genome Reads with Velvet">VelvetK 20120606 &ndash; Find a reasonable K-mer size to Assemble Genome Reads with Velvet<br /></a><a href="http://www.vicbioinformatics.com/software.velvetk.shtml" target="_blank">VelvetK</a>&nbsp;can estimate the best k-mer size to use for your Velvet de novo assembly. It needs two inputs: the estimated genome size, and all your sequence read files. The genome size can be supplied as as a number (eg. 3.5M) or as a FASTA file of a closely related genome.<a href="http://www.mybiosoftware.com/velvetk-20120606-find-reasonable-k-mer-size-assemble-genome-reads-velvet.html" title="VelvetK 20120606 &ndash; Find a reasonable K-mer size to Assemble Genome Reads with Velvet"><br /><br /></a></li>
<li><a href="http://www.vicbioinformatics.com/software.vague.shtml" title="VAGUE 1.0.5 &ndash; Velvet Assembler Graphical User Environment">VAGUE 1.0.5 &ndash; Velvet Assembler Graphical User Environment<br /></a><a href="http://www.vicbioinformatics.com/software.vague.shtml" target="_blank">VAGUE</a>&nbsp;(Velvet Assembler Graphical Front End) is a GUI for the&nbsp;<a href="http://www.mybiosoftware.com/assembly-tools/3852">Velvet</a>&nbsp;de novo assembler.<a href="http://www.mybiosoftware.com/vague-1-0-5-velvet-assembler-graphical-user-environment.html" title="VAGUE 1.0.5 &ndash; Velvet Assembler Graphical User Environment"><br /><br /></a></li>
<li><a href="http://pritchardlab.stanford.edu/software.html" title="Transcriptome Assembler &ndash; Transcriptome Assembly used in RNA-seq of 16 Mammalian Species">Transcriptome Assembler &ndash; Transcriptome Assembly used in RNA-seq of 16 Mammalian Species<br /></a><a href="http://pritchardlab.stanford.edu/software.html" target="_blank">Transcriptome Assembler</a>&nbsp;is a software for transcriptome assembly used in RNA-seq of 16 mammalian species.<a href="http://www.mybiosoftware.com/transcriptome-assembler-transcriptome-assembly-rna-seq-16-mammalian-species.html" title="Transcriptome Assembler &ndash; Transcriptome Assembly used in RNA-seq of 16 Mammalian Species"><br /><br /></a></li>
<li><a href="http://bio.codeplex.com/wikipage?title=sequenceassembler&amp;referringTitle=sampleapps&amp;ANCHOR#sampleapps" title="BioSequenceAssembler 2.0 &ndash; Microsoft Research Sequence Assembler">BioSequenceAssembler 2.0 &ndash; Microsoft Research Sequence Assembler<br /></a><a href="http://bio.codeplex.com/wikipage?title=sequenceassembler&amp;referringTitle=sampleapps&amp;ANCHOR#sampleapps" target="_blank">BioSequenceAssembler</a>&nbsp;is intended for use by biologist and laboratory technicians who are responsible for managing next-generation genomic sequencing data for alignment, assembly, and/or BLAST identification.<a href="http://www.mybiosoftware.com/biosequenceassembler-2-0-microsoft-research-sequence-assembler.html" title="BioSequenceAssembler 2.0 &ndash; Microsoft Research Sequence Assembler"><br /><br /></a></li>
<li><a href="http://www.imperial.ac.uk/bioinformatics-data-science-group" title="BugBuilder &ndash; Microbial Genome Assembly">BugBuilder &ndash; Microbial Genome Assembly<br /></a><a href="http://www3.imperial.ac.uk/bioinfsupport/resources/software/bugbuilder" target="_blank">BugBuilder</a>&nbsp;is a pipeline for the automated assembly and annotation of microbial genomes from high-throughput sequence data. It is configurable so as not to be tied to any assembler or scaffolder, and is designed to run in a cluster environment facilitating high-throughput processing of genomes.<a href="http://www.mybiosoftware.com/bugbuilder-microbial-genome-assembly.html" title="BugBuilder &ndash; Microbial Genome Assembly"><br /></a></li>
<li><a href="http://maximuspipeline.sourceforge.net/main/">MAXIMUS 0.2 &ndash; Hybrid Reference and de novo Assembly pipeline</a><br /><a href="http://maximuspipeline.sourceforge.net/main/" target="_blank">MAXIMUS</a>&nbsp;is a genome assembly pipeline which takes the best out of multiple reference assemblies and de novo assembly. The benefits of this approach include better assembled repetitive regions, less gaps and higher accuracy for the resultant assembly.<a href="http://www.mybiosoftware.com/maximus-0-2-hybrid-reference-de-novo-assembly-pipeline.html" title="MAXIMUS 0.2 &ndash; Hybrid Reference and de novo Assembly pipeline"><br /><br /></a></li>
<li><a href="http://www.bcgsc.ca/about/pubann/the-issake-short-read-sequence-assembly-approach-for-profiling-t-cell-metagenomes" title="ISSAKE &ndash; Short Read Sequence Assembly">ISSAKE &ndash; Short Read Sequence Assembly<br /></a><a href="http://www.bcgsc.ca/about/pubann/the-issake-short-read-sequence-assembly-approach-for-profiling-t-cell-metagenomes" target="_blank">iSSAKE</a>&nbsp;(immuno-SSAKE) is a sequencing approach and assembly software for profiling T-cell metagenomes using short reads from the massively parallel sequencing platforms.<a href="http://www.mybiosoftware.com/issake-short-read-sequence-assembly.html" title="ISSAKE &ndash; Short Read Sequence Assembly"><br /><br /></a></li>
<li><a href="http://www.animalgenome.org/tools/beap/" title="IDBA / IDBA-UD 1.1.1 &ndash; De Bruijn Graph De Novo Assembler with Highly Uneven Sequencing Depth">IDBA / IDBA-UD 1.1.1 &ndash; De Bruijn Graph De Novo Assembler with Highly Uneven Sequencing Depth<br /></a><a href="http://i.cs.hku.hk/~alse/hkubrg/projects/idba/index.html" target="_blank">&nbsp;IDBA</a>&nbsp;is a practical iterative De Bruijn Graph De Novo Assembler for sequence assembly in bioinfomatics. Most assemblers based on de Bruijn graph build a de Bruijn graph with a specific k to perform the assembling task. For all of them, it is very crucial to find a specific value of k. If k is too large, there will be a lot of gap problems in the graph. If k is too small, there will a lot of branch problems. IDBA uses not only one specific k but a range of k values to build the iterative de Bruijn graph. It can keep all the information in graphs with different k values. So, it will perform better than other assemblers.<a href="http://www.mybiosoftware.com/idba-ud-1-09-de-bruijn-graph-de-novo-assembler-highly-uneven-sequencing-depth.html" title="IDBA / IDBA-UD 1.1.1 &ndash; De Bruijn Graph De Novo Assembler with Highly Uneven Sequencing Depth"><br /><br /></a></li>
<li><a href="https://code.google.com/archive/p/est2assembly/" title="est2assembly 1.13 &ndash; Assembly and Annotation of Transcriptomes for any Species">est2assembly 1.13 &ndash; Assembly and Annotation of Transcriptomes for any Species<br />The&nbsp;</a><a href="https://code.google.com/p/est2assembly/" target="_blank">est2assembly</a>&nbsp;platform is the only platform for standardising transcriptome projects: go from raw trace files to an annotated GBrowse interface driven by the Seqfeature database. It accepts both Sanger and 454 sequencing technology for a denovo assembly, annotation and data mining of EST data.<a href="http://www.mybiosoftware.com/est2assembly-1-13-assembly-annotation-transcriptomes-species.html" title="est2assembly 1.13 &ndash; Assembly and Annotation of Transcriptomes for any Species"><br /><br /></a></li>
<li><a href="https://code.google.com/archive/p/curtain/" title="Curtain 0.2.3 beta &ndash; Assembling large Genomes from Short Read Sequences">Curtain 0.2.3 beta &ndash; Assembling large Genomes from Short Read Sequences<br /></a><a href="https://code.google.com/p/curtain/" target="_blank">Curtain</a>&nbsp;is an assembler of next generation sequence. Curtain is a Java wrapper around next-generation assemblers such as Velvet, which allows the incremental introduction of read-pair information into the assembly process.<a href="http://www.mybiosoftware.com/curtain-0-2-3-beta-assembling-large-genomes-short-read-sequences.html" title="Curtain 0.2.3 beta &ndash; Assembling large Genomes from Short Read Sequences"><br /><br /></a></li>
<li><a href="http://www.comp.nus.edu.sg/~bioinfo/peasm/PE_manual.htm" title="PEAssember 1.2 &ndash; A de novo Genome Assembler">PEAssember 1.2 &ndash; A de novo Genome Assembler<br /></a><a href="http://www.comp.nus.edu.sg/~bioinfo/peasm/PE_manual.htm" target="_blank">PEAssember</a>&nbsp;is a parallel de novo genome assembler for small &ndash; mid sized genomes.<a href="http://www.mybiosoftware.com/peassember-1-2-de-novo-genome-assembler.html" title="PEAssember 1.2 &ndash; A de novo Genome Assembler"><br /><br /></a></li>
<li><a href="https://sourceforge.net/projects/contrail-bio/" title="Contrail 0.8.2 &ndash; Assembly of Large Genomes using Cloud Computing">Contrail 0.8.2 &ndash; Assembly of Large Genomes using Cloud Computing<br /></a><a href="http://contrail-bio.sourceforge.net/" target="_blank">Contrail</a>&nbsp;is a Hadoop based genome assembler for assembling large genomes in the clouds<a href="http://www.mybiosoftware.com/contrail-0-8-2-assembly-large-genomes-cloud-computing.html" title="Contrail 0.8.2 &ndash; Assembly of Large Genomes using Cloud Computing"><br /><br /></a></li>
<li><a href="http://www.mybiosoftware.com/beap-0-6-beta-blast-extension-assembly-program.html" title="BEAP 0.6 beta &ndash; Blast Extension and Assembly Program">BEAP 0.6 beta &ndash; Blast Extension and Assembly Program<br />The&nbsp;</a><a href="http://www.animalgenome.org/tools/beap/" target="_blank">BEAP</a>&nbsp;is a computer program that uses a short starting DNA fragment, often a EST or partial gene segment, as &ldquo;primer&rdquo;, to recursively blast nucleotide databases in an attempt to obtain all sequences that overlaps, directly or indirectly, with the &ldquo;primer&rdquo; therefore help to &ldquo;extend&rdquo; the length of the original sequence for constructing a &ldquo;full length&rdquo; sequence for functional analysis, or at least to obtain neighboring regions of the segment for SNP discovery and linkage disequilibrium&nbsp;<a href="http://www.mybiosoftware.com/beap-0-6-beta-blast-extension-assembly-program.html" title="BEAP 0.6 beta &ndash; Blast Extension and Assembly Program"><br /><br /></a></li>
<li><a href="http://manuals.bioinformatics.ucr.edu/home/branch" title="BRANCH 1.8.1 &ndash; boosting RNA-Seq Assemblies with Partial or related Genomic Sequences">BRANCH 1.8.1 &ndash; boosting RNA-Seq Assemblies with Partial or related Genomic Sequences<br /></a><a href="http://manuals.bioinformatics.ucr.edu/home/branch" target="_blank">BRANCH</a>&nbsp;is a software that extends de novo transfrags and identifies novel transfrags with DNA contigs or genes of close related species. BRANCH discovers novel exons first and then extends/joins fragmented de novo transfrags, so that the resulted transfrags are more complete.<a href="http://www.mybiosoftware.com/branch-1-8-1-boosting-rna-seq-assemblies-partial-related-genomic-sequences.html" title="BRANCH 1.8.1 &ndash; boosting RNA-Seq Assemblies with Partial or related Genomic Sequences"><br /><br /></a></li>
<li><a href="http://www.cbcb.umd.edu/software/quake/">Quake 0.3.5 &ndash; Detect &amp; Correct Substitution Sequencing Errors in WGS Data Sets</a><br />
<p><a href="http://www.cbcb.umd.edu/software/quake/" target="_blank">Quake</a>&nbsp;is a package to correct substitution sequencing errors in experiments with deep coverage (e.g. &gt;15X), specifically intended for Illumina sequencing reads. Quake adopts the k-mer error correction framework, first introduced by the EULER genome assembly package. Unlike EULER and similar progams, Quake utilizes a robust mixture model of erroneous and genuine k-mer distributions to determine where errors are located. Then Quake uses read quality values and learns the nucleotide to nucleotide error rates to determine what types of errors are most likely. This leads to more corrections and greater accuracy, especially with respect to avoiding mis-corrections,&nbsp;&nbsp;which create false sequence unsimilar to anything in the original genome sequence from which the read was taken.</p>
</li>
<li><a href="http://www.ebi.ac.uk/~zerbino/velvet/" title="Velvet 1.2.10 &ndash; Sequence Assembler for Very Short Reads">Velvet 1.2.10 &ndash; Sequence Assembler for Very Short Reads<br /></a><a href="http://www.ebi.ac.uk/~zerbino/velvet/" target="_blank">Velvet</a>&nbsp;is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454.Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired-end read and long read information, when available, to retrieve the repeated areas between contigs.<a href="http://www.mybiosoftware.com/velvet-1-1-07-sequence-assembler-short-reads.html" title="Velvet 1.2.10 &ndash; Sequence Assembler for Very Short Reads"><br /><br /></a></li>
<li><a href="http://www.complex.iastate.edu/download/Lucy2/index.html" title="Lucy 2.20 &ndash; DNA Sequence Quality &amp; Vector Trimming">Lucy 2.20 &ndash; DNA Sequence Quality &amp; Vector Trimming<br /></a><a href="http://www.complex.iastate.edu/download/Lucy2/index.html" target="_blank">Lucy</a>&nbsp;has been used for several years to clean sequence data from automated DNA sequencers prior to sequence assembly and other downstream uses. &nbsp;The quality trimming portion of lucy makes use of phred quality scores, such as those produced by many automated sequencers based on the Sanger sequencing method. &nbsp;As such, lucy&rsquo;s quality trimming may not be appropriate for sequence data produced by some of the new &ldquo;next-generation&rdquo; sequencers.<a href="http://www.mybiosoftware.com/lucy-2-19p-r8-dna-sequence-quality-vector-trimming.html" title="Lucy 2.20 &ndash; DNA Sequence Quality &amp; Vector Trimming"><br /><br /></a></li>
<li><a href="http://bioinfo.bti.cornell.edu/tool/iAssembler/">iAssembler 1.3.2 &ndash; de novo Assembly of Roche-454/Sanger Transcriptome Sequences</a><br /><a href="http://bioinfo.bti.cornell.edu/tool/iAssembler/" target="_blank">iAssembler</a>&nbsp;is a standalone package to assemble ESTs generated using Sanger and/or Roche-454 pyrosequencing technologies into contigs.<a href="http://www.mybiosoftware.com/iassembler-1-3-2-de-novo-assembly-roche-454sanger-transcriptome-sequences.html" title="iAssembler 1.3.2 &ndash; de novo Assembly of Roche-454/Sanger Transcriptome Sequences"><br /><br /></a></li>
<li><a href="http://www.broadinstitute.org/software/gaemr/" title="GAEMR 1.0.1 &ndash; Assembly Analysis Framework">GAEMR 1.0.1 &ndash; Assembly Analysis Framework<br /></a><a href="http://www.broadinstitute.org/software/gaemr/" target="_blank">GAEMR</a>&nbsp;(Genome Assembly Evaluation Metrics and Reportin) is a complete genome analysis package that helps you evaluate and report on a genome assembly&rsquo;s completeness, correctness, and contiguity.<a href="http://www.mybiosoftware.com/gaemr-1-0-1-assembly-analysis-framework.html" title="GAEMR 1.0.1 &ndash; Assembly Analysis Framework"><br /><br /></a></li>
<li><a href="https://mulcyber.toulouse.inra.fr/plugins/mediawiki/wiki/pyrocleaner/index.php/Main_Page" title="PyroCleaner 1.3 &ndash; Clean 454 Pyrosequencing Reads in order to ease the Assembly Process">PyroCleaner 1.3 &ndash; Clean 454 Pyrosequencing Reads in order to ease the Assembly Process<br />The&nbsp;</a><a href="https://mulcyber.toulouse.inra.fr/plugins/mediawiki/wiki/pyrocleaner/index.php/Main_Page" target="_blank">pyrocleaner</a>&nbsp;is intended to clean the reads included in the sff file in order to ease the assembly process. It enables filtering sequences on different criteria such as length, complexity, number of undetermined bases which has been proven to correlate with poor quality and multiple copy reads. It also enables to clean paired-ends sff files and generates on one side a sff with the validated paired-ends and on the other the sequences which can be used as shotgun reads.<a href="http://www.mybiosoftware.com/pyrocleaner-1-3-clean-454-pyrosequencing-reads-order-ease-assembly-process.html" title="PyroCleaner 1.3 &ndash; Clean 454 Pyrosequencing Reads in order to ease the Assembly Process"><br /><br /></a></li>
<li><a href="http://bioinformatics.rutgers.edu/Software/SLiQ/" title="SLiQ &ndash; Simple linear Inequalities based Mate-Pair reads Filtering and Scaffolding">SLiQ &ndash; Simple linear Inequalities based Mate-Pair reads Filtering and Scaffolding<br /></a><a href="http://bioinformatics.rutgers.edu/Software/SLiQ/" target="_blank">SLIQ&nbsp;</a>, a set of simple linear inequalities derived from the geometry of contigs on the line, can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph.<a href="http://www.mybiosoftware.com/sliq-simple-linear-inequalities-based-mate-pair-reads-filtering-scaffolding.html" title="SLiQ &ndash; Simple linear Inequalities based Mate-Pair reads Filtering and Scaffolding"><br /><br /></a></li>
<li><a href="http://bioinf.spbau.ru/en/rectangles" title="rectangles 2.0 &ndash; Rectangle Graph for Repeat Resolution in Genome Assembly">rectangles 2.0 &ndash; Rectangle Graph for Repeat Resolution in Genome Assembly<br /></a><a href="http://bioinf.spbau.ru/en/rectangles" target="_blank">rectangles</a>&nbsp;is an ultimate tool for resolving repeats in genome assemblies.<a href="http://www.mybiosoftware.com/rectangles-2-0-rectangle-graph-repeat-resolution-genome-assembly.html" title="rectangles 2.0 &ndash; Rectangle Graph for Repeat Resolution in Genome Assembly"><br /><br /></a></li>
<li><a href="http://archive.broadinstitute.org/crd/wiki/index.php/Arachne_Main_Page" title="Arachne 4.6233 &ndash; Whole-genome Shotgun Assembler">Arachne 4.6233 &ndash; Whole-genome Shotgun Assembler<br /></a><a href="http://www.broadinstitute.org/crd/wiki/index.php/Arachne_Main_Page" target="_blank">ARACHNE</a>&nbsp;is a program for assembling data from whole genome shotgun sequencing experiments. It was designed for long reads from Sanger sequencing technology, and has been used extensively to assemble many genomes, including many that are large and highly repetitive.<a href="http://www.mybiosoftware.com/arachne-3-2-whole-genome-shotgun-assembler.html" title="Arachne 4.6233 &ndash; Whole-genome Shotgun Assembler"><br /><br /></a></li>
<li><a href="http://terpconnect.umd.edu/~ALEKSEYZ/PhrapUMDV2/" title="Reconciliator 2.0 &ndash; The tool for Merging Assemblies">Reconciliator 2.0 &ndash; The tool for Merging Assemblies<br /></a><a href="http://terpconnect.umd.edu/~ALEKSEYZ/PhrapUMDV2/" target="_blank">Reconciliator</a>&nbsp;is the tool for merging assemblies.<a href="http://www.mybiosoftware.com/reconciliator-2-0-tool-merging-assemblies.html" title="Reconciliator 2.0 &ndash; The tool for Merging Assemblies"><br /><br /></a></li>
<li><a href="http://terpconnect.umd.edu/~ALEKSEYZ/PhrapUMDV2/" title="PhrapUMD 2 &ndash; Modified version of Phrap">PhrapUMD 2 &ndash; Modified version of Phrap<br /></a><a href="http://www.glue.umd.edu/~ALEKSEYZ/PhrapUMDV2" target="_blank">Phrap UMD</a>&nbsp;consists of the UMD Trimmer, UMD Overlapper and a modified version of Phrap.It is capable of assembling data downloaded directly from the NCBI Trace Archive. The pipeline runs in 3 stages: &nbsp;first the vector ends of the reads are examined and the vector is found. &nbsp;Then the reads are trimmed for vector and quality. &nbsp;After that the trimmed reads afe fed into the 5-pass UMD Overlapper that finds the overlaps, corrects the base caller errors and performs additional trimming if necessary. &nbsp;After the overlaps are produced, the trimmed and error-corrected reads and overlaps are input into the modified version of Phrap, whichonly puts the reads together if they overlap according to the list of overlaps produced by the UMD Overlapper.<a href="http://www.mybiosoftware.com/phrapumd-2-modified-version-phrap.html" title="PhrapUMD 2 &ndash; Modified version of Phrap"><br /><br /></a></li>
<li><a href="http://www.dna-dragon.com/" title="DNA Dragon 1.5.6 build1 &ndash; DNA Sequence Contig Assembler Software">DNA Dragon 1.5.6 build1 &ndash; DNA Sequence Contig Assembler Software<br /></a><a href="http://www.dna-dragon.com/" target="_blank">DNA Dragon</a>&nbsp;Contig Assembler assembles sequences, trace data (ABI, SCF, AB1), Illumina and Roche 454 flowgrams into contigs. It is a very fast and accurate DNA sequence assembly software. The DNA sequences are assembled into contigs and a direct comparision of trace date with nucleotide data is possible. It also allows for proofreading and base editing.<a href="http://www.mybiosoftware.com/dna-dragon-1-2-7-dna-sequence-contig-assembler-software.html" title="DNA Dragon 1.5.6 build1 &ndash; DNA Sequence Contig Assembler Software"><br /></a></li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31207/laj-viewing-and-manipulating-the-output-from-pairwise-alignment-programs</guid>
	<pubDate>Wed, 01 Mar 2017 08:35:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31207/laj-viewing-and-manipulating-the-output-from-pairwise-alignment-programs</link>
	<title><![CDATA[Laj: viewing and manipulating the output from pairwise alignment programs]]></title>
	<description><![CDATA[<p>Laj is a tool for viewing and manipulating the output from pairwise alignment programs such as <a href="http://bio.cse.psu.edu/">blastz</a>. It can display interactive dotplot, pip, and text representations of the alignments, a diagram showing the locations of exons and repeats, and annotation links to other web sites containing additional information about particular regions.</p>
<p>The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.</p><p>Address of the bookmark: <a href="http://www.bx.psu.edu/~ratan/" rel="nofollow">http://www.bx.psu.edu/~ratan/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</guid>
	<pubDate>Fri, 03 Mar 2017 10:14:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</link>
	<title><![CDATA[Multi-metagenome assembly]]></title>
	<description><![CDATA[<p>This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:</p>
<p>Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes<br><br>Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, K&aring;re L. Nielsen and Per .H. Nielsen</p>
<p>Nature Biotechnology 2013, doi:&nbsp;<a href="http://www.nature.com/nbt/journal/vaop/ncurrent/abs/nbt.2579.html">10.1038/nbt.2579</a></p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/multi-metagenome" rel="nofollow">https://github.com/MadsAlbertsen/multi-metagenome</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</guid>
	<pubDate>Mon, 06 Mar 2017 04:08:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</link>
	<title><![CDATA[CONCOCT: Clustering cONtigs with COverage and ComposiTion]]></title>
	<description><![CDATA[<p>A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.</p>
<p>Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:<a href="https://github.com/BinPro/CONCOCT/issues">https://github.com/BinPro/CONCOCT/issues</a></p><p>Address of the bookmark: <a href="https://github.com/BinPro/CONCOCT" rel="nofollow">https://github.com/BinPro/CONCOCT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:44:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</link>
	<title><![CDATA[Qualimap2: Evaluating next generation sequencing alignment data]]></title>
	<description><![CDATA[<p><strong>Qualimap 2</strong><span>&nbsp;is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.&nbsp;</span><br><br><span>Supported types of experiments include:</span></p>
<ul>
<li>Whole-genome sequencing</li>
<li>Whole-exome sequencing</li>
<li>RNA-seq (speical mode available)</li>
<li>ChIP-seq</li>
</ul><p>Address of the bookmark: <a href="http://qualimap.bioinfo.cipf.es/" rel="nofollow">http://qualimap.bioinfo.cipf.es/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31564/htslib</guid>
	<pubDate>Wed, 15 Mar 2017 11:38:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31564/htslib</link>
	<title><![CDATA[HTSlib]]></title>
	<description><![CDATA[<p>Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:</p>
<dl><dt>Samtools</dt><dd>Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format</dd><dt>BCFtools</dt><dd>Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants</dd><dt>HTSlib</dt><dd>A C library for reading/writing high-throughput sequencing data</dd></dl>
<p>Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.</p><p>Address of the bookmark: <a href="http://www.htslib.org/" rel="nofollow">http://www.htslib.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/915/researcher-in-computer-sciencebiology</guid>
  <pubDate>Mon, 15 Jul 2013 18:38:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Researcher in computer science/biology]]></title>
  <description><![CDATA[
<p>Researcher in Computer Science at the Computational Biology Unit - temporary employment</p>

<p>The Department of Informatics is a vacant position as a researcher in computer science, related to Computational Biology Unit (CBU), for 3 years.<br /> <br />The position is part of CBU Service Group and will focus on bioinformatic analysis project and especially the analysis of high-throughput data, including NGS (sequencing), and proteomics data.<br /> <br />The successful candidate will be part of the Norwegian bioinformatics platform's national helpdesk within the project ELIXIR.NO<br /> <br />Applicants must hold a PhD in a relevant subject such as computer science, mathematics, molecular biology and also possess expertise and experience in bioinformatics statistics and analysis of data from high-throughput molecular experiment.<br /> <br />Basic programming or scripting skills are required. Experience in Python, R, Perl, Linux-based operating systems and moreover knowledge of databases and web programming will be a strength for applicants.<br /> <br />We expect enthusiasm and independence and moreover the ability to work in an interdisciplinary team environment.<br /> <br />Good knowledge of English is required.<br /> <br />Salaries start at level 57 (code 1109/LR 24.1) by appointment. Further promotion occurs after<br />service seniority in the position (at grade 57-65). Of particularly highly qualified applicants may be considered a higher salary.<br /> <br />Further information about the position is available from the chair of the CBU, <br />Professor Inge Jonassen, e-mail: Inge.Jonassen @ ii.uib.no<br /> <br />The successful applicant must comply with the guidelines that apply at any given time the position.<br /> <br />State employment shall as far as possible reflect the diversity of the population. It is therefore an objective to achieve a balanced age and sex composition and the recruitment of persons with immigrant backgrounds. Persons with immigrant background are requested to apply for the position.<br /> <br />Women are particularly encouraged to apply. If the experts find that several applicants have approximately equivalent qualifications, the rules on equal in the Personnel Regulations for Academic Positions will be applied.<br /> <br />University of Bergen applies the principles of public openness when recruiting staff to scientific positions.<br /> <br />Information about the applicant may be made public even though the applicant has requested not to be named in the list of applicants. If the request does not host admitted to the result, the applicant shall be notified of this.<br /> <br />Send application, CV, certificates, diplomas, undergraduate work and a list of publications (list of publications) online by clicking on https://www.jobbnorge.no/jobbsoknet/login.aspx?returnurl=/jobbsoknet/jobapplication.aspx?jobid=95196<br /> <br />You need to upload certified translations into English or a Scandinavian language of appendices, such as diplomas and transcripts.<br /> <br />Applications sent by email to individuals at the institute will not be considered.<br /> <br />Deadline: 9 August 2013</p>
]]></description>
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