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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30149?offset=40</link>
	<atom:link href="https://bioinformaticsonline.com/related/30149?offset=40" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30074/minia</guid>
	<pubDate>Thu, 08 Dec 2016 05:07:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30074/minia</link>
	<title><![CDATA[Minia]]></title>
	<description><![CDATA[<p>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. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).</p>
<h3>Download</h3>
<p><a href="https://github.com/GATB/minia/releases/download/v2.0.7/minia-v2.0.7-bin-Linux.tar.gz">Minia 2.0.7 Linux 64-bits binaries</a>&nbsp;(<a href="https://github.com/GATB/minia/releases/download/v2.0.7/minia-v2.0.7-Source.tar.gz">Source code</a>)&nbsp;<span>(<a href="http://minia.genouest.org/files/minia-1.6906.tar.gz">Legacy codebase</a>)</span></p>
<h3>For the impatient</h3>
<p>A typical Minia command line looks like:</p>
<pre>./minia -in <span>reads.fa</span> -kmer-size <span>31</span> -abundance-min <span>3</span> -out <span>output_prefix</span></pre>
<p>Type</p>
<pre>./minia</pre>
<p><span>for a quick explanation of the parameters.</span></p>
<p>For more information, refer to the&nbsp;<a href="http://minia.genouest.org/files/minia.pdf">manual</a>.</p>
<p><a href="http://kmergenie.bx.psu.edu/">KmerGenie</a>&nbsp;can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://minia.genouest.org/" rel="nofollow">http://minia.genouest.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30090/standardized-velvet-assembly-report</guid>
	<pubDate>Fri, 09 Dec 2016 03:59:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30090/standardized-velvet-assembly-report</link>
	<title><![CDATA[Standardized velvet assembly report]]></title>
	<description><![CDATA[<p>Requirements:</p>
<ul>
<li>velvet (velveth velvetg should be in your PATH)</li>
<li>R (with Sweave)</li>
<li>pdflatex (usually part of TeTeX)</li>
<li>ggplot2 (from R prompt type install.packages("ggplot2","proto","xtable"))</li>
<li>Perl</li>
</ul>
<p>Optional:</p>
<ul>
<li>BLAT or BLAST (to generate alignments against a reference genome). If using BLAT, add faToTwoBit,gfClient,gfServer to your PATH. If using BLAST, add blastall and formatdb.</li>
</ul>
<p>Edit permute.sh to your liking, paying particular attention to the kmer, cvCut, expCov, and other flags</p>
<p>To Run:</p>
<ol>
<li><code>perl fastaAllSize mysequences.fa &gt; mysequences.stat or gunzip -c mysequences.fa.gz | fastaAllSize &gt; mysequences.stat</code>&nbsp;Substitute fastqAllSize for fastq files.</li>
<li><code>./permute.sh mysequences</code>&nbsp;(leave out the .fa)</li>
</ol>
<p>https://github.com/leipzig/standardized-velvet-assembly-report</p><p>Address of the bookmark: <a href="https://github.com/leipzig/standardized-velvet-assembly-report" rel="nofollow">https://github.com/leipzig/standardized-velvet-assembly-report</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30111/eager</guid>
	<pubDate>Sat, 10 Dec 2016 18:07:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30111/eager</link>
	<title><![CDATA[EAGER]]></title>
	<description><![CDATA[<p><span>The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.</span></p>
<p><span>EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z</p><p>Address of the bookmark: <a href="https://github.com/apeltzer/EAGER-GUI" rel="nofollow">https://github.com/apeltzer/EAGER-GUI</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30144/bima-v3-an-aligner-customized-for-mate-pair-library-sequencing</guid>
	<pubDate>Wed, 14 Dec 2016 15:20:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30144/bima-v3-an-aligner-customized-for-mate-pair-library-sequencing</link>
	<title><![CDATA[BIMA V3: an aligner customized for mate pair library sequencing]]></title>
	<description><![CDATA[<p>Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and <br>time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing. <br>Availability: http://bioinformaticstools.mayo.edu/research/bima/ <br>Contact: vasmatzis.george@mayo.edu</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30207/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</guid>
	<pubDate>Mon, 19 Dec 2016 06:07:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30207/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</link>
	<title><![CDATA[GAM-NGS: genomic assemblies merger for next generation sequencing]]></title>
	<description><![CDATA[<p><span>GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic&nbsp;</span><em>locus</em><span>&nbsp;(called&nbsp;</span><em>blocks</em><span>) are identified through reads' alignments and stored in a&nbsp;</span><em>weighted</em><span>graph. The merging phase is carried out with the help of this weighted graph that allows an&nbsp;</span><em>optimal</em><span>&nbsp;resolution of&nbsp;</span><em>local</em><span>&nbsp;problematic regions.</span></p><p>Address of the bookmark: <a href="https://github.com/vice87/gam-ngs" rel="nofollow">https://github.com/vice87/gam-ngs</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30234/last</guid>
	<pubDate>Mon, 19 Dec 2016 14:07:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30234/last</link>
	<title><![CDATA[LAST]]></title>
	<description><![CDATA[<p>LAST can:</p>
<ul>
<li>Handle&nbsp;<strong>big</strong>&nbsp;sequence data, e.g:
<ul>
<li>Compare two vertebrate genomes</li>
<li>Align billions of DNA reads to a genome</li>
</ul>
</li>
<li>Indicate the&nbsp;<a href="http://lastweb.cbrc.jp/about.html">reliability</a>&nbsp;of each aligned column.</li>
<li>Use sequence quality data&nbsp;<a href="http://nar.oxfordjournals.org/content/38/7/e100.abstract">properly</a>.</li>
<li>Compare DNA to proteins, with frameshifts.</li>
<li>Compare PSSMs to sequences</li>
<li>Calculate the likelihood of chance similarities between random sequences.</li>
<li>Do split and spliced alignment.</li>
<li><a href="http://last.cbrc.jp/doc/last-train.html">Train</a>&nbsp;alignment parameters for unusual kinds of sequence (e.g. nanopore).</li>
</ul><p>Address of the bookmark: <a href="http://last.cbrc.jp/" rel="nofollow">http://last.cbrc.jp/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</guid>
	<pubDate>Tue, 20 Dec 2016 07:56:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</link>
	<title><![CDATA[Genome Assembly Tutorial]]></title>
	<description><![CDATA[<p><span>If genomes were completely random sequences in a statistical sense, 'overlap-consensus-layout' method would have been enough to assemble large genomes from Sanger reads. In contrast, real genomes often have long repetitive regions, and they are hard to assemble using overlap-consensus-layout approach. De Bruijn graph-based assembly approach was originally proposed to handle the assembly of repetitive regions better.</span></p>
<p><span>More at&nbsp;http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</span></p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1" rel="nofollow">http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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/30550/genomering-alignment-visualization-based-on-supergenome-coordinates</guid>
	<pubDate>Wed, 18 Jan 2017 10:24:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30550/genomering-alignment-visualization-based-on-supergenome-coordinates</link>
	<title><![CDATA[GenomeRing: alignment visualization based on SuperGenome coordinates]]></title>
	<description><![CDATA[<p>The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.</p>
<p>More at https://academic.oup.com/bioinformatics/article/28/12/i7/268598/GenomeRing-alignment-visualization-based-on</p><p>Address of the bookmark: <a href="http://it.informatik.uni-tuebingen.de/?page_id=185" rel="nofollow">http://it.informatik.uni-tuebingen.de/?page_id=185</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31064/cgaln</guid>
	<pubDate>Wed, 22 Feb 2017 05:14:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31064/cgaln</link>
	<title><![CDATA[Cgaln]]></title>
	<description><![CDATA[<p>Cgaln (Coarse grained alignment) is a program designed to align a pair of whole genomic sequences of not only bacteria but also entire chromosomes of vertebrates on a nominal desktop computer. Cgaln performs an alignment job in two steps, at the block level and then at the nucleotide level. The former "coarse-grained" alignment can explore genomic rearrangements and reduce the regions to be analyzed in the next step. The latter is devoted to detailed alignment within the limited regions found in the first stage. The output of Cgaln is 'glocal' in the sense that rearrangements are taken into consideration while each alignable region is extended as long as possible. Thus, Cgaln is not only fast and memory-efficient, but also can filter noisy outputs without missing the most important homologous segment pairs.</p>
<p>http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/</p><p>Address of the bookmark: <a href="http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/" rel="nofollow">http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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