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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39213?offset=590</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</guid>
	<pubDate>Wed, 20 Dec 2017 18:59:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</link>
	<title><![CDATA[Meraculous: Haplotype-sensitive Assembly of Highly Heterozygous genomes.]]></title>
	<description><![CDATA[<p><span>Meraculous is a whole genome assembler for Next Generation Sequencing data geared for large genomes. It is a hybrid k-mer/read-based assembler that capitalizes on the high accuracy of Illumina sequence by eschewing an explicit error correction step which we argue to be redundant with the assembly process. Meraculous achieves high performance with large datasets by utilizing lightweight data structures and multi-threaded parallelization, allowing to assemble human-sized genomes on commodity clusters in under a day. The process pipeline implements a highly transparent and portable model of job control and monitoring where different assembly stages can be executed and re-executed separately or in unison on a wide variety of architectures.</span></p>
<p><span>https://jgi.doe.gov/data-and-tools/meraculous/</span></p>
<p><span>https://arxiv.org/ftp/arxiv/papers/1703/1703.09852.pdf</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/meraculous20/" rel="nofollow">https://sourceforge.net/projects/meraculous20/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</guid>
	<pubDate>Tue, 08 May 2018 04:39:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</link>
	<title><![CDATA[EvidentialGene: tr2aacds, mRNA Transcript Assembly Software]]></title>
	<description><![CDATA[<p><span>EvidentialGene is a genome informatics project, "Evidence Directed Gene Construction for Eukaryotes", to construct high quality, accurate gene sets for animals and plants, developed by Don Gilbert at Indiana University, see</span><br><a href="http://arthropods.eugenes.org/EvidentialGene/" target="_blank">http://arthropods.eugenes.org/EvidentialGene/<span></span></a><br><br><span>Construction refers to the combination of classical gene prediction, and more recent gene assembly (de-novo and genome-assisted) methods. The basic Evigene methods involve using available best-of-breed gene prediction and assembly software, combining all evidence for genes, from expressed sequences, genome assembly sequences, related species protein sequences, and any other, to annotate and score gene constructions. Over-produced constructions are classified by gene evidence for best qualities per "locus", including genome-aligned and gene-transcript aligned (genome-free) locus identification. All software developed for EvidentialGene is publicly available. See project wiki/blog for notes.</span></p>
<p><span>Download&nbsp;</span></p>
<p>http://arthropods.eugenes.org/EvidentialGene/trassembly.html</p>
<p>https://sourceforge.net/p/evidentialgene/blog/</p><p>Address of the bookmark: <a href="http://arthropods.eugenes.org/EvidentialGene/trassembly.html" rel="nofollow">http://arthropods.eugenes.org/EvidentialGene/trassembly.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43110/quasimodo-quasispecies-metric-determination-on-omics</guid>
	<pubDate>Sat, 26 Jun 2021 15:22:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43110/quasimodo-quasispecies-metric-determination-on-omics</link>
	<title><![CDATA[QuasiModo - Quasispecies Metric Determination on Omics]]></title>
	<description><![CDATA[<p><span>This repository contains the scripts and pipeline that reproduces the results of the HCMV benchmarking study. In this study we evaluated genome assemblers and variant callers on 10 in vitro generated, mixed strain HCMV sequence samples, each consisting of two lab strains in different abundance ratios. This tool can also be used to evaluate assemblies and variant calling results on other similar datasets.</span></p>
<p><span>https://academic.oup.com/bib/article/22/3/bbaa123/5868070</span></p><p>Address of the bookmark: <a href="https://github.com/hzi-bifo/Quasimodo" rel="nofollow">https://github.com/hzi-bifo/Quasimodo</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator</guid>
	<pubDate>Wed, 08 Mar 2023 01:39:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator</link>
	<title><![CDATA[ALE: Assembly Likelihood Estimator]]></title>
	<description><![CDATA[<p>Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly.&nbsp; Depending on your genome size you may want to convert the .wig files to the BigWig format.</p><p>Address of the bookmark: <a href="https://github.com/sc932/ALE" rel="nofollow">https://github.com/sc932/ALE</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly</guid>
	<pubDate>Sat, 08 Jun 2024 16:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly</link>
	<title><![CDATA[MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly]]></title>
	<description><![CDATA[<p><span>The MetaGraph framework</span><span>&nbsp;is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as&nbsp;</span><a href="https://metagraph.ethz.ch/#query">sequence search</a><span>&nbsp;or&nbsp;</span><a href="https://metagraph.ethz.ch/#assembly">differential assembly</a><span>.</span></p>
<p><span>Searcg link&nbsp;https://metagraph.ethz.ch/search&nbsp;</span></p>
<p><span>Pre-print&nbsp;https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4&nbsp;</span></p><p>Address of the bookmark: <a href="https://metagraph.ethz.ch/" rel="nofollow">https://metagraph.ethz.ch/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36817/kwip-the-k-mer-weighted-inner-product-a-de-novo-estimator-of-genetic-similarity</guid>
	<pubDate>Tue, 29 May 2018 08:37:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36817/kwip-the-k-mer-weighted-inner-product-a-de-novo-estimator-of-genetic-similarity</link>
	<title><![CDATA[kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity]]></title>
	<description><![CDATA[<p>The k-mer Weighted Inner Product.</p>
<p>This software implements a <em>de novo</em>, <em>alignment free</em> measure of sample genetic dissimilarity which operates upon raw sequencing reads. It is able to calculate the genetic dissimilarity between samples without any reference genome, and without assembling one.</p>
<p> </p>

De novo estimates of genetic relatedness from next-gen sequencing data https://kwip.readthedocs.org<p>Address of the bookmark: <a href="https://github.com/kdmurray91/kwip" rel="nofollow">https://github.com/kdmurray91/kwip</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39830/the-extensive-de-novo-te-annotator-edta</guid>
	<pubDate>Thu, 08 Aug 2019 04:05:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39830/the-extensive-de-novo-te-annotator-edta</link>
	<title><![CDATA[The Extensive de novo TE Annotator (EDTA)]]></title>
	<description><![CDATA[<p><span>The EDTA package was designed to filter out false discoveries in raw TE candidates and generate a high-quality non-redundant TE library for whole-genome TE annotations. Selection of initial search programs were based on benckmarkings on the annotation performance using a manually curated TE library in the rice genome.</span></p><p>Address of the bookmark: <a href="https://github.com/oushujun/EDTA" rel="nofollow">https://github.com/oushujun/EDTA</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</guid>
	<pubDate>Wed, 14 Nov 2018 04:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</link>
	<title><![CDATA[ANItools web: a web tool for fast genome comparison within multiple bacterial strains]]></title>
	<description><![CDATA[<p><span>ANItools is a software package written by PERL scripts that can be run in a Linux/Unix system. If you want to compare bacterial genomes and calculate their average nucleotide identity (ANI), you could download and run this program directly. Or you could send us the genome sequence by email. Then we will do the analysis work for you.</span></p>
<p><span>https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454</span></p><p>Address of the bookmark: <a href="http://ani.mypathogen.cn/" rel="nofollow">http://ani.mypathogen.cn/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</guid>
	<pubDate>Wed, 06 Oct 2021 07:01:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</link>
	<title><![CDATA[MMseqs2: ultra fast and sensitive sequence search and clustering suite]]></title>
	<description><![CDATA[<p><span>MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.</span></p><p>Address of the bookmark: <a href="https://github.com/soedinglab/MMseqs2" rel="nofollow">https://github.com/soedinglab/MMseqs2</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments</guid>
	<pubDate>Mon, 22 Sep 2025 23:51:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments</link>
	<title><![CDATA[Termal: a fast and interactive terminal-based viewer for multiple sequence alignments]]></title>
	<description><![CDATA[<p>termal, a fast, interactive, terminal-based viewer for multiple sequence alignments (MSAs), designed for use on remote systems such as high-performance computing (HPC) clusters.</p>
<p>https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true</p><p>Address of the bookmark: <a href="https://github.com/sib-swiss/termal" rel="nofollow">https://github.com/sib-swiss/termal</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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