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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36510?offset=40</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</guid>
	<pubDate>Tue, 08 May 2018 04:27:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</link>
	<title><![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]></title>
	<description><![CDATA[<p><strong>HISAT2</strong><span>&nbsp;is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs&nbsp;</span><a href="http://dl.acm.org/citation.cfm?id=2674828">[Sir&eacute;n et al. 2014]</a><span>, we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).&nbsp;</span></p>
<p><span>more at&nbsp;https://ccb.jhu.edu/software/hisat2/index.shtml</span></p><p>Address of the bookmark: <a href="https://github.com/infphilo/hisat2" rel="nofollow">https://github.com/infphilo/hisat2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</guid>
	<pubDate>Thu, 09 Aug 2018 04:21:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</link>
	<title><![CDATA[List of non-commercial NGS genotype-calling software]]></title>
	<description><![CDATA[<p><span>Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.&nbsp;</span></p><p><span>A list of programs for genotype and SNP calling :</span></p><p><br />SOAP2&nbsp;http://soap.genomics.org.cn/index.html</p><p>Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)</p><p>realSFS&nbsp;http://128.32.118.212/thorfinn/realSFS/</p><p>Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation</p><p>Samtools http://samtools.sourceforge.net/</p><p>Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)</p><p>GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)</p><p>Beagle http://faculty.washington.edu/browning/beagle/beagle.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling</p><p>IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map</p><p>QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL</p><p>Multi-sample LD &lsquo;Feasible&rsquo; genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The &lsquo;feasible&rsquo; genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)</p><p>MaCH http://genome.sph.umich.edu/wiki/Thunder</p><p>Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly</guid>
	<pubDate>Tue, 22 Jan 2019 09:39:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly</link>
	<title><![CDATA[List of tools frequently used while genome assembly]]></title>
	<description><![CDATA[<h4>List of tools frequently used while genome assembly:</h4><p>I have used the following assemblers</p><ul>
<li><a href="http://bioinf.spbau.ru/spades">Spades</a>&nbsp;(v. 3.10.1)</li>
<li><a href="http://canu.readthedocs.io/en/stable/index.html">CANU</a>&nbsp;(v. 1.6)</li>
<li><a href="https://github.com/rrwick/Unicycler">Unicycler&nbsp;</a>(v. v0.4.1)</li>
<li><a href="https://github.com/lh3/miniasm">Miniasm</a>&nbsp;(v. 0.2-r137-dirty)</li>
</ul><p>I have used the following mappers</p><ul>
<li><a href="https://github.com/lh3/minimap2">minimap2</a>&nbsp;(v.&nbsp;2.0rc1-r232)</li>
<li><a href="https://github.com/lh3/minimap">minimap&nbsp;</a>(v. 0.2-r124-dirty)</li>
<li><a href="https://github.com/lh3/bwa">bwa</a>&nbsp;(v.&nbsp;0.7.12-r1039)</li>
</ul><p>I have used the following polishing tools</p><ul>
<li><a href="https://github.com/isovic/racon">Racon</a>&nbsp;(v. not available)</li>
<li><a href="https://github.com/broadinstitute/pilon">Pilon</a>&nbsp;(v. 1.18)</li>
<li><a href="https://github.com/jts/nanopolish">Nanopolish</a>&nbsp;(v. 0.8.3)</li>
</ul><p>I have used the following tools to assess genome assembly characteristics</p><ul>
<li><a href="https://github.com/chjp/ANI">ANI.pl</a>&nbsp;(https://github.com/chjp/ANI)</li>
<li><a href="http://ecogenomics.github.io/CheckM/">CheckM</a>&nbsp;(v. 1.0.7)</li>
<li><a href="https://github.com/tseemann/prokka">Prokka</a>&nbsp;(v. 1.12)</li>
<li><a href="http://bioinf.spbau.ru/en/quast">QUAST</a>&nbsp;(v. 2.3)</li>
<li><a href="http://mummer.sourceforge.net/">mummer&nbsp;</a>(v. not available)</li>
</ul><p>If you have any ideas or superior tools we have missed please let us know in the comments.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</guid>
	<pubDate>Thu, 23 Jul 2020 05:49:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</link>
	<title><![CDATA[wgd—simple command line tools for the analysis of ancient whole-genome duplications]]></title>
	<description><![CDATA[<p><span>wgd is a easy to use command-line tool for<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>distribution construction named wgd. The wgd suite provides commonly used<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.</span></p>
<p><a href="https://academic.oup.com/bioinformatics/article/35/12/2153/5162749">https://academic.oup.com/bioinformatics/article/35/12/2153/5162749</a></p><p>Address of the bookmark: <a href="https://github.com/arzwa/wgd" rel="nofollow">https://github.com/arzwa/wgd</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</guid>
	<pubDate>Wed, 06 Jan 2021 19:49:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</link>
	<title><![CDATA[Breeding Insight]]></title>
	<description><![CDATA[<p><span><span>Breeding Insight&nbsp;at Cornell University will leverage recent improvements in genomics and open source informatics components, and in&nbsp;partnership with small breeding programs, will enable these programs to harness&nbsp;&nbsp;powerful digital tools to accelerate their genetic gains</span></span></p>
<p><span>Breeding Insight is funded by&nbsp;the&nbsp;</span><span><a href="https://www.ars.usda.gov/about-ars/" target="_blank">U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS)</a></span><span>&nbsp;through Cornell University. The USDA ARS delivers scientific solutions to national and global agricultural challenges. As a global leader&nbsp;in agricultural discovery through scientific excellence, ARS is committed to delivering cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustaining our nation&rsquo;s agroecosystems and natural resources; and ensuring the economic competitiveness and excellence of our agriculture.</span></p><p>Address of the bookmark: <a href="https://www.breedinginsight.org/" rel="nofollow">https://www.breedinginsight.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</guid>
	<pubDate>Wed, 18 Aug 2021 00:02:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</link>
	<title><![CDATA[Kmer: a suite of tools for DNA sequence analysis]]></title>
	<description><![CDATA[<p>More at&nbsp;https://help.rc.ufl.edu/doc/Kmer</p>
<p>This also includes:</p>
<ul>
<li>A2Amapper: ATAC, Assembly to Assembly Comparision tool:
<ul>
<li>Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).</li>
</ul>
</li>
</ul>
<ul>
<li>Sim4db:
<ul>
<li>Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.</li>
</ul>
</li>
</ul>
<ul>
<li>LEAFF:
<ul>
<li>LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.</li>
</ul>
</li>
</ul>
<ul>
<li>Meryl:
<ul>
<li>An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.</li>
</ul>
</li>
</ul><p>Address of the bookmark: <a href="https://help.rc.ufl.edu/doc/Kmer" rel="nofollow">https://help.rc.ufl.edu/doc/Kmer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44288/upset-plots</guid>
	<pubDate>Fri, 24 Mar 2023 22:30:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44288/upset-plots</link>
	<title><![CDATA[Upset plots !]]></title>
	<description><![CDATA[<p>Upset plots are a type of visualization used to analyze the intersection of sets or categories. They are particularly useful for displaying data with multiple categories and analyzing their overlaps.</p>
<p>In an upset plot, each row represents a category or set, and each column represents a data point. The length of the bar for each category indicates the number of data points that belong to that category. The plot also shows the intersections between categories, represented by overlapping bars.</p>
<p>Upset plots are useful for visualizing complex data with multiple categories and intersections, and can help identify patterns and relationships between categories. They are often used in fields such as bioinformatics, where they can be used to analyze gene expression data or to compare the results of different experimental conditions.</p>
<p>https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions</p><p>Address of the bookmark: <a href="https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions" rel="nofollow">https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</guid>
	<pubDate>Tue, 18 Jun 2024 02:04:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</link>
	<title><![CDATA[BioKit: a set of tools dedicated to bioinformatics, data visualisation]]></title>
	<description><![CDATA[<p><span>BioKit is a set of tools dedicated to bioinformatics, data visualisation (</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.viz" title="biokit.viz"><code><span>biokit.viz</span></code></a><span>), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis (e.g.,&nbsp;</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.stats" title="biokit.stats"><code><span>biokit.stats</span></code></a><span>). Since R is quite common in bioinformatics, we also provide a convenient module to run R inside your Python scripts or shell (:mod:biokit.rtools module).</span></p><p>Address of the bookmark: <a href="https://biokit.readthedocs.io/en/latest/index.html" rel="nofollow">https://biokit.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39821/gvolante-completeness-assessment-of-genometranscriptome-sequences</guid>
	<pubDate>Tue, 06 Aug 2019 21:37:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39821/gvolante-completeness-assessment-of-genometranscriptome-sequences</link>
	<title><![CDATA[gVolante: Completeness Assessment of Genome/Transcriptome Sequences]]></title>
	<description><![CDATA[<p><strong>gVolante</strong><span>&nbsp;provides an online interface for completeness assessment of user&rsquo;s original or publicly available sequence datasets as well as for browsing results of completeness assessment performed on publicly available genome and transcriptome assemblies.</span></p>
<p><img src="https://gvolante.riken.jp/images/assessment.png" width="937" height="545" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://gvolante.riken.jp/" rel="nofollow">https://gvolante.riken.jp/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</guid>
	<pubDate>Tue, 18 Jun 2019 05:33:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</link>
	<title><![CDATA[Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.]]></title>
	<description><![CDATA[<div id="yui_3_14_1_1_1560853173251_3865">Cogent is a tool that identifies gene&nbsp;families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check&nbsp;assemblies&nbsp;for the presence of&nbsp;these known coding sequences.</div>
<div>&nbsp;</div>
<div>
<p>Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on&nbsp;<a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Iso-Seq data</a>&nbsp;and in cases where there is no reference genome or the ref genome is highly incomplete.</p>
<p>See a&nbsp;<a href="https://www.dropbox.com/s/mn6hwhguh0pqceu/20160106_Cogent_developers_conference_slides_Cuttlefish.pdf?dl=0">recent presentation</a>&nbsp;on Cogent being applied to the Cuttlefish Iso-Seq data.</p>
<p><a href="https://www.dropbox.com/s/kz0gi7qg0w82k9a/20161026_Cogent_manuscript_forGitHub.pdf?dl=0">Cogent preliminary draft paper (updated 2016Dec version)</a>,&nbsp;<a href="https://www.dropbox.com/s/37412o8glvnfhf9/20161026_Cogent_ManuscriptPlusSupplement_forGitHub.pdf?dl=0">Supplementary</a></p>
<p>Please see&nbsp;<a href="https://github.com/Magdoll/Cogent/wiki">wiki</a>&nbsp;for details on usage.</p>
</div><p>Address of the bookmark: <a href="https://github.com/Magdoll/Cogent" rel="nofollow">https://github.com/Magdoll/Cogent</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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