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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36893?offset=340</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</guid>
	<pubDate>Sat, 02 Jun 2018 07:40:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</link>
	<title><![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]></title>
	<description><![CDATA[<p><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">MCMCTREE</a><span>&nbsp;is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the&nbsp;</span><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">PAML</a><span>&nbsp;package. In this tutorial I will analyze an easy example modified from dataset of&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20551041" target="_blank">Inoue et al. (2010)</a><span>. Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.</span></p><p>Address of the bookmark: <a href="http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html" rel="nofollow">http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</guid>
	<pubDate>Mon, 07 Jan 2019 10:35:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</link>
	<title><![CDATA[kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data]]></title>
	<description><![CDATA[<p><strong>kallisto</strong>&nbsp;is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of&nbsp;<em>pseudoalignment</em>&nbsp;for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data,&nbsp;<strong>kallisto</strong>&nbsp;can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and&nbsp;<strong>kallisto</strong>&nbsp;is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks&nbsp;<strong>kallisto</strong>&nbsp;significantly outperforms existing tools.&nbsp;<strong>kallisto</strong>&nbsp;is described in detail in:</p>
<p>Nicolas L Bray, Harold Pimentel, P&aacute;ll Melsted and Lior Pachter,&nbsp;<a href="http://www.nature.com/nbt/journal/v34/n5/full/nbt.3519.html">Near-optimal probabilistic RNA-seq quantification</a>, Nature Biotechnology&nbsp;<strong>34</strong>, 525&ndash;527 (2016), doi:10.1038/nbt.3519</p><p>Address of the bookmark: <a href="https://pachterlab.github.io/kallisto/about" rel="nofollow">https://pachterlab.github.io/kallisto/about</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</guid>
	<pubDate>Mon, 12 Aug 2019 07:52:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</link>
	<title><![CDATA[Cactus: a reference-free whole-genome multiple alignment program]]></title>
	<description><![CDATA[<p>Cactus is a reference-free whole-genome multiple alignment program. The principal algorithms are described here:&nbsp;<a href="https://doi.org/10.1101/gr.123356.111">https://doi.org/10.1101/gr.123356.111</a></p>
<p><span>Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM.</span>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/ComparativeGenomicsToolkit/cactus" rel="nofollow">https://github.com/ComparativeGenomicsToolkit/cactus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41872/autodock-vina-an-open-source-program-for-doing-molecular-docking</guid>
	<pubDate>Sat, 13 Jun 2020 07:55:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41872/autodock-vina-an-open-source-program-for-doing-molecular-docking</link>
	<title><![CDATA[AutoDock Vina: an open-source program for doing molecular docking.]]></title>
	<description><![CDATA[<p><span>AutoDock Vina is an open-source program for doing&nbsp;</span><a href="http://en.wikipedia.org/wiki/Docking_(molecular)">molecular docking</a><span>. It was designed and implemented by&nbsp;</span><a href="http://olegtrott.com/">Dr. Oleg Trott</a><span>&nbsp;in the Molecular Graphics Lab at The Scripps Research Institute.</span>&nbsp;It is especially effective for protein-ligand docking. AutoDock 4 is available under the GNU General Public License. AutoDock is one of the most cited docking software applications in the research community.</p>
<p><img src="http://vina.scripps.edu/img/accuracy.png" width="352" height="264" alt="image" style="border: 0px;"></p>
<p><a href="http://vina.scripps.edu/">http://vina.scripps.edu/</a></p><p>Address of the bookmark: <a href="http://vina.scripps.edu/" rel="nofollow">http://vina.scripps.edu/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/16686/sequence-viewer-download-transcripts-exons-and-proteins</guid>
	<pubDate>Mon, 15 Sep 2014 17:30:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/16686/sequence-viewer-download-transcripts-exons-and-proteins</link>
	<title><![CDATA[Sequence Viewer: Download Transcripts, Exons and Proteins]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/ZWnLyYKozaI" frameborder="0" allowfullscreen></iframe>How to download FASTA sequence for certain gene features while in the NCBI's Sequence Viewer.

Sequence Viewer homepage:
www.ncbi.nlm.nih.gov/projects/sviewer/

Sequence Viewer playlist:
https://www.youtube.com/playlist?list=PL76D7EE6A6A8AC1C3]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27331/andi</guid>
	<pubDate>Fri, 13 May 2016 05:16:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27331/andi</link>
	<title><![CDATA[Andi]]></title>
	<description><![CDATA[<p>This is the <code>andi</code> program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because <code>andi</code> does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.</p>
<p>This readme covers all necessary instructions for the impatient to get <code>andi</code> up and running. For extensive instructions please consult the <a href="https://github.com/EvolBioInf/andi/blob/master/andi-manual.pdf">manual</a>.</p>
<p>More at https://github.com/evolbioinf/andi/</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34565/fogsaa-fast-optimal-global-sequence-alignment-algorithm</guid>
	<pubDate>Fri, 08 Dec 2017 14:41:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34565/fogsaa-fast-optimal-global-sequence-alignment-algorithm</link>
	<title><![CDATA[FOGSAA: Fast Optimal Global Sequence Alignment Algorithm]]></title>
	<description><![CDATA[<p>Sequence alignment algorithms are widely used to infer similarirty and the point of differences between pair of sequences. FOGSAA is a fast Global alignment algorithm. It is basically a branch and bound approach which starts branch expansion in a greedy way taking the symbols from the given pair of sequences (protein or nucleotide) and results in an optimal alignment faster than conventional dymanic programming techniques. It is also better than the heuristic methods with respect to alignment quality.</p><p>Address of the bookmark: <a href="http://www.isical.ac.in/~bioinfo_miu/FOGSAA.htm" rel="nofollow">http://www.isical.ac.in/~bioinfo_miu/FOGSAA.htm</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41689/medaka-sequence-correction-provided-by-ont-research</guid>
	<pubDate>Mon, 18 May 2020 16:28:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41689/medaka-sequence-correction-provided-by-ont-research</link>
	<title><![CDATA[medaka: Sequence correction provided by ONT Research]]></title>
	<description><![CDATA[<p><code>medaka</code><span>&nbsp;is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.</span></p><p>Address of the bookmark: <a href="https://github.com/nanoporetech/medaka" rel="nofollow">https://github.com/nanoporetech/medaka</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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