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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26573?offset=130</link>
	<atom:link href="https://bioinformaticsonline.com/related/26573?offset=130" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30976/brig</guid>
	<pubDate>Thu, 16 Feb 2017 13:14:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30976/brig</link>
	<title><![CDATA[BRIG]]></title>
	<description><![CDATA[<p>BRIG is a free cross-platform (Windows/Mac/Unix) application that can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data. The application is available at:<a href="http://sourceforge.net/projects/brig">http://sourceforge.net/projects/brig</a></p>
<p>If you have any questions or comments, post them on&nbsp;<a href="http://sourceforge.net/tracker/?group_id=328245">one of the trackers</a>&nbsp;on BRIG&rsquo;s SourceForge page:<a href="http://sourceforge.net/tracker/?group_id=328245">http://sourceforge.net/tracker/?group_id=328245</a>.</p>
<p>Features:</p>
<ul>
<li>Images show similarity between a central reference sequence and other sequences as concentric rings.</li>
<li>BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.</li>
<li>Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.</li>
<li>Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.</li>
<li>BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously</li>
</ul>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://brig.sourceforge.net/" rel="nofollow">http://brig.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31087/bedtools</guid>
	<pubDate>Fri, 24 Feb 2017 04:50:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31087/bedtools</link>
	<title><![CDATA[bedtools]]></title>
	<description><![CDATA[<p>Collectively, the&nbsp;<strong>bedtools</strong>&nbsp;utilities are a swiss-army knife of tools for a wide-range of genomics analysis tasks. The most widely-used tools enable&nbsp;<em>genome arithmetic</em>: that is, set theory on the genome. For example,&nbsp;<strong>bedtools</strong>&nbsp;allows one to<em>intersect</em>,&nbsp;<em>merge</em>,&nbsp;<em>count</em>,&nbsp;<em>complement</em>, and&nbsp;<em>shuffle</em>&nbsp;genomic intervals from multiple files in widely-used genomic file formats such as BAM, BED, GFF/GTF, VCF. While each individual tool is designed to do a relatively simple task (e.g.,&nbsp;<em>intersect</em>&nbsp;two interval files), quite sophisticated analyses can be conducted by combining multiple bedtools operations on the UNIX command line.</p>
<p><strong>bedtools</strong>&nbsp;is developed in the&nbsp;<a href="http://quinlanlab.org/">Quinlan laboratory</a>&nbsp;at the&nbsp;<a href="http://www.utah.edu/">University of Utah</a>&nbsp;and benefits from fantastic contributions made by scientists worldwide.</p><p>Address of the bookmark: <a href="http://bedtools.readthedocs.io/en/latest/index.html" rel="nofollow">http://bedtools.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 27 Feb 2017 09:49:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC: a repeat-aware and scalable tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><span>FinisherSC, a repeat-aware and scalable tool for upgrading&nbsp;</span><em>de novo</em><span>&nbsp;assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.</span></p><p>Address of the bookmark: <a href="http://kakitone.github.io/finishingTool/" rel="nofollow">http://kakitone.github.io/finishingTool/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</guid>
	<pubDate>Mon, 06 Mar 2017 03:44:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</link>
	<title><![CDATA[MetaBAT:  An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities]]></title>
	<description><![CDATA[<p>MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities</p>
<p>Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (&gt;200kb) in 3 hours, where 621 genome bins are &gt;50% complete with &lt;5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.</p><p>Address of the bookmark: <a href="https://bitbucket.org/berkeleylab/metabat" rel="nofollow">https://bitbucket.org/berkeleylab/metabat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32154/decostar-detection-of-co-evolution</guid>
	<pubDate>Fri, 14 Apr 2017 06:27:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32154/decostar-detection-of-co-evolution</link>
	<title><![CDATA[DeCoSTAR - Detection of Co-evolution]]></title>
	<description><![CDATA[<p><span>DeCoSTAR is a software which aims at reconstructing ancestral gene or genome organizations, in the form of sets of neighborhood relations -adjacencies- between pairs of ancestral genes or gene domains.</span><br><span>Ancestral genes or domains are deduced from reconciled gene trees in a context of birth, speciation, duplication, loss, transfer, which are either given as input or computed with the&nbsp;</span><a href="http://mbb.univ-montp2.fr/MBB/download_sources/16__TERA">ecceTERA package</a><span>, to which DeCoSTAR is integrated. DeCoSTAR constructs parsimonious scenarios of gains and breakages of adjacencies, and contains in particular all the features of previous software DeCo, DeCoLT, ArtDeCo and DeClone. It provides statistical supports on ancestral adjacencies, or the possibility to handle badly assembled genomes.&nbsp;</span><br><span>DeCoSTAR is able to reconstruct the histories of domains inside genes, including gene fusion and fission events, as well as ancestral genome structures for dozens of whole genomes from all kingdoms of life in a few minutes.</span></p><p>Address of the bookmark: <a href="http://pbil.univ-lyon1.fr/software/DeCoSTAR/" rel="nofollow">http://pbil.univ-lyon1.fr/software/DeCoSTAR/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/32629/bienko-and-crosetto-labs</guid>
  <pubDate>Fri, 12 May 2017 07:42:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bienko and Crosetto Labs]]></title>
  <description><![CDATA[
<p>We are two groups of scientists doing frontier research in quantitative biology and biomedicine. The Bienko group is interested in exploring the fundamental design principles controlling how DNA is packed in the eukaryotic nucleus and its relation to gene expression regulation. The Crosetto group engineers new molecular methods for single-cell and spatially resolved omic measurements of DNA, RNA, and proteins, with a strong focus on tumor heterogeneity. By sharing ideas and resources, we work synergistically towards a more quantitative understanding of life’s processes in healthy and diseased conditions.</p>

<p>https://bienkocrosettolabs.org/</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/32713/salzberg-lab</guid>
  <pubDate>Mon, 15 May 2017 05:14:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[Salzberg lab]]></title>
  <description><![CDATA[
<p>We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.</p>

<p>https://salzberg-lab.org/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32730/ncbi-prokaryotic-genome-annotation-pipeline</guid>
	<pubDate>Tue, 16 May 2017 08:56:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32730/ncbi-prokaryotic-genome-annotation-pipeline</link>
	<title><![CDATA[NCBI Prokaryotic Genome Annotation Pipeline]]></title>
	<description><![CDATA[<p>NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).</p>
<p>Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.</p>
<p>NCBI has developed an automatic prokaryotic genome annotation pipeline that combines&nbsp;<em>ab initio</em>&nbsp;gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP;&nbsp;<a href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=pubmed&amp;dopt=Abstract&amp;list_uids=18416670">see Pubmed Article</a>) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of&nbsp;the pipeline in&nbsp;<a href="https://www.ncbi.nlm.nih.gov/books/NBK174280/">NCBI Handbook chapter</a>. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.</p>
<p>https://www.ncbi.nlm.nih.gov/genome/annotation_prok/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/genome/annotation_prok/" rel="nofollow">https://www.ncbi.nlm.nih.gov/genome/annotation_prok/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/4004/33rd-annual-convention-of-indian-association-for-cancer-research-from-13th-to-15th-february-2014</guid>
  <pubDate>Tue, 27 Aug 2013 10:37:08 -0500</pubDate>
  <link></link>
  <title><![CDATA[33rd Annual Convention of Indian Association for Cancer Research from 13th to 15th February 2014]]></title>
  <description><![CDATA[
<p>RGCB is organizing the 33rd Annual Convention of Indian Association for Cancer Research from 13th to 15th February 2014 with the theme "Discovery, Innovation and Translation in Cancer Research"</p>

<p>Kindly log on to conference website http://rgcb.res.in/IACR2014 for further details and timely updates and registration. We shall truly appreciate if the same be circulated among your friends, scholars and students encouraging them to participate in the meet.</p>

<p>http://210.212.237.38/iacrconference/</p>
]]></description>
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