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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44703?offset=180</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31139/pbsuite-software-for-long-read-sequencing-data-from-pacbio</guid>
	<pubDate>Mon, 27 Feb 2017 09:54:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31139/pbsuite-software-for-long-read-sequencing-data-from-pacbio</link>
	<title><![CDATA[PBSuite: Software for Long-Read Sequencing Data from PacBio]]></title>
	<description><![CDATA[<p><span>PBJelly - the genome upgrading tool.&nbsp;</span><br><span>PBHoney - the structural variation discovery tool&nbsp;</span><br><br><span>Both are contained within the PBSuite code found in downloads.</span><br><br><span>----- PBJelly -----</span><br><span>Read The Paper&nbsp;</span><br><a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768" target="_blank">http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768</a><br><br><span>PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes.&nbsp;</span><br><br><span>----- PBHoney -----</span><br><span>Read The Paper</span><br><a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a><br><br><span>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.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/pb-jelly/" rel="nofollow">https://sourceforge.net/projects/pb-jelly/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31295/mycc-accurate-binning-of-metagenomic-contigs-via-automated-clustering-sequences-using-information-of-genomic-signatures-and-marker-genes</guid>
	<pubDate>Fri, 03 Mar 2017 08:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31295/mycc-accurate-binning-of-metagenomic-contigs-via-automated-clustering-sequences-using-information-of-genomic-signatures-and-marker-genes</link>
	<title><![CDATA[MyCC: Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes]]></title>
	<description><![CDATA[<p><span>MyCC, an automated binning tool that combines genomic signatures, marker genes and optional contig coverages within one or multiple samples, in order to visualize the metagenomes and to identify the reconstructed genomic fragments.</span></p>
<p><span>More at&nbsp;http://www.nature.com/articles/srep24175</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/sb2nhri/files/MyCC/" rel="nofollow">https://sourceforge.net/projects/sb2nhri/files/MyCC/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31345/prokka-tool-for-the-rapid-annotation-of-prokaryotic-genomes</guid>
	<pubDate>Mon, 06 Mar 2017 03:49:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31345/prokka-tool-for-the-rapid-annotation-of-prokaryotic-genomes</link>
	<title><![CDATA[Prokka: tool for the rapid annotation of prokaryotic genomes]]></title>
	<description><![CDATA[<p>Prokka is a software tool for the rapid annotation of prokaryotic genomes. A typical 4 Mbp genome can be fully annotated in less than 10 minutes on a quad-core computer, and scales well to 32 core SMP systems. It produces GFF3, GBK and SQN files that are ready for editing in Sequin and ultimately submitted to Genbank/DDJB/ENA.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://www.vicbioinformatics.com/software.prokka.shtml" rel="nofollow">http://www.vicbioinformatics.com/software.prokka.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</guid>
	<pubDate>Tue, 07 Mar 2017 08:50:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</link>
	<title><![CDATA[COCACOLA (binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge)]]></title>
	<description><![CDATA[<p>COCACOLA is a general framework that combines different types of information: sequence COmposition, CoverAge across multiple samples, CO-alignment to reference genomes and paired-end reads LinkAge to automatically bin contigs into OTUs. Furthermore, COCACOLA seamlessly embraces customized prior knowledge to facilitate binning accuracy.</p>
<p>News: Python version of COCACOLA is available now!</p><p>Address of the bookmark: <a href="https://github.com/younglululu/COCACOLA" rel="nofollow">https://github.com/younglululu/COCACOLA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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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/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>
</item>

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  <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>
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	<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>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41905/research-associate-bioinformatics-in-iisc-recruitment-2020</guid>
  <pubDate>Tue, 23 Jun 2020 21:53:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatics in IISc Recruitment 2020]]></title>
  <description><![CDATA[
<p>Research Associate Bioinformatics in IISc Recruitment 2020</p>

<p>Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.</p>

<p>Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.</p>

<p>Desirable Qualifications (Research Associate/s): i)  Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment). </p>

<p>ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv)  Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.</p>

<p>The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.</p>

<p>Emoluments: As per DST, GOI norms and commensurate with experience.</p>

<p>More at https://www.iisc.ac.in/positions-open/</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42206/pollard-lab</guid>
  <pubDate>Fri, 25 Sep 2020 20:20:50 -0500</pubDate>
  <link></link>
  <title><![CDATA[Pollard Lab]]></title>
  <description><![CDATA[
<p>We are a bioinformatics research lab focused on developing novel methods and using them to study genome evolution, organization, and regulation. Our mission is to decode biomedical knowledge that is missed without rigorous statistical approaches.</p>

<p>http://docpollard.org/</p>

<p>Tools</p>

<p>http://docpollard.org/resources/software/</p>
]]></description>
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