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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/14215?offset=220</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31156/splitbam-splits-a-bam-by-chromosomes</guid>
	<pubDate>Tue, 28 Feb 2017 09:01:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31156/splitbam-splits-a-bam-by-chromosomes</link>
	<title><![CDATA[splitbam: splits a BAM by chromosomes]]></title>
	<description><![CDATA[<p><strong>splitbam</strong>&nbsp;splits a BAM by chromosomes.</p>
<p>Using the reference sequence dictionary (<code>*.dict</code>), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.</p>
<h1>Usage</h1>
<p><code>java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)</code></p>
<h1>Options</h1>
<ul>
<li>-h help; This screen.</li>
<li>-R (indexed reference file) REQUIRED.</li>
<li>-u (unmapped chromosome name): default:Unmapped</li>
<li>-e | --empty : generate EMPTY bams for chromosome having no read mapped</li>
<li>-m | --mock : if option '-e', add a mock pair of sam records to the empty bam</li>
<li>-p (output file/bam pattern) REQUIRED. MUST contain&nbsp;<strong><code>__CHROM__</code></strong>&nbsp;and end with .bam</li>
<li>-s assume input is sorted.</li>
<li>-x | --index create index.</li>
<li>-t | --tmp (dir) tmp file directory</li>
<li>-G (file) chrom-group file (see below)</li>
</ul><p>Address of the bookmark: <a href="https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki" rel="nofollow">https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</guid>
	<pubDate>Fri, 03 Mar 2017 09:57:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</link>
	<title><![CDATA[CLgenomics]]></title>
	<description><![CDATA[<p>CLgenomics is a standalone desktop software specifically designed for bacterial genome analysis. This program has a powerful multi-genome browser, which enables rapid and responsive exploration of bacterial genomes.</p>
<p>To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics).&nbsp;Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers &gt;40,000 CLG files of publicly available Bacterial and Archaeal genomes.</p><p>Address of the bookmark: <a href="https://chunlab.wordpress.com/clgenomics-software/" rel="nofollow">https://chunlab.wordpress.com/clgenomics-software/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31564/htslib</guid>
	<pubDate>Wed, 15 Mar 2017 11:38:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31564/htslib</link>
	<title><![CDATA[HTSlib]]></title>
	<description><![CDATA[<p>Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:</p>
<dl><dt>Samtools</dt><dd>Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format</dd><dt>BCFtools</dt><dd>Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants</dd><dt>HTSlib</dt><dd>A C library for reading/writing high-throughput sequencing data</dd></dl>
<p>Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.</p><p>Address of the bookmark: <a href="http://www.htslib.org/" rel="nofollow">http://www.htslib.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/view/138</guid>
	<pubDate>Wed, 10 Jul 2013 16:30:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/138</link>
	<title><![CDATA[Selecting between Python 2 and Python 3?]]></title>
	<description><![CDATA[<p>Which version is best for bioinformatician to work on?</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/841/research-fellow</guid>
  <pubDate>Sat, 13 Jul 2013 11:23:20 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Fellow]]></title>
  <description><![CDATA[
<p>UNIVERSITY OF CALCUTTA</p>

<p>A walk-in-interview will be held on 15.7.2013 at 12 noon in the Department of Biophysics, Molecular Biology and Bioinformatics, 92 A.P.C Road, Kolkata-700 009 to select one trainee research fellow and two students under DIC. The positions are purely temporary and would be for a period of six months from the date of joining which may be extended by another six months subject to successful performance.</p>

<p>Qualification:</p>

<p>For the Trainee Research Fellow: Should have a master degree in Bioinformatics or allied subjects and should have biological database development experience. Must have at least one publication.</p>

<p>For the Students: Should have a master degree in Bioinformatics or allied subjects and should be proficient in C-programming language. Must be familiar with techniques of Developmental Biology.</p>

<p>The Trainee Research Fellow would be paid a consolidated sum of Rs 10000/- per month and the students would be paid a sum amount Rs. 7000/- per month during the tenure of the project.</p>

<p>Advertisement:<br />www.caluniv.ac.in/News%20&amp;%20Announcement/trainee_rf_DIC.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32048/json</guid>
	<pubDate>Tue, 04 Apr 2017 08:02:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32048/json</link>
	<title><![CDATA[JSON]]></title>
	<description><![CDATA[<p><strong>JSON</strong>&nbsp;(JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the&nbsp;<a href="http://javascript.crockford.com/">JavaScript Programming Language</a>,&nbsp;<a href="http://www.ecma-international.org/publications/files/ecma-st/ECMA-262.pdf">Standard ECMA-262 3rd Edition - December 1999</a>. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.</p>
<p>JSON is built on two structures:</p>
<ul>
<li>A collection of name/value pairs. In various languages, this is realized as an&nbsp;<em>object</em>, record, struct, dictionary, hash table, keyed list, or associative array.</li>
<li>An ordered list of values. In most languages, this is realized as an&nbsp;<em>array</em>, vector, list, or sequence.</li>
</ul>
<p>These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures.</p><p>Address of the bookmark: <a href="http://json.org/" rel="nofollow">http://json.org/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/843/structural-polymorphism-analysis-from-ngs-data</guid>
  <pubDate>Sat, 13 Jul 2013 17:12:47 -0500</pubDate>
  <link></link>
  <title><![CDATA[Structural polymorphism analysis from NGS data]]></title>
  <description><![CDATA[
<p>The LabEx BASC (Biodiversity, Agroecosystems, Society, Climate), a network of 13 laboratories of the Paris-Saclay Scientific Cluster, is seeking a bioinformatician to analyze Next Generation Sequencing (NGS) data analysis. In the context of a flagship project aiming at understanding and improving the adaptive capacity of agroecosystems it will be critical to establish a link between sequence variation, functional variation, gene/protein expression and phenotypic adaptation.</p>

<p>The successful candidate will be in charge of the detection of polymorphisms including structural variants, of the comparison of multiple and diverse genomes of a same species and of the construction of pan- and core-genomes. These challenging tasks will require bioinformatics developments and implementation of methods for accommodating the high level of repetitiveness of complex genomes. The tools will be integrated into pipelines and made available to end-users through the Galaxy platform. The bioinformatician will therefore also have to provide researchers with advices on their experimental designs in order to ensure compliance of produced datasets with pipelines requirements. He/she will be hosted by a bioinformatics/informatics team (7 people) (http://moulon.inra.fr/index.php/fr/equipestransversales/atelier-de-bioinformatique) which has computational facilities and expertise in NGS data analysis, and will benefit as well from national and international collaborative networks (Aplibio http://www.renabi.fr/platforms/aplibio/, Transplant http://transplantdb.eu, AMAIZING http://www.amaizing.fr/).</p>

<p>The position requires a doctoral degree (PhD) in bioinformatics with strong expertise in script writing (Python/Perl) and pipeline development. </p>

<p>Applicants should send a CV and the names of 2 referees willing to provide a letter of recommendation to joets@moulon.inra.fr.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32134/lifemap</guid>
	<pubDate>Mon, 10 Apr 2017 05:42:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32134/lifemap</link>
	<title><![CDATA[Lifemap]]></title>
	<description><![CDATA[<p><strong>Lifemap</strong> is an interactive tool to explore the WHOLE NCBI TAXONOMY. The concept used in <strong>Lifemap</strong> is similar to the one used in cartography with tools like Google Maps&copy; or Open Street Maps: exploring is done by zooming and panning.</p>
<div>
<p>&nbsp;The current tree contains ALL species present in NCBI taxonomy as of <span style="text-decoration: underline;">October 18th, 2016</span>: 1,135,169 species including 10,545 Archaea, 418,777 Bacteria and 705,847 Eukaryotes. The Lifemap tree is updated every two weeks.</p>
</div>
<p>&nbsp;All the nodes in the tree are clickable. This displays various information and options:</p>
<ul>
<li>The species name (and the associated common name if there is one)</li>
<li>The rank (kingdom, family, class, species...)</li>
<li>Ability to go to the corresponding node/species on NCBI web site (displayed in a new window)</li>
<li>Possibility to download the corresponding subtree in newick extended format</li>
<li>Possibilty to get the whole lineage from the current node/tip to the root of the tree.</li>
</ul><p>Address of the bookmark: <a href="http://lifemap-ncbi.univ-lyon1.fr/" rel="nofollow">http://lifemap-ncbi.univ-lyon1.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/862/dumontier-lab</guid>
  <pubDate>Sun, 14 Jul 2013 12:51:42 -0500</pubDate>
  <link></link>
  <title><![CDATA[Dumontier Lab]]></title>
  <description><![CDATA[
<p>Our research aims to better understand how living systems respond to chemical agents. A key aspect of our approach involves using computational frameworks that are powered by formal (i.e. machine understandable) semantics to make effectively use of vast and diverse amounts of biomedical knowledge. We are particularly interested in understanding how the response to chemical exposure is modulated by genetic and physiological variation among individuals and how this translates into altered capabilities at the molecular level.</p>

<p>Research Area</p>

<p>the discovery and on-demand use of biomedical data and services<br />the formulation, discovery and evaluation of scientific hypotheses<br />the simulation of biological systems at the level of individual molecules</p>

<p>Link @ http://dumontierlab.com/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32187/chromhmm-chromatin-state-discovery-and-characterization</guid>
	<pubDate>Wed, 19 Apr 2017 04:06:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32187/chromhmm-chromatin-state-discovery-and-characterization</link>
	<title><![CDATA[ChromHMM: Chromatin state discovery and characterization]]></title>
	<description><![CDATA[<p><span>ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.&nbsp;</span><br><br></p>
<ul>
<li><a href="http://compbio.mit.edu/ChromHMM/ChromHMM.zip">ChromHMM software v1.12</a>&nbsp;(<a href="http://compbio.mit.edu/ChromHMM/versionlog.txt">version log</a>)</li>
<li><a href="http://compbio.mit.edu/ChromHMM/ChromHMM_manual.pdf">ChromHMM manual</a></li>
</ul><p>Address of the bookmark: <a href="http://compbio.mit.edu/ChromHMM/" rel="nofollow">http://compbio.mit.edu/ChromHMM/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

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