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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31137?offset=1110</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30555/yaha</guid>
	<pubDate>Fri, 20 Jan 2017 05:38:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30555/yaha</link>
	<title><![CDATA[YAHA]]></title>
	<description><![CDATA[<p>YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints.</p>
<p><strong>Availability:</strong> YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from <a href="http://faculty.virginia.edu/irahall/YAHA" target="pmc_ext">http://faculty.virginia.edu/irahall/YAHA</a>.</p>
<p><strong>Contact:</strong></p>
<p>http://genome.wustl.edu/people/groups/detail/hall-lab/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/30658/srf-bioinformatics-at-jnu</guid>
  <pubDate>Tue, 24 Jan 2017 07:34:35 -0600</pubDate>
  <link></link>
  <title><![CDATA[SRF Bioinformatics at JNU]]></title>
  <description><![CDATA[
<p>School of Life Sciences <br />Jawaharlal Nehru University <br />New Delhi 110067</p>

<p>Positions available</p>

<p>Applications were invited from for the following posts in an industry sponsored project. The project entitled "OsHK3b technology and Know How", valid for a period upto February, 2018.</p>

<p>Post 3: Senior Research Fellow (Computational Biologist / Metabolic engineering)</p>

<p>Salary: As per DBT rule.</p>

<p>Duration: All the above posts are purely temporary and liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency.</p>

<p>Age limit: The upper age limit for SRF shall be 32 years, which is relaxed upto 5 years in the case of candidates belonging to Schedule Castes/Schedule Tribes, Women, Physically Handicapped and OBC applicants.</p>

<p>Essential Qualifications: Masters/B Tech/Mtech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building &amp; maintenance, high throughput data handling and analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must. Selection preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.</p>

<p>Desired Skills: Will be expected to manage ongoing research activities in the project, interact with Experimental group, manage the project data analysis, prepare file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.</p>

<p>1. The post applied for must be clearly written on the Envelope containing the application <br />2. Applications received after last date shall not be entertained, School will not be responsible for any postal delay. <br />3. No application will be accepted via hand delivery or via e-mail. Please send printed &amp; signed applications with detailed CV on or before 31st January, 2017 by post to the following address:</p>

<p>Prof. Ashwani Pareek <br />(Project Investigator) <br />Stress Physiology and Molecular Biology Laboratory (Room No-413), <br />School of Life Sciences, <br />Jawaharlal Nehru University, <br />New Delhi, India – 110067 <br />Email: ashwanipareek@gmail.com</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30698/itol-interactive-tree-of-life</guid>
	<pubDate>Tue, 31 Jan 2017 05:56:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30698/itol-interactive-tree-of-life</link>
	<title><![CDATA[iTOL: interactive Tree Of Life]]></title>
	<description><![CDATA[<p><strong>Interactive Tree Of Life</strong><span>&nbsp;is an online tool for the display and manipulation of phylogenetic trees. It provides most of the features available in other tree viewers, and offers a novel circular tree layout, which makes it easy to visualize mid-sized tree (up to several thousand leaves). Trees can be exported to several graphical formats, both bitmap and vector based.</span></p>
<p><img src="http://itol.embl.de/img/home/ex3.png" alt="image" style="border: 0px;"><br><span>There are several pre-computed trees available for display, including the main Tree Of Life, described in&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/16513982">Ciccarelli, et al., 2006</a><span>. In addition to the precomputed trees, users can upload and display personal trees and data, using the 'Data upload' page or through a personal user account.</span></p><p>Address of the bookmark: <a href="http://itol.embl.de/" rel="nofollow">http://itol.embl.de/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</guid>
	<pubDate>Thu, 25 Jan 2018 05:47:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</link>
	<title><![CDATA[RGFA: powerful and convenient handling of assembly graphs]]></title>
	<description><![CDATA[<p><span>RGFA, an implementation of the proposed GFA specification in Ruby. It allows the user to conveniently parse, edit and write GFA files. Complex operations such as the separation of the implicit instances of repeats and the merging of linear paths can be performed. A typical application of RGFA is the editing of a graph, to finish the assembly of a sequence, using information not available to the assembler. We illustrate a use case, in which the assembly of a repetitive metagenomic fosmid insert was completed using a script based on RGFA.</span></p>
<p><span>https://github.com/ggonnella/rgfa</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</guid>
	<pubDate>Mon, 06 Feb 2017 04:26:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</link>
	<title><![CDATA[FSA: Fast Statistical Alignment]]></title>
	<description><![CDATA[<p><span>FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in&nbsp;</span><a href="http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-39.html">"Posterior Decoding Methods for Optimization and Control of Multiple Alignments</a><span>."</span></p>
<p>FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:</p>
<ul>
<li>FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.</li>
<li>FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.</li>
<li>FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.</li>
<li>The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and&nbsp;<a href="http://fsa.sourceforge.net/images/Suchard_SIV.fsa.mov">movie</a>&nbsp;at the top of the page).</li>
</ul>
<p><span>You can see more information on the&nbsp;</span><a href="http://fsa.sourceforge.net/FAQ.html">FAQ</a><span>.&nbsp;</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://fsa.sourceforge.net/" rel="nofollow">http://fsa.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30901/ideoplot</guid>
	<pubDate>Mon, 13 Feb 2017 09:47:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30901/ideoplot</link>
	<title><![CDATA[Ideoplot]]></title>
	<description><![CDATA[<p>Simple ideogram plotting and annotation in R.</p>
<p>Basic usage:</p>
<p>Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf<br> -or-<br> Rscript Ideoplot.R --annotate annotations.bed</p>
<pre>Options
  --ideobed, i      A bed file of reference contig lengths/chromosome names
  --heatmap, -h     Fill chromosomes with normalized heatmap
                   (described below)
  --annotate, -a    Add character annotations.
  --out, -o         PDF output name.
  --stripes, -s     Specify a file containing the layout of the
                    annotations (description below)
  --bars, -b        Add track annotations
  --reference, -f   Either hg19, or hg38
  --topdown, r      Flag, when set, flips the orientation (P arms
                    drawn on top).
</pre><p>Address of the bookmark: <a href="https://github.com/mchaisso/Ideoplot" rel="nofollow">https://github.com/mchaisso/Ideoplot</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31207/laj-viewing-and-manipulating-the-output-from-pairwise-alignment-programs</guid>
	<pubDate>Wed, 01 Mar 2017 08:35:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31207/laj-viewing-and-manipulating-the-output-from-pairwise-alignment-programs</link>
	<title><![CDATA[Laj: viewing and manipulating the output from pairwise alignment programs]]></title>
	<description><![CDATA[<p>Laj is a tool for viewing and manipulating the output from pairwise alignment programs such as <a href="http://bio.cse.psu.edu/">blastz</a>. It can display interactive dotplot, pip, and text representations of the alignments, a diagram showing the locations of exons and repeats, and annotation links to other web sites containing additional information about particular regions.</p>
<p>The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.</p><p>Address of the bookmark: <a href="http://www.bx.psu.edu/~ratan/" rel="nofollow">http://www.bx.psu.edu/~ratan/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/31251/bioinformatics-opening-at-icgeb-new-delhi</guid>
  <pubDate>Thu, 02 Mar 2017 04:16:36 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics opening at ICGEB NEW DELHI]]></title>
  <description><![CDATA[
<p>ICGEB NEW DELHI</p>

<p>Applications are invited for:</p>

<p>Junior Research Fellow, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi</p>

<p>Qualifications:</p>

<p>Education: M.Sc. (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.</p>

<p>Fellowship: As per DBT guidelines.</p>

<p>Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.</p>

<p>Closing date for applications: 04/03/2017</p>

<p>Please send a "TWO PAGE" CV by email to:  th.icgeb@gmail.com on or before the last date.</p>

<p>Research Associate, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi</p>

<p>Qualifications:</p>

<p>Education: Ph.D. (in Biology, Biotechnology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. </p>

<p>Fellowship: As per DBT guidelines.</p>

<p>Tenure: The position is purely on temporary basis with an initial tenure of six months and  based on satisfactory performance may continue until the completion of the project.</p>

<p>Closing date for applications: 04/03/2017</p>

<p>Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</guid>
	<pubDate>Thu, 30 Mar 2017 17:38:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</link>
	<title><![CDATA[SNPGenie]]></title>
	<description><![CDATA[<p>SNPGenie is a Perl script for estimating evolutionary parameters, mainly from pooled next-generation sequencing (NGS) single-nucleotide polymorphism (SNP) variant data. SNP reports (acceptable in a variety of formats) much each correspond to a single population, with variants called relative to a single reference sequence (one sequence in one FASTA file). Just run the main script, <strong>snpgenie.pl</strong>, in a directory containing the necessary <a href="https://github.com/hugheslab/snpgenie#snpgenie-input">input files</a>, and we take care of the rest! For the earlier version, see <a href="http://ww2.biol.sc.edu/~austin/">Hughes Lab Bioinformatics Resource</a>.</p><p>Address of the bookmark: <a href="https://github.com/hugheslab/snpgenie" rel="nofollow">https://github.com/hugheslab/snpgenie</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</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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