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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30212?offset=1250</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41209/juicebox-visualization-and-analysis-software-for-hi-c-data</guid>
	<pubDate>Fri, 21 Feb 2020 00:33:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41209/juicebox-visualization-and-analysis-software-for-hi-c-data</link>
	<title><![CDATA[Juicebox: Visualization and analysis software for Hi-C data]]></title>
	<description><![CDATA[<p>Juicebox is visualization software for Hi-C data. This distribution includes the source code for Juicebox,&nbsp;<a href="https://github.com/theaidenlab/juicer/wiki/Download">Juicer Tools</a>, and&nbsp;<a href="https://aidenlab.org/assembly/">Assembly Tools</a>.&nbsp;<a href="https://github.com/theaidenlab/juicebox/wiki/Download">Download Juicebox here</a>, or use&nbsp;<a href="https://aidenlab.org/juicebox">Juicebox on the web</a>. Detailed documentation is available&nbsp;<a href="https://github.com/theaidenlab/juicebox/wiki">on the wiki</a>. Instructions below pertain primarily to usage of command line tools and the Juicebox jar files.</p>
<p>Juicebox can now be used to visualize and interactively (re)assemble genomes. Check out the Juicebox Assembly Tools Module website&nbsp;<a href="https://aidenlab.org/assembly">https://aidenlab.org/assembly</a>&nbsp;for more details on how to use Juicebox for assembly.</p>
<p>GUI at&nbsp;<a href="https://aidenlab.org/juicebox/">https://aidenlab.org/juicebox/</a></p><p>Address of the bookmark: <a href="https://github.com/aidenlab/Juicebox" rel="nofollow">https://github.com/aidenlab/Juicebox</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29886/research-officer-at-national-tea-research-foundation</guid>
  <pubDate>Fri, 18 Nov 2016 04:19:24 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Officer at National Tea Research Foundation]]></title>
  <description><![CDATA[
<p>National Tea Research Foundation (NTRF) a registered body, requires qualified and experienced agricultural research scientist for contractual appointment as per details below :</p>

<p>Post Research Officer, NTRF, Kolkata</p>

<p>Essential Post Graduate degree in Life Science having special paper in Bioinformatics. Post Graduate degree in Bioinformatics.</p>

<p>Desirable Ph. D. in the area of computational biology or bioinformatics.</p>

<p>Job Experience Work experience on database development, capable to work independently and efficient in bioinformatics related project.</p>

<p>Job Responsibility</p>

<p>To develop database on various aspects of tea research.</p>

<p>Screening of the In-house projects of NTRF at the preliminary level.</p>

<p>Review and evaluation of In-house projects of NTRF. Technical monitoring of the In-house projects of NTRF.</p>

<p>Reviewing the final technical report of the In-house projects of NTRF.</p>

<p>Putting-up of same proposals for taking approval and sanction of the competent authority.</p>

<p>Physical and technical verification of the In-house projects of NTRF.</p>

<p>To assist in organizing seminars / workshops / meetings etc.</p>

<p>Interested candidate may appear for walk-in Interview on 17 th November, 2016</p>

<p>More Info :</p>

<p>http://www.teaboard.gov.in/pdf/Recruitment_for_the_post_of_Research_Officer_pdf7538.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</guid>
	<pubDate>Thu, 23 Jul 2020 05:49:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</link>
	<title><![CDATA[wgd—simple command line tools for the analysis of ancient whole-genome duplications]]></title>
	<description><![CDATA[<p><span>wgd is a easy to use command-line tool for<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>distribution construction named wgd. The wgd suite provides commonly used<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.</span></p>
<p><a href="https://academic.oup.com/bioinformatics/article/35/12/2153/5162749">https://academic.oup.com/bioinformatics/article/35/12/2153/5162749</a></p><p>Address of the bookmark: <a href="https://github.com/arzwa/wgd" rel="nofollow">https://github.com/arzwa/wgd</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30015/scripts</guid>
	<pubDate>Wed, 30 Nov 2016 10:35:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30015/scripts</link>
	<title><![CDATA[Scripts]]></title>
	<description><![CDATA[<p>Useful script for NGS analysis.</p><p>Address of the bookmark: <a href="http://augustus.gobics.de/binaries/scripts/" rel="nofollow">http://augustus.gobics.de/binaries/scripts/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</guid>
	<pubDate>Wed, 18 Aug 2021 00:02:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</link>
	<title><![CDATA[Kmer: a suite of tools for DNA sequence analysis]]></title>
	<description><![CDATA[<p>More at&nbsp;https://help.rc.ufl.edu/doc/Kmer</p>
<p>This also includes:</p>
<ul>
<li>A2Amapper: ATAC, Assembly to Assembly Comparision tool:
<ul>
<li>Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).</li>
</ul>
</li>
</ul>
<ul>
<li>Sim4db:
<ul>
<li>Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.</li>
</ul>
</li>
</ul>
<ul>
<li>LEAFF:
<ul>
<li>LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.</li>
</ul>
</li>
</ul>
<ul>
<li>Meryl:
<ul>
<li>An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.</li>
</ul>
</li>
</ul><p>Address of the bookmark: <a href="https://help.rc.ufl.edu/doc/Kmer" rel="nofollow">https://help.rc.ufl.edu/doc/Kmer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30102/prism</guid>
	<pubDate>Sat, 10 Dec 2016 15:19:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30102/prism</link>
	<title><![CDATA[PRISM]]></title>
	<description><![CDATA[<p><span>PRISM is a software for split read (reads which span across a structrual variant -- SV ) mapping and SV calling from the mapping result. PRISM is able to detect small insertions and abitrary size deletions, inversions and tandom duplications with the direction of discordant read pairs. PRISM_CTX is a tool for detecting inter-chromosome trans-location events.&nbsp;</span><br><br><span>PRISM and PRISM_CTX were originally designed and written by&nbsp;</span><a href="http://www.cs.toronto.edu/~brudno">Michael Brudno</a><span>&nbsp;and Yue Jiang, The original PRISM publication can be found&nbsp;</span><a href="http://bioinformatics.oxfordjournals.org/content/early/2012/07/31/bioinformatics.bts484.abstract">here</a><span>.&nbsp;</span><br><br><span>The authors may be contacted via e-mail at:&nbsp;</span><em>prism at cs.toronto.edu</em><span>.&nbsp;</span><br><br><span>Additional information is available in the&nbsp;</span><a href="http://compbio.cs.toronto.edu/prism/PRISM_README">PRISM README</a><span>&nbsp;file and&nbsp;</span><a href="http://compbio.cs.toronto.edu/prism/PRISM_CTX_README">PRISM_CTX README</a><span>&nbsp;file.&nbsp;</span></p>
<p>http://compbio.cs.toronto.edu/prism/</p><p>Address of the bookmark: <a href="http://compbio.cs.toronto.edu/prism/" rel="nofollow">http://compbio.cs.toronto.edu/prism/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams</guid>
	<pubDate>Wed, 29 Jun 2022 03:22:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams</link>
	<title><![CDATA[InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams]]></title>
	<description><![CDATA[<p><span>InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets&rsquo; elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.</span></p>
<p><span>More at&nbsp;https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0611-3</span></p>
<p><span><img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs12859-015-0611-3/MediaObjects/12859_2015_611_Fig1_HTML.gif?as=webp" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="http://www.interactivenn.net/" rel="nofollow">http://www.interactivenn.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/30334/postdoc-at-ubritishcolumbia-in-troutgenomics</guid>
  <pubDate>Thu, 22 Dec 2016 08:18:46 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoc at UBritishColumbia in TroutGenomics]]></title>
  <description><![CDATA[
<p>Landscape Genomics Postdoc at UBC A research team at the University of British Columbia’s Department of Zoology and Biodiversity Research Centre is seeking a postdoctoral researcher in landscape genetics of native rainbow trout (Oncorhynchus mykiss). </p>

<p>This project is part of a larger Genome Canada project on genetics and physiology of adaptation to climate change in rainbow trout, and the population genomics component is in the labs of Eric Taylor and Michael Whitlock. The landscape genomics component primarily involves whole genome sequencing approaches to understanding the genomic basis of adaptation to features of habitat, but also to provide insights into phylogeography and the influence of watershed structure on population subdivision in rainbow trout. A PhD in a related field with expertise in basic theory and bioinformatic analysis of population genomics data is required. </p>

<p>The position is available for one year with renewal for up to three additional years. Salary is $55,000 per year plus benefits. To apply, please send a brief cover letter summarizing your qualifications for the position, a CV, and the names, addresses, phone numbers and emails of three references. </p>

<p>Review of applications will begin January 16, 2017. Address application materials to etaylor@zoology.ubc.ca to whom any questions can also be addressed. etaylor@zoology.ubc.ca</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline</guid>
	<pubDate>Mon, 10 Jul 2017 05:56:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline</link>
	<title><![CDATA[Omega2: metagenome assembly pipeline]]></title>
	<description><![CDATA[<p><span>Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.</span></p><p>Address of the bookmark: <a href="http://omega.omicsbio.org/" rel="nofollow">http://omega.omicsbio.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</guid>
	<pubDate>Mon, 27 Nov 2017 07:58:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</link>
	<title><![CDATA[miniasm: very fast OLC-based de novo assembler for noisy long reads]]></title>
	<description><![CDATA[<p>Miniasm is a very fast OLC-based&nbsp;<em>de novo</em>&nbsp;assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>) as input and outputs an assembly graph in the&nbsp;<a href="https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md">GFA</a>&nbsp;format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final&nbsp;<a href="http://wgs-assembler.sourceforge.net/wiki/index.php/Celera_Assembler_Terminology">unitig</a>&nbsp;sequences. Thus the per-base error rate is similar to the raw input reads.</p>
<p>So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-Bacterial-Assembly">PacBio E. coli sample</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS473430">ERS473430</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS544009">ERS544009</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS554120">ERS554120</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS605484">ERS605484</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS617393">ERS617393</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS646601">ERS646601</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS659581">ERS659581</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS670327">ERS670327</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS685285">ERS685285</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS743109">ERS743109</a>&nbsp;and a&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-20kb-Size-Selected-Library-with-P6-C4/ce0533c1d2a957488594f0b29da61ffa3e4627e8">deprecated PacBio E. coli data set</a>. ONT data are acquired from the&nbsp;<a href="http://lab.loman.net/2015/09/24/first-sqk-map-006-experiment/">Loman Lab</a>.</p>
<p>For a&nbsp;<em>C. elegans</em>&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/C.-elegans-data-set">PacBio data set</a>&nbsp;(only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the&nbsp;<a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP">HGAP3</a>produces a 104Mb assembly with N50 1.61Mb.&nbsp;<a href="http://lh3lh3.users.sourceforge.net/download/ce-miniasm.png">This dotter plot</a>&nbsp;gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.</p>
<p>Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>&nbsp;can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as&nbsp;<a href="https://github.com/marbl/MHAP">MHAP</a>&nbsp;and&nbsp;<a href="https://github.com/thegenemyers/DALIGNER">DALIGNER</a>. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.</p>
<p>Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)</p>
<p>We start with an all against all comparison:</p>
<div>
<pre><code>minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 &gt; reads.paf.gz
</code></pre>
</div>
<p>Then we can assemble</p>
<div>
<pre><code>miniasm -f reads.fq reads.paf.gz &gt; reads.gfa
</code></pre>
</div>
<p>Convert GFA to FASTA:</p>
<div>
<pre><code>awk <span>'/^S/{print "&gt;"$2"\n"$3}'</span> reads.gfa | fold &gt; reads.fa
</code></pre>
</div>
<p>And then count how many contigs:</p>
<div>
<pre><code>grep <span>"&gt;"</span> reads.fa | wc -l</code></pre>
</div>
<p>&nbsp;</p>
<pre><span><span>#</span> Download sample PacBio from the PBcR website</span>
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz <span>|</span> tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
<span><span>#</span> Install minimap and miniasm (requiring gcc and zlib)</span>
git clone https://github.com/lh3/minimap <span>&amp;&amp;</span> (cd minimap <span>&amp;&amp;</span> make)
git clone https://github.com/lh3/miniasm <span>&amp;&amp;</span> (cd miniasm <span>&amp;&amp;</span> make)
<span><span>#</span> Overlap</span>
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq <span>|</span> gzip -1 <span>&gt;</span> reads.paf.gz
<span><span>#</span> Layout</span>
miniasm/miniasm -f reads.fq reads.paf.gz <span>&gt;</span> reads.gfa</pre><p>Address of the bookmark: <a href="https://github.com/lh3/miniasm" rel="nofollow">https://github.com/lh3/miniasm</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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