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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38316?offset=240</link>
	<atom:link href="https://bioinformaticsonline.com/related/38316?offset=240" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22317/project-associate-bioinformatics-central-food-technological-research-institute-cftri</guid>
  <pubDate>Tue, 19 May 2015 07:01:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[Project Associate Bioinformatics @ Central Food Technological Research Institute (CFTRI)]]></title>
  <description><![CDATA[
<p>Central Food Technological Research Institute (CFTRI)</p>

<p>Project Assistant (Level-II) job position in Central Food Technological Research Institute (CFTRI) on a temporary contractual basis in the research project (GAP 0469) funded by Science &amp; Engineering Research Board (SERB), Government of India, New Delhi tenable at the Lipidomics Centre, CSIR-CFTRI, Mysore, Karnataka</p>

<p>Name of the Position : </p>

<p>Qualification : First class M. Sc. in Biochemistry/Microbiology/Genetics/ Bioinformatics with good academic record and preferably with experience in molecular biology techniques and basic knowledge of molecular biology and biological chemistry</p>

<p>Emoluments : Rs. 12,000/- per month (Consolidated)</p>

<p>Age Limit : The upper age limit for applying shall be 28 years (as on 22-5-2015), which is relaxed for candidates belonging to Scheduled Castes/Schedule Tribes, Women, Persons with Disabilities (PWD) and OBCs as per GoI norms. <br /> <br />How to apply</p>

<p>Eligible candidates may send their complete Bio-data with e-mail address/contact phone number along with attested copies of the necessary certificates through post to Prof. Ram Rajasekharan, Lipidomics Centre, Department of Lipid Science, CSIR-CFTRI, Mysore-570 020, Karnataka (email: ram@cftri.res.in) on or before 22.05.2015</p>

<p>More at http://www.cftri.com/pa_gap0469.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/42418/scientist-b-bioinformatics-at-aiims-delhi</guid>
  <pubDate>Sun, 20 Dec 2020 04:34:55 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist-B (Bioinformatics) at AIIMS, Delhi]]></title>
  <description><![CDATA[
<p>Name of the Project: “Artificial intelligence in Oncology, Harnessing big data and advanced computing to provide personalized diagnosis and treatment for Cancer patients”</p>

<p>Age Limit: 35</p>

<p>How to Apply for the AIIMS Life Science Job:</p>

<p>Interested applicants are asked to send out a detailed CV to Dr Ashok Sharma (aioncoaiims@gmail.com). Laboratory of Chromatin and also Cancer Epigenetics, Department of Biochemistry with the subject line “Application for Scientist-B position for MeitY project” latest by January 01st, 2021.<br />Complete Information of the year of passing, experience, marks, etc. ought to be mentioned in the CV Incomplete. applications will certainly be rejected Just shortlisted applicants will be called for interview. Chosen candidates will certainly be intimated by email/phone.<br />No TA/DA will certainly be paid for appearing in the interview.<br />Note, The institute reserved the right to fill up or not to fill up the post advertised.</p>

<p>Emoluments: Rs. 56,000/- plus 24 percent HRA</p>

<p>Eligibility:<br />2nd class Master’s Degree with a PhD in a pertinent subject (Bioinformatics) from.a recognized University<br />1st class Master’s degree in Life Sciences (Bioinformatics) from a recognized university OR.<br />Bachelor’s Degree in Engineering or-Technology with minimal 60% marks from a recognized University or equivalent.</p>

<p>Desirable Qualifications:<br />Experience in Bioinformatics/NGS data. Analysis/System Biology/Computer Science/ statistics with experience in Machine learning/Al project.<br />Experience of Deep learning applications in biological data ( image/text).<br />Proficient in Rf Python machine learning libraries.<br />Prior experience in the cancer-related project (ML-based) will be advantageous.<br />Experience with PyTorch/TensorFlow will certainly be very desirable.<br />Applicant should have strong scientific writing as well as. verbal abilities.<br />Papers in sci-indexed journals demonstrating ML skill sets.<br />Database handling will certainly be plus yet not required.</p>

<p>More detail at https://www.aiims.edu/images/pdf/recruitment/advertisement/biochem-16-12-20.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45257/earth-biogenome-project-reading-the-book-of-life</guid>
	<pubDate>Sun, 23 Aug 2026 11:17:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45257/earth-biogenome-project-reading-the-book-of-life</link>
	<title><![CDATA[Earth Biogenome Project - Reading the Book of Life]]></title>
	<description><![CDATA[<p>Imagine a library containing the story of every living species on Earth. Millions of books, each written in a language we are only beginning to understand.</p><p>That library is nature&mdash;and its language is DNA.</p><p>The Earth BioGenome Project (EBP) is a global scientific initiative working to read that language by creating high-quality genome sequences for Earth's biodiversity. Its vision is to build a genomic foundation for understanding animals, plants, fungi and other eukaryotic life.</p><p>Why does this matter?</p><p>Because every species carries information shaped by millions of years of evolution. A wild plant may hold genetic clues for surviving drought. A marine organism could reveal new biological processes. A threatened species may carry genetic diversity that could help it adapt to a changing environment.</p><p>By sequencing genomes, scientists can move beyond simply knowing <em>what species exist</em>. They can begin to understand <em>how they evolved, how they adapt and what makes them resilient</em>.</p><p>The possibilities reach far beyond biology. This knowledge could support biodiversity conservation, climate resilience, sustainable agriculture, medicine and new discoveries in biotechnology.</p><p>But there is also a race against time. Habitats are changing, populations are declining, and species can disappear before we fully understand them.</p><p>That makes EBP more than a technology project. It is an attempt to create a lasting genetic record of life on Earth&mdash;while that life is still here.</p><p>For centuries, humans have explored, named and documented the natural world.</p><p>Now, we have the opportunity to read it.</p><p>Every genome is a story.</p><p>The Earth BioGenome Project is helping us write the world's most extraordinary library&mdash;the book of life itself.<br /><br />Read and explore more about it @ https://www.earthbiogenome.org/</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33912/mesquite-a-modular-system-for-evolutionary-analysis</guid>
	<pubDate>Tue, 18 Jul 2017 07:42:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33912/mesquite-a-modular-system-for-evolutionary-analysis</link>
	<title><![CDATA[Mesquite: A modular system for evolutionary analysis]]></title>
	<description><![CDATA[<p><span>Mesquite is modular, extendible software for evolutionary biology, designed to help biologists organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics, while others do non-phylogenetic multivariate analysis. Because it is modular, the analyses available depend on the modules installed.</span></p>
<p><span>http://mesquiteproject.wikispaces.com/</span></p><p>Address of the bookmark: <a href="https://github.com/MesquiteProject/MesquiteCore/releases" rel="nofollow">https://github.com/MesquiteProject/MesquiteCore/releases</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</guid>
	<pubDate>Sat, 15 Feb 2020 01:49:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</link>
	<title><![CDATA[CLARK: Fast, accurate and versatile sequence classification system]]></title>
	<description><![CDATA[<p><span></span><a href="http://dx.doi.org/10.1186/s12864-015-1419-2"><strong>CLARK</strong></a><span>, a method based on a supervised sequence classification using discriminative&nbsp;</span><em>k</em><span>-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.</span></p>
<p><span><a href="http://clark.cs.ucr.edu/Spaced/">http://clark.cs.ucr.edu/Spaced/</a></span></p><p>Address of the bookmark: <a href="http://clark.cs.ucr.edu/Spaced/" rel="nofollow">http://clark.cs.ucr.edu/Spaced/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39898/itis-the-integrated-taxonomic-information-system</guid>
	<pubDate>Fri, 30 Aug 2019 23:07:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39898/itis-the-integrated-taxonomic-information-system</link>
	<title><![CDATA[ITIS: the Integrated Taxonomic Information System!]]></title>
	<description><![CDATA[<p><span>ITIS, the Integrated Taxonomic Information System! Here you will find authoritative taxonomic information on plants, animals, fungi, and microbes of North America and the world. We are a&nbsp;</span><a href="https://www.itis.gov/organ.html">partnership</a><span>&nbsp;of U.S.,&nbsp;</span><a href="http://www.cbif.gc.ca/eng/home/?id=1370403266262" target="_blank">Canadian</a><span>, and&nbsp;</span><a href="http://www.conabio.gob.mx/" target="_blank">Mexican</a><span>&nbsp;agencies (</span><a href="http://www.cbif.gc.ca/eng/integrated-taxonomic-information-system-itis/?id=1381347793621" target="_blank">ITIS-North America</a><span>); other organizations; and taxonomic specialists. ITIS is also a partner of&nbsp;</span><a href="http://www.sp2000.org/" target="_blank">Species 2000</a><span>&nbsp;and the&nbsp;</span><a href="http://www.gbif.org/" target="_blank">Global Biodiversity Information Facility (GBIF)</a><span>. The ITIS and Species 2000&nbsp;</span><a href="http://www.catalogueoflife.org/annual-checklist/" target="_blank">Catalogue of Life (CoL)</a><span>partnership is proud to provide the taxonomic backbone to the&nbsp;</span><a href="http://www.eol.org/" target="_blank">Encyclopedia of Life (EOL)</a><span>.&nbsp;</span></p>
<p><span><a href="https://www.itis.gov/pdf/twb_ug.pdf">https://www.itis.gov/pdf/twb_ug.pdf</a></span></p><p>Address of the bookmark: <a href="https://www.itis.gov/" rel="nofollow">https://www.itis.gov/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</guid>
	<pubDate>Tue, 12 Dec 2017 17:23:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</link>
	<title><![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]></title>
	<description><![CDATA[<p><span>MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a&nbsp;</span><em>k</em><span>-mer based&nbsp;</span><a href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard similarity</a><span>&nbsp;using a combination of&nbsp;</span><a href="http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/p76-schleimer.pdf">Winnowing</a><span>&nbsp;and&nbsp;</span><a href="https://en.wikipedia.org/wiki/MinHash">MinHash</a><span>. This is then converted to an estimate of sequence identity using the&nbsp;</span><a href="http://mash.readthedocs.org/">Mash</a><span>&nbsp;distance. An appropriate&nbsp;</span><em>k</em><span>-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.</span></p><p>Address of the bookmark: <a href="https://github.com/marbl/MashMap" rel="nofollow">https://github.com/marbl/MashMap</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>

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