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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31156?offset=1230</link>
	<atom:link href="https://bioinformaticsonline.com/related/31156?offset=1230" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44322/genome-context-viewer-gcv</guid>
	<pubDate>Sun, 21 May 2023 19:33:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44322/genome-context-viewer-gcv</link>
	<title><![CDATA[Genome Context Viewer (GCV)]]></title>
	<description><![CDATA[<p><span>The Genome Context Viewer (GCV) is a web-app that visualizes genomic context data provided by third party services. Specifically, it uses functional annotations as a unit of search and comparison. By adopting a common set of annotations, data-store operators can deploy federated instances of GCV, allowing users to compare genomes from different providers in a single interface.</span></p><p>Address of the bookmark: <a href="https://github.com/legumeinfo/gcv" rel="nofollow">https://github.com/legumeinfo/gcv</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/9868/raghavas-group</guid>
  <pubDate>Tue, 15 Apr 2014 23:59:48 -0500</pubDate>
  <link></link>
  <title><![CDATA[Raghava's Group]]></title>
  <description><![CDATA[
<p>Raghava's group is known for developing open source software or web servers. Group have developed large number of web-based services.</p>

<p>Find more at http://www.imtech.res.in/raghava/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</guid>
	<pubDate>Thu, 08 Aug 2024 23:31:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</link>
	<title><![CDATA[Tools to access the quality of your assembled genome !]]></title>
	<description><![CDATA[<ul dir="auto">
<li><a href="https://github.com/linsalrob/fasta_validator">FASTA VALIDATOR</a>&nbsp;+&nbsp;<a href="https://github.com/shenwei356/seqkit">SEQKIT RMDUP</a>: FASTA validation</li>
<li><a href="https://genometools.org/tools/gt_gff3validator.html">GENOMETOOLS GT GFF3VALIDATOR</a>: GFF3 validation</li>
<li><a href="https://github.com/PlantandFoodResearch/assemblathon2-analysis/blob/a93cba25d847434f7eadc04e63b58c567c46a56d/assemblathon_stats.pl">ASSEMBLATHON STATS</a>: Assembly statistics</li>
<li><a href="https://genometools.org/tools/gt_stat.html">GENOMETOOLS GT STAT</a>: Annotation statistics</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS ADAPTOR</a>: Adaptor contamination pass/fail</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS GX</a>: Foreign organism contamination pass/fail</li>
<li><a href="https://gitlab.com/ezlab/busco">BUSCO</a>: Gene-space completeness estimation</li>
<li><a href="https://github.com/tolkit/telomeric-identifier">TIDK</a>: Telomere repeat identification</li>
<li><a href="https://github.com/oushujun/LTR_retriever/blob/master/LAI">LAI</a>: Continuity of repetitive sequences</li>
<li><a href="https://github.com/DerrickWood/kraken2">KRAKEN2</a>: Taxonomy classification</li>
<li><a href="https://github.com/igvteam/juicebox.js">HIC CONTACT MAP</a>: Alignment and visualisation of HiC data</li>
<li><a href="https://github.com/mummer4/mummer">MUMMER</a>&nbsp;&rarr;&nbsp;<a href="http://circos.ca/documentation/">CIRCOS</a>&nbsp;+&nbsp;<a href="https://plotly.com/">DOTPLOT</a>&nbsp;&amp;&nbsp;<a href="https://github.com/lh3/minimap2">MINIMAP2</a>&nbsp;&rarr;&nbsp;<a href="https://github.com/schneebergerlab/plotsr">PLOTSR</a>: Synteny analysis</li>
<li><a href="https://github.com/marbl/merqury">MERQURY</a>: K-mer completeness, consensus quality and phasing assessment</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10127/assistant-professor-at-sardar-patel-university</guid>
  <pubDate>Mon, 21 Apr 2014 21:03:55 -0500</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor at SARDAR PATEL UNIVERSITY]]></title>
  <description><![CDATA[
<p>SARDAR PATEL UNIVERSITY<br />Centre for Interdisciplinary Studies in Science and Technology</p>

<p>No.: SPU/CISST/Advt./2014-15/519</p>

<p>ADVERTISEMENT for Teaching Positions (Contractual)</p>

<p>Applications for the following Contractual Teaching Position are invited for Centre for Interdisciplinary Studies in Science and Technology (CISST), Sardar Patel University:</p>

<p>2. Assistant Professor (ONE) (Contractual)</p>

<p>For the subject of Bioinformatics</p>

<p>Qualifications:</p>

<p>(I) Good academic record as defined by the concerned university with at least 55 % marks (or an equivalent grade in a point scale wherever grading system is followed) at the Master’s level</p>

<p>(II) Ph.D. degree in the concerned subject or in a relevant interdisciplinary subject<br />from an Indian University or NET/SLET clearance Contractual appointment carries a total Fixed Emoluments of Rs. 30,000/- p.m without any assurance of permanent Positions and related benefits.</p>

<p>An Application Form in prescribed Performa, available on University Website: www.spuvvn.edu should be filled in completely in Twelve Copies with self attested copies of certificates of qualifications and experience. Only one copy of each mark sheet be attached with the first copy of the application form. All 12 (Twelve) Application forms should be sent to Registrar’s office along with Demand Draft of Application form fee of Rs. 250/- (Non-refundable) in favour of “REGISTRAR, SARDAR PATEL UNIVERSITY, VALLABH VIDYANAGAR”. The S.C. and S.T. category candidates need not to pay Application fee.</p>

<p>Applicants who are in service should apply through their present employers. Candidates called for interview shall be required to attend at their own cost.</p>

<p>In absence of suitable candidate, the University may relax the eligibility criteria, for conditional appointment.</p>

<p>The last date of receipt of application by the University is 30th April, 2014</p>

<p>Advertisement: www.spuvvn.edu/careers/CISST%20Advt.%20April%202014.pdf</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design</guid>
	<pubDate>Fri, 21 Feb 2025 10:39:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design</link>
	<title><![CDATA[NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design]]></title>
	<description><![CDATA[<p>NVIDIA and the Arc Institute have introduced <strong style="font-size: 12.8px;">Evo 2</strong>, a groundbreaking AI model designed to <strong style="font-size: 12.8px;">understand, predict, and generate DNA sequences</strong>. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.</p><h3><strong>The Power of Evo 2: AI Meets DNA</strong></h3><p>Evo 2 is <strong>the largest AI model for biology ever created</strong>, trained on an astonishing <strong>9.3 trillion DNA "letters"</strong> (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.</p><p>For the first time, scientists can <strong>design DNA with AI</strong>, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to <strong>predict, modify, and even create entire genetic sequences</strong>, opening new possibilities in medicine, agriculture, and synthetic biology.</p><h3><strong>Decoding the Dark Genome</strong></h3><p>One of the biggest challenges in genetics is understanding the <strong>non-coding regions</strong> of DNA&mdash;vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.</p><p>Evo 2 is designed to <strong>decode these non-coding elements</strong>, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.</p><h3><strong>From Reading DNA to Writing It</strong></h3><p>To put Evo 2&rsquo;s impact into perspective:</p><ul>
<li><strong>Previous AI models could "read" DNA</strong> like a book, analyzing genetic sequences and identifying patterns.</li>
<li><strong>Evo 2 can "write" entirely new DNA</strong>, designing functional genes, chromosomes, and even full genomes from scratch.</li>
</ul><p>This means scientists can now <strong>engineer biological systems with AI</strong>, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.</p><h3><strong>A Step Toward Generative Biology</strong></h3><p>The Arc Institute describes Evo 2 as a major step toward <strong>"generative biology"</strong>&mdash;a revolutionary approach where AI is used to create <strong>novel biological structures</strong> rather than just analyzing existing ones. This could lead to breakthroughs such as:</p><ul>
<li><strong>New medicines</strong>: AI-generated enzymes and proteins tailored for targeted therapies.</li>
<li><strong>Disease-resistant crops</strong>: Genetically optimized plants for higher yield and climate resilience.</li>
<li><strong>Synthetic organisms</strong>: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.</li>
</ul><h3><strong>An Open-Source Revolution</strong></h3><p>Unlike many proprietary AI models, <strong>Evo 2 is open source</strong>, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can <strong>collaborate, experiment, and innovate</strong>, accelerating discoveries in genetic engineering and synthetic biology.</p><p>With Evo 2, the boundaries of what&rsquo;s possible in <strong>DNA design, genetic engineering, and biological innovation</strong> are being redrawn. The future of life sciences is no longer just about understanding life&rsquo;s code&mdash;it&rsquo;s about writing it.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10394/bioinformatics-protocols</guid>
	<pubDate>Mon, 05 May 2014 10:21:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10394/bioinformatics-protocols</link>
	<title><![CDATA[Bioinformatics Protocols]]></title>
	<description><![CDATA[<h2><span> RNA Seq </span></h2>
<p><strong> Basic Galaxy Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y">RNA-Seq tutorial</a> based on <a href="http://www.nature.com/protocolexchange/protocols/2327">Trapnell et al. (2012)</a> <em>Nature Protocols</em></li>
</ul>
<dl><dd>In this tutorial we cover the concepts of <a href="http://en.wikipedia.org/wiki/RNA-Seq">RNA-Seq</a> differential gene expression (DGE) analysis using a very small synthetic dataset from a well studied organism.</dd></dl>
<p><strong> Advanced Galaxy Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1fQ1XfeOKhezJUDTzMXtZVY20c3RGoHe-HLvFOGzqU4s/pub">RNA-Seq (Advanced) Tutorial</a></li>
</ul>
<dl><dd>In this tutorial we compare the performance of three statistically-based differential expression tools:</dd><dd>* CuffDiff</dd><dd>* EdgeR</dd><dd>* DESeq2</dd></dl>
<p><strong> Advanced Command Line Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1ayJXtgBP1OXtnV7o7lq4QHKMNk5SdPHFq4hGkqndBtI/pub">Graphical Output with CummeRbund</a> introduces some basic commands using the cummeRbund package of the R programming language</li>
</ul>
<dl><dd>You will need to install R, RStudio and cummeRbund on your PC (explained in the Tutorial). You will learn how to produce graphical output from RNA-Seq analysis previously done using a Cuffdiff analysis.</dd></dl>
<h2><span> Variant Detection </span></h2>
<p><strong> Basic Galaxy Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI">Variant Detection tutorial</a></li>
</ul>
<dl><dd>In this tutorial we cover the concepts of detecting small variants (SNVs and indels) in human genomic DNA using a small set of reads from chromosome 22.</dd></dl>
<p><strong>Advanced Galaxy Tutorial</strong></p>
<ul>
<li><a href="https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM">Variant Detection (Advanced) Tutorial</a></li>
</ul>
<dl><dd>In this tutorial we compare the performance of three statistically-based variant detection tools:</dd><dd>* SAMtools: Mpileup</dd><dd>* GATK: Unified Genotyper</dd><dd>* FreeBayes</dd><dd>Each of these tools takes as its input a BAM file of aligned reads and generates a list of likely variants in VCF format</dd></dl>
<p><strong>Pipelines</strong> are for those who are comfortable with using the UNIX command line; and often allow more control over branching and iteration logic.</p>
<ul>
<li><a href="https://github.com/claresloggett/variant_calling_pipeline">WGS/exome GATK-based variant calling pipeline</a></li>
</ul>
<dl><dd>This is a basic variant-calling and annotation pipeline developed at the Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne. It is based around BWA, GATK and ENSEMBL and was originally designed for human (or similar) data. The master branch is configured for WGS data; there is an exome branch configured for variant calling in exome data.</dd><dd>To run the pipeline you will need Rubra: <a href="https://github.com/bjpop/rubra">https://github.com/bjpop/rubra</a>. Rubra uses the python Ruffus library: <a href="http://www.ruffus.org.uk/">http://www.ruffus.org.uk/</a>.</dd></dl>
<p><strong>Protocols</strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1lfDYNzHjfDA1pHTHd-0w3xHhg7L4TipT1gRfzgiV8es/pub">Familial Variant Calling</a></li>
</ul>
<dl><dd>In this protocol we discuss and outline the process of calling familial related mutations.</dd></dl>
<ul>
<li><a href="https://docs.google.com/document/d/1PIhm8NrFGaSK0hxpDcp8wUOz11ZkOaHIrpnJshMgDec/pub">Somatic Variant Calling</a></li>
</ul>
<dl><dd>In this protocol we discuss and outline the process of identifying somatic variants or mutations.</dd></dl>
<h2><span> Assembly </span></h2>
<p><strong> Basic Galaxy Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM">Genome assembly tutorial</a></li>
</ul>
<dl><dd>In this tutorial we carry out de novo assembly of a microbial genome. We have also written a <a href="https://docs.google.com/document/d/1xs-TI5MejQARqo0pcocGlymsXldwJbJII890gnmjI0o/pub">De novo Genome Assembly for Illumina Data</a> Protocol for a more generic description of the method.</dd></dl>
<p><strong> Protocol </strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1xs-TI5MejQARqo0pcocGlymsXldwJbJII890gnmjI0o/pub">De novo Genome Assembly for Illumina Data</a></li>
</ul>
<dl><dd>In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes. Use our <a href="https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM">Genome assembly tutorial</a> to learn a specific case of using Galaxy to carry out de novo assembly of a microbial genome.</dd></dl>
<h2><span> Small RNAs </span></h2>
<p><strong> Basic Galaxy Tutorial </strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1WAObJr7M0m8U-2ku-0Y0Sdt_IHmqd1h8WaJHPhnJ1lM/pub">Quality control for small RNA</a></li>
</ul>
<dl><dd>This tutorial covers initial steps of the workflow for analysis of short RNA expression such as a quality control of the raw reads, processing of the raw reads for the subsequent analysis and initial quality assessment of the library.</dd></dl>
<h2><span> ChIP Seq </span></h2>
<p><strong> Protocol </strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1UPJC8dsiDeP5R9MH9U0IvoDgPF2Q3EOstAuzS3e6WCE/pub">ChIP-Seq</a></li>
</ul>
<dl><dd>In this protocol we discuss ChIP-Seq: a method to analyze the interaction between proteins and DNA.</dd></dl>
<h2><span> Amplicons </span></h2>
<p><strong>Protocol</strong></p>
<ul>
<li><a href="https://docs.google.com/document/d/1uW7JzxG86QzS92hTyeuNsLhX_d1XFbaZPSjh7jWxcSg/pub">Amplicon Alignment</a></li>
</ul>
<dl><dd>In this protocol we discuss and outline the process of aligning custom amplicons using primers for high precision.</dd></dl>
<h2><span> Learn Galaxy </span></h2>
<p><a href="https://docs.google.com/document/d/1wsdJDYfjZVg2uJxm9AHi_j0mY3X1M1F4gB-elkuYL7c/pub">Introduction to Galaxy,</a> for those who are very new to Galaxy.</p>
<p><a href="https://docs.google.com/document/d/1t7vVqa3mdeZYPv5-8hiHBFBYhNiynV_3mWByno9-wUM/pub">Using Histories and Workflows,</a> for those with some Galaxy knowledge.</p>
<p>The Galaxy project website has many <a href="http://wiki.galaxyproject.org/Learn">tutorials</a> and <a href="http://wiki.galaxyproject.org/Learn/Screencasts">screencasts</a> about using Galaxy and the tools, and developing new tools.</p><p>Address of the bookmark: <a href="https://genome.edu.au/wiki/Learn" rel="nofollow">https://genome.edu.au/wiki/Learn</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads</guid>
	<pubDate>Wed, 07 Jun 2017 04:18:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads</link>
	<title><![CDATA[GraphMap - A highly sensitive and accurate mapper for long, error-prone reads]]></title>
	<description><![CDATA[<p>GraphMap - A highly sensitive and accurate mapper for long, error-prone reads http://www.nature.com/ncomms/2016/160415/ncomms11307/full/ncomms11307.html<br><br><strong>Features</strong><br><br>&nbsp;&nbsp;&nbsp; Mapping position agnostic to alignment parameters.<br>&nbsp;&nbsp;&nbsp; Consistently very high sensitivity and precision across different error profiles, rates and sequencing technologies even with default parameters.<br>&nbsp;&nbsp;&nbsp; Circular genome handling to resolve coverage drops near ends of the genome.<br>&nbsp;&nbsp;&nbsp; E-value.<br>&nbsp;&nbsp;&nbsp; Meaningful mapping quality.<br>&nbsp;&nbsp;&nbsp; Various alignment strategies (semiglobal bit-vector and Gotoh, anchored).<br>&nbsp;&nbsp;&nbsp; Overlapping of reads for de novo assembly.<br>&nbsp;&nbsp;&nbsp; Transcriptome mapping through internal construction of a transcriptome from a given genomic reference and a GTF file.<br>&nbsp;&nbsp;&nbsp; ...and much more.<br><br>GraphMap is also used as an overlapper in a new de novo genome assembly project called Ra (https://github.com/mariokostelac/ra-integrate).<br>Ra attempts to create de novo assemblies from raw nanopore and PacBio reads without requiring error correction, for which a highly sensitive overlapper is required.<br><br>Currently, development of a new spliced-alignment mode for mapping RNA-seq reads is under way.<br>Description of the current effort as well as how to reach the experimental implementation can be found here: doc/rnaseq.md.</p><p>Address of the bookmark: <a href="https://github.com/isovic/graphmap" rel="nofollow">https://github.com/isovic/graphmap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10457/assistant-professor-bio-informatics-at-health-and-family-welfare-department-medical-education-in-raipur</guid>
  <pubDate>Wed, 07 May 2014 00:08:38 -0500</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor (Bio-Informatics) at Health and Family Welfare Department (Medical Education) in Raipur]]></title>
  <description><![CDATA[
<p>Advertisement No.05/2014/ Exam/Dated 17/04/2014</p>

<p>No of vacancies: 01</p>

<p>Pay scale:Rs. 15600 – 39100 + 6600/-</p>

<p>Essential Academic Qualifications / Experience : Good academic record as defined by the concerned university with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed) at the Master's Degree level in a relevant subject from an Indian University, or an equivalent degree from an accredited foreign university.</p>

<p>Besides fulfilling the above qualifications, the candidate must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET/ SET.</p>

<p>Notwithstanding anything contained in sub-clauses (a) and (b) to this Clause, candidates, who are, or have been awarded a Ph.D. Degree in accordance with the University Grants Commission (Minimum Standards and Procedure for Award of Ph.D. Degree) Regulations, 2009, shall be exempted from the requirement of the minimum eligibility condition of NET/SLET/SET for recruitment and appointment of Assistant Professor or equivalent positions in Universities/Colleges/Institutions.</p>

<p>NET/SLET/SET shall also not be required for such Masters Programmes in disciplines for which NET/SLET/SET is not conducted.</p>

<p>Apply online: http://www.psc.cg.gov.in/htm/OA_ME2014.html</p>

<p>Last Date for Online Registration: 22/05/2014</p>

<p>For more details: http://www.psc.cg.gov.in/pdf/Advertisement/ADV_ME2014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</guid>
	<pubDate>Tue, 08 May 2018 04:27:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</link>
	<title><![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]></title>
	<description><![CDATA[<p><strong>HISAT2</strong><span>&nbsp;is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs&nbsp;</span><a href="http://dl.acm.org/citation.cfm?id=2674828">[Sir&eacute;n et al. 2014]</a><span>, we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).&nbsp;</span></p>
<p><span>more at&nbsp;https://ccb.jhu.edu/software/hisat2/index.shtml</span></p><p>Address of the bookmark: <a href="https://github.com/infphilo/hisat2" rel="nofollow">https://github.com/infphilo/hisat2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</guid>
	<pubDate>Sat, 10 May 2014 04:33:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</link>
	<title><![CDATA[GPS DNA tracking - University of Sheffield]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/Aap-s1kle4Q" frameborder="0" allowfullscreen></iframe>University of Sheffield geneticist and bioinformatics expert Dr Eran Elhaik demonstrates the power of his new DNA research, which allows people to discover their genetic homeland from 1000 years ago. Find out more about our biological research here http://www.sheffield.ac.uk/aps]]></description>
	
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