<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32709?offset=450</link>
	<atom:link href="https://bioinformaticsonline.com/related/32709?offset=450" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29217/bioinformatics-openings-at-sri-venkateswara-college-university-of-delhi</guid>
  <pubDate>Tue, 20 Sep 2016 05:43:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics openings at Sri Venkateswara College, University of Delhi]]></title>
  <description><![CDATA[
<p>Bioinformatics center</p>

<p>Sri Venkateswara College (University of Delhi)</p>

<p>New Delhi- 110021</p>

<p>1. Junior Research Fellow (1 Post)</p>

<p>Applications are invited for the post of Junior Research Fellow (JRF) under DST funded project which is purely temporary and is strictly for project duration only.</p>

<p>Title of project</p>

<p>No. of post</p>

<p>Remuneration (Rs.)</p>

<p>“Computational assisted Design and Synthesis of Novel Antimalarial Agents Embodying Structural Diversity Suitable for Protease Inhibitors”</p>

<p>(One)</p>

<p>Fellowship and HRA as per DST guidelines</p>

<p>Qualification</p>

<p>Post Graduate Degree in Basic Science (M.Sc./M.Tech in Bioinformatics/Biophysics) from a recognized University in India or abroad with at least 55% marks with NET qualification or Graduate Degree in Professional Course with NET Qualification or Post Graduate Degree in Professional Course.</p>

<p>Desirable</p>

<p>Fair knowledge of Computer Aided Drug Designing (CADD), Protein Structure modeling, molecular docking, and simulations are preferable.</p>

<p>2. Traineeship (1 Post)</p>

<p>Applications are invited for the position of traineeship in DBT-BTISnet funded Bioinformatics Infrastructure Facility (BIF) to carry out project work in the area of Bioinformatics.</p>

<p>Qualification</p>

<p>Applicant should be possess PG degree/PG diploma in Bioinformatics for traineeship. The traineeship is awarded for a period of six months from the date of joining and is not extendable. The selected candidates are entitled to receive a stipend of Rs. 8000/- per month (consolidate) for a period of 6 months.</p>

<p>=====================================================================</p>

<p>3. Studentship (1 Post)</p>

<p>Applications are invited for the position of Studentship in DBT-BTISnet funded Bioinformatics Infrastructure Facility (BIF) to carry out project work in the area of Bioinformatics.</p>

<p>Qualification</p>

<p>Candidates pursuing the Final Year of Post Graduate Degree in Basic Science (M.Sc.) or Post Graduate/ Graduate Degree in Professional Course (M.Tech/B.Tech) in Bioinformatics from a recognized University in India or abroad. The selected candidates are entitled to receive a stipend of Rs. 8000/- per month (consolidate) for a period of 6 months.</p>

<p>How to Apply?</p>

<p>Applicants are required to send applications on plain paper, stating the name, address, date of birth, educational qualification, experience and Institute, along with attested photocopies of mark sheets and certificates etc. by September 20, 2016 to:</p>

<p>The Coordinator</p>

<p>Bioinformatics Center, Sri Venkateswara College</p>

<p>Benito Juarez Road, Dhaula Kuan, New Delhi- 110021</p>

<p>Applications may also be sent by email to contact@bic-svc.ac.in. Strictly mention "Application for JRF, Traineeship or Studentship" in the subject line as the case may be.</p>

<p>Short listed candidates will be called for an interview. Canvassing in any form will be a disqualification. No TA/DA will be paid either for attending the interview or joining the post.</p>

<p>For more details visit our lab webpage: http://www.bic-svc.ac.in</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</guid>
	<pubDate>Fri, 13 Jul 2018 07:49:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</link>
	<title><![CDATA[transrate: Understanding your transcriptome assembly]]></title>
	<description><![CDATA[<p><span>Transrate is software for&nbsp;</span><em>de-novo</em><span>&nbsp;transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.</span></p><p>Address of the bookmark: <a href="http://hibberdlab.com/transrate/index.html" rel="nofollow">http://hibberdlab.com/transrate/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29282/cosmic</guid>
	<pubDate>Sat, 01 Oct 2016 15:04:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29282/cosmic</link>
	<title><![CDATA[COSMIC]]></title>
	<description><![CDATA[<p>The accurate description and annotation of structural variants can be complex. &nbsp;This is due to the different resolution that variants are reported from traditional&nbsp;cytogenetic coordinates down to the actual base pair positions. Furthermore, multiple&nbsp;rearrangements in a single area of the genome can make cataloguing and interpreting&nbsp;their effects challenging.&nbsp;</p>
<p>The Rearrangement Overview page describes the one or more breakpoints which make up a structural&nbsp;variant. A breakpoint is defined as a region or point where the sample sequence has altered&nbsp;from the reference sequence. Minimum interpretation is made of this data. One variant event&nbsp;can consist of one or multiple breakpoints. The Syntax (shown above the table) gives a detailed description of the variant and its location &nbsp;(e.g. chr11:g.36585230_76606619del, a deletion of&nbsp;roughly 40Mb on chromosome 11). Syntax is based on HGVS mutation nomenclature recommendations&nbsp;[http://www.hgvs.org/rec.html].&nbsp;</p>
<p>http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview</p><p>Address of the bookmark: <a href="http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview" rel="nofollow">http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</guid>
	<pubDate>Thu, 20 Dec 2018 12:03:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</link>
	<title><![CDATA[ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data]]></title>
	<description><![CDATA[<p><span>The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps:&nbsp;</span><em>prune</em><span>,&nbsp;</span><em>partition</em><span>,&nbsp;</span><em>rescue</em><span>,&nbsp;</span><em>optimize</em><span>&nbsp;and&nbsp;</span><em>build</em><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/tangerzhang/ALLHiC/wiki" rel="nofollow">https://github.com/tangerzhang/ALLHiC/wiki</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</guid>
	<pubDate>Mon, 21 Jan 2019 17:58:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</link>
	<title><![CDATA[SvABA: Genome-wide detection of structural variants and indels by local assembly]]></title>
	<description><![CDATA[<p><span>SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of&nbsp;</span><a href="https://github.com/jts/sga">SGA</a><span>&nbsp;(String Graph Assembler) by Jared Simpson, and&nbsp;</span><a href="https://github.com/lh3/bwa">BWA-MEM</a><span>&nbsp;by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using&nbsp;</span><a href="https://github.com/walaj/VariantBam">VariantBam</a><span>&nbsp;rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.</span></p><p>Address of the bookmark: <a href="https://github.com/walaj/svaba" rel="nofollow">https://github.com/walaj/svaba</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes</guid>
	<pubDate>Tue, 20 Aug 2019 09:47:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes</link>
	<title><![CDATA[TRITEX sequence assembly pipeline for Triticeae genomes]]></title>
	<description><![CDATA[<div>
<p>The pipeline is open-source and hosted in a public Bitbucket&nbsp;<a href="https://bitbucket.org/tritexassembly/tritexassembly.bitbucket.io/src/master/">repository</a>.</p>
</div>
<div>
<p>TRITEX has been run on highly inbred genotypes of barley (<em>Hordeum vulgare</em>), tetraploid wheat (<em>Triticum turgidum</em>) and hexaploid wheat (<em>T. aestivum</em>) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.</p>
</div>
<div>
<p>A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in&nbsp;<a href="https://bio-protocol.org/e2955">Himmelbach et al. 2018</a>. Refer to the&nbsp;<a href="https://www.10xgenomics.com/resources/technical-notes/">technical notes</a>&nbsp;of 10X Genomics on how to generate Chromium data.</p>
</div><p>Address of the bookmark: <a href="https://tritexassembly.bitbucket.io/" rel="nofollow">https://tritexassembly.bitbucket.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29654/randomness-and-probability</guid>
	<pubDate>Tue, 08 Nov 2016 07:17:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29654/randomness-and-probability</link>
	<title><![CDATA[Randomness and Probability]]></title>
	<description><![CDATA[<p>Randomness and Probability</p><p>Randomness and probability are two differnet concepts: probaility is a measure (according to measure theory) which measures the randomness. Randomness is the object to be measured by probability.&nbsp;For example, probability is a mapping from randomness to the real number between 0 and 1. The similar examples are that the entropy measures the uncertanity; product of length and width measures the area of rectangle etc.</p><p><strong>Please see &ldquo;A mathematical theory of ability measure&rdquo; by N. Kong ets for more examples to answer&nbsp;this question.</strong></p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29654" length="598559" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</guid>
	<pubDate>Wed, 09 Nov 2016 16:29:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</link>
	<title><![CDATA[Method in Comparative genomics !!]]></title>
	<description><![CDATA[<p>We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change.</p>
<p>We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.</p>
<p>Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.</p><p>Address of the bookmark: <a href="http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf" rel="nofollow">http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</guid>
	<pubDate>Sun, 31 May 2020 01:59:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</link>
	<title><![CDATA[Supernova: generates phased, whole-genome de novo assemblies from a Chromium-prepared library.]]></title>
	<description><![CDATA[<p>Supernova generates phased, whole-genome&nbsp;<em>de novo</em>&nbsp;assemblies from a Chromium-prepared library.</p>
<p>Please see&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/guidance/doc/achieving-success-with-de-novo-assembly">Achieving Success with De Novo Assembly</a>&nbsp;and&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/software/overview/system-requirements">System Requirements</a>&nbsp;<em>before</em>&nbsp;creating your Chromium libraries for assembly.</p>
<p>Supernova should be run using 38-56x coverage of the genome.<br>&bull; Somewhat higher coverage is&nbsp;<em>sometimes</em>&nbsp;advantageous.<br>&bull; Supernova will exit if it finds that coverage is far from the recommended range.<br>&bull; Note that at most 2.14 billion reads are allowed.<br>&bull; Please note that we have not extensively tested genomes larger than human, and any genome above approximately 4 GB should be considered experimental and is not supported.</p><p>Address of the bookmark: <a href="https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running" rel="nofollow">https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29883/ra-bioinformatics-at-school-of-computational-integrative-sciences-jnu-india</guid>
  <pubDate>Fri, 18 Nov 2016 03:57:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at School of Computational &amp; Integrative Sciences, JNU, India]]></title>
  <description><![CDATA[
<p>School of Computational &amp; Integrative Sciences<br />Jawaharlal Nehru University<br />New Delhi – 110067</p>

<p>Date: Nov 11th. 2016                                                            Last Date:  Nov 25th. 2016</p>

<p>PROJECT ID: 632</p>

<p>The following posts are urgently required to be filled for the Department of Biotechnology, Government of India funded project entitled "Computational Core for Plant Metabolomics" administrated by Prof Indira Ghosh,  School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067</p>

<p>NB:For all Bioinformatics posts, 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>RA / Research Associate (Metabolic engineering/Computational Biologist)</p>

<p>Salary: Rs. 36000/- + HRA<br />Vacancy: 1<br />Essential Qualifications: PhD in  Bioinformatics /Mathematics/Computer Science with experience in analyzing high throughput omics-based data/ system Biology/ Analysis of Network Biology. Published paper in the field is a must to prove the experience.<br />Desired Skills: Prior experience in handling and guiding bioinformatics, metabolomics data, planning of new research area in metabolic driven network , managing the project portal, preparing and filing reports etc. Will be expected to communicate with user groups and coordinate with LIMS group in Hyderabad and the Cheminformatics group in Delhi.</p>

<p>RA / Research Associate (Chemo-informatics/Computational Biologist)</p>

<p>Salary: Rs. 36000/- + HRA<br />Vacancy: 1<br />Essential Qualifications: PhD in Bioinformatics/ computational biology/ Biophysics/Computer Science. Computational and Chemical structure related experience is a necessary qualification proven by paper published and program developed. <br />Desired Skills:  Research experience in Chemical scaffold mapping, in silico Spectral analysis, Biological Database Designing &amp; Integration is required. Individual is responsible to develop methods related to metabolite identification, Testing and refining and integrate LIMS with IIIT Hyderabad and will be expected to communicate with user groups.</p>

<p>Project SRF (Bioinformatics/Programming)</p>

<p>Salary: As per DBT rules<br />Vacancy: 1<br />Essential Qualifications: Masters/B Tech 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.Tec, Published paper  in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must.</p>

<p>More at http://www.jnu.ac.in/Career/currentjobs.htm</p>
]]></description>
</item>

</channel>
</rss>