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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31156?offset=430</link>
	<atom:link href="https://bioinformaticsonline.com/related/31156?offset=430" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27685/biodbnet</guid>
	<pubDate>Thu, 02 Jun 2016 11:11:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27685/biodbnet</link>
	<title><![CDATA[BioDBnet]]></title>
	<description><![CDATA[<p><span>Database to Database Conversions</span> </p>
<p>db2db allows for conversions of identifiers from one database to other database identifiers or annotations. To use db2db select the input type of your data, changing the input type automatically changes the output options to the ones specific for the input selected. Then select one or more output types and add your identifiers in the ID list box. Set the remove duplicate values to 'No' if you do not want duplicates to be removed. Clicking on submit then returns a table of your inputs matched against all the outputs selected in the exact order as entered. Results can be limited to a particular taxon by entering it's <a href="https://biodbnet-abcc.ncifcrf.gov/tools/orgTaxon.php">Taxon ID</a>. The performance will vary widely depending on the number of outputs and the options selected. Conversions to a single output with the default options should complete in a few seconds</p><p>Address of the bookmark: <a href="https://biodbnet-abcc.ncifcrf.gov/db/db2db.php" rel="nofollow">https://biodbnet-abcc.ncifcrf.gov/db/db2db.php</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/27713/mutabind</guid>
	<pubDate>Mon, 06 Jun 2016 13:34:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/27713/mutabind</link>
	<title><![CDATA[MutaBind]]></title>
	<description><![CDATA[<p><span>MutaBind is a new computational method and server created through NCBI research efforts that maps mutations on a protein structural complex, calculates changes in binding affinity, identifies deleterious mutations and produces a downloadable mutant structural model.&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/projects/mutabind/index.fcgi/" target="_blank">http://www.ncbi.nlm.nih.gov/projects/mutabind/index.fcgi/</a></p><p><img src="http://www.ncbi.nlm.nih.gov/projects/mutabind/prj-sunddg/static/myimgs/CirclesDiamondBlueThiner.png" width="471" height="258" alt="image" style="border: 0px;"></p><p><span>MutaBind guides you through this process, step by step, starting with selecting a protein complex and inputting PDB code or uploading PDB files. You can also retrieve results with a job ID number, view help documents, and review the MutaBind method and references.</span></p><p><span>More at&nbsp;http://www.ncbi.nlm.nih.gov/projects/mutabind/index.fcgi/</span></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/27827/guest-faculty-centre-for-bioinformatics-at-pondicherry-university</guid>
  <pubDate>Wed, 15 Jun 2016 03:44:31 -0500</pubDate>
  <link></link>
  <title><![CDATA[Guest Faculty Centre for Bioinformatics at Pondicherry University]]></title>
  <description><![CDATA[
<p>Guest Faculty Centre For Bioinformatics Jobs opportunity in Pondicherry University<br />Qualification : M.Phil. (with NET/SLET)/ M.Tech. / M.E. in Computer Science with a minimum of 55% of marks as per UGC norms.<br />Desirable : Ph.D and Teaching experience in Perl and Java programming.<br />Honorarium : Rs. 1,000/- per lecture (subject to a maximum of Rs. 25,000/- per month)<br />How to apply<br />Walk-in-Interview will be held on 29.06.2016 (Wednesday) at 2:30 P.M at the office of Centre for Bioinformatics, Pondicherry University, Puducherry — 605 014. Interested eligible candidates may attend the Walk-in-Interview along with all original certificates, self attested photocopies and testimonials with a copy of their bio-data. Candidates reporting after 2:30 P.M will not be entertained.</p>

<p>More at http://www.pondiuni.edu.in/news?quicktabs_2=5#quicktabs-2</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</guid>
	<pubDate>Thu, 16 Jun 2016 18:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</link>
	<title><![CDATA[clusterProfiler]]></title>
	<description><![CDATA[<p>statistical analysis and visulization of functional profiles for genes and gene clusters<br><br>Bioconductor version: Release (3.3)<br><br>This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.<br><br>Author: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt; with contributions from Li-Gen Wang and Giovanni Dall'Olio.<br><br>Maintainer: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt;<br><br>Citation (from within R, enter citation("clusterProfiler")):<br><br>Yu G, Wang L, Han Y and He Q (2012). &ldquo;clusterProfiler: an R package for comparing biological themes among gene clusters.&rdquo; OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287.<br>Installation<br><br>To install this package, start R and enter:<br><br>## try http:// if https:// URLs are not supported<br>source("https://bioconductor.org/biocLite.R")<br>biocLite("clusterProfiler")</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/27927/research-assistant-bioinformatics-at-andhra-university</guid>
  <pubDate>Sat, 18 Jun 2016 18:39:09 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Assistant Bioinformatics at Andhra University]]></title>
  <description><![CDATA[
<p>Advt. No. AUMLR/BIF/ RA /6-2016 <br />Research Assistant Job Position in Andhra University on temporary basis <br />No. of Post : 01<br />Eligibility : Applicants who have completed their Post Graduate degree in Bioinformatics.<br />Desirable : Undergone traineeship in BIF; at least one publication in Bioinformatics. <br />Stipend : A monthly stipend of Rs. 22,000/- + HRA (HRA is applicable only for NET qualified candidates)<br />How to apply<br />Applications on plain paper, stating the name, address, date of birth, educational qualifications and experiences, and Institute, along with attested photocopies of mark sheets and certificates, should be submitted to K. UMADEVI, Coordinator, BIF Programme, Department of Marine Living Resources, Andhra University, Visakhapatnam-530 003, Andhra Pradesh, on or before 15th July, 2016. </p>

<p>Candidates are required to appear for an interview, with all the necessary certificates in original along with a set of attested copies in the office of the Principal, AU College of Science &amp; Technology, Andhra University, Visakhapatnam. Applications may be sent by Email to andhrauniv.btisnet@nic.in / katruumadevi@gmail.com.</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/28112/ngs-glossary</guid>
	<pubDate>Mon, 27 Jun 2016 08:56:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/28112/ngs-glossary</link>
	<title><![CDATA[NGS Glossary !!]]></title>
	<description><![CDATA[<p><strong>alignment</strong>: the mapping of a raw sequence read to a location within a reference genome. The mapping occurs because the sequences within the raw read match or align to sequences within the reference genome. Alignment information is stored in the <strong>SAM</strong> or <strong>BAM</strong> file formats.</p><p><strong>bcftools</strong>: a set of companion tools, currently bundled with SAMtools, for identifying and filtering genomics variants.</p><p><strong>bowtie</strong>: widely used, open source alignment software for aligning raw sequence reads to a reference genome.</p><p><strong>BAM Format</strong>: binary, compressed format for storing <strong>SAM</strong> data.</p><p><strong>BCF Format</strong>: Binary call format. Binary, compressed format for storing <strong>VCF</strong> data.</p><p><strong>CIGAR String</strong>: Compact Idiosyncratic Gapped Alignment Report. A compact string that (partially) summarizes the alignment of a raw sequence read to the reference genome. Three core abbreviations are used: M for alignment match; I for insertion; and D for Deletion. For example, a CIGAR string of 5M2I63M indicates that the first 5 base pairs of the read align to the reference, followed by 2 base pairs, which are unique to the read, and not in the reference genome, followed by an additional 63 base pairs of alignment.</p><p><strong>FASTA Format</strong>: text format for storing raw sequence data. For example, the FASTA file at: <a href="http://www.ncbi.nlm.nih.gov/nuccore/NC_008253">http://www.ncbi.nlm.nih.gov/nuccore/NC_008253</a> contains entire genome for Escherichia coli 536.</p><p><strong>FASTQ Format</strong>: text format for storing raw sequence data along with quality scores for each base; usually generated by sequencing machines.</p><p><strong>genotype likelihood</strong>: the probability that a specific genotype is present in the sample of interest. Genotype likelihoods are usually expressed as a <strong>Phred-scaled probability</strong>, where P = 10 ^ (-Q/10). For example, if the genotype TT (both alleles are T) at position 1,299,132 in human chromosome 12 (reference G) is 37, this translates to a probability of 10<sup>-37/10</sup> = 0.0001995, meaning that there is very low probability that the reads in your sample support a TT genotype. On the other hand, a genotype of AA at the same position with a score of 0 translates into a probability of 10<sup>-0</sup> = 1, indicating extremely high probability that your sample contains a homozygous mutation of G to A.</p><p><strong>mate-pair</strong>: in paired-end sequencing, both ends of a single DNA or RNA fragment are sequenced, but the intermediate region is not. The two ends which are sequenced form a pair, and are frequently referred to as mate-pairs.</p><p><strong>QNAME</strong>: unique identifier of a raw sequence read (also known as the Query Name). Used in <strong>FASTQ</strong> and <strong>SAM</strong> files.</p><p><strong>paired-end sequencing</strong>: sequencing process where both ends of a single DNA or RNA fragment are sequenced, but the intermediate region is not. Particularly useful for identifying structural rearrangements, including gene fusions.</p><p><strong>Phred-scaled probability</strong>: a scaled value (Q) used to compactly summarize a probability, where P = 10<sup>-Q/10</sup>. For example, a Phred Q score of 10 translates to probability (P) = 10<sup>-10/10</sup> = 0.1. Phred-scaled probabilities are common in next-generation sequencing, and are used to represent multiple types of quality metrics, including quality of base calls, quality of mappings, and probabilities associated with specific genotypes. The name Phred refers to the original Phred base-calling software, which first used and developed the scale.</p><p><strong>Phred quality score</strong>: a score assigned to each base within a sequence, quantifying the probability that the base was called incorrectly. Scores use a <strong>Phred-scaled probability</strong> metric. For example, a Phred Q score of 10 translates to P=10<sup>-10/10</sup> = 0.1, indicating that the base has a 0.1 probability of being incorrect. Higher Phred score correspond to higher accuracy. In the <strong>FASTQ format</strong>, Phred scores are represented as single ASCII letters. For details on translating between Phred scores and ASCII values, refer to <a href="http://www.somewhereville.com/?p=1508">Table 1 of this useful blog post from Damian Gregory Allis</a>.</p><p><strong>read-length</strong>: the number of base pairs that are sequenced in an individual sequence read.</p><p><strong>read-depth</strong>: the number of sequence reads that pile up at the same genomic location. For example, 30X read-depth coverage indicates that the genomic location is covered by 30 independent sequencing reads. Increased read-depth translates into higher confidence for calling genomic variants.</p><p><strong>RNAME</strong>: reference genome identifier (also known as the Reference Name). Within a SAM formatted file, the RNAME identifies the reference genome where the raw read aligns.</p><p><strong>SAM Flag</strong>: a single integer value (e.g. 16), which encodes multiple elements of meta-data regarding a read and its alignment. Elements include: whether the read is one part of a paired-end read, whether the read aligns to the genome, and whether the read aligns to the forward or reverse strand of the genome. A <a href="http://picard.sourceforge.net/explain-flags.html">useful online utility</a> decodes a single SAM flag value into plain English.</p><p><strong>SAM Format</strong>: Text file format for storing sequence alignments against a reference genome. See also <strong>BAM</strong> Format.</p><p><strong>SAMtools</strong>: widely used, open source command line tool for manipulating SAM/BAM files. Includes options for converting, sorting, indexing and viewing SAM/BAM files. The SAMtools distribution also includes bcftools, a set of command line tools for identifying and filtering genomics variants. Created by <a href="http://lh3lh3.users.sourceforge.net/">Heng Li</a>, currently of the Broad Institute.</p><p><strong>single-read sequencing</strong>: sequencing process where only one end of a DNA or RNA fragment is sequenced. Contrast with <strong>paired-end</strong> sequencing.</p><p><strong>VCF Format</strong>: Variant call format. Text file format for storing genomic variants, including single nucleotide polymorphisms, insertions, deletions and structural rearrangements. See also <strong>BCF</strong> format.</p><p><strong>Next</strong><strong>Generation</strong><strong>Sequencing</strong><br /> A high-throughput sequencing method which parallelizes the sequencing process, producing thousands or millions of sequences at once.</p><p><strong>Deep</strong><strong>Sequencing</strong><br /> Techniques of nucleotide sequence analysis that increase the range, complexity, sensitivity, and accuracy of results by greatly increasing the scale of operations and thus the number of nucleotides, and the number of copies of each nucleotide sequenced.</p><p><strong>Paired-End</strong><strong>Sequencing</strong><br /> Sequence both ends of the same fragment and keep track of the paired data.</p><p><strong>Adapter</strong><br /> Short oligonucleotides which are attached to the DNA to be sequenced. An adapter can provide a priming site for both amplification and sequencing of the adjoining, unknown nucleic acid.</p><p><strong>Library</strong><br /> A collection of DNA fragments with adapters ligated to each end.</p><p><strong>Bridge</strong><strong>Amplification</strong><br /> Generation of in situ copies of a specific DNA molecule on an oligo-decorated solid support.</p><p><strong>Emulsion</strong><strong>PCR</strong><br /> A method for bead-based amplification of a library. A single adapter-bound fragment is attached to the surface of a bead, and an oil emulsion containing necessary amplification reagents is formed around the bead/fragment component. Parallel amplification of millions of beads with millions of single strand fragments produces a sequencer-ready library.</p><p><strong>Alignment</strong><br /> Mapping of sequence reads to a known reference sequence</p><p><strong>Reference</strong><strong>sequence</strong><strong>/</strong><strong>genome</strong><strong>&nbsp; </strong><br /> A fully assembled version of a genome that can be used for mapping short DNA sequence reads for comparisons of genomes from various individuals</p><p><strong>Coverage</strong><strong>Depth</strong><br /> The number of nucleotides from reads that are mapped to a given position of reference genome.</p><p><strong>Specificity</strong><strong>&nbsp; </strong><br /> The percentage of sequences that map to the intended targets out of total bases per run.</p><p><strong>Uniformity</strong><strong>&nbsp; </strong><br /> The variability in sequence coverage across target regions.</p><p><strong>Homopolymer</strong><br /> Uninterrupted stretch of a single nucleotide type (e.g., TTT or GGGGGG)</p><p><strong>InDel</strong><br /> InDel stands for Insertion or deletion. A form of structural variation in which a DNA segment is either deleted or inserted.</p><p><strong>SNP</strong><strong>&nbsp; </strong></p><p>SNP stands for Single Nucleotide Polymorphism. A single base difference found when comparing the same DNA sequence from two different individuals.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28272/bioinformatics-openings-at-icgeb-new-delhi-india</guid>
  <pubDate>Mon, 04 Jul 2016 01:04:05 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics openings at ICGEB NEW DELHI, INDIA]]></title>
  <description><![CDATA[
<p>Applications are invited for:</p>

<p>ICGEB NEW DELHI, INDIA</p>

<p>Biotechnology research positions</p>

<p>Projects include:</p>

<p>a) protein structure determination<br />b) malaria parasite biology<br />c) genomics and metagenomics<br />d) molecular and cellular biology<br />e) bioinformatics and computational biology</p>

<p>Minimum eligibility for students who have already obtained a MSc:</p>

<p>1) INSPIRE award for PhD<br />2) SPM award for PhD<br />3) CSIR/DBT/DST JRF for PhD</p>

<p>Applicants should submit their curriculum vitae by email to: sb.icgeb@gmail.com by 30 August 2016</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28449/aravind-j-shankar-gets-all-india-rank-1-in-binc-2016</guid>
	<pubDate>Tue, 19 Jul 2016 05:19:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28449/aravind-j-shankar-gets-all-india-rank-1-in-binc-2016</link>
	<title><![CDATA[Aravind J Shankar gets all India rank 1 in BINC, 2016]]></title>
	<description><![CDATA[<p>Aravind J Shankar, a bioinformatics graduate of SASTRA University, has secured the all India rank 1 in the Bioinformatics National Certification (BINC) 2016, organised by the Department of Biotechnology, Government of India.</p><p>The BINC is a nationwide examination aimed at certifying professionals in bioinformatics and tests their theoretical and practical knowledge across three phases of examination. He is entitled to receive a DBT research fellowship leading to a Ph.D. from any premier research institute in India.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35635/ete-3-reconstruction-analysis-and-visualization-of-phylogenomic-data</guid>
	<pubDate>Mon, 19 Feb 2018 06:46:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35635/ete-3-reconstruction-analysis-and-visualization-of-phylogenomic-data</link>
	<title><![CDATA[ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data]]></title>
	<description><![CDATA[<p><span>ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics. </span></p>
<p><span>The new features include </span></p>
<p><span>(i) building gene-based and supermatrix-based phylogenies using a single command, </span></p>
<p><span>(ii) testing and visualizing evolutionary models, </span></p>
<p><span>(iii) calculating distances between trees of different size or including duplications, and </span></p>
<p><span>(iv) providing seamless integration with the NCBI taxonomy database. </span></p>
<p><span>ETE is freely available at&nbsp;</span><a href="http://etetoolkit.org/" target="">http://etetoolkit.org</a></p><p>Address of the bookmark: <a href="http://etetoolkit.org" rel="nofollow">http://etetoolkit.org</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28564/dbt-%E2%80%93-bioinformatics-industrial-training-programme-biitp-2016-%E2%80%93-17</guid>
	<pubDate>Wed, 27 Jul 2016 04:09:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28564/dbt-%E2%80%93-bioinformatics-industrial-training-programme-biitp-2016-%E2%80%93-17</link>
	<title><![CDATA[DBT – Bioinformatics Industrial Training Programme (BIITP) 2016 – 17]]></title>
	<description><![CDATA[<p>BIITP is a programme of Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India, managed by Biotech Consortium India Limited (BCIL).The objective of BIITP is to provide an opportunity to bioinformatics students to acquire practical skills and experience by working on projects alongside industry experts as well as to provide an opportunity for the industry to identify potential employees.</p><p><strong>DBT Invites online applications from the bioinformatics&nbsp;students and requisitions from biotech/bioinformatics companies.</strong></p><p><strong>Biotech Industry</strong>&nbsp;:</p><p>Biotech/Bioinformatics companies interested to provide hands on industrial training to the students of Bioinformatics under BIITP may apply online. The companies would have no obligation towards any payments to trainees. The companies would be paid bench fee to cover expenses towards training. Trainees would be provided to companies subject to availability.</p><p><strong>Attn: Bioinformatics Students</strong></p><p>Bioinformatics students interested in training in biotech / bioinformatics companies may apply online.&nbsp;<strong>Stipend of Rs. 10,000/- per month</strong>&nbsp;will be paid to candidates placed for training. The candidates will be selected for training through an interview.</p><p><strong>Eligiblity</strong>&nbsp;:</p><p>a) B.E /B.Tech./M.Sc./M.Tech./Advanced Post Graduate Diploma in Bioinformatics from an Indian recognized university with minimum 55% marks or equivalent grade at highest degree/diploma completed in the year 2015 or 2016 are only eligible to apply.</p><p>b) The Advanced Post Graduate diploma should be of at least one year duration after graduation.</p><p>c)&nbsp; Students whose result of last semester/final year is not declared can also apply mentioning their marks upto the semester/year upto which result declared. The final result with original mark sheet(s) of all the semesters/years will have to be produced at the time of interview.</p><p><strong>Application Procedure</strong>&nbsp;:</p><p>The online application form is available below :</p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Fregistration1.asp" target="_blank">Application Form For Students (New User)</a></strong></p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Fregistration.asp%3FT1%3DCompany" target="_blank">Requisition form for companies (New User)</a></strong></p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Findex1.asp" target="_blank">Already registered User Click Here</a></strong></p><p>The following documents are to be sent to Mr. Manoj Gupta, Manager, Biotech Consortium India Limited, 5th floor, Anuvrat Bhawan, 210, Deen Dayal UpadhyayaMarg, New Delhi-110002.</p><p>More at&nbsp;http://www.bcil.nic.in/biitp2016-17/index.asp</p>]]></description>
	<dc:creator>Jit</dc:creator>
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