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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26617?offset=1330</link>
	<atom:link href="https://bioinformaticsonline.com/related/26617?offset=1330" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</guid>
	<pubDate>Tue, 07 Nov 2017 05:33:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</link>
	<title><![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]></title>
	<description><![CDATA[<div><p><span>kallisto</span></p></div><div><p>Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)</p><p>Software (C++)</p><p><a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a></p><p>Sailfish</p><p>Estimation of isoform abundances from reference sequences and RNA-seq data (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="http://www.cs.cmu.edu/~ckingsf/software/sailfish/">http://www.cs.cmu.edu/~ckingsf/software/sailfish/</a></p><p>Salmon</p><p>Quantification of the expression of transcripts using RNA-seq data (uses&nbsp;<em>k</em>-mers)</p><p><a href="https://combine-lab.github.io/salmon/">https://combine-lab.github.io/salmon/</a></p><p>RNA-Skim</p><p>RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses&nbsp;<em>sig</em>-mers, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C++)</p><p><a href="http://www.csbio.unc.edu/rs/">http://www.csbio.unc.edu/rs/</a></p><p>Variant calling</p><p>ChimeRScope</p><p>Fusion transcript prediction using gene&nbsp;<em>k</em>-mers profiles of the RNA-seq paired-end reads</p><p>Software (Java)</p><p><a href="https://github.com/ChimeRScope/ChimeRScope/wiki">https://github.com/ChimeRScope/ChimeRScope/wiki</a></p><p>FastGT</p><p>Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique&nbsp;<em>k</em>-mers</p><p>Software (C)</p><p><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></p><p>Phy-Mer</p><p>Reference-independent mitochondrial haplogroup classifier from NGS data (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/danielnavarrogomez/phy-mer">https://github.com/danielnavarrogomez/phy-mer</a></p><p>LAVA</p><p>Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (<em>k</em>-mer based)</p><p>Software (C)</p><p><a href="http://lava.csail.mit.edu/">http://lava.csail.mit.edu/</a></p><p>MICADo</p><p>Detection of mutations in targeted third-generation NGS data (can distinguish patients&rsquo; specific mutations; algorithm uses&nbsp;<em>k</em>-mers and is based on colored de Bruijn graphs)</p><p>Software (Python)</p><p><a href="http://github.com/cbib/MICADo">http://github.com/cbib/MICADo</a></p><p>General mapper</p><p>Minimap</p><p>Lightweight and fast read mapper and read overlap detector (uses the concept of &ldquo;minimazers&rdquo;, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C)</p><p><a href="https://github.com/lh3/minimap">https://github.com/lh3/minimap</a></p><p>Assembly</p><p>De novo genome assembly</p><p>MHAP</p><p>Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash</p><p>Software (Java)</p><p><a href="https://github.com/marbl/MHAP">https://github.com/marbl/MHAP</a></p><p>Miniasm</p><p>Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)</p><p>Software (C)</p><p><a href="https://github.com/lh3/miniasm">https://github.com/lh3/miniasm</a></p><p>LINKS</p><p>Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)</p><p>Software (Perl)</p><p><a href="https://github.com/warrenlr/LINKS/">https://github.com/warrenlr/LINKS/</a></p><p>Read clustering</p><p>afcluster</p><p>Clustering of reads from different genes and different species based on&nbsp;<em>k</em>-mer counts</p><p>Software (C++)</p><p><a href="https://github.com/luscinius/afcluster">https://github.com/luscinius/afcluster</a></p><p>QCluster</p><p>Clustering of reads with alignment-free measures (<em>k</em>-mer based) and quality values</p><p>Software (C++)</p><p><a href="http://www.dei.unipd.it/~ciompin/main/qcluster.html">http://www.dei.unipd.it/~ciompin/main/qcluster.html</a></p><p>Reads error correction</p><p>Lighter</p><p>Correction of sequencing errors in raw, whole genome sequencing reads (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="https://github.com/mourisl/Lighter">https://github.com/mourisl/Lighter</a></p><p>QuorUM</p><p>Error corrector for Illumina reads using k-mers</p><p>Software (C++)</p><p><a href="https://github.com/gmarcais/Quorum">https://github.com/gmarcais/Quorum</a></p><p>Trowel</p><p>Software (C++)</p><p><a href="https://sourceforge.net/projects/trowel-ec/">https://sourceforge.net/projects/trowel-ec/</a></p><p>Metagenomics</p><p>Assembly-free phylogenomics</p><p>AAF</p><p>Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/fanhuan/AAF">https://github.com/fanhuan/AAF</a></p><p>kSNP v3</p><p>Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on&nbsp;<em>k</em>-mer analysis)</p><p>Software (C)</p><p><a href="https://sourceforge.net/projects/ksnp/files/">https://sourceforge.net/projects/ksnp/files/</a></p><p>NGS-MC</p><p>Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2&nbsp;S&nbsp;under different Markov chain models (using&nbsp;<em>k</em>-words)</p><p>R package</p><p><a href="http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html">http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html</a></p><p>Species identification/taxonomic profiling</p><p>CLARK</p><p>Taxonomic classification of metagenomic reads to known bacterial genomes using&nbsp;<em>k</em>-mer search and LCA assignment</p><p>Software (C++)</p><p><a href="http://clark.cs.ucr.edu/">http://clark.cs.ucr.edu/</a></p><p>FOCUS</p><p>Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)</p><p>Web service Software (Python)</p><p><a href="http://edwards.sdsu.edu/FOCUS/">http://edwards.sdsu.edu/FOCUS/</a></p><p>GSM</p><p>Estimation of abundances of microbial genomes in metagenomic samples (<em>k</em>-mer based)</p><p>Software (Go)</p><p><a href="https://github.com/pdtrang/GSM">https://github.com/pdtrang/GSM</a></p><p>Mash</p><p>Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)</p><p>Software (C++)</p><p><a href="https://github.com/marbl/mash">https://github.com/marbl/mash</a></p><p>Kraken</p><p>Taxonomic assignment in metagenome analysis by exact&nbsp;<em>k</em>-mer search; LCA assignment of short reads based on a comprehensive sequence database</p><p>Software (C++)</p><p><a href="https://ccb.jhu.edu/software/kraken/">https://ccb.jhu.edu/software/kraken/</a></p><p>LMAT</p><p>Assignment of taxonomic labels to reads by&nbsp;<em>k</em>-mers searches in precomputed database</p><p>Software (C++/Python)</p><p><a href="https://sourceforge.net/projects/lmat/">https://sourceforge.net/projects/lmat/</a></p><p>stringMLST</p><p><em>k</em>-mer-based tool for MLST directly from the genome sequencing reads</p><p>Software (Python)</p><p><a href="http://jordan.biology.gatech.edu/page/software/stringMLST">http://jordan.biology.gatech.edu/page/software/stringMLST</a></p><p>Taxonomer</p><p><em>k</em>-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples</p><p>Web service</p><p><a href="http://taxonomer.iobio.io/">http://taxonomer.iobio.io/</a></p><p>Other</p><p>d2-tools</p><p>Word-based (<em>k</em>-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads</p><p>Software (Python/R)</p><p><a href="https://code.google.com/p/d2-tools/">https://code.google.com/p/d2-tools/</a></p><p>VirHostMatcher</p><p>Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)</p><p>Software (C++)</p><p><a href="https://github.com/jessieren/VirHostMatcher">https://github.com/jessieren/VirHostMatcher</a></p><p>MetaFast</p><p>Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray&ndash;Curtis dissimilarity measure</p><p>Software (Java)</p><p><a href="https://github.com/ctlab/metafast">https://github.com/ctlab/metafast</a></p></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</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/bookmarks/view/37915/dna-nucleotide-counter</guid>
	<pubDate>Fri, 12 Oct 2018 04:37:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37915/dna-nucleotide-counter</link>
	<title><![CDATA[DNA Nucleotide Counter]]></title>
	<description><![CDATA[<p style="margin: 2px 5px 4px 6px; color: #000011; font-size: 12px; font-style: normal; font-weight: 400; text-align: justify;">DNA Nucleotide Counter is delivered in a DNA Baser package together with other free molecular biology tools.<span>&nbsp;</span><a href="http://www.dnabaser.com/download/biology-tools-package-download-count.html">Download</a><span>&nbsp;</span>the package and double click it. The programs inside the package will be extracted to the destination folder (specified by you). Go to the destination folder&nbsp;and double click the program you want to use.</p>
<p style="margin: 2px 5px 4px 6px; color: #000011; font-size: 12px; font-style: normal; font-weight: 400; text-align: justify;">It<span>&nbsp;</span><a href="http://www.dnabaser.com/download/install-anywhere.html">installs in any computer</a><span>&nbsp;</span>even if you don't have administrator rights!</p><p>Address of the bookmark: <a href="http://www.dnabaser.com/download/DNA-Counter/index.html" rel="nofollow">http://www.dnabaser.com/download/DNA-Counter/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</guid>
	<pubDate>Sat, 08 Aug 2020 08:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</link>
	<title><![CDATA[ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.]]></title>
	<description><![CDATA[<p>How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).</p><p>During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.</p><p>ENCODE&rsquo;s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.</p><p><span>The&nbsp;</span><a href="https://www.nature.com/articles/s41586-020-2493-4">Encyclopedia paper of ENCODE 3</a><span>, published in&nbsp;</span><em>Nature</em><span>, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate&nbsp;</span><em>cis</em><span>-regulatory elements (cCREs).</span></p><p>More at&nbsp;<a href="https://www.nature.com/immersive/d42859-020-00027-2/index.html">https://www.nature.com/immersive/d42859-020-00027-2/index.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades</guid>
	<pubDate>Mon, 31 Jan 2022 07:18:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades</link>
	<title><![CDATA[Short-read assembly using Spades !]]></title>
	<description><![CDATA[<h2 id="short-read-assembly-a-comparison">If we only had Illumina reads, we could also assemble these using the tool Spades.</h2><p>You can try this here, or try it later on your own data.</p><h2 id="get-data">Get data</h2><p>We will use the same Illumina data as we used above:</p><ul>
<li>illumina_R1.fastq.gz: the Illumina forward reads</li>
<li>illumina_R2.fastq.gz: the Illumina reverse reads</li>
</ul><h2 id="assemble">Assemble</h2><p>Run Spades:</p><div><pre>spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina
</pre></div><ul>
<li><code>-1</code>&nbsp;is input file of forward reads</li>
<li><code>-2</code>&nbsp;is input file of reverse reads</li>
<li><code>--careful</code>&nbsp;minimizes mismatches and short indels</li>
<li><code>--cov-cutoff auto</code>&nbsp;computes the coverage threshold (rather than the default setting, &ldquo;off&rdquo;)</li>
<li><code>-o</code>&nbsp;is the output directory</li>
</ul><h2 id="results">Results</h2><p>Move into the output directory and look at the contigs:</p><div><pre>infoseq contigs.fasta</pre></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr</guid>
	<pubDate>Sat, 08 Jun 2024 15:59:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr</link>
	<title><![CDATA[Bioinformatic tools for pathogens informatics at CVR]]></title>
	<description><![CDATA[<div><div><div><div><div><p>Novel sequencing and analytical approaches focused on studying viruses and virus-host interactions. Below you will find summaries and links to a number of bioinformatic tools that have been developed @ CVR.</p></div><div><h3><a href="http://giffordlabcvr.github.io/DIGS-tool/" target="_blank" title="DIGS">DIGS</a></h3></div><div><p>The database-integrated genome-screening (DIGS) tool provides a framework for implementing automated in silico screening of sequence databases using BLAST in combination with a relational database (MySQL).</p></div><div><h3><a href="https://bioinformatics.cvr.ac.uk/software/discvr/" target="" title="DisCVR">DisCVR</a></h3></div><div><p>DisCVR is a Diagnostic tool for detecting known human viruses in clinical samples from Next-Generation Sequencing (NGS) data. The tool uses a simple and straightforward Graphical User Interface and is optimized on Windows OS without compromising speed and accuracy.</p></div><div><h3><a href="http://josephhughes.github.io/DiversiTools/" target="_blank" title="DiversiTools">DiversiTools</a></h3></div><div><p>DiversiTools is a computational tool that is specifically tailored towards viral HTS data sets and the analysis of the underlying viral populations that they represent. It was initially developed in collaboration with a number of virologists interested in characterising the intra-host diversity of viral populations and studying their evolution across transmission chains at the micro-evolutionary scale.</p></div><div><h3><a href="http://glue-tools.cvr.gla.ac.uk/" target="_blank" title="GLUE">GLUE</a></h3></div><div><p>GLUE is a flexible data-centric bioinformatics environment for virus sequence data, with a focus on virus evolution and genomic variation. GLUE has been applied to a range of viruses. A GLUE-based resource focused on Hepatitis C virus is HCV-GLUE.</p></div><div><h3><a href="https://bioinformatics.cvr.ac.uk/tanoti/" target="_blank" title="Tanoti">Tanoti</a></h3></div><div><p>Tanoti is a BLAST guided reference based short read aligner. It is developed for maximising alignment in highly variable next generation sequence data sets (Illumina).</p></div><div><h3><a href="https://bioinformatics.cvr.ac.uk/victree/" target="_blank" title="VicTREE">ViCTree</a></h3></div><div><p>ViCTree is a bioinformatic framework that automatically selects new candidate virus sequences from GenBank, generates multiple sequence alignments, calculates a maximum likelihood phylogeny and integrates the sequences into the existing phylogenetic trees.&nbsp;<span>For more information click&nbsp;</span><a href="https://bioinformatics.cvr.ac.uk/victree_web/" target="_blank">here</a>.</p></div></div></div></div></div><div><div><div><div><div><h3><a href="https://bioinformatics.cvr.ac.uk/software/viral-host-predictor/" target="" title="Viral Host Predictor">Viral Host Predictor</a></h3></div><div><p>Viral Host Predictor provides a fast and simple way to predict the hosts and vectors of RNA viruses from viral sequences.</p></div><div><h3><a href="https://github.com/salvocamiolo/GRACy/releases/tag/v0.4.4" target="_blank" title="GRACy">GRACy</a></h3></div><div><p>GRACy is a bioinformatic tool designed for the analysis of Illumina data originated from Human cytomegalovirus samples. GRACy can be used to perform read quality filtering, genotyping, de novo assembly, variant detection, annotation and data submission to public database.</p></div><div><h3><a href="https://github.com/salvocamiolo/LoReTTA/releases/tag/v0.1" target="_blank" title="LoReTTA">LoReTTA</a></h3></div><div><p>LoReTTA (Long Read Template Targeted Assembler) is a reference assisted de novo assembler specifically designed to deal with PacBio reads generated from viral genomes.&nbsp;</p></div><div><h3><a href="https://bioinformatics.cvr.ac.uk/software/bingleseq/" target="" title="BingleSeq">BingleSeq</a></h3></div><div><p>BingleSeq is a R-package enables the user-friendly analysis of count tables obtained by both Bulk RNA-Seq and single-cell RNA-Seq protocols. The development of BingleSeq focused on providing a flexible and intuitive user experience.</p></div></div></div></div></div>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21625/agricul-agricultural-scientists-recruitment-board-tural-scientists-recruitment-board-new-delhi-110-012</guid>
  <pubDate>Wed, 11 Mar 2015 09:18:37 -0500</pubDate>
  <link></link>
  <title><![CDATA[AGRICUL AGRICULTURAL SCIENTISTS RECRUITMENT BOARD TURAL SCIENTISTS RECRUITMENT BOARD NEW DELHI-110 012]]></title>
  <description><![CDATA[
<p>ADVERTISEMENT NO. 01/2015</p>

<p>PRINCIPAL SCIENTIST Pay Band: Minimum pay of `43,000 in the PB-4 of `37400-67000/- + RGP of `10,000/-.</p>

<p>Age: The candidates must not have attained the age of 52 years as on 24.03.2015. There shall be no age limit for the Council’s employees.</p>

<p>ICAR-NATIONAL INSTITUTE OF BIOTIC STRESS MANAGEMENT, RAIPUR (CHHATTISGARH)</p>

<p>57. Principal Scientist (Agricultural Entomology) (Two post)</p>

<p>Qualifications Essential:</p>

<p>(i) Doctoral degree in Agricultural Entomology including relevant basic sciences.</p>

<p>(ii) 10 years experience in the relevant subject out of which at least 8 years should be as Scientist/ Lecturer/Extension Specialist or in an equivalent position in the Pay Band- 3 of `15600-39100 with Grade Pay of `5400/`6000/`7000/`8000 and 2 years as a Senior Scientist or in an equivalent position in the Pay Band- 4 of ` 37400-67000 with Grade Pay of ` 8700/ ` 9000.</p>

<p>(iii) The candidate should have made contribution to research/teaching/extension education as evidenced by published work/innovations and impact.</p>

<p>Desirable:</p>

<p>(i) Experience of using frontiers research tools in management of insect pests of crop plants.</p>

<p>(ii) Evidence of contributions to relevant field through publications/ patents/citation index to suggest a vision/perspective in biotic stress research.</p>

<p>61. Principal Scientist (Bioinformatics) (One post)</p>

<p>Qualifications Essential:</p>

<p>(i) Doctoral degree in Bioinformatics including relevant basic sciences. (ii) &amp; (iii) As in item no. 57 above.</p>

<p>Desirable:</p>

<p>(i) Experience of using bioinformatics for advancement of knowledge and for research on biotic stress management.</p>

<p>(ii) Evidence of contributions to relevant field through publications/patents/citation index to suggest a vision/perspective in biotic stress research.</p>

<p>http://asrb.org.in/administrator/uploads_dir/1424859407english.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21680/research-associate-at-national-research-centre-on-plant-biotechnology-new-delhi</guid>
  <pubDate>Mon, 16 Mar 2015 03:22:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate at National Research Centre on Plant Biotechnology New Delhi]]></title>
  <description><![CDATA[
<p>Walk-in interview will be held on 24-03-2015 at 10:00 AM at NRCPB, New Delhi for filling Research Associate and Senior Research Fellow positions as mentioned below. The positions are temporary and are initially offered for a period of one year. Details such as emoluments, qualifications, application format etc., are given below. Desirous candidates should report for interview latest by 10:30 AM with the application in the prescribed format, copies and originals of certificates, thesis and documents. No TA/DA will be provided for attending the interview.</p>

<p>ICAR-NPTC: Fibre development in flax/linseed.</p>

<p>(Job # 1) Research Associate (one) (Bioinformatics)</p>

<p>Rs.24000+ 30% HRA) for Ph.D. and for M. Sc Rs.23000/‐ (+ 30% HRA)</p>

<p>Ph.D. Degree in Bioinformatics/Molecular Biology/Biotechnology/ Genetics/allied sciences; or M. Sc in Bioinformatics/ Biotechnology/Life Sciences/ allied sciences with 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from Fellowship/ Associate ship. 2 years research experience in bioinformatic data analysis/molecular biology techniques, and high throughput DNA/RNA sequencing, and transcriptome data analysis. Research paper with IF&gt;1 will be desirable</p>

<p>ICAR-NPTC: Shade avoidance/low-light tolerance in rice.</p>

<p>General Terms &amp; Conditions applicable to all the positions: <br />Age Limit: 35 years max. (5 years relaxation for SC/ST/OBC and woman candidates as per ICAR rules). <br />The positions are purely temporary, on a contractual basis and are initially offered for one year. <br />Originals must be shown for verification. 7. Research experience (Experience certificate from previous employer to be attached): I hereby declare that the information provided above is true to the best of my knowledge. Date: Signature</p>

<p>Advertisement:</p>

<p>www.nrcpb.org/sites/default/files/ICAR-NPTC%20DBT%20RA%20SRF%20interview%2024th%20March.pdf</p>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22393/narcis-fernandez-fuentes-lab</guid>
  <pubDate>Mon, 25 May 2015 07:30:00 -0500</pubDate>
  <link></link>
  <title><![CDATA[Narcis Fernandez-Fuentes Lab]]></title>
  <description><![CDATA[
<p>Welcome to our web-site compiling all the research-related activities of the group. Our research interests relate to a number of areas within Bioinformatics. We have a long-standing interest in protein structure prediction and structure-to-function relationships. We work in the study of biomolecular interactions, modeling of protein complexes, the study and characterization of protein-protein interactions, peptide design, modeling of genetic variation, structure-based protein design and different aspects of Plant Bioinformatics. Take a look at the our databases and servers and the list of publications for more information.</p>

<p>More at http://www.bioinsilico.org/</p>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24762/postdoctoral-fellowship-in-bioinformatics-at-pesolelab</guid>
  <pubDate>Thu, 01 Oct 2015 07:20:48 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral Fellowship in Bioinformatics at pesolelab]]></title>
  <description><![CDATA[
<p>Job Description: Bioinformatics postdoc positions are available in the area of genomics with main focus on exome and RNAseq technologies by ultra high-throughput sequencing platforms. Successful applicants should have the following qualities:</p>

<p>1) demonstrated experience in Bioinformatics research,<br />2) programing experience (python and/or R, C and C++ are very welcome),<br />3) knowledge of Linux/Unix environment,<br />4) experience in handling deep-seq data,<br />5) highly motivated and hard working, and<br />6) interested to work with a multi-disciplinary team combining bioinformatics, genomics, computational biology approaches with experimental biology.</p>

<p>Our research interest covers different areas of bioinformatics and genomics in order to achieve a deeper understanding of gene and genome structure and function (please look at our PubMed publications for more details about our research http://www.ncbi.nlm.nih.gov/pubmed/?term=pesole+g).</p>

<p>Interested applicants should email the curriculum vitae to Prof. Graziano Pesole at graziano.pesole@uniba.it or Dr. Ernesto Picardi at Ernesto.picardi@uniba.it.</p>

<p>Start date: immediate</p>

<p>Duration: up to 24 months<br />Contact Person (Referent): Ernesto Picardi<br />Ref. E-Mail: ernesto.picardi@uniba.it<br />Tel: +390805443308<br />Fax: +390805443317</p>

<p>Group Web Page: http://www.pesolelab.it/</p>
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