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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31714?offset=940</link>
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	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17187/urdip-bioinformatics-rajrf-vacancies</guid>
  <pubDate>Sat, 20 Sep 2014 20:52:56 -0500</pubDate>
  <link></link>
  <title><![CDATA[URDIP Bioinformatics RA/JRF Vacancies]]></title>
  <description><![CDATA[
<p>CSIR - UNIT FOR RESEARCH AND DEVELOPMENT OF INFORMATION PRODUCTS (CSIR- URDIP)</p>

<p>Adv. No. URDIP/ 6/2014</p>

<p>Opportunity for young Bioinformatics Professionals to make a career in the area of Intellectual Property CSIR has set up a Unit for Research and Development of Information Products (CSIR-URDIP) at Pune to work in the area of scientific informatics. One of the major focus areas of research work at CSIR-URDIP is PATENT INFORMATICS. With the increasing applications of Bioinformatics in the areas of life sciences industry such as Agriculture and Health Care (Diagnostics and Drugs), the output of research in these area is being protected by different forms of Intellectual Property rights. Realizing the importance of IP in the Bioinformatics field, Department of Biotechnology (DBT) has sanctioned a project on “Development, Facilitation and Harvesting of Bioinformatics related Intellectual Property” at CSIR-URDIP.</p>

<p>The project will involve application of Patent Informatics tools and techniques to Bioinformatics (including creation of patent landscapes, preparation of techno-legal reports of patentability, freedom to operate studies) to help protect IPRs and develop and conduct training programmes on IPRs related to Bioinformatics.</p>

<p>CSIR-URDIP invites applications from young Bioinformatics professionals to work on this emerging area which offers challenging opportunities and attractive career possibilities in future.</p>

<p>Position I: Research Associate</p>

<p>No of Positions: One</p>

<p>Consolidated amount Payable: Rs. 22,000/- per month + 20% HRA= Rs.26,400</p>

<p>Qualification:  PhD in Bioinformatics. In exceptional cases, candidature of M. Tech. candidates with First class in Bioinformatics with three years of relevant work experience will also be considered.</p>

<p>Age Limit: 35 years. The age should not exceed the limit indicated as on a closing date of receipt of completed application form.</p>

<p>Upper age limit is relaxable for 5 years for SC/ST, OBC, Physically handicapped and female candidates as per CSIR/Government of India rules.</p>

<p>Position II: Junior Research Fellow</p>

<p>No of Positions: one</p>

<p>Consolidated amount Payable: Rs. 16,000/- + 20% HRA = 19,200</p>

<p>Qualification: M.Sc / BE or equivalent in Bioinformatics with minimum of 55% marks in aggregate Job requirement: Scientific literature and patent search, analysis and Report Writing</p>

<p>Preference: Preference will be given to candidates with knowledge of patents and or 1-2 years of experience + Knowledge of Computers (MS Excel + Word Processing)</p>

<p>Age Limit: 28 years. The age should not exceed the limit indicated as on a closing date of receipt of completed application form.</p>

<p>For details please visit our website (www.urdip.res.in/careers) for further details and apply online by 30th September, 2014.</p>

<p>Advertisement: http://www.urdip.res.in/download/Advt6_2014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</guid>
	<pubDate>Fri, 26 Jul 2019 00:58:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</link>
	<title><![CDATA[jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator]]></title>
	<description><![CDATA[<p><code>jackalope</code> simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations&mdash;the latter of which can include selection, recombination, and demographic fluctuations. <code>jackalope</code> can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. All outputs can be written to standard file formats.</p>
<p><span>A swift, versatile phylogenomic and high-throughput sequencing simulator </span> <span><a href="https://jackalope.lucasnell.com">https://jackalope.lucasnell.com</a></span></p><p>Address of the bookmark: <a href="https://github.com/lucasnell/jackalope" rel="nofollow">https://github.com/lucasnell/jackalope</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17176/arvados</guid>
	<pubDate>Sat, 20 Sep 2014 16:54:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17176/arvados</link>
	<title><![CDATA[Arvados]]></title>
	<description><![CDATA[<p>Arvados is a free and open&nbsp;source bioinformatics&nbsp;platform for genomic and&nbsp;biomedical data. User can&nbsp;Store | Organize | Compute | Share the data for free.&nbsp;</p>
<p><img src="https://arvados.org/images/dax.png" width="400" height="535" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://arvados.org/" rel="nofollow">https://arvados.org/</a></p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 03:21:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</link>
	<title><![CDATA[Kevler: Reference-free variant discovery in large eukaryotic genomes]]></title>
	<description><![CDATA[<p><span>Welcome to&nbsp;</span><span>kevlar</span><span>, software for predicting&nbsp;</span><em>de novo</em><span>&nbsp;genetic variants without mapping reads to a reference genome! kevlar's&nbsp;</span><em>k</em><span>-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://kevlar.readthedocs.io/en/latest/">https://kevlar.readthedocs.io/en/latest/</a></span></p>
<p><span><a href="https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf">https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf</a></span></p><p>Address of the bookmark: <a href="https://github.com/kevlar-dev/kevlar" rel="nofollow">https://github.com/kevlar-dev/kevlar</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</guid>
	<pubDate>Wed, 12 Dec 2018 08:33:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</link>
	<title><![CDATA[genoPlotR - plot gene and genome maps project!]]></title>
	<description><![CDATA[<p>genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.</p>
<h3>Features</h3>
<ul>
<li>Linear representation of several segments of DNA</li>
<li>Comparisons represented by areas between the segments (like Artemis, for example)</li>
<li>Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files</li>
<li>Plot several subsegments of the same segment on the same line, separated by a //</li>
<li>Automatic or manual placement of the segments on the plot</li>
<li>Add annotations to all the lines</li>
<li>Create smart, automatic annotations for genomes, based on gene names</li>
<li>Add a user-generated tree</li>
<li>Add a global scale or a scale to each line</li>
<li>Use user-defined graphical functions to represent genes</li>
<li></li>
</ul><p>Address of the bookmark: <a href="http://genoplotr.r-forge.r-project.org/" rel="nofollow">http://genoplotr.r-forge.r-project.org/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</guid>
	<pubDate>Mon, 23 Mar 2020 06:22:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</link>
	<title><![CDATA[tinycov: standalone command line utility written in python to plot coverage from a BAM file]]></title>
	<description><![CDATA[<p>Tinycov is a small standalone command line utility written in python to plot the coverage of a BAM file quickly. This software was inspired by&nbsp;<a href="https://github.com/matted/genome_coverage_plotter">Matt Edwards' genome coverage plotter</a>.</p>
<p>To install the stable version:&nbsp;<code>pip3 install --user tinycov</code></p>
<p>To install the development version:</p>
<pre><code>git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .</code></pre><p>Address of the bookmark: <a href="https://github.com/cmdoret/tinycov" rel="nofollow">https://github.com/cmdoret/tinycov</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18819/jrfsrf-at-jawaharlal-nehru-institute-ofadvanced-studies-jnias-hyderabad</guid>
  <pubDate>Fri, 31 Oct 2014 08:48:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at Jawaharlal Nehru Institute ofAdvanced Studies (JNIAS), Hyderabad]]></title>
  <description><![CDATA[
<p>Applications for Academic Projects in Biotechnology, Bioinformatics, Environmental Sciences and Computer Science &amp; Engineering</p>

<p>About JNIAS<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering &amp; Technology.</p>

<p>Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.</p>

<p>Projects &amp; Facilities</p>

<p>JNIAS offers wide range of projects:</p>

<p>Biotechnology area:</p>

<p>Molecular Biology<br />Microbiology<br />Nanotechnology<br />Bioinformatics (Schrodinger Software)<br />In Silico studies &amp; Drug Designing<br />Sequence analysis<br />Protein structure function studies</p>

<p>Registration<br />Tuition Fees: Interested students need to pay the following tuition fees:<br />1. Six Month’s Project: Rs. 20,000/-<br />2. Four Month’s Project: Rs. 15,000/-<br />3. Three Month’s Project: Rs. 10,000/-<br />4. One Month - Hands on Training : Rs. 8,000/-</p>

<p>For enquires call:<br />91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)</p>

<p>Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in</p>

<p>Address<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS)<br />6th Floor, Buddha Bhavan, M.G Road,<br />Secunderabad - 500 003<br />Andhra Pradesh, India<br />Tele/Fax: 040- 27541551; 27541553<br />Mobile: 08885541554<br />Web site: www.jnias.in</p>

<p>Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/18382/google-genomics</guid>
	<pubDate>Fri, 17 Oct 2014 02:14:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/18382/google-genomics</link>
	<title><![CDATA[Google Genomics]]></title>
	<description><![CDATA[<p>Google Genomics provides an API to store, process, explore, and share DNA sequence reads, reference-based alignments, and variant calls, using Google's cloud infrastructure.</p>
<ul>
<li><strong>Store</strong> alignments and variant calls for one genome or a million.</li>
<li><strong>Process</strong> genomic data in batch by running principal component analysis or Hardy-Weinberg equilibrium, in minutes or hours, by using parallel computing frameworks like MapReduce.</li>
<li><strong>Explore</strong> data by slicing alignments and variants by genomic range across one or multiple samples -- for your own algorithms or for visualization; or interactively process entire cohorts to find transition/transversion ratios, allelic frequency, genome-wide association and more using BigQuery.</li>
<li><strong>Share</strong> genomic data with your research group, collaborators, the broader community, or the public. You decide.</li>
</ul>
<p>Google Genomics is implementing the API defined by the <a href="http://genomicsandhealth.org/">Global Alliance for Genomics and Health</a> for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.</p><p>Address of the bookmark: <a href="https://cloud.google.com/genomics/" rel="nofollow">https://cloud.google.com/genomics/</a></p>]]></description>
	<dc:creator>Reshma Khatun</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</guid>
	<pubDate>Mon, 27 Nov 2017 07:58:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</link>
	<title><![CDATA[miniasm: very fast OLC-based de novo assembler for noisy long reads]]></title>
	<description><![CDATA[<p>Miniasm is a very fast OLC-based&nbsp;<em>de novo</em>&nbsp;assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>) as input and outputs an assembly graph in the&nbsp;<a href="https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md">GFA</a>&nbsp;format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final&nbsp;<a href="http://wgs-assembler.sourceforge.net/wiki/index.php/Celera_Assembler_Terminology">unitig</a>&nbsp;sequences. Thus the per-base error rate is similar to the raw input reads.</p>
<p>So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-Bacterial-Assembly">PacBio E. coli sample</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS473430">ERS473430</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS544009">ERS544009</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS554120">ERS554120</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS605484">ERS605484</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS617393">ERS617393</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS646601">ERS646601</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS659581">ERS659581</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS670327">ERS670327</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS685285">ERS685285</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS743109">ERS743109</a>&nbsp;and a&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-20kb-Size-Selected-Library-with-P6-C4/ce0533c1d2a957488594f0b29da61ffa3e4627e8">deprecated PacBio E. coli data set</a>. ONT data are acquired from the&nbsp;<a href="http://lab.loman.net/2015/09/24/first-sqk-map-006-experiment/">Loman Lab</a>.</p>
<p>For a&nbsp;<em>C. elegans</em>&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/C.-elegans-data-set">PacBio data set</a>&nbsp;(only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the&nbsp;<a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP">HGAP3</a>produces a 104Mb assembly with N50 1.61Mb.&nbsp;<a href="http://lh3lh3.users.sourceforge.net/download/ce-miniasm.png">This dotter plot</a>&nbsp;gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.</p>
<p>Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>&nbsp;can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as&nbsp;<a href="https://github.com/marbl/MHAP">MHAP</a>&nbsp;and&nbsp;<a href="https://github.com/thegenemyers/DALIGNER">DALIGNER</a>. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.</p>
<p>Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)</p>
<p>We start with an all against all comparison:</p>
<div>
<pre><code>minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 &gt; reads.paf.gz
</code></pre>
</div>
<p>Then we can assemble</p>
<div>
<pre><code>miniasm -f reads.fq reads.paf.gz &gt; reads.gfa
</code></pre>
</div>
<p>Convert GFA to FASTA:</p>
<div>
<pre><code>awk <span>'/^S/{print "&gt;"$2"\n"$3}'</span> reads.gfa | fold &gt; reads.fa
</code></pre>
</div>
<p>And then count how many contigs:</p>
<div>
<pre><code>grep <span>"&gt;"</span> reads.fa | wc -l</code></pre>
</div>
<p>&nbsp;</p>
<pre><span><span>#</span> Download sample PacBio from the PBcR website</span>
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz <span>|</span> tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
<span><span>#</span> Install minimap and miniasm (requiring gcc and zlib)</span>
git clone https://github.com/lh3/minimap <span>&amp;&amp;</span> (cd minimap <span>&amp;&amp;</span> make)
git clone https://github.com/lh3/miniasm <span>&amp;&amp;</span> (cd miniasm <span>&amp;&amp;</span> make)
<span><span>#</span> Overlap</span>
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq <span>|</span> gzip -1 <span>&gt;</span> reads.paf.gz
<span><span>#</span> Layout</span>
miniasm/miniasm -f reads.fq reads.paf.gz <span>&gt;</span> reads.gfa</pre><p>Address of the bookmark: <a href="https://github.com/lh3/miniasm" rel="nofollow">https://github.com/lh3/miniasm</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18578/research-scientist-%E2%80%93-national-institute-of-cholera-and-enteric-diseases</guid>
  <pubDate>Wed, 22 Oct 2014 10:26:46 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Scientist – National Institute of Cholera and Enteric Diseases]]></title>
  <description><![CDATA[
<p>The following post is to be filled up on purely temporary basis under the project entitled "Second phase of Task Force Biomedical Informatics Center of ICMR" under Dr. Santasabuj Das, Scientist 'D' of this Institute:-</p>

<p>01. Scientist II 01<br />Essential: Ph.D. degree in Life Sciences from a recognized university along with a minimum of 2 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.</p>

<p>OR<br />Ph.D. degree in Bioinformatics from a recognized university.</p>

<p>OR<br />M.Sc. in Bioinformatics from a recognized university along with a minimum of 3 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.</p>

<p>Desirable:<br />Thorough Knowledge about In silico genome analysis and comparative genomics.<br />Experience with in silico identification of novel virulence factors of pathogens, host-pathogen interactions and Systems Biology.<br />Additional Postdoctoral research experience in relevant subjects from a recognized institutions.</p>

<p>Rs. 44,000/- p.m. (consolidated) plus 30% HRA</p>

<p>Below 40 years</p>

<p>Applications along with Bio-Data containing detail work experience and full list of publications may be sent via email tosantasabujdas@yahoo.com latest by October 27, 2014.</p>

<p>Short-listed candidates will be called via email for an interview to be held at the institute in the second week of November, 2014.</p>

<p>Advertisement: www.niced.org.in/placements.htm</p>
]]></description>
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