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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31566?offset=660</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26363/flo</guid>
	<pubDate>Wed, 10 Feb 2016 10:52:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26363/flo</link>
	<title><![CDATA[flo]]></title>
	<description><![CDATA[<p>flo - same species annotations lift over pipeline</p>
<p>Lift over is the process of transferring annotations from one genome assembly to another. Usually lift over is done because there is a new, improved genome assembly for the species and good quality annotations (maybe manually curated or experimentally verified) are available on the old assembly.</p>
<p>The idea is simple: align the new assembly with the old one (e.g., with BLAT), process the alignment data to define how a coordinate or coordinate range on the old assembly should be transformed to the new assembly (e.g., as a chain file), transform the coordinates (e.g., with liftOver).</p>
<p>&nbsp;</p>
<p>https://github.com/wurmlab/flo</p><p>Address of the bookmark: <a href="https://github.com/wurmlab/flo" rel="nofollow">https://github.com/wurmlab/flo</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37959/rainbow-an-integrated-tool-for-efficient-clustering-and-assembling-rad-seq-reads</guid>
	<pubDate>Fri, 19 Oct 2018 08:23:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37959/rainbow-an-integrated-tool-for-efficient-clustering-and-assembling-rad-seq-reads</link>
	<title><![CDATA[Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads]]></title>
	<description><![CDATA[<p><span>Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top&ndash;down manner. And along a guided tree, it iteratively merges sibling leaves in a bottom&ndash;up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, we show that Rainbow is more competent than the other tools in dealing with RAD-seq data</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/bio-rainbow/files/" rel="nofollow">https://sourceforge.net/projects/bio-rainbow/files/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/26382/project-assistant-jrf-at-north-eastern-hill-university</guid>
  <pubDate>Fri, 12 Feb 2016 09:23:01 -0600</pubDate>
  <link></link>
  <title><![CDATA[Project Assistant/ JRF at North Eastern Hill University]]></title>
  <description><![CDATA[
<p>Project Assistant/ JRF</p>

<p>Eligibility : ME/M.Tech, Any Graduate, MSc</p>

<p>Location : Shillong</p>

<p>Last Date : 23 Feb 2016</p>

<p>Hiring Process : Face to Face Interview<br />North Eastern Hill University</p>

<p>Project Assistant/ JRF Jobs recruitment in North Eastern Hill University on temporary basis</p>

<p>Project Title : "Next Generation Sequencing (NGS)-based de novo assembly of expressed transcripts and genome information of Orchids in North-East India"</p>

<p>Project Assistant</p>

<p>No. of Post : 01</p>

<p>Qualifications : Graduation</p>

<p>Desirable : Experience of working in a Life Science/Plant Biotechnology lab. and familiarity with computers and field work viz. collection of samples.</p>

<p>JRF</p>

<p>No. of Post : 01</p>

<p>Qualifications : M.Tech/M.Sc in Life Sciences/Botany/Biotechnology/ Bioinformatics;</p>

<p>Desirable : Aptitude for Bioinformatics and Computer Programming/ Next generation sequencing data analysis<br />How to apply</p>

<p>The applications through email bicnehu@gmail.com or post must reach the Dr. Devendra Kumar Biswal, Co-Principal Investigator (Co-PI) within 15 days from the date of publication of this advertisement.</p>

<p>More at http://www.nehu.ac.in/Advertisements/projectVacancy.php</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40889/rcorrector-efficient-and-accurate-error-correction-for-illumina-rna-seq-reads</guid>
	<pubDate>Tue, 04 Feb 2020 23:23:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40889/rcorrector-efficient-and-accurate-error-correction-for-illumina-rna-seq-reads</link>
	<title><![CDATA[Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads]]></title>
	<description><![CDATA[<p><span>Rcorrector has an accuracy higher than or comparable to existing methods, including the only other method (SEECER) designed for RNA-seq reads, and is more time and memory efficient. With a 5 GB memory footprint for 100 million reads, it can be run on virtually any desktop or server. The software is available free of charge under the GNU General Public License from&nbsp;</span><a href="https://github.com/mourisl/Rcorrector/" target="_blank">https://github.com/mourisl/Rcorrector/</a><span>.</span></p>
<pre><code>Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (&lt;=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.</code></pre><p>Address of the bookmark: <a href="https://github.com/mourisl/Rcorrector/" rel="nofollow">https://github.com/mourisl/Rcorrector/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</guid>
	<pubDate>Mon, 17 Aug 2020 05:21:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</link>
	<title><![CDATA[SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis]]></title>
	<description><![CDATA[<p><span>SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, &hellip;).</span><br><span>The bioinformatics treatment is based on Vsearch [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/27781170">Rognes 2016</a><span>] which showed to be both accurate and fast [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/26664811">Wescott 2015</a><span>].The statistical analysis is based on DESeq2 R package [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20979621">Anders and Huber 2010</a><span>] which robustly identifies the differential abundant features as suggested in [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/">McMurdie and Holmes 2014</a><span>] and [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/">Jonsson2016</a><span>]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/25516281">Love 2014</a><span>].</span><br><span>SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_presentation.pdf">here</a><span>&nbsp;and a poster&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_poster.pdf">here</a><span>.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4</a></span></p><p>Address of the bookmark: <a href="https://github.com/aghozlane/shaman" rel="nofollow">https://github.com/aghozlane/shaman</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/26438/scientist-at-regional-medical-research-centre-icmr-rmrc-port-blair</guid>
  <pubDate>Mon, 22 Feb 2016 04:38:48 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist at Regional Medical Research Centre (ICMR), RMRC, Port Blair]]></title>
  <description><![CDATA[
<p>Scientist</p>

<p>Eligibility : MSc, M Phil / Phd, BE/B.Tech</p>

<p>Location : Delhi</p>

<p>Last Date : 08 Mar 2016</p>

<p>Hiring Process : Walk - In<br />Regional Medical Research Centre - </p>

<p>Notification Order No.1-51/Proj/RMRC/PB/</p>

<p>Scientist – II (Post Code: BIC-II) job position in Regional Medical Research Centre (ICMR)</p>

<p>Essential Qualification: 1 st class Master’s degree in Bioinformatics / Computational Biology.  B.E / B.Tech (Bioinformatics / Computer Science / Biotechnology) OR 2nd Class M.Sc. with Ph.D. in Bioinformatics / Computational Biology / Life Science.</p>

<p>Desirable Qualification:  Post-doctoral research experience in Bioinformatics / Computational Biology / Computer Science / Life Science at a recognized institution.  Experience in handling and analyzing sequencing data.  Experience in scripting languages (PERL/PYTHON) etc./ Statistical software.  Experience in developing research projects.</p>

<p>Number of Post: 1 UR</p>

<p>Place of Posting: RMRC, Port Blair </p>

<p>Age Limit: 40 years</p>

<p>Pay Scale : Rs.45,954<br />How to apply</p>

<p>Interested candidates are invited to submit applications along with copies of all the certificates of educational qualifications, date of birth, working experience etc . on the affixing a passport size photograph by Application Format to attend a walk-in interview on 8th March 2016 at 10:30 AM.</p>

<p>More at http://www.icmr.nic.in/icmrnews/port%20blair%20Bioinf%2003-2016.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</guid>
	<pubDate>Sat, 26 Dec 2020 08:35:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</link>
	<title><![CDATA[FastProNGS: fast preprocessing of next-generation sequencing reads]]></title>
	<description><![CDATA[<p><span>FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/Megagenomics/FastProNGS" rel="nofollow">https://github.com/Megagenomics/FastProNGS</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26535/svelter</guid>
	<pubDate>Mon, 29 Feb 2016 17:33:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26535/svelter</link>
	<title><![CDATA[svelter]]></title>
	<description><![CDATA[<p>This software is designed to identify both simple and complex rearrangements from paired-end sequencing data. Users could ran it easily by just alling&nbsp;<em>SVelter.py</em>&nbsp;with proper parameters. It's also possible to ran it on multiple cores by calling different sub-functions separately.</p>
<p>More at&nbsp;https://github.com/mills-lab/svelter/</p><p>Address of the bookmark: <a href="https://github.com/mills-lab/svelter/" rel="nofollow">https://github.com/mills-lab/svelter/</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</guid>
	<pubDate>Sat, 06 Jul 2024 04:29:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</link>
	<title><![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]></title>
	<description><![CDATA[<p dir="auto">SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:</p>
<ol dir="auto">
<li>Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome</li>
<li>Co-assembly of a large number of metagenomes via merging of individual metagenomes</li>
<li>Includes binning and bin checking, for retrieving individual genomes</li>
<li>The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.</li>
<li>Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.</li>
<li>Metatranscriptomic support via mapping of cDNA reads against reference metagenomes</li>
</ol><p>Address of the bookmark: <a href="https://github.com/jtamames/SqueezeMeta" rel="nofollow">https://github.com/jtamames/SqueezeMeta</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/26562/jrf-at-icgeb</guid>
  <pubDate>Fri, 04 Mar 2016 05:34:42 -0600</pubDate>
  <link></link>
  <title><![CDATA[JRF at ICGEB]]></title>
  <description><![CDATA[
<p>Vacancy Notice PU/TS/01-16</p>

<p>JRF jobs in International Centre for Genetic Engineering and Biotechnology</p>

<p>Area of research: Computational analysis of protein-protein interactions and metabolic reconstruction of pathways</p>

<p>Qualification : Suitable candidate must have completed Masters in Computer Science/Physics/Mathematics/Bioinformatics or PG diploma in Bioinformatics with very sound programming skills shown in the form of completed project or paper. Programming Skill required: Java/perl/python or any other scripting language and working knowledge of MySQL. Candidate should have some familiarity with R statistical package.</p>

<p>The salary will be as per the guidelines of DBT/DST, based on qualification and experience.</p>

<p>How to apply</p>

<p>Interested candidates may send application along with CV via email to hemant@icgeb.res.in  (Dr. Hemant Ritturaj Kushwaha) on or before 08/03/2016. Candidates applying through Email must mention “Application for JRF” in their subject line.</p>

<p>More at http://www.icgeb.org/vacancies.html</p>
]]></description>
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