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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31205?offset=740</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</guid>
	<pubDate>Mon, 06 Mar 2017 03:44:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</link>
	<title><![CDATA[MetaBAT:  An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities]]></title>
	<description><![CDATA[<p>MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities</p>
<p>Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (&gt;200kb) in 3 hours, where 621 genome bins are &gt;50% complete with &lt;5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.</p><p>Address of the bookmark: <a href="https://bitbucket.org/berkeleylab/metabat" rel="nofollow">https://bitbucket.org/berkeleylab/metabat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Tue, 02 Apr 2019 21:54:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31371/phenogram</guid>
	<pubDate>Tue, 07 Mar 2017 08:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31371/phenogram</link>
	<title><![CDATA[PhenoGram]]></title>
	<description><![CDATA[<p><span>With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail.</span></p><p>Address of the bookmark: <a href="http://ritchielab.psu.edu/software/phenogram-downloads" rel="nofollow">http://ritchielab.psu.edu/software/phenogram-downloads</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</guid>
	<pubDate>Sun, 05 Jan 2020 04:08:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</link>
	<title><![CDATA[NextDenovo: string graph-based de novo assembler for TGS long reads]]></title>
	<description><![CDATA[<p>NextDenovo is a string graph-based<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use<span>&nbsp;</span><a href="https://github.com/Nextomics/NextPolish">NextPolish</a>.</p>
<p>NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of<span>&nbsp;</span><a href="https://github.com/lh3/minimap2">minimap2</a><span>&nbsp;</span>for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see<span>&nbsp;</span><a href="https://github.com/Nextomics/NextDenovo/blob/master/doc/UTILITY.md">here</a><span>&nbsp;</span>for more details).</p><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextDenovo" rel="nofollow">https://github.com/Nextomics/NextDenovo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/31520/research-associate-openings-at-iasri-india</guid>
  <pubDate>Fri, 10 Mar 2017 03:53:03 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Associate openings at IASRI, India]]></title>
  <description><![CDATA[
<p>Research Associate (RA) Two (2) </p>

<p>Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. </p>

<p>Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics <br />Knowledge in computer programming, LINUX OS. <br />Expertise in use of R/other Bioinformatics software </p>

<p>More at http://iasri.res.in/employment/2017/cabin_advertisement_RA_SRF_YP_Mar2017.pdf</p>

<p>Phenomics of Moisture Deficit Stress Tolerance and Nitrogen Use December 31, 2019 </p>

<p>Research Associate (RA) Two (2) </p>

<p>Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or System Administrator/ Computer expert for database development, development of phenome data bank and virtual phenomics facility, data archiving and Efficiency in Rice and Wheat-Phase II (Funded by National Agricultural Science Fund, ICAR) Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. maintenance; Development of image analysis algorithms, APIs and IAPs. </p>

<p>Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics <br />Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software &amp; tools. <br />December 31, 2019 </p>

<p>Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science / Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. </p>

<p>Knowledge of Statistical and Computational Genomics/ Bioinformatics. <br />Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software &amp; tools. <br />March 31, 2020</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41959/rna-bloom-a-fast-and-memory-efficient-de-novo-transcript-sequence-assembler</guid>
	<pubDate>Thu, 09 Jul 2020 03:13:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41959/rna-bloom-a-fast-and-memory-efficient-de-novo-transcript-sequence-assembler</link>
	<title><![CDATA[RNA-Bloom: a fast and memory-efficient de novo transcript sequence assembler]]></title>
	<description><![CDATA[<p><strong>RNA-Bloom</strong><span>&nbsp;</span>is a fast and memory-efficient<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>transcript sequence assembler. It is designed for the following sequencing data types:</p>
<ul>
<li>single-end/paired-end bulk RNA-seq (strand-specific/agnostic)</li>
<li>paired-end single-cell RNA-seq (strand-specific/agnostic)</li>
<li>nanopore RNA-seq (PCR cDNA/direct cDNA/direct RNA)</li>
</ul>
<p>Written by<span>&nbsp;</span><a>Ka Ming Nip</a><span>&nbsp;</span>✉️</p><p>Address of the bookmark: <a href="https://github.com/bcgsc/RNA-Bloom" rel="nofollow">https://github.com/bcgsc/RNA-Bloom</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools</guid>
	<pubDate>Wed, 15 Mar 2017 14:19:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools</link>
	<title><![CDATA[Pacbio Long Reads Compatible Software and Tools]]></title>
	<description><![CDATA[<p>The following software packages are known to be compatible with PacBio&reg; data, in addition to PacBio's own SMRT&reg; Analysis suite. All packages are believed to be open source or freely available for non-commercial use. See the individual project sites for up-to-date license information. A separate page lists&nbsp;<a href="http://pacb.com/community/partner_program/current_partners/">commercial software</a>.</p>
<p>Know of any other open source software for PacBio data?&nbsp;<a href="mailto:devnet@pacificbiosciences.com">Email us</a>.</p>
<p>Software categories:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#denovo">De novo assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#svdetection">Structural Variations Detection</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#aligners">Reference-based alignment</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#variants">Consensus and variant calling</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#RNA">RNA analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#basemods">Epigenetic base modifications and methylation</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#browsers">Genome Browsers</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#qc">Run QC</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#frameworks">Frameworks and APIs</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software" rel="nofollow">https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/view/138</guid>
	<pubDate>Wed, 10 Jul 2013 16:30:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/138</link>
	<title><![CDATA[Selecting between Python 2 and Python 3?]]></title>
	<description><![CDATA[<p>Which version is best for bioinformatician to work on?</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</guid>
	<pubDate>Thu, 30 Mar 2017 17:38:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</link>
	<title><![CDATA[SNPGenie]]></title>
	<description><![CDATA[<p>SNPGenie is a Perl script for estimating evolutionary parameters, mainly from pooled next-generation sequencing (NGS) single-nucleotide polymorphism (SNP) variant data. SNP reports (acceptable in a variety of formats) much each correspond to a single population, with variants called relative to a single reference sequence (one sequence in one FASTA file). Just run the main script, <strong>snpgenie.pl</strong>, in a directory containing the necessary <a href="https://github.com/hugheslab/snpgenie#snpgenie-input">input files</a>, and we take care of the rest! For the earlier version, see <a href="http://ww2.biol.sc.edu/~austin/">Hughes Lab Bioinformatics Resource</a>.</p><p>Address of the bookmark: <a href="https://github.com/hugheslab/snpgenie" rel="nofollow">https://github.com/hugheslab/snpgenie</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/841/research-fellow</guid>
  <pubDate>Sat, 13 Jul 2013 11:23:20 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Fellow]]></title>
  <description><![CDATA[
<p>UNIVERSITY OF CALCUTTA</p>

<p>A walk-in-interview will be held on 15.7.2013 at 12 noon in the Department of Biophysics, Molecular Biology and Bioinformatics, 92 A.P.C Road, Kolkata-700 009 to select one trainee research fellow and two students under DIC. The positions are purely temporary and would be for a period of six months from the date of joining which may be extended by another six months subject to successful performance.</p>

<p>Qualification:</p>

<p>For the Trainee Research Fellow: Should have a master degree in Bioinformatics or allied subjects and should have biological database development experience. Must have at least one publication.</p>

<p>For the Students: Should have a master degree in Bioinformatics or allied subjects and should be proficient in C-programming language. Must be familiar with techniques of Developmental Biology.</p>

<p>The Trainee Research Fellow would be paid a consolidated sum of Rs 10000/- per month and the students would be paid a sum amount Rs. 7000/- per month during the tenure of the project.</p>

<p>Advertisement:<br />www.caluniv.ac.in/News%20&amp;%20Announcement/trainee_rf_DIC.pdf</p>
]]></description>
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