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	<title><![CDATA[BOL: Related items]]></title>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/5166/role-of-microbes-in-forensic-science</guid>
	<pubDate>Sun, 29 Sep 2013 10:07:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/5166/role-of-microbes-in-forensic-science</link>
	<title><![CDATA[Role of microbes in forensic science]]></title>
	<description><![CDATA[<p>"<span>Studies on decomposing mice suggest that the microbial content of a corpse can offer clues as to how old a body is and the approximate time that death occurred."</span><span><br /><br />Read more:&nbsp;<a href="http://www.digitaljournal.com/article/359185#ixzz2gIJFVHRo">http://www.digitaljournal.com/article/359185#ixzz2gIJFVHRo</a></span></p><p><span><a href="http://www.colorado.edu/news/releases/2013/09/24/new-cu-boulder-led-study-finds-%E2%80%98microbial-clock%E2%80%99-may-help-determine-time">http://www.colorado.edu/news/releases/2013/09/24/new-cu-boulder-led-study-finds-%E2%80%98microbial-clock%E2%80%99-may-help-determine-time</a></span></p><p><span>Paper:</span></p><p><span><a href="http://www.elifesciences.org/wp-content/uploads/2013/09/eLife.01104_INPRESS.pdf">http://www.elifesciences.org/wp-content/uploads/2013/09/eLife.01104_INPRESS.pdf</a></span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/5817/the-first-50-plant-genomes</guid>
	<pubDate>Mon, 21 Oct 2013 11:19:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/5817/the-first-50-plant-genomes</link>
	<title><![CDATA[The First 50 Plant Genomes]]></title>
	<description><![CDATA[<p>Plant scientists can exploit available 50 plant complete genomic data for their resequencing or other related projects for understanding the genetic mechanism behind their different traits and molecular evolution. Complete information about these plant genomes given in paper link.</p>
<p><a href="https://www.crops.org/publications/tpg/articles/6/2/plantgenome2013.03.0001in">https://www.crops.org/publications/tpg/articles/6/2/plantgenome2013.03.0001in</a></p><p>Address of the bookmark: <a href="https://www.crops.org/publications/tpg/pdfs/6/2/plantgenome2013.03.0001in" rel="nofollow">https://www.crops.org/publications/tpg/pdfs/6/2/plantgenome2013.03.0001in</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/9676/bioinformatics-job-in-genotypic-tech-india</guid>
  <pubDate>Mon, 07 Apr 2014 08:20:54 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics job in Genotypic Tech, India]]></title>
  <description><![CDATA[
<p>Genotypic Technology, the first Genomics Company of India is poised to become the next generation life sciences company. We are hiring professionals for our high end Genomics Labs (Molecular Biology/ Microarray/NGS) and Bioinformatics groups.</p>

<p>Apply to Genotypic Technology if you are a PhD in Life Sciences/ Molecular Biology/ Biotechnology/ Human Genetics/ Bioinformatics with minimum 4-5 years post doctoral experience as well as publications in peer reviewed journals.</p>

<p>Source: http://www.genotypic.co.in/Careers/2/Current-Openings.aspx</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17885/international-conference-on-bioinformatics-models-methods-and-algorithms</guid>
	<pubDate>Sun, 05 Oct 2014 11:42:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17885/international-conference-on-bioinformatics-models-methods-and-algorithms</link>
	<title><![CDATA[International Conference on Bioinformatics Models, Methods and Algorithms]]></title>
	<description><![CDATA[<p><span>The purpose of the International Conference on Bioinformatics Models, Methods and Algorithms is to bring together researchers and practitioners interested in the application of computational systems and information technologies to the field of molecular biology, including for example the use of statistics and algorithms to understanding biological processes and systems, with a focus on new developments in genome bioinformatics and computational biology. Areas of interest for this community include sequence analysis, biostatistics, image analysis, scientific data management and data mining, machine learning, pattern recognition, computational evolutionary biology, computational genomics and other related fields.</span></p>
<p><span><span>Position Paper Submission Extension:</span><span>&nbsp;</span><span>October 9, 2014</span><span>&nbsp;</span><br><span>Regular Paper Authors Notification:</span><span>&nbsp;</span><span>November 3, 2014</span><span>&nbsp;</span><br><span>Position Paper Authors Notification:</span><span>&nbsp;</span><span>November 6, 2014</span><span>&nbsp;</span><br><span>Regular and Position Paper Camera Ready and Registration:</span><span>&nbsp;</span><span>November 17, 2014</span><span>&nbsp;</span></span></p><p>Address of the bookmark: <a href="http://www.bioinformatics.biostec.org/" rel="nofollow">http://www.bioinformatics.biostec.org/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</guid>
	<pubDate>Mon, 27 Nov 2017 10:38:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</link>
	<title><![CDATA[INC-Seq: accurate single molecule reads using nanopore sequencing]]></title>
	<description><![CDATA[<p><span>INC-Seq reads enabled accurate species-level classification, identification of species at 0.1&nbsp;% abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.</span></p><p>Address of the bookmark: <a href="https://github.com/CSB5/INC-Seq" rel="nofollow">https://github.com/CSB5/INC-Seq</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</guid>
	<pubDate>Fri, 02 Feb 2018 04:23:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</link>
	<title><![CDATA[TelomereHunter]]></title>
	<description><![CDATA[<p><span>TelomereHunter is a tool for estimating telomere content from human whole-genome sequencing data. It is designed to take BAM files from a tumor and a matching control sample as input. However, it is also possible to run TelomereHunter with one input file. TelomereHunter extracts and sorts telomeric reads from the input sample(s). For the estimation of telomere content, GC biases are taken into account. Finally, the results of TelomereHunter are visualized in several diagrams.</span><br><br><span>TelomereHunter is available for download at the following address:&nbsp;</span><a href="https://pypi.python.org/pypi/telomerehunter/" target="_blank">https://pypi.python.org/pypi/telomerehunter/</a></p><p>Address of the bookmark: <a href="http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html" rel="nofollow">http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</guid>
	<pubDate>Tue, 15 May 2018 07:35:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</link>
	<title><![CDATA[HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies]]></title>
	<description><![CDATA[HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to:

NGS short reads (Illumina HiSeq)
clone-based sequencing (Fosmid or BAC clones)
SMRT reads (PacBio)
Oxford Nanopore reads
10X Genomics Linked-Reads
proximity-ligation (Hi-C) reads
high-coverage sequencing (&gt;40x coverage-per-SNP) using above technologies
combinations of the above technologies (e.g. scaffold long reads with Hi-C reads)
See below for specific examples of command line options and best practices for some of these technologies.

NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants.

If you use HapCUT2 in your research, please cite:

Edge, P., Bafna, V. &amp; Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116<p>Address of the bookmark: <a href="https://github.com/vibansal/HapCUT2" rel="nofollow">https://github.com/vibansal/HapCUT2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</guid>
	<pubDate>Tue, 03 Jul 2018 04:14:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</link>
	<title><![CDATA[ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data]]></title>
	<description><![CDATA[ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.<p>Address of the bookmark: <a href="https://github.com/bcgsc/ChopStitch" rel="nofollow">https://github.com/bcgsc/ChopStitch</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</guid>
	<pubDate>Thu, 06 Sep 2018 16:21:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</link>
	<title><![CDATA[LoRMA: A tool for correcting sequencing errors in long reads]]></title>
	<description><![CDATA[<p><span>An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of&nbsp;</span><em>k</em><span>-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75&times;, the throughput of the new method is at least 20% higher.</span></p>
<blockquote>
<p><span>conda install -c atgc-montpellier lorma</span></p>
</blockquote><p>Address of the bookmark: <a href="https://gite.lirmm.fr/lorma/lorma-releases/wikis/home" rel="nofollow">https://gite.lirmm.fr/lorma/lorma-releases/wikis/home</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</guid>
	<pubDate>Thu, 04 Oct 2018 05:23:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</link>
	<title><![CDATA[nQuire: a statistical framework for ploidy estimation using next generation sequencing]]></title>
	<description><![CDATA[<p>nQuire provides a statistical framework to study organisms with intraspecific variation in ploidy. nQuire is likely to be useful in epidemiological studies of pathogens, artificial selection experiments, and for historical or ancient samples where intact nuclei are not preserved. It is implemented as a stand-alone Linux command line tool in the C programming language and is available at https://github.com/clwgg/nQuireunder the MIT license.</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuireunder" rel="nofollow">https://github.com/clwgg/nQuireunder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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