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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43892?offset=230</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</guid>
	<pubDate>Sun, 04 May 2014 18:46:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</link>
	<title><![CDATA[Your stress/depression came from ancestor]]></title>
	<description><![CDATA[<p>"A study published in&nbsp;<em>Nature Neuroscience</em>&nbsp;finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."</p><p>Source:</p><p>http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment</guid>
	<pubDate>Wed, 05 Jul 2017 16:47:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment</link>
	<title><![CDATA[GeneProf: analysis of high-throughput sequencing experiment]]></title>
	<description><![CDATA[<div>GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!</div>
<p>Some of GeneProf's highlights include:</p>
<ul>
<li><strong>Easy-to-use web-based interface:</strong>Access your data at any time from any computer with a working internet connection -- no need to install software! (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_introduction.jsp#section:SystemRequirements">Section 'System Requirements'</a>).</li>
<li><strong>Analysis wizards make your life easy:</strong>Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_conceptsexplained.jsp#subconcept:AnalysisWizards">SubConcept 'Analysis Wizards'</a>).</li>
<li><strong>Versatile modules:</strong>Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_conceptsexplained.jsp#concept:Workflows">Concept 'Workflows'</a>).</li>
<li><strong>Integrated Analysis:</strong>Analysis of&nbsp;<em>ChIP-seq</em>&nbsp;and&nbsp;<em>RNA-seq</em>&nbsp;data in one place, plus support for the integration of other external data (e.g. from microarrays).</li>
<li><strong>Comprehensive Resource:</strong>GeneProf provides a comprehensive resource of&nbsp;<em>fully analyzed</em>&nbsp;next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_tutorials.jsp#tutorial:ExaminingPublicNext-GenDatausingGeneProf">Tutorial 'Examining Public Next-Gen Data..'</a>).</li>
<li><strong>Extensibility:</strong>Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_advancedtopics.jsp#section:ModuleDevelopment:AddingnewFunctionalitytoGeneProf">Section 'Module Development: Adding new..'</a>) and data from GeneProf may be used externally (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_advancedtopics.jsp#section:WebAPI:RetrievingDatafromGeneProf">Section 'Web API: Retrieving Data from ..'</a>).</li>
</ul>
<p>&nbsp;</p>
<p>GeneProf is academic software developed at the&nbsp;<a href="http://www.crm.ed.ac.uk/">Centre for Regenerative Medicine</a>&nbsp;/&nbsp;<a href="http://www.crm.ed.ac.uk/about/institute-stem-cell-research">Institute for Stem Cell Research</a>,&nbsp;<a href="http://www.ed.ac.uk/">University of Edinburgh</a>&nbsp;and has benefited from funding by the&nbsp;<a href="http://www.mrc.ac.uk/">Medical Research Council</a>&nbsp;and the&nbsp;<a href="http://www.eurosystemproject.eu/">EU Framework 7 Project "EuroSyStem"</a>.</p><p>Address of the bookmark: <a href="https://www.geneprof.org/GeneProf/index.jsp" rel="nofollow">https://www.geneprof.org/GeneProf/index.jsp</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<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>
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	<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/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</guid>
	<pubDate>Wed, 23 May 2018 06:54:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</link>
	<title><![CDATA[BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,]]></title>
	<description><![CDATA[<p><span>BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.</span></p>
<p>Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The &ldquo;target.fasta.sa&rdquo; is the suffix array from &ldquo;target.fasta&rdquo; generated by sawriter.</p>
<blockquote>
<p>blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15</p>
</blockquote>
<p>the output format option &ldquo;-m 4&Prime; generate the alignment coordinate. Not fully documented, but I can explain that to you.&nbsp;</p>
<p>I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.</p><p>Address of the bookmark: <a href="http://bix.ucsd.edu/projects/blasr/" rel="nofollow">http://bix.ucsd.edu/projects/blasr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</guid>
	<pubDate>Mon, 06 Aug 2018 17:19:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</link>
	<title><![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]></title>
	<description><![CDATA[<p><span>gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ml.ssu.ac.kr/gSearch/index.html" rel="nofollow">http://ml.ssu.ac.kr/gSearch/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</guid>
	<pubDate>Wed, 22 Aug 2018 10:40:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</link>
	<title><![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]></title>
	<description><![CDATA[<p>SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.</p>
<p>Reads are simulated from both strands of a provided or randomly generated reference sequence.</p>
<div id="rst-header-features">
<ul>
<li>The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)</li>
<li>The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length</li>
<li>Quality values and number of passes depend on fragment length.</li>
<li>Provided subread error probabilities are modified according to number of passes</li>
<li>Outputs reads in FASTQ format and alignments in SAM format</li>
</ul>
</div><p>Address of the bookmark: <a href="https://bitbucket.org/genomeinformatics/simlord/" rel="nofollow">https://bitbucket.org/genomeinformatics/simlord/</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</guid>
	<pubDate>Thu, 20 Sep 2018 08:38:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</link>
	<title><![CDATA[d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity]]></title>
	<description><![CDATA[<p><code>d2Tools</code>&nbsp;are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the&nbsp;<strong>d2-style</strong>(d2/d2<code>*</code>/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.</p>
<p>Manual at&nbsp;https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki</p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/d2-tools/" rel="nofollow">https://code.google.com/archive/p/d2-tools/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</guid>
	<pubDate>Thu, 10 Jan 2019 04:42:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</link>
	<title><![CDATA[NGS Platforms launched by BGI’s MGI Tech]]></title>
	<description><![CDATA[<p>MGI Tech Co., Ltd. (MGI), a subsidiary of BGI Group, is committed to enabling effective and affordable healthcare solutions for all. Based on its proprietary technology, MGI produces sequencing devices, equipment, consumables and reagents to support life science research, medicine and healthcare. MGI's multi-omics platforms include genetic sequencing, mass spectrometry and medical imaging. Providing real-time, comprehensive, life-long solutions, its mission&nbsp;is to&nbsp;develop and promote advanced life science tools for future healthcare.</p><p>MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to&nbsp;$5&nbsp;per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.</p><p>In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for&nbsp;$1 million.</p><p>The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.</p><p><span>Powered by DNBseq&trade;, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.</span></p><p><span><span>SOURCE MGI</span></span></p><p>https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/</p><p>http://en.mgitech.cn/</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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