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	<title><![CDATA[BOL: All site bookmarks]]></title>
	<link>https://bioinformaticsonline.com/bookmarks/all?offset=820</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34750/japanese-multi-omics-reference-panel-jmorp</guid>
	<pubDate>Fri, 22 Dec 2017 20:18:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34750/japanese-multi-omics-reference-panel-jmorp</link>
	<title><![CDATA[Japanese Multi Omics Reference Panel, jMorp]]></title>
	<description><![CDATA[<p><span>Japanese Multi Omics Reference Panel, jMorp, is a database of metabolome and proteome data in plasma obtained from volunteers in ToMMo. Metabolome data were measured by proton NMR and LC-MS, and proteome data were obtained by nanoLC-MS. We could measure several thousands of metabolites including the uncharacterized ones, and several hundreds of proteins until now.</span></p>
<p><span>https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkx978/4563306</span></p><p>Address of the bookmark: <a href="https://jmorp.megabank.tohoku.ac.jp/2017/compounds" rel="nofollow">https://jmorp.megabank.tohoku.ac.jp/2017/compounds</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing</guid>
	<pubDate>Fri, 22 Dec 2017 00:31:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing</link>
	<title><![CDATA[AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing]]></title>
	<description><![CDATA[<p><span>AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to &sim;98% reduction of false variants when combined to conventional public pipelines and &sim;48% to the in-house commercial solution, with a minimal loss of sensitivity</span></p>
<p><span><span>The program with a detailed manual is available at&nbsp;</span><a href="https://sourceforge.net/projects/airvf/" target="">https://sourceforge.net/projects/airvf/</a></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/airvf/" rel="nofollow">https://sourceforge.net/projects/airvf/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34746/trrust-v2-an-expanded-reference-database-of-human-and-mouse-transcriptional-regulatory-interactions</guid>
	<pubDate>Thu, 21 Dec 2017 17:01:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34746/trrust-v2-an-expanded-reference-database-of-human-and-mouse-transcriptional-regulatory-interactions</link>
	<title><![CDATA[TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions]]></title>
	<description><![CDATA[<p>TRRUST contains 8,444 and 6,552 TF-target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively. They have been derived from 11,237 pubmed articles, which describe small-scale experimental studies of transcriptional regulations. To efficiently search for regulatory relationships from over 20 million pubmed articles, we used sentence-based text mining approach.</p>
<p>TRRUST database also provides information of mode of regulation (activation or repression). Currently 8,972 (59.8%) regulatory relationships are known for mode of regulation.</p>
<p>Search at :&nbsp;http://www.grnpedia.org/trrust/Network_search_form.php</p><p>Address of the bookmark: <a href="http://www.grnpedia.org/trrust/" rel="nofollow">http://www.grnpedia.org/trrust/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34744/foldit-solve-puzzles-for-science</guid>
	<pubDate>Thu, 21 Dec 2017 15:17:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34744/foldit-solve-puzzles-for-science</link>
	<title><![CDATA[Foldit: Solve Puzzles for Science]]></title>
	<description><![CDATA[<p><span>Foldit</span><span>&nbsp;is an online puzzle video game about protein&nbsp;</span><span>folding. It</span><span>&nbsp;is part of an experimental research project developed by the University of Washington, Center for Game Science, in collaboration with the UW Department of Biochemistry. The objective of&nbsp;</span><span>Foldit</span><span>&nbsp;is to&nbsp;</span><span>fold</span><span>&nbsp;the structures of selected proteins as perfectly as possible</span></p>
<p>https://fold.it/portal/</p><p>Address of the bookmark: <a href="https://fold.it/" rel="nofollow">https://fold.it/</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34734/smash-an-alignment-free-tool-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences</guid>
	<pubDate>Thu, 21 Dec 2017 08:26:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34734/smash-an-alignment-free-tool-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences</link>
	<title><![CDATA[SMASH: An alignment-free tool to find and visualise rearrangements between pairs of DNA sequences]]></title>
	<description><![CDATA[<p style="text-align: justify;"><span>SMASH is a completely alignment-free method to find and visualise rearrangements between pairs of DNA sequences</span>. The detection is based on&nbsp;<span>relative compression</span>, namely using a FCM, also known as Markov model, of high context order (typically 20). The method has been approached with a tool (also called SMASH). For visualization, SMASH outputs a SVG image, with an ideogram output architecture, where the patterns are represented with several HSV values (only value varies). The following image, illustrating the information maps between human and chimpanzee for the several chromosomes, depicts an example:</p>
<p><a href="https://github.com/pratas/smash/blob/master/imgs/HC.png" target="_blank"><img src="https://github.com/pratas/smash/raw/master/imgs/HC.png" alt="ScreenShot" style="border: 0px;"></a></p>
<p>&nbsp;</p>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/pratas/smash" rel="nofollow">https://github.com/pratas/smash</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</guid>
	<pubDate>Wed, 20 Dec 2017 18:59:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</link>
	<title><![CDATA[Meraculous: Haplotype-sensitive Assembly of Highly Heterozygous genomes.]]></title>
	<description><![CDATA[<p><span>Meraculous is a whole genome assembler for Next Generation Sequencing data geared for large genomes. It is a hybrid k-mer/read-based assembler that capitalizes on the high accuracy of Illumina sequence by eschewing an explicit error correction step which we argue to be redundant with the assembly process. Meraculous achieves high performance with large datasets by utilizing lightweight data structures and multi-threaded parallelization, allowing to assemble human-sized genomes on commodity clusters in under a day. The process pipeline implements a highly transparent and portable model of job control and monitoring where different assembly stages can be executed and re-executed separately or in unison on a wide variety of architectures.</span></p>
<p><span>https://jgi.doe.gov/data-and-tools/meraculous/</span></p>
<p><span>https://arxiv.org/ftp/arxiv/papers/1703/1703.09852.pdf</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/meraculous20/" rel="nofollow">https://sourceforge.net/projects/meraculous20/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34718/dipspades-assembler-for-highly-polymorphic-diploid-genomes</guid>
	<pubDate>Wed, 20 Dec 2017 18:35:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34718/dipspades-assembler-for-highly-polymorphic-diploid-genomes</link>
	<title><![CDATA[dipSPAdes: Assembler for Highly Polymorphic Diploid Genomes.]]></title>
	<description><![CDATA[<p><span>While the number of sequenced diploid genomes have been steadily increasing in the last few years, assembly of highly polymorphic (HP) diploid genomes remains challenging. As a result, there is a shortage of tools for assembling HP genomes from the next generation sequencing (NGS) data. The initial approaches to assembling HP genomes were proposed in the pre-NGS era and are not well suited for NGS projects. To address this limitation, we developed the first de Bruijn graph assembler, dipSPAdes, for HP genomes that significantly improves on the state-of-the-art assemblers for HP diploid genomes.</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pubmed/25734602" rel="nofollow">https://www.ncbi.nlm.nih.gov/pubmed/25734602</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</guid>
	<pubDate>Wed, 20 Dec 2017 08:49:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</link>
	<title><![CDATA[Delta: a new Web-based 3D genome visualization and analysis platform]]></title>
	<description><![CDATA[<p><em>Delta</em><span>&nbsp;is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes.&nbsp;</span><em>Delta</em><span>&nbsp;takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome.&nbsp;</span><em>Delta</em><span>features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.</span></p>
<p>https://github.com/zhangzhwlab/delta</p><p>Address of the bookmark: <a href="https://github.com/zhangzhwlab/delta" rel="nofollow">https://github.com/zhangzhwlab/delta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization</guid>
	<pubDate>Mon, 18 Dec 2017 04:16:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization</link>
	<title><![CDATA[NanoSim: nanopore sequence read simulator based on statistical characterization.]]></title>
	<description><![CDATA[<p><span>NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim</span></p>
<p><span>https://github.com/bcgsc/NanoSim</span></p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/nanosim" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/nanosim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34699/biological-file-format-tutorial</guid>
	<pubDate>Sun, 17 Dec 2017 18:13:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34699/biological-file-format-tutorial</link>
	<title><![CDATA[Biological file format tutorial]]></title>
	<description><![CDATA[<p>This section explains some of the commonly used file formats in bioinformatics. The information provided here is basic and designed to help users to distinguish the difference between different formats. Please refer user manual or other information resources on web for more details.</p>
<ol>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_fasta">FASTA</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_fastq">FASTQ</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_sam">SAM</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_bam">BAM</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_vcf">VCF</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_gff">GFF</a></li>
<li><a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/#fileformats_gtf">GTF</a></li>
</ol><p>Address of the bookmark: <a href="https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/" rel="nofollow">https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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