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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40613?offset=380</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</guid>
	<pubDate>Wed, 21 Feb 2024 06:19:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</link>
	<title><![CDATA[pipesnake: bioinformatics best-practice analysis pipeline for phylogenomic reconstruction]]></title>
	<description><![CDATA[<p dir="auto"><span>ausarg/pipesnake</span>&nbsp;is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.</p>
<p dir="auto">The pipeline is built using&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The&nbsp;<a href="https://www.nextflow.io/docs/latest/dsl2.html">Nextflow DSL2</a>&nbsp;implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.</p><p>Address of the bookmark: <a href="https://github.com/AusARG/pipesnake" rel="nofollow">https://github.com/AusARG/pipesnake</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38661/gene-ontology-consortium</guid>
	<pubDate>Fri, 11 Jan 2019 05:51:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38661/gene-ontology-consortium</link>
	<title><![CDATA[Gene Ontology Consortium]]></title>
	<description><![CDATA[<p>The GO knowledgebase is composed of two primary components:</p>
<ul>
<li>the&nbsp;<strong><a href="http://geneontology.org/page/ontology-documentation">Gene Ontology (GO)</a></strong>, which provides the logical structure of the biological functions (&lsquo;terms&rsquo;) and their relationships to one another, manifested as a directed acyclic graph</li>
<li>the corpus of&nbsp;<strong><a href="http://geneontology.org/page/go-annotations">GO annotations</a></strong>, evidence-based statements relating a specific gene product (a protein, non-coding RNA, or macromolecular complex, which we often refer to as &lsquo;genes&rsquo; for simplicity) to a specific ontology term</li>
</ul>
<p>Together, the ontology and annotations aim to describe a comprehensive model of biological systems. Currently, the GO knowledgebase includes experimental findings from over&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=loprovGeneOntol[SB]">140 000 published papers</a>, represented as over 600 000 experimentally-supported GO annotations. These provide the core dataset for additional inference of over 6 million functional annotations for a diverse set of organisms spanning the tree of life.</p>
<p>In addition to this core knowledgebase, GOC resources also include software to edit and perform logical reasoning over the ontologies, web access to the ontology and annotations, and analytical tools that use the GO knowledgebase to support biomedical research.</p><p>Address of the bookmark: <a href="http://www.geneontology.org/" rel="nofollow">http://www.geneontology.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</guid>
	<pubDate>Sun, 03 Dec 2017 15:19:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</link>
	<title><![CDATA[MinION_GC: An R script to do some QC on MinION data]]></title>
	<description><![CDATA[<p><span>Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.</span></p>
<p>https://github.com/roblanf/minion_qc</p><p>Address of the bookmark: <a href="https://github.com/roblanf/minion_qc" rel="nofollow">https://github.com/roblanf/minion_qc</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</guid>
	<pubDate>Tue, 08 May 2018 04:58:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</link>
	<title><![CDATA[MIX: Combining multiple assemblies from NGS data]]></title>
	<description><![CDATA[<p>Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.</p>
<p>The Mix algorithm, approach and results were published in BMC bioinformatics :&nbsp;<a href="http://www.biomedcentral.com/1471-2105/14/S15/S16">http://www.biomedcentral.com/1471-2105/14/S15/S16</a>.</p><p>Address of the bookmark: <a href="https://github.com/cbib/MIX" rel="nofollow">https://github.com/cbib/MIX</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</guid>
	<pubDate>Mon, 06 Aug 2018 17:24:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</link>
	<title><![CDATA[NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data]]></title>
	<description><![CDATA[<p>NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.</p>
<p>&nbsp;</p>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextSV" rel="nofollow">https://github.com/Nextomics/NextSV</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38172/bamview-a-free-interactive-display-of-read-alignments-in-bam-data-files</guid>
	<pubDate>Fri, 09 Nov 2018 13:43:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38172/bamview-a-free-interactive-display-of-read-alignments-in-bam-data-files</link>
	<title><![CDATA[BamView: a free interactive display of read alignments in BAM data files]]></title>
	<description><![CDATA[<p>To run the application on UNIX from the downloaded jar file run the UNIX:</p>
<p><tt>java -mx512m -jar BamView.jar</tt></p>
<p>and extra command line options are given when '-h' is used:</p>
<p><tt>java -jar BamView.jar -h</tt></p>
<p>BAM files can be specified on the command line with the '-a' option:</p>
<p><tt>java -mx512m -jar BamView.jar -a pathToFile/sorted.bam</tt></p>
<p>If a BAM filename is not given on the command line BamView will prompt for a file to be entered. The BAM index file should have the same name as the BAM file but with a '.bai' suffix. Multiple BAM files can be loaded and overlaid in the viewer. To make this easier BamView will read in files that contain a list of filenames.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://bamview.sourceforge.net/" rel="nofollow">http://bamview.sourceforge.net/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38598/zenbu-a-collaborative-omics-data-integration-and-interactive-visualization-system</guid>
	<pubDate>Fri, 04 Jan 2019 13:35:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38598/zenbu-a-collaborative-omics-data-integration-and-interactive-visualization-system</link>
	<title><![CDATA[ZENBU: a collaborative, omics data integration and interactive visualization system]]></title>
	<description><![CDATA[<p><span>ZENBU</span><span>&nbsp;</span><span>is a data integration, data analysis, and visualization system enhanced for RNAseq, ChipSeq, CAGE and other types of next-generation-sequence-tag (NGS) based data. ZENBU allows for novel data exploration through "on-demand" data processing and interactive linked-visualizations and is able to make many-views from the same primary sequence alignment data which users can uploaded from BAM, BED, GFF and tab-text files.&nbsp;<br>Please check our&nbsp;<a href="http://fantom.gsc.riken.jp/zenbu/wiki">documentation wiki</a>&nbsp;for details on how to use the system, or check out some of the views above.</span></p><p>Address of the bookmark: <a href="http://fantom.gsc.riken.jp/zenbu/" rel="nofollow">http://fantom.gsc.riken.jp/zenbu/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39200/omtools-a-software-package-for-visualizing-and-processing-optical-mapping-data</guid>
	<pubDate>Fri, 29 Mar 2019 01:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39200/omtools-a-software-package-for-visualizing-and-processing-optical-mapping-data</link>
	<title><![CDATA[OMTools: a software package for visualizing and processing optical mapping data]]></title>
	<description><![CDATA[<p><span>OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses.</span></p>
<p><span><a href="https://github.com/TF-Chan-Lab/OMTools">https://github.com/TF-Chan-Lab/OMTools</a></span></p><p>Address of the bookmark: <a href="https://github.com/TF-Chan-Lab/OMTools" rel="nofollow">https://github.com/TF-Chan-Lab/OMTools</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data</guid>
	<pubDate>Tue, 28 Jan 2020 03:44:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data</link>
	<title><![CDATA[vt: a variant tool set that discovers short variants from Next Generation Sequencing data.]]></title>
	<description><![CDATA[<p><span>vt is a variant tool set that discovers short variants from Next Generation Sequencing data.</span></p>
<p><span><a href="https://genome.sph.umich.edu/wiki/Vt">https://genome.sph.umich.edu/wiki/Vt</a></span></p>
<p><a href="https://github.com/atks/vt">https://github.com/atks/vt</a></p><p>Address of the bookmark: <a href="https://genome.sph.umich.edu/wiki/Vt" rel="nofollow">https://genome.sph.umich.edu/wiki/Vt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42040/proactiv-estimation-of-promoter-activity-from-rna-seq-data</guid>
	<pubDate>Thu, 13 Aug 2020 10:21:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42040/proactiv-estimation-of-promoter-activity-from-rna-seq-data</link>
	<title><![CDATA[proActiv: Estimation of Promoter Activity from RNA-Seq data]]></title>
	<description><![CDATA[<p>proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described in&nbsp;<a href="https://github.com/GoekeLab/proActiv#reference">Demircioglu et al</a>.</p>
<p>Additional data on differential promoters in tissues and cancers from TCGA, ICGC, GTEx, and PCAWG can be downloaded here:&nbsp;<a href="https://jglab.org/data-and-software/">https://jglab.org/data-and-software/</a></p><p>Address of the bookmark: <a href="https://github.com/GoekeLab/proActiv" rel="nofollow">https://github.com/GoekeLab/proActiv</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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