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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37905?offset=350</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/23149/raphael-lab</guid>
  <pubDate>Sat, 04 Jul 2015 19:05:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Raphael Lab]]></title>
  <description><![CDATA[
<p>Raphael Lab research is focused on Bioinformatics and Computational Biology.</p>

<p>Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.</p>

<p>More athttp://compbio.cs.brown.edu/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects</guid>
	<pubDate>Tue, 04 Aug 2015 05:14:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects</link>
	<title><![CDATA[Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?]]></title>
	<description><![CDATA[<p>Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?</p>]]></description>
	<dc:creator>Strand</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools</guid>
	<pubDate>Mon, 08 Feb 2016 15:39:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools</link>
	<title><![CDATA[n50PlottingTools]]></title>
	<description><![CDATA[<p><span>Tools to create plots showing N-statistics for genome assemblies </span></p>
<p><span>More at https://github.com/dentearl/n50PlottingTools</span></p><p>Address of the bookmark: <a href="https://github.com/dentearl/n50PlottingTools" rel="nofollow">https://github.com/dentearl/n50PlottingTools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26828/bioinfolab</guid>
  <pubDate>Fri, 25 Mar 2016 11:05:35 -0500</pubDate>
  <link></link>
  <title><![CDATA[BioinfoLab]]></title>
  <description><![CDATA[
<p>Laboratory of Statistics and Computational tools for Bioinformatics</p>

<p>The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&amp;C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments. </p>

<p>More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</guid>
	<pubDate>Tue, 26 Apr 2016 03:48:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</link>
	<title><![CDATA[HOMER:  Software for motif discovery and next-gen sequencing analysis]]></title>
	<description><![CDATA[<p><span>This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/computerSetup.html">Setting up your computing environment</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/retrieveFiles.html">Retrieving and storing sequencing files</a>&nbsp;(your own data or from public sources)</li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/fastqFiles.html">Checking sequence quality, trimming, general sequence manipulation</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/mapping.html">Mapping reads to a reference genome</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/samfiles.html">Manipulating SAM/BAM alignment files</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/genomeBrowsers.html">Visualizing data in a genome browser</a></li>
</ol>
<p><br>RNA-Seq</p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqCufflinks.html">De novo transcript discovery and differential analysis with Cufflinks</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqR.html">Differential expression analysis with R/Bioconductor</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/clustering.html">Clustering of large expression datasets (microarray or RNA-Seq)</a></li>
</ol>
<p><br><span>Microarray</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/affymetrix.html">Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor</a></li>
</ol>
<p><span>General Tips for Data Analysis</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/excelTips.html">Excel workarounds, adding gene annotation, X-Y plots tips, etc.</a></li>
</ol><p>Address of the bookmark: <a href="http://homer.salk.edu/homer/basicTutorial/" rel="nofollow">http://homer.salk.edu/homer/basicTutorial/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</guid>
	<pubDate>Wed, 27 Apr 2016 11:07:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</link>
	<title><![CDATA[RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes]]></title>
	<description><![CDATA[<p>The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.</p>
<p>More at http://www.nature.com/articles/srep08365</p><p>Address of the bookmark: <a href="http://rast.nmpdr.org/" rel="nofollow">http://rast.nmpdr.org/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</guid>
	<pubDate>Fri, 20 May 2016 18:53:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</link>
	<title><![CDATA[MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping]]></title>
	<description><![CDATA[<p><span>MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</guid>
	<pubDate>Thu, 16 Jun 2016 17:55:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</link>
	<title><![CDATA[CNIDARIA: fast, reference-free phylogenomic clustering]]></title>
	<description><![CDATA[<p>Motivation: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but these do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.</p>
<p>Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.</p>
<p>Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.</p>
<p>Contact: Saulo Aflitos - sauloal@gmail.com</p>
<p>Supplementary information: Supplementary data are available at Bioinformatics online.</p><p>Address of the bookmark: <a href="https://github.com/sauloal/cnidaria/wiki" rel="nofollow">https://github.com/sauloal/cnidaria/wiki</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28168/sam-flags</guid>
	<pubDate>Wed, 29 Jun 2016 15:38:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28168/sam-flags</link>
	<title><![CDATA[SAM flags]]></title>
	<description><![CDATA[<p>Decoding SAM flags</p>
<p>This utility makes it easy to identify what are the properties of a read based on its SAM flag value, or conversely, to find what the SAM Flag value would be for a given combination of properties.</p>
<p>To decode a given SAM flag value, just enter the number in the field below. The encoded properties will be listed under Summary below, to the right.</p><p>Address of the bookmark: <a href="https://broadinstitute.github.io/picard/explain-flags.html" rel="nofollow">https://broadinstitute.github.io/picard/explain-flags.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</guid>
	<pubDate>Mon, 27 Jun 2016 11:23:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</link>
	<title><![CDATA[Kaiju]]></title>
	<description><![CDATA[<p>Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.</p>
<p>By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database <em>nr</em> used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.</p>
<p>Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.</p>
<p>Kaiju is described in <a href="http://www.nature.com/ncomms/2016/160413/ncomms11257/full/ncomms11257.html">Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em> 7:11257</a> (open access).</p><p>Address of the bookmark: <a href="http://kaiju.binf.ku.dk/" rel="nofollow">http://kaiju.binf.ku.dk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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