<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37957?offset=230</link>
	<atom:link href="https://bioinformaticsonline.com/related/37957?offset=230" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27113/picard</guid>
	<pubDate>Fri, 29 Apr 2016 08:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27113/picard</link>
	<title><![CDATA[Picard]]></title>
	<description><![CDATA[<p>Picard is a set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. These file formats are defined in the <a href="http://samtools.github.io/hts-specs/">Hts-specs</a> repository. See especially the <a href="http://samtools.github.io/hts-specs/SAMv1.pdf">SAM specification</a> and the <a href="http://samtools.github.io/hts-specs/VCFv4.3.pdf">VCF specification</a>.</p>
<p>Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on <a href="https://github.com/broadinstitute/picard">GitHub</a>.</p>
<p>The Picard toolkit is open-source under the <a href="https://tldrlegal.com/license/mit-license">MIT license</a> and free for all uses.</p>
<p>Enjoy!</p><p>Address of the bookmark: <a href="http://broadinstitute.github.io/picard/" rel="nofollow">http://broadinstitute.github.io/picard/</a></p>]]></description>
	<dc:creator>Neel</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/26999/discovar</guid>
	<pubDate>Mon, 18 Apr 2016 11:59:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26999/discovar</link>
	<title><![CDATA[DISCOVAR]]></title>
	<description><![CDATA[<p><strong>DISCOVAR</strong> is a new variant caller and <strong>DISCOVAR <em>de novo</em></strong> a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer &mdash; produced on MiSeq or HiSeq 2500 &mdash; and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.</p>
<p><strong>DISCOVAR</strong> can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.</p>
<p><strong>DISCOVAR <em>de novo</em></strong> can generate <em>de novo</em> assemblies for both large and small genomes. It currently does not call variants.</p>
<p>More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/discovar/blog/" rel="nofollow">https://www.broadinstitute.org/software/discovar/blog/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</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/pages/view/27799/bbmapbbtools-package-multipurpose-tool-designed-for-converting-reads-or-other-nucleotide-data-between-different-formats</guid>
	<pubDate>Mon, 13 Jun 2016 05:47:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/27799/bbmapbbtools-package-multipurpose-tool-designed-for-converting-reads-or-other-nucleotide-data-between-different-formats</link>
	<title><![CDATA[BBMap/BBTools package: Multipurpose tool designed for converting reads or other nucleotide data between different formats.]]></title>
	<description><![CDATA[<div id="post_message_148585"><a href="https://sourceforge.net/projects/bbmap/" target="_blank">Reformat</a>is a member of the <a href="https://sourceforge.net/projects/bbmap/" target="_blank">BBMap/BBTools package</a>. It is a multipurpose tool designed for converting reads or other nucleotide data between different formats. It supports, and can inter-convert:<br /> <br /> fastq<br /> fasta<br /> fasta+qual<br /> sam<br /> scarf (an old Illumina format)<br /> bam (if samtools is installed)<br /> gzip<br /> zip<br /> ascii-33 (sanger)<br /> ascii-64 (old Illumina)<br /> paired files<br /> interleaved files<br /> <br /> It is multithreaded and can process data at over 500 megabytes per second, and can accept streams from standard in and write to standard out, allowing it to be easily dropped into the middle of a pipeline for format conversion. Reformat autodetects formats based on file extensions and content, making it very easy to use; and the autodetection can be overridden, allowing flexibility for people who don't like to follow naming conventions, or out-of-spec fastq files with qualities values like -17 or 120.<br /> <br /> The program has been gradually expanded, and can now perform various other functions. None of these will break pairing, if the input is paired.<br /> <br /> Quality trimming (either or both ends)<br /> Quality filtering<br /> Fixed-length trimming<br /> Generation of histograms (base composition, quality, etc)<br /> Subsampling (to a fraction of input reads, or an exact number of reads or bases)<br /> Changing fasta line-wrapping length<br /> Reverse-complementing (all reads or only read 2)<br /> Adding /1 and /2 suffix to read names<br /> GC-content filtering<br /> Length-filtering<br /> Testing for corrupted interleaved files<br /> <br /> Reformat is compatible with any platform that supports Java 1.7 or higher. It also has a bash shellscript for simpler invocation. Typical usage examples:<br /> <br /> Reformat fastq into fasta:<br /> <strong>reformat.sh in=x.fq out=y.fa</strong><br /> <br /> Interleave paired reads:<br /> <strong>reformat.sh in1=x1.fq in2=x2.fq out=y.fq</strong><br /> <br /> Note - you can actually use a shortcut if paired read files have the same name with a 1 and a 2. This is equivalent to the above command:<br /> <strong>reformat.sh in=x#.fq out=y.fq</strong><br /> <br /> De-interleave reads:<br /> <strong>reformat.sh in=x.fq out1=y1.fq out2=y2.fq</strong><br /> <br /> Verify that interleaving appears correct, assuming Illumina namimg conventions:<br /> <strong>reformat.sh in=x.fq vint</strong><br /> <br /> Convert ASCII-33 to ASCII-64:<br /> <strong>reformat.sh in=x.fq out=y.fq qin=33 qout=64</strong><br /> <br /> Quality-trim paired reads to Q10 on the left and right ends and discard reads shorter than 50bp after trimming:<br /> <strong>reformat.sh in1=x1.fq in2=x2.fq out1=y1.fq out2=y2.fq outsingle=singletons.fq qtrim=rl trimq=10 minlength=50</strong><br /> <br /> Subsample 10% of the first 20000 pairs in an interleaved file:<br /> <strong>reformat.sh in=x.fq out=y.fq reads=20000 samplerate=0.1 int=t</strong><br /> (in this case "int=t" overrides interleaving autodetection, to ensure reads are treated as pairs)<br /> <br /> Pipe in a gzipped sam file and pipe out fasta:<br /> <strong>reformat.sh in=stdin.sam.gz out=stdout.fa</strong><br /> <br /> Reverse-complement reads:<br /> <strong>reformat.sh in=x.fq out=y.fq rcomp</strong><br /> <br /> For reformatting a file with very long sequences, Reformat will need more memory; just add the additional flag "-Xmx2g". For example, to change the line-wrapping length on the human genome (which has individual sequences over 200Mbp long) to 70 characters:<br /> <strong>reformat.sh -Xmx2g in=HG19.fa.gz out=HG19_wrapped.fa.gz fastawrap=70</strong><br /> <br /> For additional functions, please run the shellscript with no arguments, or just read it with a text editor. If you have any questions, please post them in this thread.<br /> <br /> For people using a non-bash terminal, you may need to type "bash reformat.sh" instead of just "reformat.sh".<br /> For users of Windows or other platforms that do not support bash shellscripts, replace "reformat.sh" with "java -ea -Xmx200m /path/to/bbmap/current/ jgi.ReformatReads"<br /> for example,<br /> <strong>java -ea -Xmx200m C:\bbmap\current\ jgi.ReformatReads in=x.fq out=y.fa</strong><br /> <br /> Reformat can be downloaded with BBTools here:<br /> <a href="https://sourceforge.net/projects/bbmap/" target="_blank">https://sourceforge.net/projects/bbmap/</a></div>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27847/anvio</guid>
	<pubDate>Thu, 16 Jun 2016 18:15:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27847/anvio</link>
	<title><![CDATA[Anvio]]></title>
	<description><![CDATA[<p>In a nutshell</p>
<p>Anvi&rsquo;o is an analysis and visualization platform for &lsquo;omics data.</p>
<p>Please find the methods paper here: https://peerj.com/articles/1319/</p>
<p>Anvi&rsquo;o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper</p>
<p><span>An analysis and visualization platform for 'omics data</span><span>&nbsp;</span><span><a href="http://merenlab.org/projects/anvio">http://merenlab.org/projects/anvio</a></span></p>
<p><span>Paper&nbsp;https://peerj.com/articles/1839/</span></p><p>Address of the bookmark: <a href="https://github.com/meren/anvio" rel="nofollow">https://github.com/meren/anvio</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/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>