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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37241?offset=60</link>
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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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</guid>
	<pubDate>Tue, 15 May 2018 09:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</link>
	<title><![CDATA[Circlator: automated circularization of genome assemblies using long sequencing reads]]></title>
	<description><![CDATA[A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. 

Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.<p>Address of the bookmark: <a href="http://sanger-pathogens.github.io/circlator/" rel="nofollow">http://sanger-pathogens.github.io/circlator/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37211/jbrowse-embeddable-genome-browser-built-completely-with-javascript-and-html5</guid>
	<pubDate>Fri, 29 Jun 2018 09:19:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37211/jbrowse-embeddable-genome-browser-built-completely-with-javascript-and-html5</link>
	<title><![CDATA[JBrowse: Embeddable genome browser built completely with JavaScript and HTML5]]></title>
	<description><![CDATA[JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl.

Headline Features:
Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed.
Scales easily to multi-gigabase genomes and deep-coverage sequencing.
Quickly open and view data files on your computer without uploading them to any server.
Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more.  BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed.
Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks.
Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance.
Can run as a stand-alone app on OSX and Windows using the Electron platform
Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry

https://jbrowse.org/<p>Address of the bookmark: <a href="https://github.com/GMOD/jbrowse" rel="nofollow">https://github.com/GMOD/jbrowse</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37759/pandaseq-is-a-program-to-align-illumina-reads-optionally-with-pcr-primers-embedded-in-the-sequence-and-reconstruct-an-overlapping-sequence</guid>
	<pubDate>Fri, 21 Sep 2018 10:19:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37759/pandaseq-is-a-program-to-align-illumina-reads-optionally-with-pcr-primers-embedded-in-the-sequence-and-reconstruct-an-overlapping-sequence</link>
	<title><![CDATA[PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.]]></title>
	<description><![CDATA[<p>Development packages for zlib and libbz2 are needed, as well as a standard compiler environment. On Ubuntu, this can be installed via:</p>
<pre><code>sudo apt-get install build-essential libtool automake zlib1g-dev libbz2-dev pkg-config
</code></pre>
<p>On MacOS, the Apple Developer tools and Fink (or MacPorts or Brew) must be installed, then:</p>
<pre><code>sudo fink install bzip2-dev pkgconfig</code></pre><p>Address of the bookmark: <a href="https://github.com/neufeld/pandaseq" rel="nofollow">https://github.com/neufeld/pandaseq</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38481/arcs-scaffolding-genome-drafts-with-linked-reads</guid>
	<pubDate>Mon, 17 Dec 2018 17:40:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38481/arcs-scaffolding-genome-drafts-with-linked-reads</link>
	<title><![CDATA[ARCS: scaffolding genome drafts with linked reads]]></title>
	<description><![CDATA[<p>ARCS requires two input files:</p>
<ul>
<li>Draft assembly fasta file</li>
<li>Interleaved linked reads file (Barcode sequence expected in the BX tag of the read header or in the form "@readname_barcode" ; Run&nbsp;<a href="https://support.10xgenomics.com/genome-exome/software/pipelines/latest/what-is-long-ranger">Long Ranger basic</a>&nbsp;on raw chromium reads to produce this interleaved file)</li>
<li></li>
</ul><p>Address of the bookmark: <a href="https://github.com/bcgsc/ARCS/" rel="nofollow">https://github.com/bcgsc/ARCS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads</guid>
	<pubDate>Tue, 28 Jan 2020 03:27:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads</link>
	<title><![CDATA[FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads]]></title>
	<description><![CDATA[<p>FastGT is a program package for whole-genome genotyping of genome variants directly from raw sequencing reads. It is written in C and runs in Linux. FastGT uses a list of variant-specific k-mer pairs that are unique in human genome, counts the frequency of k-mers in sequencing data and predicts the genotype. All this takes less than 1 hour on average low-cost Linux server.</p>
<p><a href="http://bioinfo.ut.ee/FastGT/">http://bioinfo.ut.ee/FastGT/</a></p>
<p><strong><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></strong></p><p>Address of the bookmark: <a href="http://bioinfo.ut.ee/FastGT/" rel="nofollow">http://bioinfo.ut.ee/FastGT/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40994/biological-databases</guid>
	<pubDate>Wed, 12 Feb 2020 01:16:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40994/biological-databases</link>
	<title><![CDATA[Biological databases !]]></title>
	<description><![CDATA[<p>Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ...</p>
<p>ftp://ftp.ncbi.nih.gov/genomes/</p>
<p>https://hgdownload.soe.ucsc.edu/downloads.html</p><p>Address of the bookmark: <a href="ftp://ftp.ncbi.nih.gov/genomes/" rel="nofollow">ftp://ftp.ncbi.nih.gov/genomes/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</guid>
	<pubDate>Mon, 17 Aug 2020 05:21:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</link>
	<title><![CDATA[SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis]]></title>
	<description><![CDATA[<p><span>SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, &hellip;).</span><br><span>The bioinformatics treatment is based on Vsearch [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/27781170">Rognes 2016</a><span>] which showed to be both accurate and fast [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/26664811">Wescott 2015</a><span>].The statistical analysis is based on DESeq2 R package [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20979621">Anders and Huber 2010</a><span>] which robustly identifies the differential abundant features as suggested in [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/">McMurdie and Holmes 2014</a><span>] and [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/">Jonsson2016</a><span>]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/25516281">Love 2014</a><span>].</span><br><span>SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_presentation.pdf">here</a><span>&nbsp;and a poster&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_poster.pdf">here</a><span>.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4</a></span></p><p>Address of the bookmark: <a href="https://github.com/aghozlane/shaman" rel="nofollow">https://github.com/aghozlane/shaman</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</guid>
	<pubDate>Sat, 26 Dec 2020 08:35:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</link>
	<title><![CDATA[FastProNGS: fast preprocessing of next-generation sequencing reads]]></title>
	<description><![CDATA[<p><span>FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/Megagenomics/FastProNGS" rel="nofollow">https://github.com/Megagenomics/FastProNGS</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26453/stacks</guid>
	<pubDate>Wed, 24 Feb 2016 15:52:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26453/stacks</link>
	<title><![CDATA[Stacks]]></title>
	<description><![CDATA[<p>Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.</p>
<p>More at http://catchenlab.life.illinois.edu/stacks/</p><p>Address of the bookmark: <a href="http://catchenlab.life.illinois.edu/stacks/" rel="nofollow">http://catchenlab.life.illinois.edu/stacks/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

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