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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38625?offset=20</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</guid>
	<pubDate>Thu, 20 Dec 2018 11:55:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</link>
	<title><![CDATA[FGENESH - Program for predicting multiple genes in genomic DNA sequences]]></title>
	<description><![CDATA[<p>FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu&nbsp;<em>et al</em>. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff&nbsp;<em>et al</em>. (2002) Science 296:79).</p><p>Address of the bookmark: <a href="http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind" rel="nofollow">http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33223/tbl2asn-a-command-line-program-that-automates-the-creation-of-sequence-records-for-submission-to-genbank</guid>
	<pubDate>Mon, 29 May 2017 07:37:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33223/tbl2asn-a-command-line-program-that-automates-the-creation-of-sequence-records-for-submission-to-genbank</link>
	<title><![CDATA[Tbl2asn: a command-line program that automates the creation of sequence records for submission to GenBank]]></title>
	<description><![CDATA[<p>Tbl2asn is a command-line program that automates the creation of sequence records for submission to GenBank. It uses many of the same functions as Sequin but is driven generally by data files. Tbl2asn generates .sqn files for submission to GenBank. Additional manual editing is not required before submission.</p>
<p>Tbl2asn is available by anonymous&nbsp;<a href="ftp://ftp.ncbi.nih.gov/toolbox/ncbi_tools/converters/by_program/tbl2asn/">FTP</a>. Copy the right version for your platform, then uncompress the file, rename it to "tbl2asn", and set the permissions, as necessary for the platform.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/" rel="nofollow">https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</guid>
	<pubDate>Thu, 31 May 2018 06:00:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</link>
	<title><![CDATA[CrossMap: a program for convenient conversion of genome coordinates]]></title>
	<description><![CDATA[CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) &lt;&gt; hg19 (GRCh37), Mouse mm9 (MGSCv37) &lt;&gt; mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. 

It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.<p>Address of the bookmark: <a href="http://crossmap.sourceforge.net" rel="nofollow">http://crossmap.sourceforge.net</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38166/pygenometracks-standalone-program-and-library-to-plot-beautiful-genome-browser-tracks</guid>
	<pubDate>Fri, 09 Nov 2018 12:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38166/pygenometracks-standalone-program-and-library-to-plot-beautiful-genome-browser-tracks</link>
	<title><![CDATA[pyGenomeTracks: Standalone program and library to plot beautiful genome browser tracks]]></title>
	<description><![CDATA[<p>pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot:</p>
<ul>
<li>bigwig</li>
<li>bed (many options)</li>
<li>bedgraph</li>
<li>links (represented as arcs)</li>
<li>Hi-C matrices (if&nbsp;<a href="http://hicexplorer.readthedocs.io/">HiCExplorer</a>&nbsp;is installed)</li>
</ul><p>Address of the bookmark: <a href="https://github.com/deeptools/pyGenomeTracks" rel="nofollow">https://github.com/deeptools/pyGenomeTracks</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</guid>
	<pubDate>Mon, 12 Aug 2019 07:52:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</link>
	<title><![CDATA[Cactus: a reference-free whole-genome multiple alignment program]]></title>
	<description><![CDATA[<p>Cactus is a reference-free whole-genome multiple alignment program. The principal algorithms are described here:&nbsp;<a href="https://doi.org/10.1101/gr.123356.111">https://doi.org/10.1101/gr.123356.111</a></p>
<p><span>Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM.</span>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/ComparativeGenomicsToolkit/cactus" rel="nofollow">https://github.com/ComparativeGenomicsToolkit/cactus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41872/autodock-vina-an-open-source-program-for-doing-molecular-docking</guid>
	<pubDate>Sat, 13 Jun 2020 07:55:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41872/autodock-vina-an-open-source-program-for-doing-molecular-docking</link>
	<title><![CDATA[AutoDock Vina: an open-source program for doing molecular docking.]]></title>
	<description><![CDATA[<p><span>AutoDock Vina is an open-source program for doing&nbsp;</span><a href="http://en.wikipedia.org/wiki/Docking_(molecular)">molecular docking</a><span>. It was designed and implemented by&nbsp;</span><a href="http://olegtrott.com/">Dr. Oleg Trott</a><span>&nbsp;in the Molecular Graphics Lab at The Scripps Research Institute.</span>&nbsp;It is especially effective for protein-ligand docking. AutoDock 4 is available under the GNU General Public License. AutoDock is one of the most cited docking software applications in the research community.</p>
<p><img src="http://vina.scripps.edu/img/accuracy.png" width="352" height="264" alt="image" style="border: 0px;"></p>
<p><a href="http://vina.scripps.edu/">http://vina.scripps.edu/</a></p><p>Address of the bookmark: <a href="http://vina.scripps.edu/" rel="nofollow">http://vina.scripps.edu/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</guid>
	<pubDate>Tue, 09 Jun 2020 05:21:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</link>
	<title><![CDATA[StringTie Transcript assembly and quantification for RNA-Seq]]></title>
	<description><![CDATA[<p><strong>StringTie</strong><span>&nbsp;is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional&nbsp;</span><em>de novo</em><span>&nbsp;assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like&nbsp;</span><a href="https://github.com/alyssafrazee/ballgown">Ballgown</a><span>,&nbsp;</span><a href="http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html">Cuffdiff</a><span>&nbsp;or other programs (DESeq2, edgeR, etc.).</span></p><p>Address of the bookmark: <a href="https://ccb.jhu.edu/software/stringtie/" rel="nofollow">https://ccb.jhu.edu/software/stringtie/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41686/catbat-tool-for-taxonomic-classification-of-contigs-and-metagenome-assembled-genomes-mags</guid>
	<pubDate>Mon, 18 May 2020 10:53:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41686/catbat-tool-for-taxonomic-classification-of-contigs-and-metagenome-assembled-genomes-mags</link>
	<title><![CDATA[CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs)]]></title>
	<description><![CDATA[<p>Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formated appropriately (see <a href="https://github.com/dutilh/CAT#usage">Usage</a>).</p><p>Address of the bookmark: <a href="https://github.com/dutilh/CAT" rel="nofollow">https://github.com/dutilh/CAT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38749/clipcrop-a-tool-for-detecting-structural-variations-with-single-base-resolution-using-soft-clipping-information</guid>
	<pubDate>Sun, 20 Jan 2019 06:34:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38749/clipcrop-a-tool-for-detecting-structural-variations-with-single-base-resolution-using-soft-clipping-information</link>
	<title><![CDATA[ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information]]></title>
	<description><![CDATA[<p><span>ClipCrop for detecting SVs with single-base resolution using soft-clipping information. A soft-clipped sequence is an unmatched fragment in a partially mapped read. To assess the performance of ClipCrop with other SV-detecting tools, we generated various patterns of simulation data &ndash; SV lengths, read lengths, and the depth of coverage of short reads &ndash; with insertions, deletions, tandem duplications, inversions and single nucleotide alterations in a human chromosome.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/shinout/clipcrop" rel="nofollow">https://github.com/shinout/clipcrop</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>

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