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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40721?offset=220</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</guid>
	<pubDate>Thu, 24 May 2018 02:53:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</link>
	<title><![CDATA[%MinMax: A versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding.]]></title>
	<description><![CDATA[%MM calculates whether a given gene sequence encodes amino acids using the most common codons possible, the least common codons possible, or (most typically) some combination of these extremes. See our PLoS ONE paper for more details on how the %MinMax algorithm works. 

%MinMax results are averaged over an 18-codon sliding window; hence the result for "codon window = 1" is the average codon usage for codons 1-18, codon window 2 = codons 2-19, etc.<p>Address of the bookmark: <a href="http://www.codons.org/" rel="nofollow">http://www.codons.org/</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</guid>
	<pubDate>Mon, 04 Jun 2018 03:45:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</link>
	<title><![CDATA[“One code to find them all”: a perl tool to conveniently parse RepeatMasker output files]]></title>
	<description><![CDATA[One code to find them all is a set of perl scripts to extract useful information from RepeatMasker about transposable elements, retrieve their sequences and get some quantitative information.

Assemble RepeatMasker hits into complete TE copies, including LTR-retrotransposon
Retrieve corresponding TE sequences, and flanking sequences, from the local fasta files
Compute summary statistics for each TE family (number of TE copies, genome coverage...)
Ambiguous cases such as nested TE can be assembled into copies automatically or manually
Allow for working with a TE user-defined library
Allow for working with only a user-chosen set of TE families


http://doua.prabi.fr/software/one-code-to-find-them-all<p>Address of the bookmark: <a href="http://doua.prabi.fr/software/one-code-to-find-them-all" rel="nofollow">http://doua.prabi.fr/software/one-code-to-find-them-all</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</guid>
	<pubDate>Fri, 15 Jun 2018 04:01:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</link>
	<title><![CDATA[SALSA: A tool to scaffold long read assemblies with Hi-C]]></title>
	<description><![CDATA[This code is used to scaffold your assemblies using Hi-C data. This version implements some improvements in the original SALSA algorithm. If you want to use the old version, it can be found in the old_salsa branch.

To use the latest version, first run the following commands:

  cd SALSA
  make
To run the code, you will need Python 2.7, BOOST libraries and Networkx(version lower than 1.2).

If you consider using this tool, please cite our publication which describes the methods used for scaffolding.

Ghurye, J., Pop, M., Koren, S., Bickhart, D., &amp; Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC genomics, 18(1), 527. Link

Ghurye, J., Rhie, A., Walenz, B.P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A.M. and Koren, S., 2018. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. bioRxiv, p.261149 Link

For any queries, please either ask on github issue page or send an email to Jay Ghurye (jayg@cs.umd.edu).<p>Address of the bookmark: <a href="https://github.com/machinegun/SALSA" rel="nofollow">https://github.com/machinegun/SALSA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</guid>
	<pubDate>Mon, 06 Aug 2018 17:19:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</link>
	<title><![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]></title>
	<description><![CDATA[<p><span>gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ml.ssu.ac.kr/gSearch/index.html" rel="nofollow">http://ml.ssu.ac.kr/gSearch/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 09:35:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</link>
	<title><![CDATA[GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences]]></title>
	<description><![CDATA[<p>GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:</p>
<ul>
<li>Step 1. Run mugsy to get multiple sequence alignment results.</li>
<li>Step 2 &amp; 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.</li>
<li>Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.</li>
<li></li>
<li></li>
</ul>
<p>https://github.com/DanwangJessica/GRSR</p><p>Address of the bookmark: <a href="https://github.com/DanwangJessica/GRSR" rel="nofollow">https://github.com/DanwangJessica/GRSR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence</guid>
	<pubDate>Thu, 16 May 2019 00:20:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence</link>
	<title><![CDATA[iRNAD: a computational tool for identifying D modification sites in RNA sequence]]></title>
	<description><![CDATA[<p><span>iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification.&nbsp;</span></p>
<p><span><a href="http://lin-group.cn/server/iRNAD/">http://lin-group.cn/server/iRNAD/</a></span></p><p>Address of the bookmark: <a href="http://lin-group.cn/server/iRNAD/" rel="nofollow">http://lin-group.cn/server/iRNAD/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40465/airlift-a-methodology-and-tool-for-comprehensively-moving-mappings-and-annotations-from-one-genome-to-another-similar-genome</guid>
	<pubDate>Mon, 23 Dec 2019 10:20:13 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40465/airlift-a-methodology-and-tool-for-comprehensively-moving-mappings-and-annotations-from-one-genome-to-another-similar-genome</link>
	<title><![CDATA[AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome]]></title>
	<description><![CDATA[<p>We propose AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome while maintaining the accuracy of a full mapper.</p><p>Address of the bookmark: <a href="https://github.com/CMU-SAFARI/AirLift" rel="nofollow">https://github.com/CMU-SAFARI/AirLift</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny</guid>
	<pubDate>Mon, 17 Feb 2020 05:38:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny</link>
	<title><![CDATA[Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny]]></title>
	<description><![CDATA[<p>Chromonomer is a program designed to integrate a genome assembly with a genetic map. Chromonomer tries very hard to identify and remove markers that are out of order in the genetic map, when considered against their local assembly order; and to identify scaffolds that have been incorrectly assembled according to the genetic map, and split those scaffolds.</p><p>Address of the bookmark: <a href="http://catchenlab.life.illinois.edu/chromonomer/" rel="nofollow">http://catchenlab.life.illinois.edu/chromonomer/</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>

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