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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41571?offset=280</link>
	<atom:link href="https://bioinformaticsonline.com/related/41571?offset=280" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads</guid>
	<pubDate>Tue, 15 May 2018 02:53:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads</link>
	<title><![CDATA[TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads]]></title>
	<description><![CDATA[<p><strong>TA</strong>ndem&nbsp;<strong>RE</strong>peat&nbsp;<strong>AN</strong>alyzer -TAREAN &ndash; is a computational pipeline for&nbsp;<strong>unsupervised identification of satellite repeats</strong>&nbsp;from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.</p>
<p><em><strong>How to use TAREAN</strong></em>:</p>
<ul>
<li>Install a local instance of the pipeline using its source code available from&nbsp;<a href="https://bitbucket.org/petrnovak/repex_tarean" target="_blank" title="TAREAN source code">bitbucket repository</a>.</li>
<li>Use&nbsp; public Galaxy-based server at&nbsp;<a href="https://repeatexplorer-elixir.cerit-sc.cz/" target="_blank">https://repeatexplorer-elixir.cerit-sc.cz/</a>. The server is provided in frame of the&nbsp;<a href="https://www.elixir-czech.cz/" target="_blank">Elixir CZ project</a>&nbsp;and is maintained by&nbsp;<a href="https://www.cesnet.cz/" target="_blank">CESNET</a>&nbsp;and&nbsp;<a href="https://www.cerit-sc.cz/en/index.html" target="_blank">CERIT-SC</a>. Simple registration is required to use this service.</li>
</ul>
<p>Development of TAREAN was supported by&nbsp;<a href="https://www.elixir-czech.cz/" target="_blank" title="ELIXIR-CZ">ELIXIR CZ</a>&nbsp;research infrastructure project (MEYS Grant No: LM2015047).</p>
<p><strong><em>References</em></strong></p>
<p>Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) &ndash;&nbsp;<a href="https://academic.oup.com/nar/article/3574061/" target="_blank">TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads</a>.&nbsp;<em>Nucleic Acids Res.</em>, doi:10.1093/nar/gkx257</p><p>Address of the bookmark: <a href="https://bitbucket.org/petrnovak/repex_tarean" rel="nofollow">https://bitbucket.org/petrnovak/repex_tarean</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</guid>
	<pubDate>Thu, 31 May 2018 09:35:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</link>
	<title><![CDATA[BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data]]></title>
	<description><![CDATA[BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.

The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC.

https://github.com/lh3/bfc<p>Address of the bookmark: <a href="https://github.com/lh3/bfc" rel="nofollow">https://github.com/lh3/bfc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</guid>
	<pubDate>Mon, 11 Jun 2018 09:44:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</link>
	<title><![CDATA[HiGlass: a tool for exploring genomic contact matrices and tracks.]]></title>
	<description><![CDATA[HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices. To load private data, HiGlass can be run locally within a Docker container. The HiC data in the examples below is from Rao et al. (2014)

http://higlass.io/<p>Address of the bookmark: <a href="http://higlass.io/" rel="nofollow">http://higlass.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</guid>
	<pubDate>Thu, 02 Aug 2018 07:39:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</link>
	<title><![CDATA[LSC :a long read error correction tool]]></title>
	<description><![CDATA[<h2>Getting Started</h2>
<p>These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.</p>
<ul>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_requirements.asp">LSC_requirements</a>&nbsp;for running LSC.</li>
<li><a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_download.asp">Download</a>&nbsp;and set-up the LSC package.</li>
<li>Follow the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_tutorial.asp">tutorial</a>&nbsp;to see how LSC works on some example data.</li>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_manual.asp">manual</a>&nbsp;if anything is unclear.</li>
<li>You're ready, Happy LSCing!</li>
</ul>
<h2>Latest publication</h2>
<p><span>Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong&nbsp;</span><br><strong>Improving PacBio Long Read Accuracy by Short Read Alignment&nbsp;</strong><span>[</span><a href="http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0046679">Manuscript</a><span>]&nbsp;</span><br><em>PLoS ONE</em><span>&nbsp;2012. 7(10): e46679. doi:10.1371/journal.pone.0046679</span></p><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/LSC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/LSC/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37554/finishersca-repeat-aware-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 20 Aug 2018 04:08:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37554/finishersca-repeat-aware-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC:a repeat-aware tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><br>Here is the command to run the tool:</p>
<pre><code>python finisherSC.py destinedFolder mummerPath
</code></pre>
<p>If you are running on server computer and would like to use multiple threads, then the following commands can generate 20 threads to run FinisherSC.</p>
<pre><code>python finisherSC.py -par 20 destinedFolder mummerPath
</code></pre>
<p>Sometimes, if the names of raw reads and contigs consists of special characters/formats, FinisherSC/MUMmer may not parse them correctly. In that case, you want to have a quick renaming of the names of contigs/reads in contigs.fasta or raw_reads.fasta using the following command.</p>
<pre><code>    perl -pe 's/&gt;[^\$]*$/"&gt;Seg" . ++$n ."\n"/ge' raw_reads.fasta &gt; newRaw_reads.fasta
    cp newRaw_reads.fasta raw_reads.fasta
    perl -pe 's/&gt;[^\$]*$/"&gt;Seg" . ++$n ."\n"/ge' contigs.fasta &gt; newContigs.fasta
    cp newContigs.fasta contigs.fasta</code></pre><p>Address of the bookmark: <a href="https://github.com/kakitone/finishingTool" rel="nofollow">https://github.com/kakitone/finishingTool</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool</guid>
	<pubDate>Sun, 23 Sep 2018 05:12:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool</link>
	<title><![CDATA[rHAT: a seed-and-extension-based noisy long read alignment tool]]></title>
	<description><![CDATA[<p><span>rHAT is a seed-and-extension-based noisy long read alignment tool. It is suitable for aligning 3rd generation sequencing reads which are in large read length with relatively high error rate, especially Pacbio's Single Molecule Read-time (SMRT) sequencing reads.</span></p><p>Address of the bookmark: <a href="https://github.com/dfguan/rHAT" rel="nofollow">https://github.com/dfguan/rHAT</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies</guid>
	<pubDate>Tue, 30 Oct 2018 10:49:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies</link>
	<title><![CDATA[Synima: a Synteny imaging tool for annotated genome assemblies]]></title>
	<description><![CDATA[<p><span>Synima written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima &ndash; and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from&nbsp;</span><a href="https://github.com/rhysf/Synima" target="_blank">https://github.com/rhysf/Synima</a><span>&nbsp;under the MIT License.</span></p><p>Address of the bookmark: <a href="https://github.com/rhysf/Synima" rel="nofollow">https://github.com/rhysf/Synima</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing</guid>
	<pubDate>Sun, 16 Feb 2020 08:47:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing</link>
	<title><![CDATA[iSeqQC: a tool for expression-based quality control in RNA sequencing]]></title>
	<description><![CDATA[<p><span>iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.</span></p>
<p><a href="http://cancerwebpa.jefferson.edu/iSeqQC/">http://cancerwebpa.jefferson.edu/iSeqQC/</a></p>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8</a></p><p>Address of the bookmark: <a href="https://github.com/gkumar09/iSeqQC" rel="nofollow">https://github.com/gkumar09/iSeqQC</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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