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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36597?offset=690</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</guid>
	<pubDate>Tue, 18 Feb 2020 03:24:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</link>
	<title><![CDATA[LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data]]></title>
	<description><![CDATA[<p>LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.</p>
<p>https://github.com/CSB5/lofreq</p>
<p>http://csb5.github.io/lofreq/installation/</p>
<p>https://github.com/CSB5/lofreq/tree/master/dist</p><p>Address of the bookmark: <a href="http://csb5.github.io/lofreq/" rel="nofollow">http://csb5.github.io/lofreq/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</guid>
	<pubDate>Wed, 18 Aug 2021 00:02:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</link>
	<title><![CDATA[Kmer: a suite of tools for DNA sequence analysis]]></title>
	<description><![CDATA[<p>More at&nbsp;https://help.rc.ufl.edu/doc/Kmer</p>
<p>This also includes:</p>
<ul>
<li>A2Amapper: ATAC, Assembly to Assembly Comparision tool:
<ul>
<li>Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).</li>
</ul>
</li>
</ul>
<ul>
<li>Sim4db:
<ul>
<li>Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.</li>
</ul>
</li>
</ul>
<ul>
<li>LEAFF:
<ul>
<li>LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.</li>
</ul>
</li>
</ul>
<ul>
<li>Meryl:
<ul>
<li>An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.</li>
</ul>
</li>
</ul><p>Address of the bookmark: <a href="https://help.rc.ufl.edu/doc/Kmer" rel="nofollow">https://help.rc.ufl.edu/doc/Kmer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</guid>
	<pubDate>Wed, 03 Jan 2018 05:05:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</link>
	<title><![CDATA[Seal: SEquence ALignment evaluation suite]]></title>
	<description><![CDATA[<p><span>Seal</span>&nbsp;is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:</p>
<ul>
<li>Reading a pre-existing reference genome from one or more FASTA files.</li>
<li>Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).</li>
<li>Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.</li>
<li>Applying alignment tools to the genome and the reads through a standardized interface.</li>
<li>Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.</li>
<li>Computing run times and measures of accuracy.</li>
</ul>
<p><span>Seal</span>&nbsp;has interfaces to evaluate the following software packages:</p>
<ul>
<li>Bowtie</li>
<li>BWA</li>
<li>MAQ</li>
<li>mrFAST</li>
<li>mrsFAST</li>
<li>Novoalign</li>
<li>SHRiMP</li>
<li>SOAPv2</li>
</ul><p>Address of the bookmark: <a href="http://compbio.case.edu/seal/" rel="nofollow">http://compbio.case.edu/seal/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats</guid>
	<pubDate>Thu, 07 Mar 2019 21:13:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats</link>
	<title><![CDATA[HipSTR: Haplotype inference and phasing for Short Tandem Repeats]]></title>
	<description><![CDATA[<p><span>HipSTR</span>&nbsp;was specifically developed to deal with these errors in the hopes of obtaining more robust STR genotypes. In particular, it accomplishes this by:</p>
<ol>
<li>Learning locus-specific PCR stutter models using an&nbsp;<a href="http://en.wikipedia.org/wiki/Expectation-maximization_algorithm">EM algorithm</a></li>
<li>Mining candidate STR alleles from population-scale sequencing data</li>
<li>Employing a specialized hidden Markov model to align reads to candidate alleles while accounting for STR artifacts</li>
<li>Utilizing phased SNP haplotypes to genotype and phase STRs</li>
</ol><p>Address of the bookmark: <a href="https://github.com/tfwillems/HipSTR" rel="nofollow">https://github.com/tfwillems/HipSTR</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>

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