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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/23174?offset=160</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</guid>
	<pubDate>Wed, 27 Dec 2017 20:36:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</link>
	<title><![CDATA[Ra assembler - a de novo DNA assembler for third generation sequencing data]]></title>
	<description><![CDATA[<p>Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).</p>
<p>Ra is in development since 2014 in the form of several separate components that used to be run individually.<br>This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.</p>
<p>Unlike other state-of-the-art assemblers,&nbsp;<span>Ra does not have an error correction step.</span>&nbsp;Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler",&nbsp;<a href="https://github.com/isovic/graphmap">https://github.com/isovic/graphmap</a>) and constructing and reducing an overlap graph (Ra layout,&nbsp;<a href="https://github.com/mariokostelac/ra">https://github.com/mariokostelac/ra</a>).</p><p>Address of the bookmark: <a href="https://github.com/mariokostelac/ra-integrate/" rel="nofollow">https://github.com/mariokostelac/ra-integrate/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<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/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</guid>
	<pubDate>Wed, 23 May 2018 06:54:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</link>
	<title><![CDATA[BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,]]></title>
	<description><![CDATA[<p><span>BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.</span></p>
<p>Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The &ldquo;target.fasta.sa&rdquo; is the suffix array from &ldquo;target.fasta&rdquo; generated by sawriter.</p>
<blockquote>
<p>blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15</p>
</blockquote>
<p>the output format option &ldquo;-m 4&Prime; generate the alignment coordinate. Not fully documented, but I can explain that to you.&nbsp;</p>
<p>I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.</p><p>Address of the bookmark: <a href="http://bix.ucsd.edu/projects/blasr/" rel="nofollow">http://bix.ucsd.edu/projects/blasr/</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/37602/indexcov-fast-coverage-quality-control-for-whole-genome-sequencing</guid>
	<pubDate>Wed, 29 Aug 2018 09:20:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37602/indexcov-fast-coverage-quality-control-for-whole-genome-sequencing</link>
	<title><![CDATA[Indexcov: fast coverage quality control for whole-genome sequencing]]></title>
	<description><![CDATA[<p><em>indexcov</em><span>, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large-scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample.&nbsp;</span><em>Indexcov</em><span>&nbsp;is available at&nbsp;</span><a href="https://github.com/brentp/goleft" target="_blank">https://github.com/brentp/goleft</a><span>&nbsp;under the MIT license.</span></p><p>Address of the bookmark: <a href="https://github.com/brentp/goleft" rel="nofollow">https://github.com/brentp/goleft</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</guid>
	<pubDate>Tue, 13 Nov 2018 07:25:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</link>
	<title><![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]></title>
	<description><![CDATA[<p><strong>Required python modules</strong></p>
<ul>
<li>biopython</li>
<li>intervaltree</li>
<li>numpy</li>
<li>scipy</li>
<li>tqdm</li>
<li>PyYAML</li>
</ul><p>Address of the bookmark: <a href="https://github.com/cerebis/sim3C" rel="nofollow">https://github.com/cerebis/sim3C</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Sat, 06 Jul 2019 03:48:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</guid>
	<pubDate>Fri, 25 Feb 2022 04:42:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</link>
	<title><![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]></title>
	<description><![CDATA[<p dir="auto">This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.</p>
<p dir="auto">Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:</p>
<p dir="auto"><a href="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" target="_blank"><img src="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" alt="smudgeexample" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/KamilSJaron/smudgeplot" rel="nofollow">https://github.com/KamilSJaron/smudgeplot</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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