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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37512?offset=200</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</guid>
	<pubDate>Tue, 03 Jul 2018 04:14:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</link>
	<title><![CDATA[ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data]]></title>
	<description><![CDATA[ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.<p>Address of the bookmark: <a href="https://github.com/bcgsc/ChopStitch" rel="nofollow">https://github.com/bcgsc/ChopStitch</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</guid>
	<pubDate>Thu, 20 Sep 2018 08:38:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</link>
	<title><![CDATA[d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity]]></title>
	<description><![CDATA[<p><code>d2Tools</code>&nbsp;are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the&nbsp;<strong>d2-style</strong>(d2/d2<code>*</code>/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.</p>
<p>Manual at&nbsp;https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki</p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/d2-tools/" rel="nofollow">https://code.google.com/archive/p/d2-tools/</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/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Tue, 02 Apr 2019 21:54:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39213/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>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing</guid>
	<pubDate>Wed, 13 Nov 2019 22:20:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing</link>
	<title><![CDATA[mosdepth: fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing]]></title>
	<description><![CDATA[<p>mosdepth can output:</p>
<p>per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.<br>mean per-window depth given a window size--as would be used for CNV calling.<br>the mean per-region given a BED file of regions.<br>a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.<br>quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)<br>threshold output to indicate how many bases in each region are covered at the given thresholds.<br>A summary of mean depths per chromosome and within specified regions per chromosome.</p><p>Address of the bookmark: <a href="https://github.com/brentp/mosdepth" rel="nofollow">https://github.com/brentp/mosdepth</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</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>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</guid>
	<pubDate>Thu, 28 May 2020 21:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</link>
	<title><![CDATA[Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data]]></title>
	<description><![CDATA[<p>Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.</p>
<p>Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.</p><p>Address of the bookmark: <a href="https://github.com/dnanexus/parliament2" rel="nofollow">https://github.com/dnanexus/parliament2</a></p>]]></description>
	<dc:creator>Jit</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads</guid>
	<pubDate>Mon, 14 May 2018 05:25:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads</link>
	<title><![CDATA[GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads]]></title>
	<description><![CDATA[<p><span>This software is provided ``as is&rdquo; without warranty of any kind. In no event shall the author be held responsible for any damage resulting from the use of this software. The program package, including source codes, executables, and this documentation, is distributed free of charge. If you use this program in a publication, please cite the following reference:</span><br><span>Chong Chu, Xin Li, and Yufeng Wu. "GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads." bioRxiv (2017): 125534.</span></p><p>Address of the bookmark: <a href="https://github.com/Reedwarbler/GAPPadder" rel="nofollow">https://github.com/Reedwarbler/GAPPadder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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