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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36597?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</guid>
	<pubDate>Tue, 05 Sep 2023 07:31:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</link>
	<title><![CDATA[MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads]]></title>
	<description><![CDATA[<p dir="auto">MitoHiFi v3.2 is a python pipeline distributed under&nbsp;<a href="https://github.com/marcelauliano/MitoHiFi/blob/master/LICENSE">MIT License</a>&nbsp;!</p>
<p dir="auto">MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project (DToL)</p>
<p dir="auto">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05385-y&nbsp;</p>
<p dir="auto"><a href="https://github.com/marcelauliano/MitoHiFi/blob/master/docs/dtol-logo-round-300x132.png" target="_blank"><img src="https://github.com/marcelauliano/MitoHiFi/raw/master/docs/dtol-logo-round-300x132.png" alt="" style="border: 0px; border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marcelauliano/MitoHiFi" rel="nofollow">https://github.com/marcelauliano/MitoHiFi</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</guid>
	<pubDate>Fri, 20 May 2016 19:08:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</link>
	<title><![CDATA[Hagfish - assess an assembly through creative use of coverage plots]]></title>
	<description><![CDATA[<p>Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of&nbsp;<em style="font-size: 12.8px;">de novo</em>&nbsp;genome assembly or identification of structural variation in a genome re-sequencing experiment.</p>
<p>Hagfish requires a reference sequence and a&nbsp;<span>paired end</span>&nbsp;re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.</p>
<p>Quick links:&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Install">Installation</a>,<a href="https://github.com/mfiers/hagfish/wiki/Operation">Operation</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/ReadMappers">Read mappers</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Scripts">Hagfish scripts</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Plots">Hagfish plots</a></p><p>Address of the bookmark: <a href="https://github.com/mfiers/hagfish" rel="nofollow">https://github.com/mfiers/hagfish</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32709/cabog-celera-assembler-with-best-overlap-graph</guid>
	<pubDate>Mon, 15 May 2017 05:04:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32709/cabog-celera-assembler-with-best-overlap-graph</link>
	<title><![CDATA[CABOG: Celera Assembler with Best Overlap Graph]]></title>
	<description><![CDATA[<p>CABOG (Celera Assembler with Best Overlap Graph) is scientific software for&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/24/24/2818.abstract">DNA research</a>. CABOG has been a critical component of many genome sequencing projects. CABOG operates on small genomes such as bacterial as well as large genomes such as mammalian. CABOG is an extension of the Celera Assembler software that was originally developed at&nbsp;<a href="http://www.celera.com/">Celera</a>&nbsp;for the 2001 publication of the first draft human genome sequence. The software was released to the public domain in 2004. Its open source&nbsp;<a href="http://wgs-assembler.sf.net/">repository</a>&nbsp;on Source Forge is an internet resource for scientists around the world.&nbsp;</p>
<p>CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.</p>
<p>The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (<em>Drosophila melanogaster</em>, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.</p><p>Address of the bookmark: <a href="http://www.jcvi.org/cms/research/projects/cabog/overview/" rel="nofollow">http://www.jcvi.org/cms/research/projects/cabog/overview/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37211/jbrowse-embeddable-genome-browser-built-completely-with-javascript-and-html5</guid>
	<pubDate>Fri, 29 Jun 2018 09:19:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37211/jbrowse-embeddable-genome-browser-built-completely-with-javascript-and-html5</link>
	<title><![CDATA[JBrowse: Embeddable genome browser built completely with JavaScript and HTML5]]></title>
	<description><![CDATA[JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl.

Headline Features:
Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed.
Scales easily to multi-gigabase genomes and deep-coverage sequencing.
Quickly open and view data files on your computer without uploading them to any server.
Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more.  BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed.
Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks.
Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance.
Can run as a stand-alone app on OSX and Windows using the Electron platform
Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry

https://jbrowse.org/<p>Address of the bookmark: <a href="https://github.com/GMOD/jbrowse" rel="nofollow">https://github.com/GMOD/jbrowse</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37759/pandaseq-is-a-program-to-align-illumina-reads-optionally-with-pcr-primers-embedded-in-the-sequence-and-reconstruct-an-overlapping-sequence</guid>
	<pubDate>Fri, 21 Sep 2018 10:19:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37759/pandaseq-is-a-program-to-align-illumina-reads-optionally-with-pcr-primers-embedded-in-the-sequence-and-reconstruct-an-overlapping-sequence</link>
	<title><![CDATA[PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.]]></title>
	<description><![CDATA[<p>Development packages for zlib and libbz2 are needed, as well as a standard compiler environment. On Ubuntu, this can be installed via:</p>
<pre><code>sudo apt-get install build-essential libtool automake zlib1g-dev libbz2-dev pkg-config
</code></pre>
<p>On MacOS, the Apple Developer tools and Fink (or MacPorts or Brew) must be installed, then:</p>
<pre><code>sudo fink install bzip2-dev pkgconfig</code></pre><p>Address of the bookmark: <a href="https://github.com/neufeld/pandaseq" rel="nofollow">https://github.com/neufeld/pandaseq</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly</guid>
	<pubDate>Mon, 04 Feb 2019 04:53:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly</link>
	<title><![CDATA[wtdbg2: A fuzzy Bruijn graph approach to long noisy reads assembly]]></title>
	<description><![CDATA[<p><span>Wtdbg2 is a&nbsp;</span><em>de novo</em><span>&nbsp;sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output.&nbsp;</span></p>
<pre>./wtdbg2 -x rs -g 4.6m -t 16 -i reads.fa.gz -fo prefix
./wtpoa-cns -t 16 -i prefix.ctg.lay.gz -fo prefix.ctg.fa</pre><p>Address of the bookmark: <a href="https://github.com/ruanjue/wtdbg2" rel="nofollow">https://github.com/ruanjue/wtdbg2</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</guid>
	<pubDate>Sat, 08 May 2021 21:25:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</link>
	<title><![CDATA[HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding]]></title>
	<description><![CDATA[<p><span>HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/esolares/HapSolo" rel="nofollow">https://github.com/esolares/HapSolo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36861/eagler-a-scaffolding-tool-for-long-reads</guid>
	<pubDate>Mon, 04 Jun 2018 05:26:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36861/eagler-a-scaffolding-tool-for-long-reads</link>
	<title><![CDATA[EAGLER: a scaffolding tool for long reads.]]></title>
	<description><![CDATA[<p>EAGLER is a scaffolding tool for long reads. The scaffolder takes as input a draft genome created by any NGS assembler and a set of long reads. The long reads are used to extend the contigs present in the NGS draft and possibly join overlapping contigs. EAGLER supports both PacBio and Oxford Nanopore reads.</p>
<p>The tool should be compatible with most UNIX flavors and has been successfully tested on the following operating systems:</p>
<ul>
<li>Mac OS X 10.11.1</li>
<li>Mac OS X 10.10.3</li>
<li>Ubuntu 14.04 LTS</li>
</ul>

https://bib.irb.hr/datoteka/844447.Diplomski_2015_Luka_terbi.pdf<p>Address of the bookmark: <a href="https://github.com/mculinovic/EAGLER" rel="nofollow">https://github.com/mculinovic/EAGLER</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31156/splitbam-splits-a-bam-by-chromosomes</guid>
	<pubDate>Tue, 28 Feb 2017 09:01:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31156/splitbam-splits-a-bam-by-chromosomes</link>
	<title><![CDATA[splitbam: splits a BAM by chromosomes]]></title>
	<description><![CDATA[<p><strong>splitbam</strong>&nbsp;splits a BAM by chromosomes.</p>
<p>Using the reference sequence dictionary (<code>*.dict</code>), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.</p>
<h1>Usage</h1>
<p><code>java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)</code></p>
<h1>Options</h1>
<ul>
<li>-h help; This screen.</li>
<li>-R (indexed reference file) REQUIRED.</li>
<li>-u (unmapped chromosome name): default:Unmapped</li>
<li>-e | --empty : generate EMPTY bams for chromosome having no read mapped</li>
<li>-m | --mock : if option '-e', add a mock pair of sam records to the empty bam</li>
<li>-p (output file/bam pattern) REQUIRED. MUST contain&nbsp;<strong><code>__CHROM__</code></strong>&nbsp;and end with .bam</li>
<li>-s assume input is sorted.</li>
<li>-x | --index create index.</li>
<li>-t | --tmp (dir) tmp file directory</li>
<li>-G (file) chrom-group file (see below)</li>
</ul><p>Address of the bookmark: <a href="https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki" rel="nofollow">https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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