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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41599?offset=10</link>
	<atom:link href="https://bioinformaticsonline.com/related/41599?offset=10" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27035/spades</guid>
	<pubDate>Tue, 19 Apr 2016 08:37:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27035/spades</link>
	<title><![CDATA[SPAdes]]></title>
	<description><![CDATA[<p>SPAdes &ndash; St. Petersburg genome assembler &ndash; is intended for both standard isolates and single-cell MDA bacteria assemblies. This manual will help you to install and run SPAdes. SPAdes version 3.7.1 was released under GPLv2 on March 8, 2016 and can be downloaded from <a href="http://bioinf.spbau.ru/en/spades" target="_blank">http://bioinf.spbau.ru/en/spades</a>.</p>
<p>Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html</p><p>Address of the bookmark: <a href="http://bioinf.spbau.ru/spades" rel="nofollow">http://bioinf.spbau.ru/spades</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</guid>
	<pubDate>Tue, 12 Dec 2017 17:23:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</link>
	<title><![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]></title>
	<description><![CDATA[<p><span>MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a&nbsp;</span><em>k</em><span>-mer based&nbsp;</span><a href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard similarity</a><span>&nbsp;using a combination of&nbsp;</span><a href="http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/p76-schleimer.pdf">Winnowing</a><span>&nbsp;and&nbsp;</span><a href="https://en.wikipedia.org/wiki/MinHash">MinHash</a><span>. This is then converted to an estimate of sequence identity using the&nbsp;</span><a href="http://mash.readthedocs.org/">Mash</a><span>&nbsp;distance. An appropriate&nbsp;</span><em>k</em><span>-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.</span></p><p>Address of the bookmark: <a href="https://github.com/marbl/MashMap" rel="nofollow">https://github.com/marbl/MashMap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</guid>
	<pubDate>Wed, 22 Aug 2018 10:40:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</link>
	<title><![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]></title>
	<description><![CDATA[<p>SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.</p>
<p>Reads are simulated from both strands of a provided or randomly generated reference sequence.</p>
<div id="rst-header-features">
<ul>
<li>The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)</li>
<li>The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length</li>
<li>Quality values and number of passes depend on fragment length.</li>
<li>Provided subread error probabilities are modified according to number of passes</li>
<li>Outputs reads in FASTQ format and alignments in SAM format</li>
</ul>
</div><p>Address of the bookmark: <a href="https://bitbucket.org/genomeinformatics/simlord/" rel="nofollow">https://bitbucket.org/genomeinformatics/simlord/</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40460/sviper-swipe-your-structural-variants-called-on-long-ontpacbio-reads-with-short-exact-illumina-reads</guid>
	<pubDate>Sun, 22 Dec 2019 03:48:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40460/sviper-swipe-your-structural-variants-called-on-long-ontpacbio-reads-with-short-exact-illumina-reads</link>
	<title><![CDATA[SViper: Swipe your Structural Variants called on long (ONT/PacBio) reads with short exact (Illumina) reads.]]></title>
	<description><![CDATA[<p>Call sviper</p>
<pre><code>~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants
</code></pre>
<p>This will output a&nbsp;<code>polished_variants.vcf</code>&nbsp;file, that contains all the refined variants.</p>
<p>Sometimes it is helpful to look at the polished sequence, e.g. with the IGV browser. In that case you want SViper to output the polished and aligned sequences in a bam file via the option&nbsp;<code>--output-polished-bam</code>:</p>
<pre><code>~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants --output-</code>polished-bam</pre><p>Address of the bookmark: <a href="https://github.com/smehringer/SViper" rel="nofollow">https://github.com/smehringer/SViper</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore</guid>
	<pubDate>Fri, 19 Oct 2018 08:48:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore</link>
	<title><![CDATA[Wtdbg2: a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore]]></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. Wtdbg2 is able to assemble the human and even the 32Gb&nbsp;</span><a href="https://www.nature.com/articles/nature25458">Axolotl</a><span>&nbsp;genome at a speed tens of times faster than&nbsp;</span><a href="https://github.com/marbl/canu">CANU</a><span>&nbsp;and&nbsp;</span><a href="https://github.com/PacificBiosciences/FALCON">FALCON</a><span>while producing contigs of comparable base accuracy.</span></p><p>Address of the bookmark: <a href="https://github.com/ruanjue/wtdbg2" rel="nofollow">https://github.com/ruanjue/wtdbg2</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</guid>
	<pubDate>Mon, 27 Nov 2017 08:05:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</link>
	<title><![CDATA[SPAdes hybrid genome assembly]]></title>
	<description><![CDATA[<p>When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the&nbsp;<a href="https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0101-6">B fragilis assembly</a>&nbsp;by Mick Watson&rsquo;s group.</p><p>Again, running spades.py will show you the options:</p><div><pre><code>spades.py
</code></pre></div><p>This produces:</p><div><pre><code>SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o &lt;output_dir&gt;

Basic options:
-o      &lt;output_dir&gt;    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    &lt;filename&gt;      file with interlaced forward and reverse paired-end reads
-1      &lt;filename&gt;      file with forward paired-end reads
-2      &lt;filename&gt;      file with reverse paired-end reads
-s      &lt;filename&gt;      file with unpaired reads
--pe&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-&lt;or&gt;    orientation of reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--s&lt;#&gt;          &lt;filename&gt;      file with unpaired reads for single reads library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-&lt;or&gt;    orientation of reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--hqmp&lt;#&gt;-12    &lt;filename&gt;      file with interlaced reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-1     &lt;filename&gt;      file with forward reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-2     &lt;filename&gt;      file with reverse reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-s     &lt;filename&gt;      file with unpaired reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-&lt;or&gt;  orientation of reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--nxmate&lt;#&gt;-1   &lt;filename&gt;      file with forward reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--nxmate&lt;#&gt;-2   &lt;filename&gt;      file with reverse reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--sanger        &lt;filename&gt;      file with Sanger reads
--pacbio        &lt;filename&gt;      file with PacBio reads
--nanopore      &lt;filename&gt;      file with Nanopore reads
--tslr  &lt;filename&gt;      file with TSLR-contigs
--trusted-contigs       &lt;filename&gt;      file with trusted contigs
--untrusted-contigs     &lt;filename&gt;      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  &lt;cp&gt;    restart run with updated options and from the specified check-point ('ec', 'as', 'k&lt;int&gt;', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       &lt;filename&gt;      file with dataset description in YAML format
-t/--threads    &lt;int&gt;           number of threads
                                [default: 16]
-m/--memory     &lt;int&gt;           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       &lt;dirname&gt;       directory for temporary files
                                [default: &lt;output_dir&gt;/tmp]
-k              &lt;int,int,...&gt;   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    &lt;float&gt;         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  &lt;33 or 64&gt;      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]
</code></pre></div><p>As you can see this is also a &ldquo;pipeline&rdquo; of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:</p><div><pre><code>spades.py -t 4 <span>\</span>
          -m 32 <span>\</span>
          -k 31,51,71 <span>\</span>
          --only-assembler <span>\</span>
          -1 miseq.1.fastq -2 miseq.2.fastq <span>\</span>
          --nanopore minion.fastq <span>\</span>
          -o hybrid_assembly
</code></pre></div><p>In turn, these parameters mean</p><ul>
<li>use 4 threads</li>
<li>max memory is 32Gb</li>
<li>use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71</li>
<li>only run the assembler, not the correction algorithm (for speed)</li>
<li>read 1 and read 2 of the MiSeq data</li>
<li>the nanopore data</li>
<li>put the output in folder &ldquo;hybrid_assembly&rdquo;</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41691/genobuntu-package-for-next-generation-sequencing-and-genome-assembly</guid>
	<pubDate>Mon, 18 May 2020 16:47:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41691/genobuntu-package-for-next-generation-sequencing-and-genome-assembly</link>
	<title><![CDATA[Genobuntu: Package for Next Generation Sequencing and Genome Assembly]]></title>
	<description><![CDATA[<div>
<p>Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.<br><br>Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one&rsquo;s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.<br><br>Therefore, Genobuntu offers a well-tailored environment for both novices and experts working in the field of genome assembly.</p>
</div>
<div>
<h3>Features</h3>
<ul>
<li>Velvet</li>
<li>MiB</li>
<li>SSAKE</li>
<li>EULER</li>
<li>VCAKE</li>
<li>ABySS</li>
<li>ALLPATHS</li>
<li>Celera</li>
<li>SHARCGS</li>
<li>Allpaths</li>
<li>IDBA</li>
<li>TAIPAN</li>
<li>Edena</li>
<li>SOAPdenovo</li>
<li>Maq</li>
<li>IDBA-UD</li>
<li>No. of Reads present in the Ref. Seq.</li>
<li>ART NGS Reads Simulator</li>
<li>HiTEC, FASTQC</li>
<li>Minimum Description Length</li>
<li>SOAPaligner</li>
<li>Sequencing Read Archive Toolkit</li>
</ul>
</div><p>Address of the bookmark: <a href="https://sourceforge.net/projects/genobuntu/" rel="nofollow">https://sourceforge.net/projects/genobuntu/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</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>

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