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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30140?offset=100</link>
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	<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/28809/kissplice</guid>
	<pubDate>Tue, 16 Aug 2016 08:34:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</link>
	<title><![CDATA[KisSplice]]></title>
	<description><![CDATA[<p>KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.</p>
<p>KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.</p>
<p>KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.</p><p>Address of the bookmark: <a href="http://kissplice.prabi.fr/" rel="nofollow">http://kissplice.prabi.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28417/wisescaffolder</guid>
	<pubDate>Wed, 13 Jul 2016 08:08:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28417/wisescaffolder</link>
	<title><![CDATA[WiseScaffolder]]></title>
	<description><![CDATA[<p>Function</p>
<p>WiseScaffolder is a stand-alone semi-automatic application for genome scaffolding of pre-assembled contigs using mate-pair data. It also produces editable scaffold maps, allowing either to build gapped scaffolds or usable as a common thread for the manual improvement of scaffolds.</p>
<p>Description&nbsp;</p>
<p>WiseScaffolder includes 4 subcommands: dumpconfig generates a configuration file that notably specifies the average insert size of the mate-pair library preprocess allows the detection and correction of chimerae, the estimation of contigs copy number and produces valuable outputs for the manual improvement of scaffolds scaffold constitutes the central scaffold-builder and comprises two modules:</p>
<p>i) the interative_scaffold_extender, which works with big, unambiguous contigs, or when they run out, single copy contigs, and</p>
<p>ii) the small_contig_inserter, which inserts the small contigs within scaffolds buildfasta converts the scaffold(s) map(s) into Fasta sequences.</p><p>Address of the bookmark: <a href="http://abims.sb-roscoff.fr/wisescaffolder" rel="nofollow">http://abims.sb-roscoff.fr/wisescaffolder</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</guid>
	<pubDate>Mon, 05 Sep 2016 04:07:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<ul>
<li>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between&nbsp;<em>different assemblies</em>&nbsp;(such as Human&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</li>
<li>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</li>
<li>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s&nbsp;<em>not</em>&nbsp;a program for aligning sequences to reference genome.</li>
<li>We&nbsp;<em>do not</em>&nbsp;recommend using CrossMap to convert genome coordinates between species.</li>
</ul><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28844/teannot</guid>
	<pubDate>Thu, 18 Aug 2016 10:02:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28844/teannot</link>
	<title><![CDATA[TEannot]]></title>
	<description><![CDATA[<p>We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto" rel="nofollow">https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</guid>
	<pubDate>Thu, 25 Aug 2016 08:05:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</link>
	<title><![CDATA[LUMPY]]></title>
	<description><![CDATA[<p>A probabilistic framework for structural variant discovery.</p>
<p>Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84.&nbsp;<a href="http://dx.doi.org/10.1186/gb-2014-15-6-r84">doi:10.1186/gb-2014-15-6-r84</a>.</p>
<p>More at&nbsp;https://github.com/arq5x/lumpy-sv</p><p>Address of the bookmark: <a href="https://github.com/arq5x/lumpy-sv" rel="nofollow">https://github.com/arq5x/lumpy-sv</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</guid>
	<pubDate>Mon, 29 Aug 2016 11:44:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</link>
	<title><![CDATA[Ka, Ks and Ka/Ks calculations]]></title>
	<description><![CDATA[<p>gKaKs is a codon-based genome-level Ka/Ks computation pipeline developed and based on programs from four widely used packages: BLAT, BLASTALL (including bl2seq, formatdb and fastacmd), PAML (including codeml and yn00) and KaKs_Calculator (including 10 substitution rate estimation methods). gKaKs can automatically detect and eliminate frameshift mutations and premature stop codons to compute the substitution rates (Ka, Ks and Ka/Ks) between a well-annotated genome and a non-annotated genome or even a poorly assembled scaffold dataset. It is especially useful for newly sequenced genomes that have not been well annotated.&nbsp;</p>
<p>Look for KaKs calculation:</p>
<p>https://github.com/fumba/kaks-calculator</p>
<p>http://longlab.uchicago.edu/?q=gKaKs</p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/23314322</p><p>Address of the bookmark: <a href="http://longlab.uchicago.edu/?q=gKaKs" rel="nofollow">http://longlab.uchicago.edu/?q=gKaKs</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29108/assembly-tutorial-ppt</guid>
	<pubDate>Wed, 07 Sep 2016 03:12:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29108/assembly-tutorial-ppt</link>
	<title><![CDATA[Assembly tutorial PPT]]></title>
	<description><![CDATA[<p>Saved Cornell University assembly workshop PPT.</p><p>Reference:&nbsp;</p><p>http://cbsu.tc.cornell.edu/lab/doc/assembly_workshop_20150420_lecture1.pdf</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29108" length="1617402" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29142/opera-optimal-paired-end-read-assembler</guid>
	<pubDate>Fri, 09 Sep 2016 05:28:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29142/opera-optimal-paired-end-read-assembler</link>
	<title><![CDATA[OPERA : Optimal Paired-End Read Assembler]]></title>
	<description><![CDATA[<p>OPERA (Optimal Paired-End Read Assembler) is a sequence assembly program (<a href="http://en.wikipedia.org/wiki/Sequence_assembly">http://en.wikipedia.org/wiki/Sequence_assembly</a>). It uses information from paired-end/mate-pair/long reads to order and orient the intermediate contigs/scaffolds assembled in a genome assembly project, in a process known as Scaffolding. OPERA is based on an exact algorithm that is guaranteed to minimize the discordance of scaffolds with the information provided by the paired-end/mate-pair/long reads (for further details see Gao et al, 2011).</p>
<p>Note that since the original publication, we have made significant changes to OPERA (v1.0 onwards) including refinements to its basic algorithm (to reduce local errors, improve efficiency etc.) and incorporated features that are important for scaffolding large genomes (multi-library support, better repeat-handling etc.), in addition to other scalability and usability improvements (bam and gzip support, smaller memory footprint). We therefore encourage you to download and use our latest version: OPERA-LG. In our benchmarks, it has significantly improved corrected N50 and reduced the number of scaffolding errors. Furthermore, our latest release contains the wrapper script OPERA-long-read that enables scaffolding with long-reads from third-generation sequencing technologies (PacBio or Oxford Nanopore). The manuscript describing the new features and algorithms is available at&nbsp;<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y">Genome Biology</a>. We look forward to getting your feedback to improve it further.</p><p>Address of the bookmark: <a href="https://sourceforge.net/p/operasf/wiki/The%20OPERA%20wiki/" rel="nofollow">https://sourceforge.net/p/operasf/wiki/The%20OPERA%20wiki/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29485/ribbon</guid>
	<pubDate>Fri, 21 Oct 2016 04:54:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29485/ribbon</link>
	<title><![CDATA[Ribbon !!]]></title>
	<description><![CDATA[<p><span>Visualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes. We have addressed this problem by creating Ribbon (genomeribbon.com) an interactive online visualization tool that displays alignments along both reference and query sequences, along with any associated variant calls in the sample. This way Ribbon shows patterns in alignments of many reads across multiple chromosomes, while allowing detailed inspection of individual reads (Supplementary Note 1). For example, here we show a gene fusion in the SK-BR-3 breast cancer cell line linking the genes CYTH1 and EIF3H. While it has been found in the transcriptome previously, genome sequencing did not identify a direct chromosomal fusion between these two genes. After SMRT sequencing, Ribbon shows that there are indeed long reads that span from one gene to the other, going through not one but two variants, for the first time showing the genomic link between these two genes (Figure 1a). More gene fusions of this cancer cell line are investigated in Supplementary Note 2. Figure 1b shows another complex event in this sample made simple in Ribbon: the translocation of a 4.4 kb sequence deleted from chr19 and inserted into chr16 (Figure 1b). Thus, Ribbon enables understanding of complex variants, and it may also help in the detection of sequencing and sample preparation issues, testing of aligners and variant-callers, and rapid curation of structural variant candidates (Supplementary Note 3). In addition to SAM and BAM files with long, short, or paired-end reads, Ribbon can also load coordinate files from whole genome aligners such as MUMmer. Therefore, Ribbon can be used to test assembly algorithms or inspect the similarity between species. Supplementary Note 4 shows a comparison of gorilla and human genomes using Ribbon, highlighting major structural differences. In conclusion, Ribbon is a powerful interactive web tool for viewing complex genomic alignments.</span></p>
<p>Script at&nbsp;https://github.com/MariaNattestad/ribbon</p><p>Address of the bookmark: <a href="http://genomeribbon.com/" rel="nofollow">http://genomeribbon.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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