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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31018?offset=50</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29274/strudel</guid>
	<pubDate>Fri, 30 Sep 2016 09:47:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29274/strudel</link>
	<title><![CDATA[Strudel]]></title>
	<description><![CDATA[<p>Strudel is our graphical tool for visualizing genetic and physical maps of genomes for comparative purposes. The application aims to let the user examine their data at a variety of different levels of resolution, from entire maps to individual markers, and explore syntenic relationships between genomes. All browsing and interaction with Strudel happens in real-time &ndash; there is no need to wait while the maps are generated. It is built using Java 1.6 and ships with its own JRE, so there is no need for users to install or update Java.</p><p>Address of the bookmark: <a href="https://ics.hutton.ac.uk/strudel/" rel="nofollow">https://ics.hutton.ac.uk/strudel/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29384/phymmbl</guid>
	<pubDate>Mon, 10 Oct 2016 08:56:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29384/phymmbl</link>
	<title><![CDATA[PHYMMBL]]></title>
	<description><![CDATA[<p><span>Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies.&nbsp;</span><strong>Phymm</strong><span>, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods.&nbsp;</span><strong>PhymmBL</strong><span>&nbsp;(rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/BLAST">BLAST</a><span>, produces even higher accuracy.</span></p><p>Address of the bookmark: <a href="http://www.cbcb.umd.edu/software/phymm/" rel="nofollow">http://www.cbcb.umd.edu/software/phymm/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</guid>
	<pubDate>Fri, 04 Nov 2016 05:02:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</link>
	<title><![CDATA[HybPiper]]></title>
	<description><![CDATA[<p>HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.</p>
<p>Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.</p>
<p>HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information,&nbsp;<a href="https://github.com/mossmatters/HybPiper/wiki/">please see our wiki</a>.</p>
<p>HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.</p><p>Address of the bookmark: <a href="https://github.com/mossmatters/HybPiper" rel="nofollow">https://github.com/mossmatters/HybPiper</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30130/scaffmatch</guid>
	<pubDate>Tue, 13 Dec 2016 10:23:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30130/scaffmatch</link>
	<title><![CDATA[ScaffMatch]]></title>
	<description><![CDATA[<p>caffMatch is a novel scaffolding tool based on Maximum-Weight Matching able to produce high-quality scaffolds from NGS data (reads and contigs). The tool is written in Python 2.7. It also includes a bash script wrapper that calls aligner in case one needs to first map reads to contigs (instead of providing .sam files).</p>
<p>The arguments accepted by ScaffMatch are:</p>
<p>&nbsp; -w) Working directory -- this is the directory where ScaffMatch files are stored. These are .sam files produced after mapping reads to contigs and the resulting scaffolds file `scaffolds.fa` fasta file;</p>
<p>&nbsp; -c) Contig fasta file;</p>
<p>&nbsp; -m) Command line argument with no options. It is used when .sam files are used instead of reads .fastq files. Do not use this option if you provide reads files;</p>
<p>&nbsp; -1) (Comma separated list of) either .fastq or .sam file(s) corresponding to the first read of the read pair;</p>
<p>&nbsp; -2) (Comma separated list of) either .fastq or .sam file(s) corresponding to the second read of the read pair;</p>
<p>&nbsp; -i) (Comma separated list of) insert size(s) of the library(-ies);</p>
<p>&nbsp; -s) (Comma separated list of) library(-ies) standard deviation(s) of insert size(s);</p>
<p>&nbsp; -t) Bundle threshold. Pairs of contigs supported by number of read pairs less than the value of this argument are discarded. Optional argument, by default it is equal to 5;</p>
<p>&nbsp; -g) Matching heuristics: use `max_weight` for Maximum Weight Matching heuristics with the Insertion step, use `backbone` for Maximum Weight Matching heuristics without the Insertion step, use `greedy` for Greedy Matching heuristics;</p>
<p>&nbsp; -l) Log file - where to store the logs. Optional argument. By default, stdout is used.</p><p>Address of the bookmark: <a href="http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch" rel="nofollow">http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30304/mcscan</guid>
	<pubDate>Thu, 22 Dec 2016 03:53:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30304/mcscan</link>
	<title><![CDATA[MCscan]]></title>
	<description><![CDATA[<p><span>MCscan is a computer program that can simultaneously scan multiple genomes to identify homologous chromosomal regions and subsequently align these regions using genes as anchors. This is the toolset for generating the synteny correspondences in&nbsp;</span><a href="http://chibba.agtec.uga.edu/duplication">Plant Genome Duplication Database</a><span>. It is intended as an easy-to-use and quick way to identify conserved gene arrays both within the same genome and across different genomes.</span></p>
<p><span>More at&nbsp;http://chibba.agtec.uga.edu/duplication/mcscan/</span></p><p>Address of the bookmark: <a href="http://chibba.agtec.uga.edu/duplication/mcscan/" rel="nofollow">http://chibba.agtec.uga.edu/duplication/mcscan/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27333/satsuma-highly-sensitive-whole-genome-synteny-alignments</guid>
	<pubDate>Fri, 13 May 2016 05:25:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27333/satsuma-highly-sensitive-whole-genome-synteny-alignments</link>
	<title><![CDATA[SATSUMA : Highly sensitive whole-genome synteny alignments.]]></title>
	<description><![CDATA[<p>Satsuma is a whole-genome synteny alignment program. It takes two genomes, computes alignments, and then keeps only the parts that are orthologous, i.e. following the conserved order and orientation of features, such as protein coding genes, non-coding genes, or neutral sequences. Satsuma does not require any pre-processing, such as repeat masking, since it will automatically detect ambiguous mappings.<br> <br> Satsuma has parallelization built-in and is designed to run on multi-core architectures. The run-time for aligning two bird-size genomes (~1.2 Gb) is around two days on 24 CPUs. <br> <br> You can find the manual <a href="http://satsuma.sourceforge.net/manual.html">here</a>.<br> Download the latest source code from <a href="https://sourceforge.net/projects/satsuma/">here.</a><br> Stable versions can also be downloaded from the <a href="https://www.broadinstitute.org/science/programs/genome-biology/spines">Broad Institute's</a> web site.<br> <br> An incomplete list of questions and answers (yes, these have really been asked by our users! Please feel free to add your own by e-mailing us) is <a href="http://satsuma.sourceforge.net/faq.html">here</a>.<br> <br> If you use Satsuma in your research, please cite:<br> <a href="http://bioinformatics.oxfordjournals.org/content/26/9/1145.long">Grabherr, M. G., Russell, P., Meyer, M., Mauceli, E., Alf&ouml;ldi, J., Di Palma, F., &amp; Lindblad-Toh, K. (2010). Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics, 26(9), 1145-51</a>.</p>
<p><strong>Tutorial at http://evomics.org/learning/genomics/satsuma/</strong></p><p>Address of the bookmark: <a href="http://satsuma.sourceforge.net/" rel="nofollow">http://satsuma.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30555/yaha</guid>
	<pubDate>Fri, 20 Jan 2017 05:38:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30555/yaha</link>
	<title><![CDATA[YAHA]]></title>
	<description><![CDATA[<p>YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints.</p>
<p><strong>Availability:</strong> YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from <a href="http://faculty.virginia.edu/irahall/YAHA" target="pmc_ext">http://faculty.virginia.edu/irahall/YAHA</a>.</p>
<p><strong>Contact:</strong></p>
<p>http://genome.wustl.edu/people/groups/detail/hall-lab/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30976/brig</guid>
	<pubDate>Thu, 16 Feb 2017 13:14:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30976/brig</link>
	<title><![CDATA[BRIG]]></title>
	<description><![CDATA[<p>BRIG is a free cross-platform (Windows/Mac/Unix) application that can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data. The application is available at:<a href="http://sourceforge.net/projects/brig">http://sourceforge.net/projects/brig</a></p>
<p>If you have any questions or comments, post them on&nbsp;<a href="http://sourceforge.net/tracker/?group_id=328245">one of the trackers</a>&nbsp;on BRIG&rsquo;s SourceForge page:<a href="http://sourceforge.net/tracker/?group_id=328245">http://sourceforge.net/tracker/?group_id=328245</a>.</p>
<p>Features:</p>
<ul>
<li>Images show similarity between a central reference sequence and other sequences as concentric rings.</li>
<li>BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.</li>
<li>Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.</li>
<li>Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.</li>
<li>BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously</li>
</ul>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://brig.sourceforge.net/" rel="nofollow">http://brig.sourceforge.net/</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/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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