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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19631?offset=130</link>
	<atom:link href="https://bioinformaticsonline.com/related/19631?offset=130" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29912/maq-mapping-and-assembly-with-quality</guid>
	<pubDate>Tue, 22 Nov 2016 04:51:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29912/maq-mapping-and-assembly-with-quality</link>
	<title><![CDATA[Maq: Mapping and Assembly with Quality]]></title>
	<description><![CDATA[<p><strong>Maq</strong>&nbsp;stands for&nbsp;<em>Mapping and Assembly with Quality</em>&nbsp;It builds assembly by mapping short reads to reference sequences. Maq is a project hosted by&nbsp;<a href="http://sourceforge.net/">SourceForge.net</a>. The project page is available at<a href="http://sourceforge.net/projects/maq/">http://sourceforge.net/projects/maq/</a>. Maq is previously known as mapass2.</p>
<h2>Run Maq Now</h2>
<p>Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.</p>
<ol>
<li>Prepare a reference sequence (ref.fasta). Better a bacterial genome.</li>
<li>Download maq, maq-data and maqview at the&nbsp;<a href="http://sourceforge.net/project/showfiles.php?group_id=191815">download page</a>.</li>
<li>Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.</li>
<li>Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:
<pre>maq.pl demo ref.fasta calib-30.dat
</pre>
where&nbsp;<em>calib-30.dat</em>&nbsp;is contained in maq-data.</li>
<li>View the alignment:
<pre>cd maqdemo/easyrun;
maqindex -i -c consensus.cns all.map;
maqview -c consensus.cns all.map</pre>
</li>
</ol>
<p><strong>Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.</strong></p><p>Address of the bookmark: <a href="http://maq.sourceforge.net" rel="nofollow">http://maq.sourceforge.net</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30203/e-rga-enhanced-reference-guided-assembly-of-complex-genomes</guid>
	<pubDate>Mon, 19 Dec 2016 05:56:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30203/e-rga-enhanced-reference-guided-assembly-of-complex-genomes</link>
	<title><![CDATA[e-RGA: enhanced Reference Guided Assembly of Complex Genomes]]></title>
	<description><![CDATA[<p><span>Next Generation Sequencing has totally changed genomics: we are able to produce huge amounts of data at an incredibly low cost compared to Sanger sequencing. Despite this, some old problems have become even more difficult, de novo assembly being on top of this list. Despite efforts to design tools able to assemble, de novo, an organism sequenced with short reads, the results are still far from those achievable with long reads. In this paper, we propose a novel method that aims to improve de novo assembly in the presence of a closely related reference. The idea is to combine de novo and reference-guided assembly in order to obtain enhanced results.</span></p><p>Address of the bookmark: <a href="http://journal.embnet.org/index.php/embnetjournal/article/view/208" rel="nofollow">http://journal.embnet.org/index.php/embnetjournal/article/view/208</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/30104/structural-variation-the-hidden-genomic-treasure</guid>
	<pubDate>Sat, 10 Dec 2016 16:19:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/30104/structural-variation-the-hidden-genomic-treasure</link>
	<title><![CDATA[Structural variation: the hidden genomic treasure]]></title>
	<description><![CDATA[<p>Genome re-sequencing projects have revealed substantial amounts of genetic variation between individuals extending beyond single nucleotide polymorphisms (SNPs) and short indels. Structural Variations (SVs) and Copy Number Variations (CNVs) are a major source of genomic variation. However, compared to SNPs, accurate detection, genotyping and understanding of CNVs is lagging behind due to much greater analytical challenges related to SV/CNV detection and analysis. In our lab we analyse SVs/CNVs using high-throughput sequencing and different analytical approaches.&nbsp;The most‐studied structural variants are copy number variations (CNVs) which can be generated by several different mechanisms including non‐allelic homologous recombination, non‐homologous end‐joining and deoxyribonucleic acid (DNA) replication‐related fork stalling and template switching. CNVs are closely related to segmental duplications (SDs): SDs can stimulate the formation of CNVs and themselves started out as CNVs, but became fixed in a species. Structural variation can be neutral but has also influenced our phenotypic evolution, for example our susceptibility to disease and our ability to digest certain types of food. Our understanding of the extent of structural variation is increasing rapidly, but it will be much more difficult to understand its phenotypic consequences.&nbsp;</p><p><img src="http://www.nature.com/nmeth/journal/v9/n2/images/nmeth.1858-F3.jpg" alt="image" width="946" height="603" style="border: 0px; border: 0px;"></p><p>Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation.&nbsp;SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.</p><p><strong>Structure variation sequencing signature (Source: NatRev Genetics)</strong></p><p><img src="http://www.nature.com/nrg/journal/v12/n5/images/nrg2958-f2.jpg" alt="image" width="800" height="824" style="border: 0px; border: 0px;"></p><p>Related tools, databases and publications are listed below. If you know any interesing papers, please let us know in comment section:</p><p><br /><strong>Key concepts</strong></p><p>Structural variation includes balanced variants such as inversions and translocations, and unbalanced ones such as duplications and deletions (copy number variations or CNVs).</p><p>Structural variants can arise by several mechanisms, including nonallelic homologous recombination (NAHR), nonhomologous end‐joining (NHEJ) and DNA replication‐based fork stalling and template switching (FoSTeS).</p><p>CNV is closely linked to segmental duplication, but is not exactly the same. Segmental duplications can stimulate CNV formation by NAHR, and themselves arise from CNVs that have become fixed.</p><p>Segmental duplications did not appear uniformly during the evolution of the Great Ape species, but rather during a burst of activity around the time of the divergence of gorilla from the human/chimpanzee ancestor.</p><p>Duplicated genes play a critical role in the evolution of a genome as they act as &lsquo;spare parts&rsquo; than can evolve to perform new or more specialized functions.</p><p>Effects of structural variation on gene expression can be identified but only a few examples of the consequences for species biology have been documented.</p><p><strong style="font-size: 12.8px;">Tools</strong></p><p><a href="http://sv.gersteinlab.org/cnvnator">CNVnator</a>a tool for CNV discovery and genotyping from depth of read mapping.<a href="http://www.ncbi.nlm.nih.gov/pubmed/21293372">2011a</a>,<a href="http://www.ncbi.nlm.nih.gov/pubmed/21324876">2011b</a></p><p><a href="http://sv.gersteinlab.org/age">AGE</a>a tools that implements an algorithm for optimal alignment of sequences with SVs.<a href="http://www.ncbi.nlm.nih.gov/pubmed/21233167">2011</a></p><p><a href="http://sv.gersteinlab.org/breakseq">BreakSeq</a>a pipeline for annotation, classification and analysis of SVs at single nucleotide resolution.<a href="http://www.ncbi.nlm.nih.gov/pubmed/20037582">2010</a></p><p><a href="http://sv.gersteinlab.org/pemer">PEMer</a>a computational and simulation framework for discovering SVs by paired-end read mapping.<a href="http://www.ncbi.nlm.nih.gov/pubmed/19236709">2009</a>,<a href="http://www.ncbi.nlm.nih.gov/pubmed/17901297">2007</a></p><p>GASV https://code.google.com/archive/p/gasv/</p><p>PAIROSCOPE http://pairoscope.sourceforge.net/</p><p>SVDetect&nbsp;http://svdetect.sourceforge.net/Site/Home.html</p><p>BreakPtr, discovery of unbalanced structural variants (copy-number variants) with tiling microarrays&nbsp;<a href="http://tiling.mbb.yale.edu/BreakPtr/" target="_top">Link</a>&nbsp;</p><p>R Package&nbsp;https://www.bioconductor.org/help/course-materials/2010/EMBL2010/Practical-4-StructuralVariants.pdf<br /><br />BreakSeq, structural variant genotyping using split reads&nbsp;<a href="http://sv.gersteinlab.org/breakseq/" target="_top">Link</a>&nbsp;<br /><br />CopySeq, genotyping of unbalanced structural variants (copy-number variants) using read-depth&nbsp;<a href="http://www.korbel.embl.de/CopySeq/" target="_top">Link</a>&nbsp;<br /><br />DELLY2, integrated structural variant discovery, genotyping and visualization in deep sequencing data&nbsp;<a href="https://github.com/dellytools/delly" target="_top">Link</a>&nbsp;<br /><br />PEMer, structural variant discovery in 454 sequencing data by paired-end mapping&nbsp;<a href="http://www.korbel.embl.de/PEMer/" target="_top">Link</a>&nbsp;<br /><br />TIGER, transduction inference in germline genomes using short read data&nbsp;<a href="https://github.com/jelena-tica/TIGER" target="_top">Link</a>&nbsp;</p><p>MANTA&nbsp;https://github.com/Illumina/manta</p><p>SV-Bay&nbsp;https://github.com/InstitutCurie/SV-Bay</p><p>BreakDancer&nbsp;http://breakdancer.sourceforge.net/</p><p>Variation Hunter&nbsp;http://compbio.cs.sfu.ca/software-variation-hunter</p><p>Lumpy&nbsp;https://github.com/arq5x/lumpy-sv</p><p>ForestSV&nbsp;http://sebatlab.ucsd.edu/index.php/software-data&nbsp;</p><p>PBSuites for long reads&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong>Visualization</strong></p><p>The SV visualization tool:&nbsp;<a href="http://genomesavant.com/savant/">http://genomesavant.com/savant/</a></p><p>InGAP-SV (<a href="http://ingap.sourceforge.net/">http://ingap.sourceforge.net/</a>) that is nice tools for both detection and visualisation of severals kind of structural variations (Large insertions, translocation, deletion, inversions....)&nbsp;</p><p>Tools table: http://www.nature.com/nbt/journal/v29/n8/fig_tab/nbt.1904_T2.html</p><p>Variation Viewer https://www.ncbi.nlm.nih.gov/variation/view/</p><p><strong style="font-size: 12.8px;">Papers</strong></p><p>http://www.nature.com/nmeth/journal/v9/n2/full/nmeth.1858.html</p><p>http://journal.frontiersin.org/researchtopic/1412/structural-variations-in-genomes-ecological-and-evolutionary-implications</p><p>http://www.mi.fu-berlin.de/wiki/pub/ABI/GenomicsLecture10Materials/structural-variation.pdf</p><p>http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1479-3</p><p>https://www.ncbi.nlm.nih.gov/dbvar/content/overview/</p><p>http://www.nature.com/subjects/structural-variation</p><p>https://eichlerlab.gs.washington.edu/news/NatMeth_Feb2012.pdf</p><p>https://www.ncbi.nlm.nih.gov/pubmed/19477992 ***</p><p>https://www.ncbi.nlm.nih.gov/pubmed/22452995</p><p>http://biorxiv.org/content/early/2016/09/06/073833</p><p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479793/</p><p>http://www.nature.com/articles/srep18501</p><p>http://www.genetics.org/content/202/1/351</p><p>http://www.cs.cmu.edu/~sssykim/teaching/s13/slides/Lecture_SVI.pdf</p><p>https://www.omicsonline.org/open-access/structural-variation-detection-from-next-generation-sequencing-2469-9853-S1-007.php?aid=69055</p><p>http://schatzlab.cshl.edu/presentations/2016/2016.01.12.PAG.Structural%20Variations.pdf</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</guid>
	<pubDate>Mon, 19 Dec 2016 09:42:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</link>
	<title><![CDATA[MeGAMerge: A tool to merge assembled contigs, long reads from metagenomic sequencing runs]]></title>
	<description><![CDATA[<p>MeGAMerge</p>
<p>MeGAMerge (A tool to merge assembled contigs, long reads from metagenomic sequencing runs)</p>
<p>Description</p>
<p>MeGAMerge is a perl based wrapper/tool that can accept any number of sequence (FASTA) files containing assembled contigs of any length in Multi-FASTA format to produce an improved contig set based on OLC based assembly. All overlap parameters (Minimum Overlap Length, Identity, etc) are user-declarable at runtime. It is written to run on Linux.</p>
<p>Requirements:</p>
<p>You will need to have the following tools installed and in $PATH, or added to $binpath in the tool:</p>
<p>Newbler (specifically runAssembly)<br>Minimus2 (part of AMOS, also requires MUMmer)</p><p>Address of the bookmark: <a href="https://github.com/LANL-Bioinformatics/MeGAMerge" rel="nofollow">https://github.com/LANL-Bioinformatics/MeGAMerge</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/30364/bioinformatics-contest-2017</guid>
	<pubDate>Fri, 23 Dec 2016 14:03:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/30364/bioinformatics-contest-2017</link>
	<title><![CDATA[Bioinformatics Contest 2017!]]></title>
	<description><![CDATA[<p><a href="http://contest.bioinf.me" target="_blank">Bioinformatics Contest 2017</a>! Rosalind is co-organizer.<br /> Compete with thousands of people worldwide on bioinformatics problem solving.<br /> Everything is online. Qualification round starts on <strong>January 23, 2017</strong>. Final is on <span><span>Feb 18</span></span>.</p><p>You will need to solve bioinformatics problems using programming. The goal is to correctly solve as many problems as possible within 24 hours. Some of them will be approximation problems and will have partial grades. All rounds will be held online, submissions will be auto-graded in real time.</p><p>Check more at http://contest.bioinf.me/</p><p>Good luck!</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30538/gkno</guid>
	<pubDate>Tue, 17 Jan 2017 03:35:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30538/gkno</link>
	<title><![CDATA[GKNO]]></title>
	<description><![CDATA[<p><span>gkno opens the world of complex bioinformatic analysis to people of all level of computational expertise. This site contains documentation, tutorials and information on all the tools that comprise gkno.</span></p>
<p><span>More at&nbsp;http://gkno.me/</span></p><p>Address of the bookmark: <a href="http://gkno.me/" rel="nofollow">http://gkno.me/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31018/j-circos</guid>
	<pubDate>Fri, 17 Feb 2017 09:06:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31018/j-circos</link>
	<title><![CDATA[J-Circos]]></title>
	<description><![CDATA[<p>Circos plot tool (J-Circos) that is an interactive visualization tool that can plot Circos figures, as well as being able to dynamically add data to the figure, and providing information for specific data points using mouse hover display and zoom in/out functions. J-Circos uses the Java computer language to enable it to be used on most operating systems (Windows, MacOS, Linux). Users can input data into J-Circos using flat data formats, as well as from the GUI. J-Circos will enable biologists to better study more complex chromosomal interactions and fusion transcripts that are otherwise difficult to visualize from next-generation sequencing data.</p><p>Address of the bookmark: <a href="http://www.australianprostatecentre.org/research/software/jcircos" rel="nofollow">http://www.australianprostatecentre.org/research/software/jcircos</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau</guid>
	<pubDate>Mon, 30 Sep 2013 11:34:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau</link>
	<title><![CDATA[Bioinformatics Algorithms (Part 1)  with Pavel  Pevzner, Phillip E. C. Compeau,]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/t5t_nfzdzEg" frameborder="0" allowfullscreen></iframe><p>The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.</p>]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools</guid>
	<pubDate>Wed, 15 Mar 2017 14:19:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31568/pacbio-long-reads-compatible-software-and-tools</link>
	<title><![CDATA[Pacbio Long Reads Compatible Software and Tools]]></title>
	<description><![CDATA[<p>The following software packages are known to be compatible with PacBio&reg; data, in addition to PacBio's own SMRT&reg; Analysis suite. All packages are believed to be open source or freely available for non-commercial use. See the individual project sites for up-to-date license information. A separate page lists&nbsp;<a href="http://pacb.com/community/partner_program/current_partners/">commercial software</a>.</p>
<p>Know of any other open source software for PacBio data?&nbsp;<a href="mailto:devnet@pacificbiosciences.com">Email us</a>.</p>
<p>Software categories:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#denovo">De novo assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#svdetection">Structural Variations Detection</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#aligners">Reference-based alignment</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#variants">Consensus and variant calling</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#RNA">RNA analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#basemods">Epigenetic base modifications and methylation</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#browsers">Genome Browsers</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#qc">Run QC</a></li>
<li><a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software#frameworks">Frameworks and APIs</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software" rel="nofollow">https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/32713/salzberg-lab</guid>
  <pubDate>Mon, 15 May 2017 05:14:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[Salzberg lab]]></title>
  <description><![CDATA[
<p>We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.</p>

<p>https://salzberg-lab.org/</p>
]]></description>
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