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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27440?offset=70</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30085/fqtools</guid>
	<pubDate>Thu, 08 Dec 2016 09:31:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30085/fqtools</link>
	<title><![CDATA[fqtools]]></title>
	<description><![CDATA[<p><code>fqtools</code><span>&nbsp;is a software suite for fast processing of&nbsp;</span><code>FASTQ</code><span>&nbsp;files. Various file manipulations are supported. See below for a full list of the subcommands available and a brief description of their purpose. Most of the individual subcommands will take either a single file or a pair of files as input. If no input file is specified, fqtools will attempt to read data from&nbsp;</span><code>stdin</code><span>. In this case, it is advisabe to specify the format of the data provided. For subcommands that generate FASTQ data, either a single file or a pair of files will be generated. If no&nbsp;</span><code>-o</code><span>&nbsp;argument is provided, single files will be writted to&nbsp;</span><code>stdout</code><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/alastair-droop/fqtools" rel="nofollow">https://github.com/alastair-droop/fqtools</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/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/31105/understanding-pacbio</guid>
	<pubDate>Fri, 24 Feb 2017 10:17:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</link>
	<title><![CDATA[Understanding PacBio]]></title>
	<description><![CDATA[<p>This tutorial includes resources for learning more about PacBio data and bioinformatics analysis, and includes content suitable for both beginners and experts. Below are links to training modules (webinars and PowerPoint presentations) to help you get started with your data processing, as well as information for specialized applications.</p>
<p>Training Resources:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Workshop">Bioinformatics Workshop (Webinars)</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Training-Slides">Bioinformatics Training Slides</a></li>
</ul>
<p>Specialized Applications:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/De-Novo-Assembly">De Novo Assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Transcriptome analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Base-modification-analysis">Base Modification Analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Data-Analysis-Tools">Data Analysis Tools</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Minor-Variants-and-Phasing-Analysis">Minor Variants and Phasing Analysis</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki" rel="nofollow">https://github.com/PacificBiosciences/Bioinformatics-Training/wiki</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</guid>
	<pubDate>Fri, 17 Feb 2017 08:51:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</link>
	<title><![CDATA[sockeye]]></title>
	<description><![CDATA[<p>This sockeye&nbsp;software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.</p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</guid>
	<pubDate>Fri, 03 Mar 2017 10:14:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</link>
	<title><![CDATA[Multi-metagenome assembly]]></title>
	<description><![CDATA[<p>This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:</p>
<p>Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes<br><br>Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, K&aring;re L. Nielsen and Per .H. Nielsen</p>
<p>Nature Biotechnology 2013, doi:&nbsp;<a href="http://www.nature.com/nbt/journal/vaop/ncurrent/abs/nbt.2579.html">10.1038/nbt.2579</a></p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/multi-metagenome" rel="nofollow">https://github.com/MadsAlbertsen/multi-metagenome</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</guid>
	<pubDate>Mon, 06 Mar 2017 04:08:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</link>
	<title><![CDATA[CONCOCT: Clustering cONtigs with COverage and ComposiTion]]></title>
	<description><![CDATA[<p>A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.</p>
<p>Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:<a href="https://github.com/BinPro/CONCOCT/issues">https://github.com/BinPro/CONCOCT/issues</a></p><p>Address of the bookmark: <a href="https://github.com/BinPro/CONCOCT" rel="nofollow">https://github.com/BinPro/CONCOCT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31564/htslib</guid>
	<pubDate>Wed, 15 Mar 2017 11:38:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31564/htslib</link>
	<title><![CDATA[HTSlib]]></title>
	<description><![CDATA[<p>Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:</p>
<dl><dt>Samtools</dt><dd>Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format</dd><dt>BCFtools</dt><dd>Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants</dd><dt>HTSlib</dt><dd>A C library for reading/writing high-throughput sequencing data</dd></dl>
<p>Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.</p><p>Address of the bookmark: <a href="http://www.htslib.org/" rel="nofollow">http://www.htslib.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/2042/ngs-course-medical-genomics-scheduled-for-17-20-september-2013-in-uz-leuven-belgium</guid>
	<pubDate>Mon, 12 Aug 2013 12:08:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/2042/ngs-course-medical-genomics-scheduled-for-17-20-september-2013-in-uz-leuven-belgium</link>
	<title><![CDATA[NGS course Medical Genomics, scheduled for 17-20 September 2013 in UZ Leuven (Belgium).]]></title>
	<description><![CDATA[<p>This course is open to all students and postdocs and registration for all academic participants is free of charge. To help us in organizing the course, please register online via http://gc.uzleuven.be where the preliminary program is also available.</p><p>This course is organized with support from the IAP &ldquo;Belgian Medical Genomics Initiative&rdquo;, SymBioSys and the Genomics Core.</p><p>For inquiries, please email Ms Narcisse Opdekamp ( narcisse.opdekamp@uzleuven.be ).</p><p>More at &gt;&gt;&nbsp;<a href="http://gc.uzleuven.be/">http://gc.uzleuven.be/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

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