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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27076?offset=290</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</guid>
	<pubDate>Sun, 05 Jan 2020 04:08:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</link>
	<title><![CDATA[NextDenovo: string graph-based de novo assembler for TGS long reads]]></title>
	<description><![CDATA[<p>NextDenovo is a string graph-based<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use<span>&nbsp;</span><a href="https://github.com/Nextomics/NextPolish">NextPolish</a>.</p>
<p>NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of<span>&nbsp;</span><a href="https://github.com/lh3/minimap2">minimap2</a><span>&nbsp;</span>for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see<span>&nbsp;</span><a href="https://github.com/Nextomics/NextDenovo/blob/master/doc/UTILITY.md">here</a><span>&nbsp;</span>for more details).</p><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextDenovo" rel="nofollow">https://github.com/Nextomics/NextDenovo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</guid>
	<pubDate>Fri, 28 Oct 2016 07:59:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</link>
	<title><![CDATA[Graph Genome Suite]]></title>
	<description><![CDATA[<p><span>Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the&nbsp;</span><strong>Graph Genome Suite</strong><span>&nbsp;&mdash; the most advanced population genomics tools in the world.</span></p><p>Address of the bookmark: <a href="https://www.sbgenomics.com/graph/" rel="nofollow">https://www.sbgenomics.com/graph/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29603/statistical-for-biological-research</guid>
	<pubDate>Thu, 03 Nov 2016 04:59:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29603/statistical-for-biological-research</link>
	<title><![CDATA[Statistical for biological research]]></title>
	<description><![CDATA[<p>There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.</p>
<p>This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.</p>
<p><em>Nature Methods</em>' <strong><a href="http://www.nature.com/collections/qghhqm/pointsofsignificance">Points of Significance</a></strong> column on statistics explains many key statistical and experimental design concepts. <strong><a href="http://www.nature.com/collections/qghhqm/resources">Other resources</a></strong> include an online plotting tool and links to statistics guides from other publishers.</p><p>Address of the bookmark: <a href="http://www.nature.com/collections/qghhqm" rel="nofollow">http://www.nature.com/collections/qghhqm</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29628/links</guid>
	<pubDate>Fri, 04 Nov 2016 06:19:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29628/links</link>
	<title><![CDATA[LINKS]]></title>
	<description><![CDATA[<p>LINKS is a genomics application for scaffolding genome assemblies with long reads, such as those produced by Oxford Nanopore Technologies Ltd. It can be used to scaffold high-quality draft genome assemblies with any long sequences (eg. ONT reads, PacBio reads, another draft genomes, etc)</p>
<p>Paper at&nbsp;https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0076-3</p><p>Address of the bookmark: <a href="https://github.com/warrenlr/LINKS/" rel="nofollow">https://github.com/warrenlr/LINKS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29915/professor-all-levels-in-bioinformatics-and-computational-biology</guid>
  <pubDate>Tue, 22 Nov 2016 05:43:38 -0600</pubDate>
  <link></link>
  <title><![CDATA[Professor (all levels) in Bioinformatics and Computational Biology]]></title>
  <description><![CDATA[
<p>King Abdullah University of Science and Technology (KAUST) (kaust.edu.sa) is seeking a highly motivated and skilled faculty member for the Bioinformatics track whose research focuses on development of methods and tools for Bioinformatics and Computational Biology.<br />KAUST is an international, graduate-level research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located on the shores of the Red Sea in Saudi Arabia, KAUST offers superb research facilities, generous assured research funding, and internationally competitive salaries, attracting top international faculty, scientists, engineers, and students to conduct fundamental and goal-oriented research to address the world’s pressing scientific and technological challenges in the areas of food, water, energy, and the environment.<br />The successful applicant is expected to develop world-leading research in domain of bioinformatics/computational biology with focus on development of novel computational approaches for efficient and accurate methods of analyzing biological phenomena at molecular level. The faculty member will be part of the Computational Bioscience Research Center (CBRC) within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. The position will remain open until filled.<br /> <br />Requirements:<br /> <br />PhD or equivalent in a Computer Science, Mathematics or Engineering discipline. Candidates should be well-established within the research field relevant to the position grade. They should demonstrate original research and experience at the highest international level.<br /> <br />Responsibilities and tasks:<br /> <br />Research competence in the following areas is preferred:<br />Analysis of next generation sequencing (NGS) and other ‘omics’ data (e.g. CAGE, ChIP-Seq, DHS, RNA-Seq, Ribo-Seq, proteomic, metabolic and NMR spectra, etc.).<br />Signaling, regulatory and metabolic pathways analysis.<br />Development of tools (web-based and standalone) suited for efficient computational biology/bioinformatics.<br /> <br /> <br />Visit cemse.kaust.edu.sa to apply.</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30002/excavator2tool</guid>
	<pubDate>Wed, 30 Nov 2016 04:09:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30002/excavator2tool</link>
	<title><![CDATA[EXCAVATOR2tool]]></title>
	<description><![CDATA[<p><span>EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.</span><br><span>EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it&rsquo;s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/excavator2tool/" rel="nofollow">https://sourceforge.net/projects/excavator2tool/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</guid>
	<pubDate>Tue, 29 Jul 2014 09:11:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</link>
	<title><![CDATA[The Genome 10K Project]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/B57xDIGtCT0" frameborder="0" allowfullscreen></iframe>https://genome10k.soe.ucsc.edu

The Genome 10K project aims to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species, approximately one for every vertebrate genus. The trajectory of cost reduction in DNA sequencing suggests that this project will be feasible within a few years. Capturing the genetic diversity of vertebrate species would create an unprecedented resource for the life sciences and for worldwide conservation efforts.

The growing Genome 10K Community of Scientists (G10KCOS), made up of leading scientists representing major zoos, museums, research centers, and universities around the world, is dedicated to coordinating efforts in tissue specimen collection that will lay the groundwork for a large-scale sequencing and analysis project.]]></description>
	
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30147/cisa-contig-integrator-for-sequence-assembly</guid>
	<pubDate>Thu, 15 Dec 2016 05:42:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30147/cisa-contig-integrator-for-sequence-assembly</link>
	<title><![CDATA[CISA: Contig Integrator for Sequence Assembly]]></title>
	<description><![CDATA[<p>A plethora of algorithmic assemblers have been proposed for the <em>de novo</em> assembly of genomes, however, no individual assembler guarantees the optimal assembly for diverse species. Optimizing various parameters in an assembler is often performed in order to generate the most optimal assembly. However, few efforts have been pursued to take advantage of multiple assemblies to yield an assembly of high accuracy. In this study, we employ various state-of-the-art assemblers to generate different sets of contigs for bacterial genomes. A tool, named CISA, has been developed to integrate the assemblies into a hybrid set of contigs, resulting in assemblies of superior contiguity and accuracy, compared with the assemblies generated by the state-of-the-art assemblers and the hybrid assemblies merged by existing tools. This tool is implemented in Python and requires MUMmer and BLAST+ to be installed on the local machine. The source code of CISA and examples of its use are available at <a href="http://sb.nhri.org.tw/CISA/">http://sb.nhri.org.tw/CISA/</a>.</p><p>Address of the bookmark: <a href="http://sb.nhri.org.tw/CISA/en/CISA" rel="nofollow">http://sb.nhri.org.tw/CISA/en/CISA</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<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>
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