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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34569?offset=560</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29272/decipher</guid>
	<pubDate>Fri, 30 Sep 2016 09:33:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29272/decipher</link>
	<title><![CDATA[DECIPHER]]></title>
	<description><![CDATA[<p>DECIPHER is a software toolset that can be used to maintain, analyze, and decipher large amounts of DNA sequence data. To install DECIPHER, see the <a href="http://DECIPHER.cee.wisc.edu/Download.html">Downloads</a> page.<br><br> To begin using DECIPHER read the "Getting Started DECIPHERing" tutorial. Refer to the PDF documents below for instructions on how to use DECIPHER for various tasks.</p><p>Address of the bookmark: <a href="http://decipher.cee.wisc.edu/Documentation.html" rel="nofollow">http://decipher.cee.wisc.edu/Documentation.html</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29284/genebreak-a-tool-to-systematically-identify-genes-recurrently-affected-by-the-genomic-location-of-chromosomal-cna-associated-breaks-by-a-genome-wide-approach</guid>
	<pubDate>Sat, 01 Oct 2016 15:15:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29284/genebreak-a-tool-to-systematically-identify-genes-recurrently-affected-by-the-genomic-location-of-chromosomal-cna-associated-breaks-by-a-genome-wide-approach</link>
	<title><![CDATA[GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach]]></title>
	<description><![CDATA[<p>Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).</p>
<p> </p><p>Address of the bookmark: <a href="http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html" rel="nofollow">http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30111/eager</guid>
	<pubDate>Sat, 10 Dec 2016 18:07:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30111/eager</link>
	<title><![CDATA[EAGER]]></title>
	<description><![CDATA[<p><span>The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.</span></p>
<p><span>EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z</p><p>Address of the bookmark: <a href="https://github.com/apeltzer/EAGER-GUI" rel="nofollow">https://github.com/apeltzer/EAGER-GUI</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30234/last</guid>
	<pubDate>Mon, 19 Dec 2016 14:07:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30234/last</link>
	<title><![CDATA[LAST]]></title>
	<description><![CDATA[<p>LAST can:</p>
<ul>
<li>Handle&nbsp;<strong>big</strong>&nbsp;sequence data, e.g:
<ul>
<li>Compare two vertebrate genomes</li>
<li>Align billions of DNA reads to a genome</li>
</ul>
</li>
<li>Indicate the&nbsp;<a href="http://lastweb.cbrc.jp/about.html">reliability</a>&nbsp;of each aligned column.</li>
<li>Use sequence quality data&nbsp;<a href="http://nar.oxfordjournals.org/content/38/7/e100.abstract">properly</a>.</li>
<li>Compare DNA to proteins, with frameshifts.</li>
<li>Compare PSSMs to sequences</li>
<li>Calculate the likelihood of chance similarities between random sequences.</li>
<li>Do split and spliced alignment.</li>
<li><a href="http://last.cbrc.jp/doc/last-train.html">Train</a>&nbsp;alignment parameters for unusual kinds of sequence (e.g. nanopore).</li>
</ul><p>Address of the bookmark: <a href="http://last.cbrc.jp/" rel="nofollow">http://last.cbrc.jp/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30550/genomering-alignment-visualization-based-on-supergenome-coordinates</guid>
	<pubDate>Wed, 18 Jan 2017 10:24:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30550/genomering-alignment-visualization-based-on-supergenome-coordinates</link>
	<title><![CDATA[GenomeRing: alignment visualization based on SuperGenome coordinates]]></title>
	<description><![CDATA[<p>The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.</p>
<p>More at https://academic.oup.com/bioinformatics/article/28/12/i7/268598/GenomeRing-alignment-visualization-based-on</p><p>Address of the bookmark: <a href="http://it.informatik.uni-tuebingen.de/?page_id=185" rel="nofollow">http://it.informatik.uni-tuebingen.de/?page_id=185</a></p>]]></description>
	<dc:creator>Neel</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/32849/car-reconstructing-contiguous-regions-of-an-ancestral-genome</guid>
	<pubDate>Thu, 18 May 2017 05:24:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32849/car-reconstructing-contiguous-regions-of-an-ancestral-genome</link>
	<title><![CDATA[CAR: Reconstructing Contiguous Regions of an Ancestral Genome]]></title>
	<description><![CDATA[<div id="abstract-1">
<p id="p-5">We describe a new method for predicting the ancestral order and orientation of those intervals from their observed adjacencies in modern species. We combine the results from this method with data from chromosome painting experiments to produce a map of an early mammalian genome that accounts for 96.8% of the available human genome sequence data. The precision is further increased by mapping inversions as small as 31 bp. Analysis of the predicted evolutionary breakpoints in the human lineage confirms certain published observations but disagrees with others. Although only a few mammalian genomes are currently sequenced to high precision, our theoretical analyses and computer simulations indicate that our results are reasonably accurate and that they will become highly accurate in the foreseeable future. Our methods were developed as part of a project to reconstruct the genome sequence of the last ancestor of human, dogs, and most other placental mammals;</p>
</div><p>Address of the bookmark: <a href="http://www.bx.psu.edu/miller_lab/car/" rel="nofollow">http://www.bx.psu.edu/miller_lab/car/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/33973/list-of-genome-announcement-notes-and-reporting-journals</guid>
	<pubDate>Wed, 26 Jul 2017 08:01:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/33973/list-of-genome-announcement-notes-and-reporting-journals</link>
	<title><![CDATA[List of genome announcement, notes and reporting journals]]></title>
	<description><![CDATA[<p><span>Faced with an increasing number of articles describing DNA data and a need for more appropriate venues to present these data, some publishers and journals have responded by changing the structure and format of genome papers. Specifically, certain journals have started accepting very short manuscripts (500&ndash;1500 words) that present a new chromosome sequence, its GenBank accession number and little else. These pint-sized articles go by various names, such as genome reports, genome announcements, genome notes or genome letters</span><span>, but will be referred to here broadly as genome reports. Their short length and minimal number (or complete absence) of figures, tables and article subheadings are a significant departure from long-form genome papers, which typically span 8&ndash;10 journal pages, contain many supporting items and have formal introduction, methods, results and discussion sections.</span></p><p>Following are the list of journals publishing&nbsp;<span>pint-sized articles go by various names, such as genome reports, genome announcements, genome notes or genome letters</span><span>, but will be referred to here broadly as genome reports.</span></p><p>1. <strong>Genome Announcements</strong>, American Society for Microbiology, Genome announcement, Impact factor 1.3, &nbsp;A 500-word report stating that the genome of a particular organism (prokaryote, eukaryote or virus) has been sequenced and providing a citable record of the corresponding GenBank submission. Must include abstract but no text headings can be used except for &lsquo;Acknowledgments&rsquo; and &lsquo;References&rsquo;. Cannot include figures, tables or supplemental material to present data or analysis.</p><p>Link: https://mra.asm.org/</p><p>2. <strong>Genome Biology and Evolution</strong>, Oxford University Press, Genome report, Impact factor 4.2, Focused 1500-word papers (up to six tables or figures) that publish the main evolutionary message of new genome sequences as they become submitted to GenBank. May also contain specifically focused comparative analyses of previously published genomes that contain a substantial and novel insight of broadest evolutionary significance.</p><p>Link: https://academic.oup.com/gbe</p><p>3. <strong>Journal of Biotechnology</strong>, Elsevier, Genome announcement, Impact factor 2.9, A 500-word report announcing the availability of the completely annotated genome sequence of a biotechnologically relevant organism in the corresponding database (for eukaryotes, advanced draft genomes will also be considered). Articles can contain an Abstract, a brief report on the organism and its biotechnological relevance, a table summarizing the genome features, References and an Acknowledgement. Figures are generally not allowed.</p><p>Link: https://www.journals.elsevier.com/journal-of-biotechnology</p><p>4. <strong>Journal of Genomics</strong>, Ivyspring, Genome note, Impact factor N/A, A 1000-word report (10 reference limit; conclusions not permitted) describing novel data sets from high-throughput analysis of genotypes, phenotypes, gene expression, metabolomes, proteomes or genome assemblies.Standard metrics for data quality and the experimental design must be clearly reported.</p><p>Link: http://www.jgenomics.com/</p><p>5. <strong>Mem&oacute;rias do Instituto</strong>, Oswaldo Cruz Oswaldo Cruz Foundation, Genome announcement and highlight, Impact factor 1.6, Dedicated to publishing new genome information from eukaryote parasites, virus, bacteria and their respective vectors, as well as re-sequencing or comparative genome analyses. Should occupy no more than three printed pages including figures and/or tables.</p><p>Link: http://memorias.ioc.fiocruz.br/</p><p>6. <strong>Molecular Ecology Resources,</strong> Wiley, Genomic resources note, &nbsp;Impact factor 3.7, Short notes on newly assembled and annotated transcriptomes, genome fractions or whole genomes, and/or a library of SNP/SSR markers.Authors submit a short manuscript describing how the resource was developed and where the data can be accessed. Do not appear in journal as individual papers but are instead published as part of a summary article.</p><p>Link: https://onlinelibrary.wiley.com/journal/17550998</p><p>7. <strong>Standards in Genomic Science</strong>, BioMed Central (Springer), Short genome report, Impact factor 3.2,&nbsp;<span>Short (&sim;500-word) article on newly sequenced genome. Article format must follow guidelines and template (available from journal Web site) put forward by the SGS. Any manuscripts not using template or that are missing key figures, tables and/or references (as per the guidelines) will be returned to authors. Rationale of the content model is to provide information that is consistently and uniformly presented for rapid and easy consumption by both human and machine readers.&nbsp;</span></p><p><span>Link: https://standardsingenomics.biomedcentral.com/</span></p><p><span>8. <strong>3biotech</strong>, Springer,&nbsp;<span>Short genome report, Impact factor 1.3,&nbsp;</span><span>Short (&sim;500-word) article on newly sequenced genome. Article format must follow guidelines (available from journal Web site).&nbsp;<span>&nbsp;Genome of a particular organism (prokaryote, eukaryote or virus) has been sequenced and providing a citable record of the corresponding GenBank submission.</span></span></span></p><p><span><span><span>Link: https://link.springer.com/journal/13205</span></span></span></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/35125/eugene-v-koonin-lab</guid>
  <pubDate>Tue, 09 Jan 2018 05:01:15 -0600</pubDate>
  <link></link>
  <title><![CDATA[Eugene V. Koonin Lab]]></title>
  <description><![CDATA[
<p>Interested in understanding the evolution of life. To obtain glimpses of such understanding, we employ existing and new methods of computational biology to perform research in several major areas.</p>

<p>https://www.ncbi.nlm.nih.gov/research/groups/koonin/</p>
]]></description>
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