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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41485?offset=10</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29280/nemo-%E2%80%93-a-stochastic-individual-base-genetically-explicit-simulation-platform</guid>
	<pubDate>Sat, 01 Oct 2016 14:45:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29280/nemo-%E2%80%93-a-stochastic-individual-base-genetically-explicit-simulation-platform</link>
	<title><![CDATA[Nemo – A stochastic, individual-base, genetically explicit simulation platform]]></title>
	<description><![CDATA[<ul>
<li>
<p>A&nbsp;<strong>recombination map</strong>&nbsp;has been added for all multi-locus traits. The map positions (chromosomal) for neutral markers (e.g. SNPs) and loci under selection (QTLs, deleterious mutations, DMIs) can now be specified explicitly, or set at random. The map can hold an unlimited number of loci of different types jointly, at any recombination scale (cM or lower). The effects of linkage can thus be finely explored.</p>
</li>
<li>
<p>A new trait coding for (Bateson-)<strong>Dobzhansky-Muller incompatibility loci</strong>. Multiple haploid or diploid pairs of incompatible loci can be spread throughout the genome and affect individual fitness.</p>
</li>
<li>
<p><strong>Multi-type selection</strong>:&nbsp;<a href="http://nemo2.sourceforge.net/classIndividual.html" title="This class contains traits along with other individual information (sex, pedigree, etc. ).">Individual</a>&nbsp;fitness can be jointly determined by different types of loci under selectinon, such as QTLs coding for quantitative traits under spatially variable selection, universally deleterious mutations, and Dobzhansky-Muller incompatibility loci.</p>
</li>
<li>
<p><strong>An unlimited number of quantitative traits</strong>&nbsp;under different forms of selection can be modelled, based on universally pleiotropic loci with several bi- or multi-allelic models.</p>
</li>
<li>
<p><strong>Spatial and temporal variation of selection</strong>&nbsp;on quantitative traits is possible, modelling shifts of environmental conditions over time.</p>
</li>
<li>
<p>The dispersal matrix describing the movement of individuals among sub-populations can be replaced by a connectivity matrix and a reduced dispersal matrix describing migration only among the connected sub-populations. This offers a substantial gain in computing time and system memory when simulating very large grids.</p>
</li>
<li>
<p>Input parameters' arguments may be specified in separate files. This is particularly convenient when specifying large matrices.</p>
</li>
<li>
<p>Many adjustments have been made for refined control of the input of parameters and data output. See updates in the manual.</p>
</li>
</ul><p>Address of the bookmark: <a href="http://nemo2.sourceforge.net/index.html" rel="nofollow">http://nemo2.sourceforge.net/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29679/comparative-genomics-educational-material-and-papers-bookmarks</guid>
	<pubDate>Wed, 09 Nov 2016 16:23:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29679/comparative-genomics-educational-material-and-papers-bookmarks</link>
	<title><![CDATA[Comparative genomics educational material and papers bookmarks]]></title>
	<description><![CDATA[<p><span>Alignment of the porcine genome against seven other mammalian genomes (</span><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#supplementary-information">Supplementary Information</a><span>) identified homologous synteny blocks (HSBs). Using porcine HSBs and stringent filtering criteria, 192 pig-specific evolutionary breakpoint regions (EBRs) were located. The number of porcine EBRs </span><span>is comparable to the number of bovine-lineage-specific EBRs (100) reported earlier using a slightly lower resolution (500</span><span><span>&thinsp;</span></span><span>kilobases (kb)), indicating that both lineages evolved with an average rate of ~2.1 large-scale rearrangements per million years after the divergence from a common cetartiodactyl ancestor ~60</span><span><span>&thinsp;</span></span><span>Myr ago</span><sup><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#ref2" title="Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg extinction on mammal diversification. Science 334, 521-524 (2011)">2</a></sup><span>. This rate compares to ~1.9 rearrangements per million years within the primate lineage (</span><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#supplementary-information">Supplementary Table 11</a><span>). A total of 20 and 18 cetartiodactyl EBRs (shared by pigs and cattle) were detected using the pig and human genomes as a reference, respectively.</span></p><p>Address of the bookmark: <a href="http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html" rel="nofollow">http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30236/pyscaf</guid>
	<pubDate>Mon, 19 Dec 2016 14:20:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30236/pyscaf</link>
	<title><![CDATA[pyScaf]]></title>
	<description><![CDATA[<p>pyScaf orders contigs from genome assemblies utilising several types of information:</p>
<ul>
<li>paired-end (PE) and/or mate-pair libraries (<a href="https://github.com/lpryszcz/pyScaf#ngs-based-scaffolding">NGS-based mode</a>)</li>
<li>long reads (<a href="https://github.com/lpryszcz/pyScaf#scaffolding-based-on-long-reads">NGS-based mode</a>)</li>
<li>synteny to the genome of some related species (<a href="https://github.com/lpryszcz/pyScaf#reference-based-scaffolding">reference-based mode</a>)</li>
</ul>
<p>Scaffolding&nbsp;</p>
<p>In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.</p>
<p>Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:</p>
<ul>
<li>matches not satisfying cut-offs (<code>--identity</code>&nbsp;and&nbsp;<code>--overlap</code>)</li>
<li>suboptimal matches (only best match of each query to reference is kept)</li>
<li>and removing overlapping matches on reference.</li>
</ul>
<p>In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on&nbsp;<em>C. parapsilosis</em>&nbsp;(13 Mb; CANPA) and&nbsp;<em>A. thaliana</em>&nbsp;(119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (<a href="https://www.dropbox.com/sh/bb7lwggo40xrwtc/AAAZ7pByVQQQ-WhUXZVeJaZVa/pyScaf?dl=0">Figures in dropbox</a>,&nbsp;<a href="https://docs.google.com/spreadsheets/d/1InBExy-qKDLj-upd8tlPItVSKc4mLepZjZxB31ii9OY/edit#gid=2036953672">CANPA table</a>,&nbsp;<a href="https://docs.google.com/spreadsheets/d/1InBExy-qKDLj-upd8tlPItVSKc4mLepZjZxB31ii9OY/edit#gid=1920757821">ARATH table</a>).<br>Runs took ~0.5 min for CANPA on&nbsp;<code>4 CPUs</code>&nbsp;and ~2 min for ARATH on&nbsp;<code>16 CPUs</code>.</p>
<p><span>Important remarks:</span></p>
<ul>
<li>Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.</li>
<li>pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no&nbsp;<em>de novo assembler</em>&nbsp;/ scaffolder is perfect...), which breaks synteny.</li>
<li>pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.</li>
<li>pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations.&nbsp;<span>Note however, this is experimental implementation!</span></li>
<li>Consider closing gaps after scaffolding.</li>
</ul><p>Address of the bookmark: <a href="https://github.com/lpryszcz/pyScaf" rel="nofollow">https://github.com/lpryszcz/pyScaf</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
</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/bookmarks/view/38634/eyechrom-visualizing-chromosome-count-data-from-plants</guid>
	<pubDate>Tue, 08 Jan 2019 10:20:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38634/eyechrom-visualizing-chromosome-count-data-from-plants</link>
	<title><![CDATA[EyeChrom: Visualizing Chromosome Count Data From Plants]]></title>
	<description><![CDATA[<p><span>It's goal is to show chromosmal data per genus. Select the genus, and the plot will show the records found for it in the Chromosome Counts Database. note: Report an issue via Gihub: github.com/roszenil/CCDBcurator and github.com/RodrigoRivero/EyeChrom</span></p>
<p>https://bsapubs.onlinelibrary.wiley.com/doi/pdf/10.1002/aps3.1207</p><p>Address of the bookmark: <a href="http://eyechrom.com:3838/EyeChrom/" rel="nofollow">http://eyechrom.com:3838/EyeChrom/</a></p>]]></description>
	<dc:creator>Jit</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/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</guid>
	<pubDate>Mon, 28 Feb 2022 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</link>
	<title><![CDATA[Genomicus: genome browser that enables users to navigate in genomes in several dimensions]]></title>
	<description><![CDATA[<p>Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.</p>
<p>Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.</p>
<p>All the data on extant species displayed in this browser are from&nbsp;<a href="http://www.ensembl.org/">Ensembl</a>.</p>
<p><br><strong>Summary statistics of Genomicus version 105.01:</strong><span>&nbsp;(view species tree in&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.pdf">pdf</a><span>&nbsp;or&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.nwk">newick</a><span>)</span><br><br></p>
<table id="introstats">
<tbody>
<tr><th>Number of extant species</th>
<td>200</td>
</tr>
<tr><th>Number of extant genes</th>
<td>4303993</td>
</tr>
<tr><th>&nbsp;</th></tr>
<tr><th>Number of ancestral species</th>
<td>196</td>
</tr>
<tr><th>Number of ancestral genes</th>
<td>4624213</td>
</tr>
<tr><th>Number of ancestral synteny blocks</th>
<td>83342<br><br></td>
</tr>
</tbody>
</table><p>Address of the bookmark: <a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl" rel="nofollow">https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/39217/caulobacter-ethensis-20-computer-generated-genome-of-a-living-organism</guid>
	<pubDate>Wed, 03 Apr 2019 08:45:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/39217/caulobacter-ethensis-20-computer-generated-genome-of-a-living-organism</link>
	<title><![CDATA[Caulobacter ethensis - 2.0 : Computer-generated Genome of a Living Organism]]></title>
	<description><![CDATA[<div><span>All the genome sequences of organisms known throughout the world are stored in a database belonging to the National Center for Biotechnology Information in the United States. As of today, the database has an additional entry:&nbsp;<em><strong><span>Caulobacter ethensis</span></strong></em><span><strong><span>-2.0</span></strong>.&nbsp;</span></span></div><div><span><span>&nbsp;</span></span></div><div><span><span>It is the&nbsp;<strong>world's first fully computer-generated genome of a living organism</strong>, developed by scientists at ETH Zurich.&nbsp;</span></span></div><div><span><span>&nbsp;</span></span></div><div><span><span>However, it must be emphasised that although the genome for&nbsp;</span><em>C. ethensis</em>-2.0 was physically produced in the form of a very large DNA molecule, a corresponding organism does not yet exist.</span></div><div><span>&nbsp;</span></div><div><span><strong>Source</strong>:&nbsp;<a href="https://www.sciencedaily.com/releases/2019/04/190401171343.htm?utm_source=feedburner&amp;utm_medium=email&amp;utm_campaign=Feed%3A+sciencedaily%2Fmost_popular+%28Most+Popular+News+--+ScienceDaily%29">https://www.sciencedaily.com/releases/2019/04/190401171343.htm</a></span></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</guid>
	<pubDate>Mon, 11 Jun 2018 09:44:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</link>
	<title><![CDATA[HiGlass: a tool for exploring genomic contact matrices and tracks.]]></title>
	<description><![CDATA[HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices. To load private data, HiGlass can be run locally within a Docker container. The HiC data in the examples below is from Rao et al. (2014)

http://higlass.io/<p>Address of the bookmark: <a href="http://higlass.io/" rel="nofollow">http://higlass.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</guid>
	<pubDate>Thu, 20 Dec 2018 12:03:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</link>
	<title><![CDATA[ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data]]></title>
	<description><![CDATA[<p><span>The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps:&nbsp;</span><em>prune</em><span>,&nbsp;</span><em>partition</em><span>,&nbsp;</span><em>rescue</em><span>,&nbsp;</span><em>optimize</em><span>&nbsp;and&nbsp;</span><em>build</em><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/tangerzhang/ALLHiC/wiki" rel="nofollow">https://github.com/tangerzhang/ALLHiC/wiki</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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