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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/11399?offset=220</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</guid>
	<pubDate>Mon, 29 Aug 2016 11:44:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</link>
	<title><![CDATA[Ka, Ks and Ka/Ks calculations]]></title>
	<description><![CDATA[<p>gKaKs is a codon-based genome-level Ka/Ks computation pipeline developed and based on programs from four widely used packages: BLAT, BLASTALL (including bl2seq, formatdb and fastacmd), PAML (including codeml and yn00) and KaKs_Calculator (including 10 substitution rate estimation methods). gKaKs can automatically detect and eliminate frameshift mutations and premature stop codons to compute the substitution rates (Ka, Ks and Ka/Ks) between a well-annotated genome and a non-annotated genome or even a poorly assembled scaffold dataset. It is especially useful for newly sequenced genomes that have not been well annotated.&nbsp;</p>
<p>Look for KaKs calculation:</p>
<p>https://github.com/fumba/kaks-calculator</p>
<p>http://longlab.uchicago.edu/?q=gKaKs</p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/23314322</p><p>Address of the bookmark: <a href="http://longlab.uchicago.edu/?q=gKaKs" rel="nofollow">http://longlab.uchicago.edu/?q=gKaKs</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes</guid>
	<pubDate>Thu, 01 Sep 2016 08:02:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes</link>
	<title><![CDATA[BRAKER: pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes]]></title>
	<description><![CDATA[<p><span>Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction.</span></p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/26559507</p><p>Address of the bookmark: <a href="http://bioinf.uni-greifswald.de/bioinf/braker/" rel="nofollow">http://bioinf.uni-greifswald.de/bioinf/braker/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29112/sybil</guid>
	<pubDate>Wed, 07 Sep 2016 03:20:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29112/sybil</link>
	<title><![CDATA[Sybil]]></title>
	<description><![CDATA[<p><span>The Sybil software package provides a primarily web-based front-end to comparative genome datasets warehoused in a chado relational database. It was developed by the bioinformatics department at The Institute for Genomic Research (</span><a href="http://www.tigr.org/">TIGR</a><span>) and development continues at the J. Craig Venter Institute (</span><a href="http://jcvi.org/">JCVI</a><span>) and the Institute for Genome Sciences (</span><a href="http://igs.umaryland.edu/">IGS</a><span>) at the University of Maryland: Baltimore. Sybil has been used at TIGR/JCVI, IGS, NYU, New York Medical College, Novartis Vaccines and University of Maryland: College Park to support a number of research projects that involve comparative genome analysis. The following sections provide some high-level technical details about the overall architecture and external dependencies of the Sybil package.</span></p><p>Address of the bookmark: <a href="http://sybil.sourceforge.net/" rel="nofollow">http://sybil.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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/29379/bbmap-help</guid>
	<pubDate>Mon, 10 Oct 2016 06:29:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29379/bbmap-help</link>
	<title><![CDATA[BBMap help]]></title>
	<description><![CDATA[<div>
<div>BBMAP <span> &bull; <span>a solution for everything</span></span><a href="https://www.biostarhandbook.com/"><span></span></a></div>
<div>That content has been reformatted and it is being expanded to include more information.<span><span></span></span></div>
</div>
<hr>
<p>There are common options for most BBMap suite programs and depending on the file extension the input/output format is automatically chosen/set.</p>
<hr>
<h3>Using BBMap</h3>
<h4>Mapping Nanopore reads</h4>
<p>BBMap.sh has a length cap of 6kbp. Reads longer than this will be broken into 6kbp pieces and mapped independently.</p>
<p>More at https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html</p><p>Address of the bookmark: <a href="https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html" rel="nofollow">https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/29479/how-to-install-perl-modules-on-mac-os-x-in-easy-steps</guid>
	<pubDate>Thu, 20 Oct 2016 07:26:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/29479/how-to-install-perl-modules-on-mac-os-x-in-easy-steps</link>
	<title><![CDATA[How to install Perl modules on Mac OS X in easy steps !!]]></title>
	<description><![CDATA[<p>Today at work, I learned how to install Perl modules using&nbsp;<a href="http://en.wikipedia.org/wiki/CPAN">CPAN</a>. It&rsquo;s a lot easier than I thought.</p><p>You see, for the past couple of years, I&rsquo;ve been a bit frustrated because OS X does not come with a whole lot of Perl modules pre-installed, and for all I googled, I couldn&rsquo;t find an &ldquo;idiot&rsquo;s&rdquo; guide for moderately-savvy-but-not-expert users like myself to install modules and dependencies on demand.</p><p>The only instructions I could find point to&nbsp;<a href="http://fink.sourceforge.net/">Fink</a>, which basically installs modules in a path that isn&rsquo;t included in the Perl @INC variable, meaning you have to manually specify the full path to the modules in every script &mdash; which is not a lot of fun if you&rsquo;re developing on OS X and deploying on Red Hat, for instance.</p><p>Moreover, Fink doesn&rsquo;t seem to make every module available, and it&rsquo;s not very easy to determine which Fink package you need to install if you need a particular module.</p><p>So, with a script that called on several apparently unavailable modules, and a deadline looming, I finally decided to suck it up and figure out how to use CPAN to install them:</p><h4>1) Make sure you have the Apple Developer Tools (XCode) installed.</h4><p>These are on one of your install discs, or available as a huge but free download from the&nbsp;<a href="https://developer.apple.com/xcode/">Apple Developer Connection</a>&nbsp;[free registration required] or the Mac App Store. I thought I had them, but apparently when we upgraded that computer to Tiger, they went missing.</p><p>If you don&rsquo;t have this stuff installed, your installation will fail with errors about unavailable commands.</p><h4>1.5) Install Command Line Tools (Recent XCode versions only)</h4><p>(Thank you to Tom Marchioro for informing me about this step.)</p><p>Older versions of XCode installed the command line tools (which are required to properly install CPAN modules) by default, but apparently newer ones do not. To check whether you have the command line tools already installed, run the following from the Terminal:</p><p><code>$ which make</code></p><p>This command checks the system for the &ldquo;<code>make</code>&rdquo; tool. If it spits out something like&nbsp;<code>/usr/bin/make</code>&nbsp;you&rsquo;re golden and can skip ahead to Step 2. If you just get a new prompt and no output, you&rsquo;ll need to install the tools:</p><ol>
<li>Launch XCode and bring up the Preferences panel.</li>
<li>Click on the Downloads tab</li>
<li>Click to install the Command Line Tools</li>
</ol><p>If you like, you can run&nbsp;<code>which make</code>&nbsp;again to confirm that everything&rsquo;s installed correctly.</p><h4>2) Configure CPAN.</h4><p><code>$ sudo perl -MCPAN -e shell</code></p><p><code>perl&gt; o conf init</code></p><p>This will prompt you for some settings. You can accept the defaults for almost everything (just hit &ldquo;return&rdquo;). The two things you must fill in are the path to&nbsp;<code>make</code>&nbsp;(which should be&nbsp;<code>/usr/bin/make</code>&nbsp;or the value returned when you run&nbsp;<code>which make</code>&nbsp;from the command line) and your choice of CPAN mirrors (which you actually choose don&rsquo;t really matter, but it won&rsquo;t let you finish until you select at least one). If you use a proxy or a very restrictive firewall, you may have to configure those settings as well.</p><p>If you skip Step 2, you may get errors about&nbsp;<code>make</code>&nbsp;being unavailable.</p><h4>3) Upgrade CPAN</h4><p><code>$ sudo perl -MCPAN -e 'install Bundle::CPAN'</code></p><p>Don&rsquo;t forget the&nbsp;<code>sudo</code>, or it&rsquo;ll fail with permissions errors, probably when doing something relatively unimportant like installing&nbsp;<code>man</code>&nbsp;files.</p><p>This will spend a long time downloading, testing, and compiling various files and dependencies. Bear with it. It will prompt you a few times about dependencies. You probably want to enter &ldquo;yes&rdquo;. I agreed to everything it asked me, and everything turned out fine. YMMV of course. If everything installs properly, it&rsquo;ll give you an &ldquo;OK&rdquo; at the end.</p><h4>4) Install your modules. For each module&hellip;.</h4><p><code>$ sudo perl -MCPAN -e 'install Bundle::Name'</code></p><p>or</p><p><code>$ sudo perl -MCPAN -e 'install Module::Name'</code></p><p>This will install the module&nbsp;<em>and</em>&nbsp;its dependencies. Nice, eh? Again, don&rsquo;t forget the&nbsp;<code>sudo</code>.</p><p>The first time you run this after upgrading CPAN, it may prompt you to configure again (see Step 2). If you accept its offer to try to configure itself automatically, it may just run through everything without a problem.</p><p>There are a couple of potential pitfalls with specific modules (such as the<code>LWP::UserAgent</code>&nbsp;/&nbsp;<code>HEAD</code>&nbsp;issue), but most have workarounds, and I haven&rsquo;t run into anything that wasn&rsquo;t easily recoverable.</p><p>And that&rsquo;s it!</p><p>Did you find this useful? Is there anything I missed?</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29574/beagle</guid>
	<pubDate>Thu, 27 Oct 2016 11:19:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29574/beagle</link>
	<title><![CDATA[Beagle]]></title>
	<description><![CDATA[<p>Beagle is a software package that performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection.</p>
<p>Beagle version 4.1 has a more accurate genotype phasing algorithm and a very fast and accurate genotype imputation algorithm. Version 4.1 also has several changes to the command line arguments which are described in the&nbsp;<a href="http://faculty.washington.edu/browning/beagle/release_notes" target="_blank">release notes</a>. The "ped" argument has no effect in version 4.1. If your data contains nuclear families and you want to model the parent-offspring relationships when phasing genotypes, please use&nbsp;<a href="https://faculty.washington.edu/browning/beagle/b4_0.html">version 4.0</a>.</p>
<p>If you use Beagle 4.1 in a published analysis, please report the program version and cite the appropriate article.</p>
<p>The citation for Beagle's phasing algorithm is:</p>
<p>S R Browning and B L Browning (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084-1097.<a href="http://dx.doi.org/doi:10.1086/521987" target="_blank">doi:10.1086/521987</a></p>
<p>The citation for Beagle's genotype imputation algorithm is:</p>
<p>B L Browning and S R Browning (2016). Genotype imputation with millions of reference samples. Am J Hum Genet 98:116-126.<a href="http://dx.doi.org/doi:10.1016/j.ajhg.2015.11.020" target="_blank">doi:10.1016/j.ajhg.2015.11.020</a></p>
<p>The citation for Beagle's IBD detection algorithm is:</p>
<p>B L Browning and S R Browning (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194(2):459-71.<a href="http://dx.doi.org/doi:10.1534/genetics.113.150029" target="_blank">doi:10.1534/genetics.113.150029</a></p><p>Address of the bookmark: <a href="http://faculty.washington.edu/browning/beagle/beagle.html" rel="nofollow">http://faculty.washington.edu/browning/beagle/beagle.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29586/eforgev12</guid>
	<pubDate>Fri, 28 Oct 2016 09:06:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29586/eforgev12</link>
	<title><![CDATA[eFORGE.v1.2]]></title>
	<description><![CDATA[<p><span>The eFORGE tool provides a method to view the tissue specific regulatory component of a set of EWAS DMPs. eFORGE analysis takes a set of DMPs, such as those hits above genome-wide significance threshold in an EWAS study, and analyses whether there is enrichment for overlap of putative functional elements compared to matched background DMPs. It assesses enrichment on a per cell type basis, since functional elements are differentially active in different cell types, and hence can expose tissue-specific signals of enrichment for the given test DMP set. This can reveal the sites of action underlying the EWAS signal, and provide confirmation of the validity of the EWAS where a tissue-specific mechanism is known or expected for the phenotype. Conversely unknown tissue involvements can also be revealed.</span></p><p>Address of the bookmark: <a href="http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation" rel="nofollow">http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</guid>
	<pubDate>Fri, 04 Nov 2016 05:02:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</link>
	<title><![CDATA[HybPiper]]></title>
	<description><![CDATA[<p>HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.</p>
<p>Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.</p>
<p>HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information,&nbsp;<a href="https://github.com/mossmatters/HybPiper/wiki/">please see our wiki</a>.</p>
<p>HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.</p><p>Address of the bookmark: <a href="https://github.com/mossmatters/HybPiper" rel="nofollow">https://github.com/mossmatters/HybPiper</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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>
</item>

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