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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36880?offset=1240</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/22388/perl-one-liner-basics</guid>
	<pubDate>Sun, 24 May 2015 09:28:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/22388/perl-one-liner-basics</link>
	<title><![CDATA[Perl One liner basics !!]]></title>
	<description><![CDATA[<p>Perl has a ton of command line switches (see perldoc perlrun), but I'm just going to cover the ones you'll commonly need to debug code. The most important switch is -e, for execute (or maybe "engage" :) ). The -e switch takes a quoted string of Perl code and executes it. For example:<br /><br />$ perl -e 'print "Hello, World!\n"'<br />Hello, World!<br /><br />It's important that you use single-quotes to quote the code for -e. This usually means you can't use single-quotes within the one liner code. If you're using Windows cmd.exe or PowerShell, you must use double-quotes instead.<br /><br />I'm always forgetting what Perl's predefined special variables do, and often test them at the command line with a one liner to see what they contain. For instance do you remember what $^O is?<br /><br />$ perl -e 'print "$^O\n"'<br />linux<br /><br />It's the operating system name. With that cleared up, let's see what else we can do. If you're using a relatively new Perl (5.10.0 or higher) you can use the -E switch instead of -e. This turns on some of Perl's newer features, like say, which prints a string and appends a newline to it. This saves typing and makes the code cleaner:<br /><br />$ perl -E 'say "$^O"'<br />linux<br /><br />Pretty handy! say is a nifty feature that you'll use again and again.</p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</guid>
	<pubDate>Fri, 02 Feb 2018 03:24:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</link>
	<title><![CDATA[karyoploteR: plot whole genomes with arbitrary data]]></title>
	<description><![CDATA[<p><span><a href="http://bioconductor.org/packages/karyoploteR">karyoploteR</a></span><span>&nbsp;is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.</span></p><p>Address of the bookmark: <a href="https://bernatgel.github.io/karyoploter_tutorial/" rel="nofollow">https://bernatgel.github.io/karyoploter_tutorial/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</guid>
	<pubDate>Tue, 23 May 2017 05:20:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</link>
	<title><![CDATA[GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.]]></title>
	<description><![CDATA[<p><span>GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexeyG/GRASS" rel="nofollow">https://github.com/AlexeyG/GRASS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22416/rosenberg-lab</guid>
  <pubDate>Wed, 27 May 2015 17:52:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Rosenberg lab]]></title>
  <description><![CDATA[
<p>Research. Research in the lab focuses on mathematical, statistical, and computational problems in evolutionary biology and human genetics. Long-term interests of the lab include topics such as:</p>

<p>    Human genetic variation<br />    Inference of human evolutionary history from genetic markers<br />    Statistical analysis of population-genetic data<br />    Mathematical models of gene genealogies<br />    Theoretical population genetics<br />    Combinatorics of evolutionary trees<br />    The relationship between gene trees and species trees<br />    The role of human evolutionary genetics in the search for genes that contribute to disease-susceptibility <br />More at https://web.stanford.edu/group/rosenberglab/index.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22437/jrf-bioinformatics-icar-national-research-centre-for-orchids-pakyong</guid>
  <pubDate>Thu, 28 May 2015 19:33:19 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics @ ICAR - National Research Centre for Orchids  Pakyong]]></title>
  <description><![CDATA[
<p>ICAR - National Research Centre for Orchids</p>

<p>Pakyong</p>

<p>F.No:NRCO/Admn/DBT /136 /</p>

<p>Walk-in-Interviews will be held at 737106, Sikkim for the post of 01 (One Project ‘DBT’s Twinning programme for the NE’ titled “Assessment of some fragrant orchids of north-east India for sustainable improvement of community livelihood”, indicated below. The appointment will be on contractual basis and the incumbents shall not have any regular appointment in ICAR.</p>

<p>‘DBT’s Twinning programme for the NE’ titled “Assessment of chemical and genetic divergence of some fragrant orchids of north-east India for sustainable improvement of community livelihood”</p>

<p>Junior Research Fellow (One post)</p>

<p>Essential Qualification : a. MSc (with NET qualification) / M.Tech degree (with or without NET) with minimum 55% marks in Biotechnology/ Bioinformatics/ Molecular Biology or any other related field.</p>

<p>Desirable Qualification: Computer Skills (Linux, Perl, Java, MySQL) with experience in advanced molecular Biology techniques</p>

<p>2nd June 2015</p>

<p>Advertisement: www.nrcorchids.nic.in/Employments/Vacancy%20-%20JRF.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</guid>
	<pubDate>Fri, 01 Jun 2018 07:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</link>
	<title><![CDATA[Ranbow: a haplotype assembler for polyploid genomes]]></title>
	<description><![CDATA[Ranbow is a haplotype assembler for polyploid genomes. It has been developed for the haplotype assembly of the hexaploid sweet potato genome, which is highly heterozygous. Ranbow can also be applied to other polyploid genomes. After a first phasing, Ranbow utilizes the assembled haplotypes to improve the accuracy of variant calling results and to infer the evolutionary history of the organism´s genome. Ranbow has three main modes of function:

ranbow hap: for haplotyping
ranbow eval: for evaluating of the assemble haplotypes by gold standard (long) reads 
ranbow phylo: for the phylogenetic analysis<p>Address of the bookmark: <a href="https://www.molgen.mpg.de/ranbow" rel="nofollow">https://www.molgen.mpg.de/ranbow</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/22570/frequent-words-problem-solution-by-perl</guid>
	<pubDate>Tue, 09 Jun 2015 23:38:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/22570/frequent-words-problem-solution-by-perl</link>
	<title><![CDATA[Frequent words problem solution by Perl]]></title>
	<description><![CDATA[<div><p>Solved with perl <a href="http://rosalind.info/problems/1a/">http://rosalind.info/problems/1a/</a></p><p>#Find the most frequent k-mers in a string.<br />#Given: A DNA string Text and an integer k.<br />#Return: All most frequent k-mers in Text (in any order).<br /><br />use strict;<br />use warnings;<br /><br />my $string="ACGTTGCATGTCGCATGATGCATGAGAGCT";<br />my $kmer=4; <br />my %myHash;<br />my $max=0;<br /><br />for (my $aa=0; $aa&lt;=(length($string)-4); $aa++) {<br />&nbsp;&nbsp; &nbsp;my $myStr=substr&nbsp; $string, $aa,$kmer;<br />&nbsp;&nbsp; &nbsp;#print "$myStr\n";<br />&nbsp;&nbsp; &nbsp;my $km=kmerMatch ($string, $myStr, $kmer);<br />&nbsp;&nbsp; &nbsp;if ($km &gt; $max) { $max = $km;}<br />&nbsp;&nbsp; &nbsp;#print "$km\t$myStr\n";<br />&nbsp;&nbsp; &nbsp;$myHash{$myStr}=$km;<br />&nbsp;&nbsp; &nbsp;<br />}<br /><br />#Print all key which have matching values<br />foreach my $name (keys %myHash){<br />&nbsp;&nbsp;&nbsp; print "$name " if $myHash{$name} == $max;<br />}<br /><br />sub kmerMatch { #Check the exact matching kmers with sliding window<br />my ($string, $myStr, $kmer)=@_;<br />my $count=0;<br />for (my $aa=0; $aa&lt;=(length($string)-4); $aa++) {<br />&nbsp;&nbsp; &nbsp;my $myWin=substr&nbsp; $string, $aa,$kmer;<br />&nbsp;&nbsp; &nbsp;if ($myWin eq $myStr) {<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;#print "$myWin eq $myStr\n";<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;$count++;<br />&nbsp;&nbsp; &nbsp;}<br />}<br />return $count;<br />}</p></div>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/37236/installing-salmon-for-trinity</guid>
	<pubDate>Tue, 03 Jul 2018 09:02:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/37236/installing-salmon-for-trinity</link>
	<title><![CDATA[Installing Salmon for Trinity !]]></title>
	<description><![CDATA[
<p>➜  trinityrnaseq-Trinity-v2.6.6 git:(master) ✗ conda install salmon<br />Solving environment: done</p>

<p>## Package Plan ##</p>

<p>  environment location: /home/urbe/anaconda3</p>

<p>  added / updated specs: <br />    - salmon</p>

<p>The following packages will be downloaded:</p>

<p>    package                    |            build<br />    ---------------------------|-----------------<br />    boost-1.64.0               |           py36_4         331 KB  conda-forge<br />    jemalloc-5.1.0             |       hfc679d8_0         8.2 MB  conda-forge<br />    boost-cpp-1.64.0           |                1        17.8 MB  conda-forge<br />    salmon-0.10.2              |                1         3.7 MB  bioconda<br />    conda-4.5.5                |           py36_0         624 KB  conda-forge<br />    tbb-2018_20171205          |                0         1.2 MB  conda-forge<br />    ------------------------------------------------------------<br />                                           Total:        31.8 MB</p>

<p>The following NEW packages will be INSTALLED:</p>

<p>    boost:     1.64.0-py36_4    conda-forge<br />    boost-cpp: 1.64.0-1         conda-forge<br />    jemalloc:  5.1.0-hfc679d8_0 conda-forge<br />    salmon:    0.10.2-1         bioconda   <br />    tbb:       2018_20171205-0  conda-forge</p>

<p>The following packages will be UPDATED:</p>

<p>    conda:     4.5.4-py36_0     conda-forge --&gt; 4.5.5-py36_0 conda-forge</p>

<p>Proceed ([y]/n)? y</p>

<p>Downloading and Extracting Packages<br />boost-1.64.0         |  331 KB | ####################################################################################################################################### | 100% <br />jemalloc-5.1.0       |  8.2 MB | ####################################################################################################################################### | 100% <br />boost-cpp-1.64.0     | 17.8 MB | ####################################################################################################################################### | 100% <br />salmon-0.10.2        |  3.7 MB | ####################################################################################################################################### | 100% <br />conda-4.5.5          |  624 KB | ####################################################################################################################################### | 100% <br />tbb-2018_20171205    |  1.2 MB | ####################################################################################################################################### | 100% <br />Preparing transaction: done<br />Verifying transaction: done<br />Executing transaction: done</p>
]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/22572/clump-finding-problem-solved-with-perl</guid>
	<pubDate>Wed, 10 Jun 2015 00:17:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/22572/clump-finding-problem-solved-with-perl</link>
	<title><![CDATA[Clump Finding Problem Solved with Perl]]></title>
	<description><![CDATA[<p>The question at http://rosalind.info/problems/1d/</p><p>Script are moved to&nbsp;http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</guid>
	<pubDate>Thu, 09 Aug 2018 04:21:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</link>
	<title><![CDATA[List of non-commercial NGS genotype-calling software]]></title>
	<description><![CDATA[<p><span>Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.&nbsp;</span></p><p><span>A list of programs for genotype and SNP calling :</span></p><p><br />SOAP2&nbsp;http://soap.genomics.org.cn/index.html</p><p>Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)</p><p>realSFS&nbsp;http://128.32.118.212/thorfinn/realSFS/</p><p>Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation</p><p>Samtools http://samtools.sourceforge.net/</p><p>Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)</p><p>GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)</p><p>Beagle http://faculty.washington.edu/browning/beagle/beagle.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling</p><p>IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map</p><p>QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL</p><p>Multi-sample LD &lsquo;Feasible&rsquo; genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The &lsquo;feasible&rsquo; genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)</p><p>MaCH http://genome.sph.umich.edu/wiki/Thunder</p><p>Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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