<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44229?offset=300</link>
	<atom:link href="https://bioinformaticsonline.com/related/44229?offset=300" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/29407/live-webinar-on-rna-seq-data-analysis-on-9-nov-2016</guid>
	<pubDate>Wed, 19 Oct 2016 05:25:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/29407/live-webinar-on-rna-seq-data-analysis-on-9-nov-2016</link>
	<title><![CDATA[Live Webinar on RNA-Seq Data Analysis on 9 Nov 2016]]></title>
	<description><![CDATA[<p><strong><a href="http://www.strand-ngs.com/webinar_registration">Live Webinar on RNA-Seq Data Analysis</a></strong></p><p><a href="http://www.strand-ngs.com/webinar_registration">Abstract: </a>Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.</p><p><a href="http://www.strand-ngs.com/webinar_registration">Speaker:</a> Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences</p><p>Date: 9th Nov, <a href="http://www.strand-ngs.com/webinar_registration">Session 1</a> for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, <a href="http://www.strand-ngs.com/webinar_registration">Session 2</a> for AFO/ EMEA: 9:00 AM PST</p><p>Register here <a href="http://www.strand-ngs.com/webinar_registration">http://www.strand-ngs.com/webinar_registration</a></p>]]></description>
	<dc:creator>Strand</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/file/view/29638/r-graphical-cookbook-by-winston-chang</guid>
	<pubDate>Fri, 04 Nov 2016 12:50:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29638/r-graphical-cookbook-by-winston-chang</link>
	<title><![CDATA[R Graphical Cookbook by Winston Chang]]></title>
	<description><![CDATA[<p>R Graphical Cookbook by Winston Chang</p><p>A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".</p><p>The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures  In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.</p><p>More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf</p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29638" length="37521" type="image/png" />
</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30076/sga-string-graph-assembler</guid>
	<pubDate>Thu, 08 Dec 2016 05:08:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30076/sga-string-graph-assembler</link>
	<title><![CDATA[SGA: String Graph Assembler]]></title>
	<description><![CDATA[<p><span>SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.</span></p>
<p><span>More at</span></p>
<p><span>https://github.com/jts/sga</span></p>
<p>SGA dependencies:<br> -google sparse hash library (http://code.google.com/p/google-sparsehash/)<br> -the bamtools library (https://github.com/pezmaster31/bamtools)<br> -zlib (http://www.zlib.net/)<br> -(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)</p><p>Address of the bookmark: <a href="https://github.com/jts/sga" rel="nofollow">https://github.com/jts/sga</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30102/prism</guid>
	<pubDate>Sat, 10 Dec 2016 15:19:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30102/prism</link>
	<title><![CDATA[PRISM]]></title>
	<description><![CDATA[<p><span>PRISM is a software for split read (reads which span across a structrual variant -- SV ) mapping and SV calling from the mapping result. PRISM is able to detect small insertions and abitrary size deletions, inversions and tandom duplications with the direction of discordant read pairs. PRISM_CTX is a tool for detecting inter-chromosome trans-location events.&nbsp;</span><br><br><span>PRISM and PRISM_CTX were originally designed and written by&nbsp;</span><a href="http://www.cs.toronto.edu/~brudno">Michael Brudno</a><span>&nbsp;and Yue Jiang, The original PRISM publication can be found&nbsp;</span><a href="http://bioinformatics.oxfordjournals.org/content/early/2012/07/31/bioinformatics.bts484.abstract">here</a><span>.&nbsp;</span><br><br><span>The authors may be contacted via e-mail at:&nbsp;</span><em>prism at cs.toronto.edu</em><span>.&nbsp;</span><br><br><span>Additional information is available in the&nbsp;</span><a href="http://compbio.cs.toronto.edu/prism/PRISM_README">PRISM README</a><span>&nbsp;file and&nbsp;</span><a href="http://compbio.cs.toronto.edu/prism/PRISM_CTX_README">PRISM_CTX README</a><span>&nbsp;file.&nbsp;</span></p>
<p>http://compbio.cs.toronto.edu/prism/</p><p>Address of the bookmark: <a href="http://compbio.cs.toronto.edu/prism/" rel="nofollow">http://compbio.cs.toronto.edu/prism/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30124/understanding-greedy-algorithms</guid>
	<pubDate>Mon, 12 Dec 2016 04:37:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30124/understanding-greedy-algorithms</link>
	<title><![CDATA[Understanding Greedy Algorithms]]></title>
	<description><![CDATA[<p>Learning greedy algo for biologist.&nbsp;</p>
<p>https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/</p>
<p>This webpage is also useful for the same:</p>
<p>http://learninglover.com/examples.php?id=59</p>
<p>http://www.cs.rpi.edu/~magdon/ps/conference/super_biokdd.pdf</p>
<p>https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf</p>
<p>http://schatzlab.cshl.edu/teaching/AssemblyClass/01.%20Assembly%20Intro.pdf</p>
<p>http://lsl.sinica.edu.tw/Services/Class/files/20150612449.pdf</p>
<p>http://www.cs.jhu.edu/~langmea/resources/lecture_notes/assembly_scs.pdf</p>
<p>https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-43.pdf</p><p>Address of the bookmark: <a href="https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/" rel="nofollow">https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30216/quickmerge-a-simple-and-fast-metassembler-and-assembly-gap-filler-designed-for-long-molecule-based-assemblies</guid>
	<pubDate>Mon, 19 Dec 2016 10:23:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30216/quickmerge-a-simple-and-fast-metassembler-and-assembly-gap-filler-designed-for-long-molecule-based-assemblies</link>
	<title><![CDATA[quickmerge: A simple and fast metassembler and assembly gap filler designed for long molecule based assemblies.]]></title>
	<description><![CDATA[<p><span>quickmerge uses a simple concept to improve contiguity of genome assemblies based on long molecule sequences, often with dramatic outcomes. The program uses information from assemblies made with illumina short reads and PacBio long reads to improve contiguities of an assembly generated with PacBio long reads alone. This is counterintuitive because illumina short reads are not typically considered to cover genomic regions which PacBio long reads cannot. Although we have not evaluated this program for assemblies generated with Oxford nanopore sequences, the program should work with ONP-assemblies too.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/mahulchak/quickmerge" rel="nofollow">https://github.com/mahulchak/quickmerge</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30304/mcscan</guid>
	<pubDate>Thu, 22 Dec 2016 03:53:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30304/mcscan</link>
	<title><![CDATA[MCscan]]></title>
	<description><![CDATA[<p><span>MCscan is a computer program that can simultaneously scan multiple genomes to identify homologous chromosomal regions and subsequently align these regions using genes as anchors. This is the toolset for generating the synteny correspondences in&nbsp;</span><a href="http://chibba.agtec.uga.edu/duplication">Plant Genome Duplication Database</a><span>. It is intended as an easy-to-use and quick way to identify conserved gene arrays both within the same genome and across different genomes.</span></p>
<p><span>More at&nbsp;http://chibba.agtec.uga.edu/duplication/mcscan/</span></p><p>Address of the bookmark: <a href="http://chibba.agtec.uga.edu/duplication/mcscan/" rel="nofollow">http://chibba.agtec.uga.edu/duplication/mcscan/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
</item>
<item>
	<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>
</item>

</channel>
</rss>