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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42012?offset=20</link>
	<atom:link href="https://bioinformaticsonline.com/related/42012?offset=20" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes</guid>
	<pubDate>Mon, 25 Jun 2018 17:22:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes</link>
	<title><![CDATA[chromoMap-An R package for Interactive visualization and mapping of human chromosomes]]></title>
	<description><![CDATA[
<p>chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.</p>

<p>https://cran.r-project.org/web/packages/chromoMap/index.html</p>
]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</guid>
	<pubDate>Thu, 27 Dec 2018 19:45:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</link>
	<title><![CDATA[GeneOverlap: An R package to test and visualize gene overlaps]]></title>
	<description><![CDATA[<p>Overlapping gene lists can reveal biological meanings and may lead to novel hypotheses. For example, histone modification is an important cellular mechanism that can pack and re-pack chromatin. By making the chromatin structure more dense or loose, the gene expression can be turned on or off. Tri-methylation on lysine 4 of histone H3 (H3K4me3) is associated with gene activation and its genome-wide enrichment can be mapped by using ChIP-seq experiments. Because of its activating role, if we overlap the genes that are bound by H3K4me3 with the genes that are highly expressed, we should expect a positive association. Similary, we can perform such kind of overlapping between the gene lists of different histone modifications with that of various expression groups and establish each histone modification&rsquo;s role in gene regulation.</p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf" rel="nofollow">https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure</guid>
	<pubDate>Sat, 07 Mar 2020 05:52:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure</link>
	<title><![CDATA[GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure]]></title>
	<description><![CDATA[<p><span>GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.</span></p>
<p><a href="https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1">https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1</a></p><p>Address of the bookmark: <a href="https://github.com/wanyuac/GeneMates" rel="nofollow">https://github.com/wanyuac/GeneMates</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts</guid>
	<pubDate>Sun, 28 Jun 2015 07:46:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts</link>
	<title><![CDATA[BioScripts]]></title>
	<description><![CDATA[<p>You are requested to please bookmark collection of bioinformatics tools, scripts, codes that can be pieced together in a very easy and flexible manner to perform both simple and complex bioinformatics tasks.</p>
<p>The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.</p>
<p>https://code.google.com/p/biowiki/</p>
<p>https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk</p>
<p>NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&amp;projectsearch=Search+projects</p>
<p>NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&amp;projectsearch=Search+projects</p><p>Address of the bookmark: <a href="https://code.google.com/hosting/search?q=bioinformatics&amp;sa=Search" rel="nofollow">https://code.google.com/hosting/search?q=bioinformatics&amp;sa=Search</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</guid>
	<pubDate>Mon, 25 Apr 2016 16:22:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</link>
	<title><![CDATA[Venn Diagrams on R Studio]]></title>
	<description><![CDATA[<h3>First step: Install &amp; load &ldquo;VennDiagram&rdquo; package.</h3>
<pre><code><span># install.packages('VennDiagram')</span>
<span>library</span><span>(</span><span>VennDiagram</span><span>)</span>
</code></pre>
<h3>Second step: Load data</h3>
<p>Add filepath if &ldquo;catdoge.csv&rdquo; is not in working-directory.</p>
<pre><code><span>d</span> <span>&lt;-</span> <span>read.csv</span><span>(</span><span>"catdoge.csv"</span><span>)</span></code><br><br></pre><p>Address of the bookmark: <a href="http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html" rel="nofollow">http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38176/asciigenome-genome-browser-based-on-command-line-interface-and-designed-for-running-from-console-terminals</guid>
	<pubDate>Fri, 09 Nov 2018 13:50:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38176/asciigenome-genome-browser-based-on-command-line-interface-and-designed-for-running-from-console-terminals</link>
	<title><![CDATA[ASCIIGenome: genome browser based on command line interface and designed for running from console terminals.]]></title>
	<description><![CDATA[<p><code>ASCIIGenome</code>&nbsp;is a genome browser based on command line interface and designed for running from console terminals.</p>
<p>Since&nbsp;<code>ASCIIGenome</code>&nbsp;does not require a graphical interface it is particularly useful for quickly visualizing genomic data on remote servers while offering flexibility similar to popular GUI viewers like&nbsp;<a href="https://www.broadinstitute.org/igv/">IGV</a>.</p>
<p><span>Documentation</span>&nbsp;is at&nbsp;<a href="http://asciigenome.readthedocs.io/en/latest/">readthedocs/asciigenome</a>.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/dariober/ASCIIGenome" rel="nofollow">https://github.com/dariober/ASCIIGenome</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</guid>
	<pubDate>Sat, 20 Mar 2021 00:28:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</link>
	<title><![CDATA[List of bioinformatics packages for NGS analysis !]]></title>
	<description><![CDATA[<p>Package suites gather software packages and installation tools for specific languages or platforms. We have some for bioinformatics software.</p><ul>
<li><a href="https://github.com/Bioconductor">Bioconductor</a>&nbsp;&ndash; A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [&nbsp;<a href="https://link.springer.com/article/10.1186/gb-2004-5-10-r80">paper-2004</a>&nbsp;|&nbsp;<a href="https://www.bioconductor.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/biopython/biopython">Biopython</a>&nbsp;&ndash; Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the&nbsp;<a href="http://open-bio.org/">Open Bioinformatics Foundation</a>. Contains the very useful&nbsp;<a href="https://biopython.org/DIST/docs/api/Bio.Entrez-module.html">Entrez</a>&nbsp;package for API access to the NCBI databases. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/19304878">paper-2009</a>&nbsp;|&nbsp;<a href="https://biopython.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/bioconda">Bioconda</a>&nbsp;&ndash; A channel for the&nbsp;<a href="http://conda.pydata.org/docs/intro.html">conda package manager</a>&nbsp;specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with&nbsp;<code>conda install</code>) bioinformatics packages. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29967506">paper-2018</a>&nbsp;|&nbsp;<a href="https://bioconda.github.io/">web</a>&nbsp;]</li>
<li><a href="https://github.com/BioJulia">BioJulia</a>&nbsp;&ndash; Bioinformatics and computational biology infastructure for the Julia programming language. [&nbsp;<a href="https://biojulia.net/">web</a>&nbsp;]</li>
<li><a href="https://github.com/rust-bio/rust-bio">Rust-Bio</a>&nbsp;&ndash; Rust implementations of algorithms and data structures useful for bioinformatics. [&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/early/2015/10/06/bioinformatics.btv573.short?rss=1">paper-2016</a>&nbsp;]</li>
<li><a href="https://github.com/seqan/seqan3">SeqAn</a>&nbsp;&ndash; The modern C++ library for sequence analysis.</li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32853/progressivecactus</guid>
	<pubDate>Thu, 18 May 2017 05:29:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32853/progressivecactus</link>
	<title><![CDATA[progressiveCactus]]></title>
	<description><![CDATA[<p><span>Progressive Cactus is a whole-genome alignment package.</span></p>
<p><span><span>Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules</span></span></p>
<p>https://github.com/glennhickey/progressiveCactus</p><p>Address of the bookmark: <a href="https://github.com/glennhickey/progressiveCactus" rel="nofollow">https://github.com/glennhickey/progressiveCactus</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40596/igblast-a-popular-ncbi-package-for-classifying-and-analyzing-immunoglobulin-ig-and-t-cell-receptor-tcr-variable-domain-sequences</guid>
	<pubDate>Thu, 23 Jan 2020 11:34:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40596/igblast-a-popular-ncbi-package-for-classifying-and-analyzing-immunoglobulin-ig-and-t-cell-receptor-tcr-variable-domain-sequences</link>
	<title><![CDATA[IgBLAST: a popular NCBI package for classifying and analyzing immunoglobulin (IG) and T cell receptor (TCR) variable domain sequences]]></title>
	<description><![CDATA[<p>NCBI team released a new version of IgBLAST with four new improvements. IgBLAST is a popular NCBI package for classifying and analyzing immunoglobulin (IG) and T cell receptor (TCR) variable domain sequences. Improvements are:<span style="text-decoration: underline;"></span><span style="text-decoration: underline;"></span></p><p>&nbsp;&nbsp;&nbsp; 1. Support for the new FWR4 annotation feature in the AIRR format, both in standard format and in the AIRR alignment format.<span style="text-decoration: underline;"></span><span style="text-decoration: underline;"></span></p><p>&nbsp;&nbsp;&nbsp; 2. The previous &ldquo;-penalty&rdquo; parameter was renamed as -V_penalty to be consistent with other IgBLAST penalty options.<span style="text-decoration: underline;"></span><span style="text-decoration: underline;"></span></p><p>&nbsp;&nbsp;&nbsp; 3. Restored constant internal BLAST search parameters for domain annotation (i.e., FWR/CDR) such that this process is not influenced by user parameters.<span style="text-decoration: underline;"></span><span style="text-decoration: underline;"></span></p><p>&nbsp;&nbsp;&nbsp; 4. Corrected FWR/CDR annotations for certain mouse VK and rat VH germline genes.<span style="font-size: 12.8px;">&nbsp;</span></p><p><span style="text-decoration: underline;"></span></p><p>IgBLAST 1.15.0 is available for&nbsp;<a href="https://ftp.ncbi.nih.gov/blast/executables/igblast/release/LATEST/" target="_blank">download</a>&nbsp;from the BLAST FTP area. See the the new&nbsp;<a href="https://ncbi.github.io/igblast/" target="_blank">manual</a>&nbsp;on GitHub for information about setting up and running IgBLAST.</p><p><span style="text-decoration: underline;"></span></p><p>&nbsp;If you have any questions or concerns, please contact&nbsp;<a href="mailto:blast-help@ncbi.nlm.nih.gov" target="_blank" title="Follow link">blast-help@ncbi.nlm.nih.gov</a><span style="text-decoration: underline;"></span><span style="text-decoration: underline;"></span></p><p><span style="text-decoration: underline;"></span>&nbsp;</p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42017/gromacs-a-versatile-package-to-perform-molecular-dynamics</guid>
	<pubDate>Thu, 06 Aug 2020 22:40:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42017/gromacs-a-versatile-package-to-perform-molecular-dynamics</link>
	<title><![CDATA[GROMACS: a versatile package to perform molecular dynamics]]></title>
	<description><![CDATA[<p><span>GROMACS is a versatile package to perform molecular dynamics, i.e simulate the Newtonian equations of motion for systems with hundreds to millions of particles. GROMACS is able to work with many biochemical molecules like proteins, lipids and nucleic acids. The WeNMR GROMACS web portal combines the versatility of this molecular dynamics package with the calculation power of the eNMR grid. This will enable you to perform many simulations from the comfort of your internet browser anywhere in the world. The server is furthermore aimed to provide a user friendly and efficient MD experience by performing many preparation and optimization steps automatically.</span></p>
<p>GROMACS conda&nbsp;<a href="https://bioconda.github.io/recipes/gromacs/README.html">https://bioconda.github.io/recipes/gromacs/README.html</a>&nbsp;</p><p>Address of the bookmark: <a href="http://haddock.science.uu.nl/enmr/services/GROMACS/main.php" rel="nofollow">http://haddock.science.uu.nl/enmr/services/GROMACS/main.php</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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