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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41485?offset=90</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/19732/marine-bioinformatician</guid>
	<pubDate>Wed, 24 Dec 2014 05:17:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/19732/marine-bioinformatician</link>
	<title><![CDATA[Marine bioinformatician ;)]]></title>
	<description><![CDATA[<p>A marine bioinformatician at work. Sorry&nbsp;jealous biotechnologist, this is how we work.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/19732" length="35550" type="image/gif" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</guid>
	<pubDate>Fri, 15 Jun 2018 04:01:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</link>
	<title><![CDATA[SALSA: A tool to scaffold long read assemblies with Hi-C]]></title>
	<description><![CDATA[This code is used to scaffold your assemblies using Hi-C data. This version implements some improvements in the original SALSA algorithm. If you want to use the old version, it can be found in the old_salsa branch.

To use the latest version, first run the following commands:

  cd SALSA
  make
To run the code, you will need Python 2.7, BOOST libraries and Networkx(version lower than 1.2).

If you consider using this tool, please cite our publication which describes the methods used for scaffolding.

Ghurye, J., Pop, M., Koren, S., Bickhart, D., &amp; Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC genomics, 18(1), 527. Link

Ghurye, J., Rhie, A., Walenz, B.P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A.M. and Koren, S., 2018. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. bioRxiv, p.261149 Link

For any queries, please either ask on github issue page or send an email to Jay Ghurye (jayg@cs.umd.edu).<p>Address of the bookmark: <a href="https://github.com/machinegun/SALSA" rel="nofollow">https://github.com/machinegun/SALSA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps</guid>
	<pubDate>Mon, 10 Feb 2020 01:35:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps</link>
	<title><![CDATA[Leaflet: JavaScript libraries for interactive maps]]></title>
	<description><![CDATA[<p><a href="http://leafletjs.com/">Leaflet</a><span>&nbsp;is one of the most popular open-source JavaScript libraries for interactive maps.</span></p>
<h3>Features</h3>
<ul>
<li>Interactive panning/zooming</li>
<li>Compose maps using arbitrary combinations of:
<ul>
<li>Map tiles</li>
<li>Markers</li>
<li>Polygons</li>
<li>Lines</li>
<li>Popups</li>
<li>GeoJSON</li>
</ul>
</li>
<li>Create maps right from the R console or RStudio</li>
<li>Embed maps in&nbsp;<a href="http://yihui.name/knitr/">knitr</a>/<a href="http://rmarkdown.rstudio.com/">R Markdown</a>&nbsp;documents and&nbsp;<a href="http://shiny.rstudio.com/">Shiny</a>&nbsp;apps</li>
<li>Easily render spatial objects from the&nbsp;<code>sp</code>&nbsp;or&nbsp;<code>sf</code>&nbsp;packages, or data frames with latitude/longitude columns</li>
<li>Use map bounds and mouse events to drive Shiny logic</li>
<li>Display maps in non spherical mercator projections</li>
<li>Augment map features using chosen plugins from&nbsp;<a href="http://leafletjs.com/plugins">leaflet plugins repository</a></li>
</ul>
<p><a href="https://rstudio.github.io/leaflet/">https://rstudio.github.io/leaflet/</a></p><p>Address of the bookmark: <a href="https://rstudio.github.io/leaflet/" rel="nofollow">https://rstudio.github.io/leaflet/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</guid>
	<pubDate>Thu, 18 May 2017 05:27:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</link>
	<title><![CDATA[ANGES: reconstructing ANcestral GEnomeS maps]]></title>
	<description><![CDATA[<p>This page contains the software ANGES 1.01, that aims at reconstucting ancestral genome maps from homologous markers in extant related genomes.</p>
<h3>Download</h3>
<ul>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01.tar.gz">Program, version 1.01</a>&nbsp;(July 10, 2012, documentation updated in August 2014)</li>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01_examples_with_results.tar.gz">Examples with results (featured ancestors: boreoeutherian, amniote, yeasts, Burkholderia, monocots)</a>; please refer to the documentation of the distribution above.</li>
</ul><p>Address of the bookmark: <a href="http://paleogenomics.irmacs.sfu.ca/ANGES/" rel="nofollow">http://paleogenomics.irmacs.sfu.ca/ANGES/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/4882/detect-the-sequence-pattern-and-its-location-in-fasta-file-with-match-and-mismatches-information</guid>
	<pubDate>Thu, 26 Sep 2013 15:02:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/4882/detect-the-sequence-pattern-and-its-location-in-fasta-file-with-match-and-mismatches-information</link>
	<title><![CDATA[Detect the sequence pattern and its location in fasta file with match and mismatches information.]]></title>
	<description><![CDATA[<p>This script is one of my old script to detect some centromeric pattern in chromosomes. User can also control the number of mismatches allowed through command line ..</p><p>To run:</p><p>perl centro.pl</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/4882" length="3596" type="text/x-perl" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33006/avid-a-global-alignment-program</guid>
	<pubDate>Wed, 24 May 2017 05:19:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33006/avid-a-global-alignment-program</link>
	<title><![CDATA[AVID: A Global Alignment Program]]></title>
	<description><![CDATA[<p>A new global alignment method called AVID. The method is designed to be fast, memory efficient, and practical for sequence alignments of large genomic regions up to megabases long. We present numerous applications of the method, ranging from the comparison of assemblies to alignment of large syntenic genomic regions and whole genome human/mouse alignments. We have also performed a quantitative comparison of AVID with other popular alignment tools. To this end, we have established a format for the representation of alignments and methods for their comparison. These formats and methods should be useful for future studies. The tools we have developed for the alignment comparisons, as well as the AVID program, are publicly available. See Web Site References section for AVID Web address and Web addresses for other programs discussed in this paper.</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC430967/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC430967/</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39683/gffcompare-program-for-processing-gtfgff-files</guid>
	<pubDate>Tue, 09 Jul 2019 13:35:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39683/gffcompare-program-for-processing-gtfgff-files</link>
	<title><![CDATA[GffCompare: Program for processing GTF/GFF files]]></title>
	<description><![CDATA[<p>The program gffcompare can be used to compare, merge, annotate and estimate accuracy of one or more GFF files (the &ldquo;query&rdquo; files), when compared with a reference annotation (also provided as GFF).</p><p>Address of the bookmark: <a href="https://ccb.jhu.edu/software/stringtie/gffcompare.shtml" rel="nofollow">https://ccb.jhu.edu/software/stringtie/gffcompare.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</guid>
	<pubDate>Mon, 27 Nov 2017 10:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</link>
	<title><![CDATA[Opera: An optimal genome scaffolding program]]></title>
	<description><![CDATA[<p><span>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (</span><a href="http://en.wikipedia.org/wiki/Sequence_assembly" target="_blank">http://en.wikipedia.org/wiki/Sequence_assembly&nbsp;<img src="https://a.fsdn.com/con/img/icons/external_asset.png" alt="image" style="border: 0px;"></a><span>). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.</span><br><br><span>An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.</span><br><br><span>Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.</span><br><br><span>Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.</span></p>
<p><span>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/operasf/" rel="nofollow">https://sourceforge.net/projects/operasf/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</guid>
	<pubDate>Sat, 02 Jun 2018 07:40:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</link>
	<title><![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]></title>
	<description><![CDATA[<p><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">MCMCTREE</a><span>&nbsp;is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the&nbsp;</span><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">PAML</a><span>&nbsp;package. In this tutorial I will analyze an easy example modified from dataset of&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20551041" target="_blank">Inoue et al. (2010)</a><span>. Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.</span></p><p>Address of the bookmark: <a href="http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html" rel="nofollow">http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</guid>
	<pubDate>Mon, 07 Jan 2019 10:35:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</link>
	<title><![CDATA[kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data]]></title>
	<description><![CDATA[<p><strong>kallisto</strong>&nbsp;is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of&nbsp;<em>pseudoalignment</em>&nbsp;for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data,&nbsp;<strong>kallisto</strong>&nbsp;can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and&nbsp;<strong>kallisto</strong>&nbsp;is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks&nbsp;<strong>kallisto</strong>&nbsp;significantly outperforms existing tools.&nbsp;<strong>kallisto</strong>&nbsp;is described in detail in:</p>
<p>Nicolas L Bray, Harold Pimentel, P&aacute;ll Melsted and Lior Pachter,&nbsp;<a href="http://www.nature.com/nbt/journal/v34/n5/full/nbt.3519.html">Near-optimal probabilistic RNA-seq quantification</a>, Nature Biotechnology&nbsp;<strong>34</strong>, 525&ndash;527 (2016), doi:10.1038/nbt.3519</p><p>Address of the bookmark: <a href="https://pachterlab.github.io/kallisto/about" rel="nofollow">https://pachterlab.github.io/kallisto/about</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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