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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39187?offset=60</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38625/croco-a-program-to-detect-potential-cross-contaminations-in-hts-assembled-transcriptomes-using-expression-level-quantification</guid>
	<pubDate>Mon, 07 Jan 2019 18:17:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38625/croco-a-program-to-detect-potential-cross-contaminations-in-hts-assembled-transcriptomes-using-expression-level-quantification</link>
	<title><![CDATA[CroCo: A program to detect potential cross contaminations in HTS assembled transcriptomes using expression level quantification]]></title>
	<description><![CDATA[<p>CroCo is a program to detect cross contamination events in assembled transcriptomes using sequencing reads to determine the true origin of every transcripts.<br>Such cross contaminations can be expected if several RNA-Seq experiments were prepared during the same period at the same lab, or by the same people, or if they were processed or sequenced by the same sequencing service facility.<br>Our approach first determines a subset of transcripts that are suspiciously similar across samples using a pairwise BLAST procedure. CroCo then combine all transcriptomes into a metatranscriptome and quantifies the "expression level" of all transcripts successively using every sample read data (e.g. several species sequenced by the same lab for a particular study) while allowing read multi-mappings.<br>Several mapping tools implemented in CroCo can be used to estimate expression level (default is RapMap).<br>This information is then used to categorize each transcript in the following 5 categories :</p>
<p><br>clean: the transcript origin is from the focal sample.</p>
<p>cross contamination: the transcript origin is from an alien sample of the same experiment.</p>
<p>dubious: expression levels are too close between focal and alien samples to determine the true origin of the transcript.</p>
<p>low coverage: expression levels are too low in all samples, thus hampering our procedure (which relies on differential expression) to confidently assign it to any category.</p>
<p>over expressed: expression levels are very high in at least 3 samples and CroCo will not try to categorize it. Indeed, such a pattern does not correspond to expectations for cross contaminations, but often reflect highly conserved genes such as ribosomal gene, or external contamination shared by several samples (e.g. Escherichia coli contaminations).</p><p>Address of the bookmark: <a href="https://gitlab.mbb.univ-montp2.fr/mbb/CroCo" rel="nofollow">https://gitlab.mbb.univ-montp2.fr/mbb/CroCo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line</guid>
	<pubDate>Fri, 01 Nov 2019 00:29:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line</link>
	<title><![CDATA[Gooey: Turn (almost) any Python command line program into a full GUI application with one line]]></title>
	<description><![CDATA[<p><span>Turn (almost) any Python command line program into a full GUI application with one line</span></p>
<p>The easiest way to install Gooey is via&nbsp;<code>pip</code></p>
<pre><code>pip install Gooey 
</code></pre>
<p>Alternatively, you can install Gooey by cloning the project to your local directory</p>
<pre><code>git clone https://github.com/chriskiehl/Gooey.git
</code></pre>
<p>run&nbsp;<code>setup.py</code></p>
<pre><code>python setup.py install</code></pre><p>Address of the bookmark: <a href="https://github.com/chriskiehl/Gooey" rel="nofollow">https://github.com/chriskiehl/Gooey</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</guid>
	<pubDate>Tue, 25 Jan 2022 17:59:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</link>
	<title><![CDATA[CrossMap: program for genome coordinates conversion between different assemblies]]></title>
	<description><![CDATA[<p><span>CrossMap is a program for genome coordinates conversion between&nbsp;</span><em>different assemblies</em><span>&nbsp;(such as&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a><span>&nbsp;&lt;=&gt;&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a><span>). It supports commonly used file formats including&nbsp;</span><a href="https://samtools.github.io/hts-specs/SAMv1.pdf">BAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/CRAM_(file_format)">CRAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/SAM_(file_format)">SAM</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/wiggle.html">Wiggle</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/bigWig.html">BigWig</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format1">BED</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format3">GFF</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format4">GTF</a><span>,&nbsp;</span><a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">MAF</a><span>&nbsp;</span><a href="https://samtools.github.io/hts-specs/VCFv4.2.pdf">VCF</a><span>, and&nbsp;</span><a href="https://sites.google.com/site/gvcftools/home/about-gvcf">gVCF</a><span>.</span></p><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</guid>
	<pubDate>Thu, 25 Aug 2016 08:05:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</link>
	<title><![CDATA[LUMPY]]></title>
	<description><![CDATA[<p>A probabilistic framework for structural variant discovery.</p>
<p>Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84.&nbsp;<a href="http://dx.doi.org/10.1186/gb-2014-15-6-r84">doi:10.1186/gb-2014-15-6-r84</a>.</p>
<p>More at&nbsp;https://github.com/arq5x/lumpy-sv</p><p>Address of the bookmark: <a href="https://github.com/arq5x/lumpy-sv" rel="nofollow">https://github.com/arq5x/lumpy-sv</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</guid>
	<pubDate>Tue, 26 Dec 2017 21:14:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</link>
	<title><![CDATA[PASA: Gene Structure Annotation and Analysis]]></title>
	<description><![CDATA[<p><span>PASA, acronym for Program to Assemble Spliced Alignments, is a eukaryotic genome annotation tool that exploits spliced alignments of expressed transcript sequences to automatically model gene structures, and to maintain gene structure annotation consistent with the most recently available experimental sequence data. PASA also identifies and classifies all splicing variations supported by the transcript alignments.</span></p><p>Address of the bookmark: <a href="http://pasapipeline.github.io/" rel="nofollow">http://pasapipeline.github.io/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</guid>
	<pubDate>Wed, 23 May 2018 03:24:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</link>
	<title><![CDATA[bpRNA: large-scale automated annotation and analysis of RNA secondary structure]]></title>
	<description><![CDATA[<p>bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.</p>
<p>The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.</p><p>Address of the bookmark: <a href="http://github.com/hendrixlab/bpRNA" rel="nofollow">http://github.com/hendrixlab/bpRNA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42405/caretta-%E2%80%93-a-multiple-protein-structure-alignment-and-feature-extraction-suite</guid>
	<pubDate>Fri, 18 Dec 2020 02:09:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42405/caretta-%E2%80%93-a-multiple-protein-structure-alignment-and-feature-extraction-suite</link>
	<title><![CDATA[Caretta – A multiple protein structure alignment and feature extraction suite]]></title>
	<description><![CDATA[<h3>Caretta &ndash;&nbsp;a multiple protein structure alignment and feature extraction suite</h3>
<p><span>Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning.&nbsp;</span></p><p>Address of the bookmark: <a href="http://www.bioinformatics.nl/caretta/" rel="nofollow">http://www.bioinformatics.nl/caretta/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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