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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32730?offset=280</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs</guid>
	<pubDate>Fri, 04 Nov 2016 10:48:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs</link>
	<title><![CDATA[R Graphs !!]]></title>
	<description><![CDATA[<p><span>The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided. Enjoy nice graphs !!</span></p><p>Address of the bookmark: <a href="http://rgraphgallery.blogspot.be/" rel="nofollow">http://rgraphgallery.blogspot.be/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</guid>
	<pubDate>Tue, 08 Nov 2016 07:34:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</link>
	<title><![CDATA[Statistics and probability]]></title>
	<description><![CDATA[<h3><span>Topics</span></h3>
<div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/displaying-describing-data">Displaying and describing data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data">Modeling distributions of data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data">Describing relationships in quantitative data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/designing-studies">Designing studies</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/probability-library">Probability</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library">Random variables</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library">Sampling distributions</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample">Confidence intervals (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample">Significance tests (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-confidence-intervals-two-samples">Significance tests and confidence intervals (two samples)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/inference-categorical-data-chi-square-tests">Inference for categorical data (chi-square tests)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/advanced-regression-inference-transforming">Advanced regression (inference and tran</a></div>
</div><p>Address of the bookmark: <a href="https://www.khanacademy.org/math/statistics-probability" rel="nofollow">https://www.khanacademy.org/math/statistics-probability</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27216/yass-genomic-similarity-search-tool</guid>
	<pubDate>Mon, 02 May 2016 09:26:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27216/yass-genomic-similarity-search-tool</link>
	<title><![CDATA[YASS :: genomic similarity search tool]]></title>
	<description><![CDATA[<p>YASS is a genomic similarity search tool, for nucleic (DNA/RNA) sequences in fasta or plain text format (<em>it produces local pairwise alignments</em>). Like most of the heuristic pairwise local alignment tools for DNA sequences (FASTA, BLAST, PATTERNHUNTER, BLASTZ/LASTZ, LAST ...), YASS uses <em>seeds</em> to detect potential similarity regions, and then tries to extend them to local alignments. This genomic search tool uses <em>multiple transition constrained spaced seeds</em> that enable to search more fuzzy repeats, as non-coding DNA/RNA. Another simple, but interesting feature is that you can specify the seed pattern used in the search step (as provided for example by <a href="http://bioinfo.lifl.fr/yass/iedera.php">iedera</a>).</p>
<p>Main features of YASS are:</p>
<ul>
<li>multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off</li>
<li>transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [<code>A&lt;-&gt;G</code>] or pyrimidine to pyrimidine [<code>C&lt;-&gt;T</code>])</li>
<li>using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies</li>
<li>parameterizable <em>output</em> filter for low complexity repeats</li>
<li>reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)</li>
<li>post-processing step to group gapped alignments</li>
</ul><p>Address of the bookmark: <a href="http://bioinfo.lifl.fr/yass/" rel="nofollow">http://bioinfo.lifl.fr/yass/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</guid>
	<pubDate>Fri, 14 Apr 2017 06:19:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</link>
	<title><![CDATA[UpSetR Shiny App!]]></title>
	<description><![CDATA[<p>UpSetR generates static&nbsp;<a href="http://vcg.github.io/upset/?dataset=0&amp;duration=1000&amp;orderBy=subsetSize&amp;grouping=groupByIntersectionSize&amp;selection=">UpSet plots</a>. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.</p>
<h4>To begin, input your data using one of the three input styles.</h4>
<ol>
<li>"File" takes a correctly formatted.csv file.</li>
<li>"List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2164-9-488.pdf">(Hulsen et al., 2008)</a>&nbsp;and jvenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2105-15-293.pdf">(Bardou et al., 2014)</a></li>
<li>"Expression" takes the input used by the venneuler R package&nbsp;<a href="https://cran.r-project.org/web/packages/venneuler/venneuler.pdf">(Wilkinson, 2015)</a></li>
</ol><p>Address of the bookmark: <a href="https://gehlenborglab.shinyapps.io/upsetr/" rel="nofollow">https://gehlenborglab.shinyapps.io/upsetr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32483/cla-contig-layout-authenticator</guid>
	<pubDate>Fri, 05 May 2017 05:58:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32483/cla-contig-layout-authenticator</link>
	<title><![CDATA[CLA: Contig-Layout-Authenticator]]></title>
	<description><![CDATA[<p><span>To improve upon the shortcomings associated with the construction of draft genomes with Illumina paired-end sequencing, we developed Contig-Layout-Authenticator (CLA). The CLA pipeline can scaffold reference-sorted contigs based on paired reads, resulting in better assembled genomes. Moreover, CLA also hints at probable misassemblies and contaminations, for the users to cross-check before constructing the consensus draft. The CLA pipeline was designed and trained extensively on various bacterial genome datasets for the ordering and scaffolding of large repetitive contigs. The tool has been validated and compared favorably with other widely-used scaffolding and ordering tools using both simulated and real sequence datasets. CLA is a user friendly tool that requires a single command line input to generate ordered scaffolds.</span></p>
<p><span>Script&nbsp;https://sourceforge.net/projects/c-l-authenticator/files/</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459</a></p>]]></description>
	<dc:creator>Jit</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/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</guid>
	<pubDate>Wed, 20 Dec 2017 08:49:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</link>
	<title><![CDATA[Delta: a new Web-based 3D genome visualization and analysis platform]]></title>
	<description><![CDATA[<p><em>Delta</em><span>&nbsp;is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes.&nbsp;</span><em>Delta</em><span>&nbsp;takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome.&nbsp;</span><em>Delta</em><span>features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.</span></p>
<p>https://github.com/zhangzhwlab/delta</p><p>Address of the bookmark: <a href="https://github.com/zhangzhwlab/delta" rel="nofollow">https://github.com/zhangzhwlab/delta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35384/mgcv-the-microbial-genomic-context-viewer-for-comparative-genome-analysis</guid>
	<pubDate>Mon, 29 Jan 2018 04:55:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35384/mgcv-the-microbial-genomic-context-viewer-for-comparative-genome-analysis</link>
	<title><![CDATA[MGcV: the microbial genomic context viewer for comparative genome analysis]]></title>
	<description><![CDATA[<p><span>MGcV is an interactive web-based visalization tool tailored to facilitate small scale genome analysis. To start using MGcV:</span></p>
<ol>
<li>Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by
<ul>
<li>selecting the type of identifier and pasting identifiers (one per line)</li>
<li><em><strong>or</strong></em>&nbsp;by using the&nbsp;<a>gene ID search tool</a></li>
<li><em><strong>or</strong></em>&nbsp;with the&nbsp;<a>BLAST search tool</a></li>
</ul>
</li>
<li>Click "Visualize context".</li>
</ol>
<p><span>Consult the&nbsp;</span><a href="http://mgcv.cmbi.ru.nl/help.html" target="_blank">documentation</a><span>&nbsp;to learn more about MGcV.</span></p><p>Address of the bookmark: <a href="http://mgcv.cmbi.ru.nl/" rel="nofollow">http://mgcv.cmbi.ru.nl/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies</guid>
	<pubDate>Fri, 06 Jul 2018 03:36:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies</link>
	<title><![CDATA[KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies]]></title>
	<description><![CDATA[<p>KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:</p>
<ul>
<li><span>hist</span>: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.</li>
<li><span>gcp:</span>&nbsp;K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.</li>
<li><span>comp</span>: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.</li>
<li><span>sect</span>: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.</li>
<li><span>blob</span>: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.</li>
<li><span>filter</span>: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
<ul>
<li><span>kmer</span>: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.</li>
<li><span>seq</span>: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.</li>
</ul>
</li>
<li><span>plot</span>: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
<ul>
<li><span>density</span>: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.</li>
<li><span>profile</span>: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool</li>
<li><span>spectra-cn</span>: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.</li>
<li><span>spectra-hist</span>: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.</li>
<li><span>spectra-mx</span>: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".</li>
</ul>
</li>
</ul>
<p>In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.</p>
<p>This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit:&nbsp;<a href="https://kat.readthedocs.org/en/latest/">https://kat.readthedocs.org/en/latest/</a></p>
<p><a href="https://academic.oup.com/bioinformatics/article/33/4/574/2664339">https://academic.oup.com/bioinformatics/article/33/4/574/2664339&nbsp;</a></p><p>Address of the bookmark: <a href="https://github.com/TGAC/KAT" rel="nofollow">https://github.com/TGAC/KAT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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