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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35131?offset=80</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40613/genome-in-a-bottle-giab-consortium</guid>
	<pubDate>Sat, 25 Jan 2020 13:50:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40613/genome-in-a-bottle-giab-consortium</link>
	<title><![CDATA[Genome in a Bottle (GIAB) Consortium]]></title>
	<description><![CDATA[<p><span>The</span><a href="http://www.genomeinabottle.org/"> Genome in a Bottle (GIAB) Consortium</a><span> is a public-private-academic consortium hosted by </span><a href="http://www.nist.gov/" target="_blank">NIST</a><span> to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. </span></p>
<p><span><a href="https://www.nist.gov/news-events/news/2016/09/nist-releases-new-family-standardized-genomes">https://www.nist.gov/news-events/news/2016/09/nist-releases-new-family-standardized-genomes</a></span></p><p>Address of the bookmark: <a href="https://jimb.stanford.edu/giab/" rel="nofollow">https://jimb.stanford.edu/giab/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</guid>
	<pubDate>Fri, 08 Dec 2017 16:48:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</link>
	<title><![CDATA[kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome]]></title>
	<description><![CDATA[<p><span>Sept. 20, 2017 Version 3.1 released. Major upgrade. Version 3.1 fixes the problems with SNP annotation that arose when NCBI discontinued use of GI numbers. Please read carefully the Preface (page 3) and the File of annotated genomes section (pages 9-10) in the version 3.1 User Guide. Thanks to Tom Slezak for revsing the get_genbank_file3 script and to Tod Stuber (USDA) for testing version 3.1 even though he doesn't need the annotation feature. All users are encouraged to upgrade to version 3.1.&nbsp;<br></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/ksnp/files/" rel="nofollow">https://sourceforge.net/projects/ksnp/files/</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare</guid>
	<pubDate>Wed, 19 Jan 2022 10:30:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare</link>
	<title><![CDATA[VCF Compare !]]></title>
	<description><![CDATA[<h2><span>compare two&nbsp;<strong>BWA</strong>&nbsp;mapping methods with the online hg18-mapped data</span></h2>
<p>We first operate a rapid inspection of the different BAM files using&nbsp;<strong>samtools flagstat</strong>. Illumina provided chr21 read mapping obtained with their&nbsp;<strong>GA IIx</strong>&nbsp;deep sequencing platform &lt;<a href="ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam" target="_blank">ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam</a>&gt;, aligned to the b36/hg18 reference genome)</p><p>Address of the bookmark: <a href="https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare" rel="nofollow">https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26573/efficient-genome-searching-with-biostrings-and-the-bsgenome-data-package</guid>
	<pubDate>Mon, 07 Mar 2016 05:18:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26573/efficient-genome-searching-with-biostrings-and-the-bsgenome-data-package</link>
	<title><![CDATA[Efficient genome searching with Biostrings and the BSgenome data package]]></title>
	<description><![CDATA[<p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/3.3/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf" rel="nofollow">https://www.bioconductor.org/packages/3.3/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf</a></p>]]></description>
	<dc:creator>Aasha</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</guid>
	<pubDate>Tue, 16 Aug 2016 08:34:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</link>
	<title><![CDATA[KisSplice]]></title>
	<description><![CDATA[<p>KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.</p>
<p>KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.</p>
<p>KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.</p><p>Address of the bookmark: <a href="http://kissplice.prabi.fr/" rel="nofollow">http://kissplice.prabi.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</guid>
	<pubDate>Mon, 05 Sep 2016 04:07:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<ul>
<li>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between&nbsp;<em>different assemblies</em>&nbsp;(such as Human&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</li>
<li>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</li>
<li>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s&nbsp;<em>not</em>&nbsp;a program for aligning sequences to reference genome.</li>
<li>We&nbsp;<em>do not</em>&nbsp;recommend using CrossMap to convert genome coordinates between species.</li>
</ul><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28844/teannot</guid>
	<pubDate>Thu, 18 Aug 2016 10:02:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28844/teannot</link>
	<title><![CDATA[TEannot]]></title>
	<description><![CDATA[<p>We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto" rel="nofollow">https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto</a></p>]]></description>
	<dc:creator>Jit</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>

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