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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37512?offset=110</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</guid>
	<pubDate>Sat, 26 Dec 2020 08:35:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42485/fastprongs-fast-preprocessing-of-next-generation-sequencing-reads</link>
	<title><![CDATA[FastProNGS: fast preprocessing of next-generation sequencing reads]]></title>
	<description><![CDATA[<p><span>FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/Megagenomics/FastProNGS" rel="nofollow">https://github.com/Megagenomics/FastProNGS</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</guid>
	<pubDate>Fri, 04 Oct 2024 02:45:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</link>
	<title><![CDATA[Libraries or management tools for high throughput sequencing data]]></title>
	<description><![CDATA[<ul>
<li><a href="http://gatb.inria.fr/"><span>GATB</span></a>&nbsp;Library.&nbsp;The&nbsp;<span>Genome Analysis Toolbox with de-Bruijn graph.&nbsp;</span>A large part of tools developed by the GenScale team are based on this library.<br />These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em>&nbsp;metagenomes). Among them are (the full is available here:&nbsp;<a href="https://gatb.inria.fr/software/">https://gatb.inria.fr/software/</a>):</li>
<li><a href="https://github.com/morispi/LRez"><span>LRez</span></a>: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.</li>
</ul><h2>Variant calling and/or genotyping</h2><ul>
<li><a href="https://gatb.inria.fr/software/discosnp/" title="DiscoSNP">DiscoSNP++ and&nbsp;discoSnpRAD</a>: Reference-free small variant discovery (SNPs and indels)</li>
<li><a href="https://gatb.inria.fr/software/mind-the-gap/" title="MindTheGap">MindTheGap</a>: Detection and assembly of large insertion variants</li>
<li><a href="https://gatb.inria.fr/software/takeabreak/" title="TakeABreak">TakeABreak</a>:&nbsp;reference-free inversion discovery tool</li>
<li><a href="https://github.com/llecompte/SVJedi">SVJedi</a>: Structural Variant genotyper with long read data</li>
<li><a href="https://github.com/SandraLouise/SVJedi-graph">SVJedi-graph</a>: Structural Variant genotyper with long read data using a variation graph</li>
</ul><h2>Sequence assembly</h2><ul>
<li><a href="https://github.com/cguyomar/MinYS">MinYS</a>: reference-guided genome assembly in metagenomics data</li>
<li><a href="https://github.com/anne-gcd/MTG-Link">MTG-link</a>: local assembly tool for linked-read data</li>
<li><a href="https://gatb.inria.fr/software/minia/" title="Minia">Minia</a>: De novo short read assembler</li>
<li><a href="https://gatb.inria.fr/de-novo-genome-assembly/">de-novo pipeline</a>:&nbsp;<em>de-novo</em>&nbsp;assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes</li>
<li><a href="https://gatb.inria.fr/software/mapsembler/" title="Mapsembler2">Mapsembler2</a>: Targeted assembly (not maintained)</li>
</ul><h2>Managing k-mers &amp; indexation</h2><ul>
<li><a href="https://github.com/lrobidou/findere">findere</a>:&nbsp;simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
<ul>
<li><a href="https://github.com/lrobidou/fimpera">fimpera</a>&nbsp;extends findere adding the abundance information.</li>
</ul>
</li>
<li><a href="https://github.com/tlemane/kmtricks">kmtricks</a>:&nbsp;modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.</li>
<li><a href="https://github.com/tlemane/kmindex">kmindex&nbsp;</a>is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.</li>
<li><a href="https://github.com/pierrepeterlongo/back_to_sequences">back to sequences</a>: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.</li>
<li><a href="https://github.com/vicLeva/bqf">Backpack Quotient Filter</a>:&nbsp;k-mer indexing data structure with abundance</li>
<li><a href="http://github.com/GATB/rconnector">short read connector</a>:&nbsp;Detect similar reads from potentially large read set</li>
<li><a href="https://gatb.inria.fr/software/dsk/" title="DSK">DSK</a>:&nbsp;Count K-mer in sequences</li>
</ul><h2>Pangenome graph manipulation</h2><ul>
<li><a href="https://github.com/Tharos-ux/pancat">Pancat</a>: Pangenome Comparison and Analysis Toolkit</li>
<li><a href="https://pypi.org/project/gfagraphs/">GFAGraphs</a>: a Python library to handle pangenome graph files in GFA format.</li>
</ul><h2>Comparative metagenomics with k-mers</h2><ul>
<li><a href="https://github.com/GATB/simka">Simka and SimkaMin</a>:&nbsp;Comparative metagenomics for large-scale datasets</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/compreads-metagenomic-data-analysis/">Comparead &amp; Commet</a>:&nbsp;comparison of metagenomic datasets</li>
</ul><h2>Species and bacterial strains identification</h2><ul>
<li><a href="https://github.com/gsiekaniec/ORI">ORI</a>: software using long nanopore reads to identify bacteria present in a sample at the strain level</li>
<li><a href="https://github.com/kevsilva/StrainFLAIR">StrainFLAIR</a>:&nbsp;STRAIN-level proFiLing using vArIation gRaph</li>
</ul><h2>General-purpose sequencing data manipulation</h2><ul>
<li><a href="https://team.inria.fr/genscale/ngs-software/gassst/">GASSST</a>:&nbsp;long read mapper</li>
<li><a href="https://gatb.inria.fr/software/leon/" title="Leon">Leon</a>: short read compressor (now included in GATB-core)</li>
<li><a href="https://gatb.inria.fr/software/bloocoo/" title="Bloocoo">Bloocoo</a>:&nbsp;short read corrector</li>
<li><a href="https://github.com/GATB/bcalm">BCALM</a>:&nbsp;Construct compacted de Bruijn graphs (unitigs)</li>
</ul><h2>&nbsp;Protein Structure</h2><ul>
<li><a href="https://team.inria.fr/genscale/protein-structure/a-purva-contact-map-overlap-solver/">A_Purva</a>:&nbsp;Contact Map Overlap solver</li>
<li><a href="https://team.inria.fr/genscale/protein-structure/md-jeep-distance-geomtry-solver/">MD-Jeep</a>:&nbsp;Distance Geometry solver</li>
<li><a href="https://team.inria.fr/genscale/csa-comparative-structural-alignment/">CSA</a>:&nbsp;Comparative Structural Alignment</li>
</ul><h2>Workflow</h2><ul>
<li><a href="https://team.inria.fr/genscale/workflows/slicee/">SLICEE</a>:&nbsp;parallel execution of bioinformatics workflows</li>
</ul><h3>Comparative Genomics</h3><ul>
<li><a href="https://team.inria.fr/genscale/comparative-genomics/cassis/">CASSIS</a>:&nbsp;detection of rearrangement breakpoints</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/plast-intensive-sequence-comparison/">PLAST</a>:&nbsp;intensive bank-to-bank sequence comparison</li>
<li><a href="https://github.com/stephanierobin/DrjBreakpointFinder">DRJBreakpointFinder</a>: detection and precise localization of excision sites in proviral segments</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats</guid>
	<pubDate>Thu, 07 Mar 2019 21:13:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39104/hipstr-haplotype-inference-and-phasing-for-short-tandem-repeats</link>
	<title><![CDATA[HipSTR: Haplotype inference and phasing for Short Tandem Repeats]]></title>
	<description><![CDATA[<p><span>HipSTR</span>&nbsp;was specifically developed to deal with these errors in the hopes of obtaining more robust STR genotypes. In particular, it accomplishes this by:</p>
<ol>
<li>Learning locus-specific PCR stutter models using an&nbsp;<a href="http://en.wikipedia.org/wiki/Expectation-maximization_algorithm">EM algorithm</a></li>
<li>Mining candidate STR alleles from population-scale sequencing data</li>
<li>Employing a specialized hidden Markov model to align reads to candidate alleles while accounting for STR artifacts</li>
<li>Utilizing phased SNP haplotypes to genotype and phase STRs</li>
</ol><p>Address of the bookmark: <a href="https://github.com/tfwillems/HipSTR" rel="nofollow">https://github.com/tfwillems/HipSTR</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37241/remilo-reference-assisted-misassembly-detection-algorithm-using-short-and-long-reads</guid>
	<pubDate>Fri, 06 Jul 2018 04:27:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37241/remilo-reference-assisted-misassembly-detection-algorithm-using-short-and-long-reads</link>
	<title><![CDATA[ReMILO: reference assisted misassembly detection algorithm using short and long reads.]]></title>
	<description><![CDATA[ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.<p>Address of the bookmark: <a href="https://github.com/songc001/remilo" rel="nofollow">https://github.com/songc001/remilo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</guid>
	<pubDate>Mon, 05 Aug 2024 23:01:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</link>
	<title><![CDATA[UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment]]></title>
	<description><![CDATA[<p>UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment</p>
<ul>
<li>
<p>Using state of the art tools, easily extended for other viruses</p>
</li>
<li>
<p>Tool and database updates for critical components via Conda</p>
</li>
<li>
<p>Built using modern design patterns with Conda and Snakemake</p>
</li>
<li>
<p>Extensible and easy to customize</p>
</li>
<li>
<p>Submission Ready Genomes</p>
</li>
<li>
<p>Customizable reporting with comprehensive visualization</p>
</li>
</ul>
<p>https://ikim-essen.github.io/uncovar/</p>
<p>Github&nbsp;https://github.com/IKIM-Essen/uncovar</p>
<p>&nbsp;</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://ikim-essen.github.io/uncovar/" rel="nofollow">https://ikim-essen.github.io/uncovar/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43583/pango-lineage-analysis</guid>
	<pubDate>Mon, 15 Nov 2021 03:38:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43583/pango-lineage-analysis</link>
	<title><![CDATA[Pango Lineage Analysis !]]></title>
	<description><![CDATA[<p>The Pango nomenclature is being used by researchers and public health agencies worldwide to track the transmission and spread of SARS-CoV-2, including variants of concern. This website documents all current Pango lineages and their spread, as well as various software tools which can be used by researchers to perform analyses on SARS-COV-2 sequence data.</p><p>Address of the bookmark: <a href="https://cov-lineages.org/resources/pangolin/output.html" rel="nofollow">https://cov-lineages.org/resources/pangolin/output.html</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33901/rnacon-web-server-for-the-prediction-and-classification-of-non-coding-rnas</guid>
	<pubDate>Mon, 17 Jul 2017 04:55:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33901/rnacon-web-server-for-the-prediction-and-classification-of-non-coding-rnas</link>
	<title><![CDATA[RNAcon: web-server for the prediction and classification of non-coding RNAs]]></title>
	<description><![CDATA[<p style="text-align: justify;">RNAcon is a web-server for the prediction and classification of non-coding RNAs. It uses SVM-based model for the discrimination between coding and ncRNAs and RandomForest-based prediction model for the classification of ncRNAs into different classes. The structural information based graph properties were used for the development of prediction model.</p>
<p style="text-align: justify;">The&nbsp;<a href="http://crdd.osdd.net/raghava/rnacon/RNAcon_v1.0.tar.gz">standalone version (Linux-based command-line) of RNAcon</a>&nbsp;is freely available for the global scientific community.</p>
<p style="text-align: justify;">Reference:&nbsp;<a href="http://www.biomedcentral.com/1471-2164/15/127/abstract">Panwar, B.; Arora, A. and Raghava, G.P.S. (2014) Prediction and classification of ncRNAs using structural information</a>BMC Genomics 2014, 15:127</p><p>Address of the bookmark: <a href="http://crdd.osdd.net/raghava/rnacon/" rel="nofollow">http://crdd.osdd.net/raghava/rnacon/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</guid>
	<pubDate>Thu, 26 Aug 2021 10:28:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</link>
	<title><![CDATA[k-mers tutorial - classification and taxonomy]]></title>
	<description><![CDATA[<p>DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to&nbsp;<a href="http://www.pnas.org/content/109/33/13272">store k-mer graphs efficiently</a>,&nbsp;<a href="http://ivory.idyll.org/blog/what-is-diginorm.html">discard redundant data</a>, and&nbsp;<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0101271">count them efficiently</a>.</p>
<p>More recently, we've been enthused about&nbsp;<a href="http://joss.theoj.org/papers/3d793c6e7db683bee7c03377a4a7f3c9">using k-mer based similarity measures</a>&nbsp;and&nbsp;<a href="http://ivory.idyll.org/blog/2016-sourmash-sbt.html">computing and searching k-mer-based sketch search databases for all the things</a>.</p>
<p>But I haven't spent too much talking about using k-mers for taxonomy, although that has become an&nbsp;<em>ahem</em>&nbsp;area of interest recently,&nbsp;<a href="http://www.biorxiv.org/content/early/2017/07/03/155358">if you read into our papers a bit</a>.</p>
<p>In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!</p><p>Address of the bookmark: <a href="http://ivory.idyll.org/blog/2017-something-about-kmers.html" rel="nofollow">http://ivory.idyll.org/blog/2017-something-about-kmers.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</guid>
	<pubDate>Tue, 08 Nov 2022 03:39:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</link>
	<title><![CDATA[Tools for RNA classification]]></title>
	<description><![CDATA[<p><span>barrnap</span>&nbsp;-&nbsp;<a href="https://github.com/tseemann/barrnap" target="_blank">https://github.com/tseemann/barrnap</a></p><p><span>CPAT</span>&nbsp;-&nbsp;<a href="https://github.com/liguowang/cpat" target="_blank">https://github.com/liguowang/cpat</a>,&nbsp;<a href="http://lilab.research.bcm.edu/" target="_blank">http://lilab.research.bcm.edu/</a>&nbsp;(web server)</p><p><span>CPC2</span>&nbsp;-&nbsp;<a href="https://github.com/gao-lab/CPC2_standalone" target="_blank">https://github.com/gao-lab/CPC2_standalone</a>,&nbsp;<a href="http://cpc2.gao-lab.org/" target="_blank">http://cpc2.gao-lab.org/</a>&nbsp;(web server)</p><p><span>Infernal</span>&nbsp;-&nbsp;<a href="http://eddylab.org/infernal/" target="_blank">http://eddylab.org/infernal/</a>,&nbsp;<a href="https://github.com/EddyRivasLab/infernal" target="_blank">https://github.com/EddyRivasLab/infernal</a></p><p><span>NCBI RefSeq</span>&nbsp;-&nbsp;<a href="https://www.ncbi.nlm.nih.gov/refseq/" target="_blank">https://www.ncbi.nlm.nih.gov/refseq/</a></p><p><span>Rfam</span>&nbsp;-&nbsp;<a href="http://rfam.xfam.org/" target="_blank">http://rfam.xfam.org/</a>,&nbsp;<a href="https://docs.rfam.org/en/latest/index.html" target="_blank">https://docs.rfam.org/en/latest/index.html</a></p><p><span>SILVA</span>&nbsp;-&nbsp;<a href="https://www.arb-silva.de/" target="_blank">https://www.arb-silva.de/</a></p><p><span>RNAmmer</span>&nbsp;-&nbsp;<a href="http://www.cbs.dtu.dk/services/RNAmmer/" target="_blank">http://www.cbs.dtu.dk/services/RNAmmer/</a>&nbsp;(web server, standalone download link)</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

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