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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/5166?offset=40</link>
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	<description><![CDATA[]]></description>
	
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	<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>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44516/16srna-database-download</guid>
	<pubDate>Wed, 24 Apr 2024 04:33:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44516/16srna-database-download</link>
	<title><![CDATA[16sRNA Database Download]]></title>
	<description><![CDATA[<p>Downloading 16S rRNA databases can be crucial for various bioinformatics analyses, especially in microbiome research. However, it's important to note that databases can vary based on your specific needs, such as the taxonomic coverage you require or the type of analysis you're performing. Here's a general guideline on how you can obtain 16S rRNA databases:</p><ol>
<li>
<p><span>NCBI (National Center for Biotechnology Information)</span>:</p>
<ul>
<li>NCBI provides various databases related to genetic information, including 16S rRNA sequences.</li>
<li>You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (<a href="https://www.ncbi.nlm.nih.gov/nucleotide/" target="_new">https://www.ncbi.nlm.nih.gov/nucleotide/</a>).</li>
<li>Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.</li>
<li>You can download sequences individually or in batches using the provided tools.</li>
</ul>
</li>
<li>
<p><span>GreenGenes</span>:</p>
<ul>
<li>GreenGenes is a widely used 16S rRNA gene sequence database.</li>
<li>You can access it at <a target="_new">http://greengenes.secondgenome.com/</a>.</li>
<li>GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.</li>
</ul>
</li>
<li>
<p><span>SILVA</span>:</p>
<ul>
<li>SILVA (<a href="https://www.arb-silva.de/" target="_new">https://www.arb-silva.de/</a>) is another comprehensive database for ribosomal RNA (rRNA) sequences.</li>
<li>It covers not only 16S rRNA but also other ribosomal RNA sequences.</li>
<li>SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.</li>
</ul>
</li>
<li>
<p><span>Ribosomal Database Project (RDP)</span>:</p>
<ul>
<li>RDP (<a target="_new">http://rdp.cme.msu.edu/</a>) is a curated database that offers 16S rRNA sequences.</li>
<li>It provides tools for sequence analysis and classification.</li>
<li>You can download sequences and taxonomy information from their website.</li>
</ul>
</li>
<li>
<p><span>QIIME (Quantitative Insights Into Microbial Ecology)</span>:</p>
<ul>
<li>QIIME (<a href="https://qiime2.org/" target="_new">https://qiime2.org/</a>) is a widely used bioinformatics platform for microbiome analysis.</li>
<li>It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.</li>
<li>QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.</li>
</ul>
</li>
</ol><p>Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.</p><p>&nbsp;</p><p>NCBI 16s RNA database location&nbsp;ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</guid>
	<pubDate>Wed, 17 Jul 2013 15:50:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</link>
	<title><![CDATA[Bioinformatics approach to Boar Taint]]></title>
	<description><![CDATA[<p><span>Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole&nbsp;in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to &gt;10X average coverage. Short reads generated from these samples mapped to&nbsp;<em>Sus Scrofa</em>&nbsp;version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.</span><span>&nbsp;</span></p><p><span>&nbsp;</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/989" length="19688" type="image/jpeg" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/view/1926</guid>
	<pubDate>Sun, 11 Aug 2013 11:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/1926</link>
	<title><![CDATA[Want to Know which genome assembler rule the world ?]]></title>
	<description><![CDATA[<p><span><strong>Assemblathon 2</strong>: evaluating de novo methods of genome assembly&nbsp;</span></p><p><span><a href="http://www.gigasciencejournal.com/content/2/1/10/abstract">http://www.gigasciencejournal.com/content/2/1/10/abstract</a></span></p><p><span><a href="http://blogs.nature.com/news/2013/07/genome-assembly-contest-prompts-soul-searching.html">http://blogs.nature.com/news/2013/07/genome-assembly-contest-prompts-soul-searching.html</a></span></p><p><a href="http://assemblathon.org/post/44431915644/feedback-and-analysis-of-the-assemblathon-2-p">http://assemblathon.org/post/44431915644/feedback-and-analysis-of-the-assemblathon-2-p</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4195/barber-pole-worm-sheep-pathogen-sequenced</guid>
	<pubDate>Tue, 03 Sep 2013 16:32:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4195/barber-pole-worm-sheep-pathogen-sequenced</link>
	<title><![CDATA[Barber pole worm , sheep pathogen sequenced !!!]]></title>
	<description><![CDATA[<p>Haemonchus contortus is a highly pathogenic parasitic nematode of that can infect a large number of wild and domesticated ruminant species and is the most economically important parasite of sheep and goats worldwide. Scientists at the Wellcome Trust Sanger Institute have sequenced the genome of the barber's pole worm (Haemonchus contortus), which will help to explore the this tropical parasite which&nbsp;been disseminated around the world by livestock movement.&nbsp;</p><p>H. contortus is a member of the superfamily trichostrongyloidea (Strongylida) which contains most of the economically important parasitic nematodes of grazing livestock. These parasites cost the global livestock industry billions of dollars per annum in lost production and drug costs.&nbsp;A common type of clover may be a preventative or palliative for the disease. However, some particular breeds of sheep, such as the Gulf Coast Native from the Southern United States, have been shown to have developed special resistance to H. contortus.</p><p>Getting the full genome can help to tackle the problem and understand the resistance mechanism with an ease. Moreover, the genome could now provide a comprehensive understanding of how treatments against parasitic worms work and point to further new treatments and vaccines.&nbsp;By comparing the genome of the barber's pole worm with those of worms that have acquired drug resistance, researchers expect to reveal information about how and why resistance has occurred. Till now, researchers have uncovered essential information in the fight against drug resistance in worms.</p><p>Reference:</p><p><a href="http://www.fwi.co.uk/articles/28/08/2013/140758/researchers-close-in-on-worm-resistance-in-sheep.htm">http://www.fwi.co.uk/articles/28/08/2013/140758/researchers-close-in-on-worm-resistance-in-sheep.htm</a></p><p><a href="http://www.sciencedaily.com/releases/2013/08/130828103351.htm?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+sciencedaily%2Fplants_animals+(ScienceDaily%3A+Plants+%26+Animals+News)">http://www.sciencedaily.com/releases/2013/08/130828103351.htm?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+sciencedaily%2Fplants_animals+(ScienceDaily%3A+Plants+%26+Animals+News)</a></p><p>Image source: Wikipedia</p><p><img src="http://upload.wikimedia.org/wikipedia/commons/8/8e/Haemonchus_contortus.jpg" alt="image" width="800" height="533" style="border: 0px; border: 0px;"></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy</guid>
	<pubDate>Tue, 26 Nov 2013 17:34:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy</link>
	<title><![CDATA[DNA tale of 3 to 4 years old Serbia boy]]></title>
	<description><![CDATA[<p><span>The genome of a young boy found underground at Mal&rsquo;ta near Lake Baikal of eastern Siberia around 24,000 years ago came out as close relative of Europeans and Native Indians.</span></p><p><span>Link:</span></p><p><span><a href="http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0">http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0</a></span></p><p>&nbsp;</p><p><a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html">http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10237/genome-of-rainbow-trout-sequenced</guid>
	<pubDate>Fri, 25 Apr 2014 10:36:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10237/genome-of-rainbow-trout-sequenced</link>
	<title><![CDATA[Genome of Rainbow Trout Sequenced]]></title>
	<description><![CDATA[<p>Major finding:</p><p><span>&ldquo;In humans and most vertebrates the duplication events were older so there are fewer duplicated genes still present. Most of the duplicated genes get lost or modified so much that they are no longer recognizable as duplicates over time. In the trout and salmon we can see an earlier stage in the process and many duplicated genes are still present,&rdquo; said Dr Gary Thorgaard of Washington State University, a co-author of the paper published in the journal Nature Communications.</span></p><p><span>Source:</span></p><p><span>http://www.sci-news.com/genetics/science-genome-rainbow-trout-01877.html</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10378/real-time-sequencing</guid>
	<pubDate>Sun, 04 May 2014 18:16:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10378/real-time-sequencing</link>
	<title><![CDATA[Real time Sequencing]]></title>
	<description><![CDATA[<p><span>&ldquo;... we now know we can do high-throughput sequencing at any location on Earth,&rdquo; Moroz said.</span></p><p><span>Source:</span></p><p><span>http://news.ufl.edu/2014/04/28/real-time-genome-sequencing-at-sea/</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/11365/drawback-of-exome-sequencing</guid>
	<pubDate>Mon, 02 Jun 2014 05:46:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/11365/drawback-of-exome-sequencing</link>
	<title><![CDATA[Drawback of Exome Sequencing]]></title>
	<description><![CDATA[<p><span><span>Dr Eric Londin, Assistant Professor, Thomas Jefferson University, USA, stated that analysis of 44 exome datasets from four different testing kits showed that they missed a high proportion of clinically relevant regions in the 56 ACMG genes. "At least one gene in each exome method was missing more than 40 percent of disease-causing genetic variants, and we found that the worst-performing method missed more than 90 percent of such variants in four of the 56 genes," he says.</span><br /></span></p><p><span><strong>Source</strong>:&nbsp;http://www.eurekalert.org/pub_releases/2014-05/esoh-pco052914.php</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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