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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/13842?offset=830</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42923/flanker</guid>
	<pubDate>Sat, 27 Feb 2021 22:04:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42923/flanker</link>
	<title><![CDATA[Flanker]]></title>
	<description><![CDATA[<p><span>Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of&nbsp;<span>mobile genetic elements (</span>MGEs) without prior knowledge of their structure.&nbsp;<span>Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.</span></span></p>
<p><span><img src="https://github.com/wtmatlock/flanker/raw/main/docs/frontpage.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/wtmatlock/flanker" rel="nofollow">https://github.com/wtmatlock/flanker</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43088/iva-accurate-de-novo-assembly-of-rna-virus-genomes</guid>
	<pubDate>Wed, 23 Jun 2021 07:51:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43088/iva-accurate-de-novo-assembly-of-rna-virus-genomes</link>
	<title><![CDATA[IVA: accurate de novo assembly of RNA virus genomes]]></title>
	<description><![CDATA[<p>IVA (Iterative Virus Assembler) designed specifically for read pairs sequenced at highly variable depth from RNA virus samples. We tested IVA on datasets from 140 sequenced samples from human immunodeficiency virus-1 or influenza-virus-infected people and demonstrated that IVA outperforms all other virus de novo assemblers.</p>
<p><strong> Availability and implementation: </strong> The software runs under Linux, has the GPLv3 licence and is freely available from http://sanger-pathogens.github.io/iva</p>
<p>https://pubmed.ncbi.nlm.nih.gov/25725497/</p><p>Address of the bookmark: <a href="https://github.com/sanger-pathogens/iva" rel="nofollow">https://github.com/sanger-pathogens/iva</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</guid>
	<pubDate>Sat, 11 Sep 2021 00:28:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</link>
	<title><![CDATA[RagTag: a collection of software tools for scaffolding and improving modern genome assemblies]]></title>
	<description><![CDATA[<p>RagTag is a collection of software tools for scaffolding and improving modern genome assemblies. Tasks include:</p>
<ul>
<li>Homology-based misassembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/correct">correction</a></li>
<li>Homology-based assembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/scaffold">scaffolding</a>&nbsp;and&nbsp;<a href="https://github.com/malonge/RagTag/wiki/patch">patching</a></li>
<li>Scaffold&nbsp;<a href="https://github.com/malonge/RagTag/wiki/merge">merging</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/malonge/RagTag" rel="nofollow">https://github.com/malonge/RagTag</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps</guid>
	<pubDate>Fri, 10 Dec 2021 06:22:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps</link>
	<title><![CDATA[Illumina based assembly pipeline steps !]]></title>
	<description><![CDATA[<h3 id="illumina">Illumina<a href="https://nf-co.re/viralrecon#illumina"><span></span></a></h3><ol>
<li>Merge re-sequenced FastQ files (<a href="http://www.linfo.org/cat.html"><code>cat</code></a>)</li>
<li>Read QC (<a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/"><code>FastQC</code></a>)</li>
<li>Adapter trimming (<a href="https://github.com/OpenGene/fastp"><code>fastp</code></a>)</li>
<li>Removal of host reads (<a href="http://ccb.jhu.edu/software/kraken2/"><code>Kraken 2</code></a>; <em>optional</em>)</li>
<li>Variant calling<ol>
<li>Read alignment (<a href="http://bowtie-bio.sourceforge.net/bowtie2/index.shtml"><code>Bowtie 2</code></a>)</li>
<li>Sort and index alignments (<a href="https://sourceforge.net/projects/samtools/files/samtools/"><code>SAMtools</code></a>)</li>
<li>Primer sequence removal (<a href="https://github.com/andersen-lab/ivar"><code>iVar</code></a>; <em>amplicon data only</em>)</li>
<li>Duplicate read marking (<a href="https://broadinstitute.github.io/picard/"><code>picard</code></a>; <em>optional</em>)</li>
<li>Alignment-level QC (<a href="https://broadinstitute.github.io/picard/"><code>picard</code></a>, <a href="https://sourceforge.net/projects/samtools/files/samtools/"><code>SAMtools</code></a>)</li>
<li>Genome-wide and amplicon coverage QC plots (<a href="https://github.com/brentp/mosdepth/"><code>mosdepth</code></a>)</li>
<li>Choice of multiple variant calling and consensus sequence generation routes (<a href="https://github.com/andersen-lab/ivar"><code>iVar variants and consensus</code></a>; <em>default for amplicon data</em> <em>||</em> <a href="http://samtools.github.io/bcftools/bcftools.html"><code>BCFTools</code></a>, <a href="https://github.com/arq5x/bedtools2/"><code>BEDTools</code></a>; <em>default for metagenomics data</em>)
<ul>
<li>Variant annotation (<a href="http://snpeff.sourceforge.net/SnpEff.html"><code>SnpEff</code></a>, <a href="http://snpeff.sourceforge.net/SnpSift.html"><code>SnpSift</code></a>)</li>
<li>Consensus assessment report (<a href="http://quast.sourceforge.net/quast"><code>QUAST</code></a>)</li>
<li>Lineage analysis (<a href="https://github.com/cov-lineages/pangolin"><code>Pangolin</code></a>)</li>
<li>Clade assignment, mutation calling and sequence quality checks (<a href="https://github.com/nextstrain/nextclade"><code>Nextclade</code></a>)</li>
<li>Individual variant screenshots with annotation tracks (<a href="https://asciigenome.readthedocs.io/en/latest/"><code>ASCIIGenome</code></a>)</li>
</ul>
</li>
<li>Intersect variants across callers (<a href="http://samtools.github.io/bcftools/bcftools.html"><code>BCFTools</code></a>)</li>
</ol></li>
<li><em>De novo</em> assembly<ol>
<li>Primer trimming (<a href="https://cutadapt.readthedocs.io/en/stable/guide.html"><code>Cutadapt</code></a>; <em>amplicon data only</em>)</li>
<li>Choice of multiple assembly tools (<a href="http://cab.spbu.ru/software/spades/"><code>SPAdes</code></a> <em>||</em> <a href="https://github.com/rrwick/Unicycler"><code>Unicycler</code></a> <em>||</em> <a href="https://github.com/GATB/minia"><code>minia</code></a>)
<ul>
<li>Blast to reference genome (<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch"><code>blastn</code></a>)</li>
<li>Contiguate assembly (<a href="https://www.sanger.ac.uk/science/tools/pagit"><code>ABACAS</code></a>)</li>
<li>Assembly report (<a href="https://github.com/BU-ISCIII/plasmidID"><code>PlasmidID</code></a>)</li>
<li>Assembly assessment report (<a href="http://quast.sourceforge.net/quast"><code>QUAST</code></a>)</li>
</ul>
</li>
</ol></li>
<li>Present QC and visualisation for raw read, alignment, assembly and variant calling results (<a href="http://multiqc.info/"><code>MultiQC</code></a>)</li>
</ol>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</guid>
	<pubDate>Thu, 03 Feb 2022 04:01:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</link>
	<title><![CDATA[chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.]]></title>
	<description><![CDATA[<p>chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.</p>
<p dir="auto">USAGE:</p>
<ul dir="auto">
<li>-query: sequence A in fasta format</li>
<li>-db: sequence B in fasta format</li>
<li>-out: output matrix</li>
<li>-kmer Integer: k&gt;1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria</li>
<li>-diffuse Integer: z&gt;0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1</li>
<li>-dimension Size of the output matrix and plot. Integer: d&gt;0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended</li>
</ul><p>Address of the bookmark: <a href="https://github.com/estebanpw/chromeister" rel="nofollow">https://github.com/estebanpw/chromeister</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</guid>
	<pubDate>Thu, 31 Mar 2022 23:58:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</link>
	<title><![CDATA[The complete sequence of a human genome]]></title>
	<description><![CDATA[<p><span>The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.</span></p><p>Address of the bookmark: <a href="https://www.science.org/doi/10.1126/science.abj6987" rel="nofollow">https://www.science.org/doi/10.1126/science.abj6987</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</guid>
	<pubDate>Tue, 25 Apr 2023 20:58:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</link>
	<title><![CDATA[JBrowse 2: a modular genome browser with views of synteny and structural variation]]></title>
	<description><![CDATA[<ul dir="auto">
<li>igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)</li>
<li>jb2-web - stock instance of jbrowse-web v1.7.5</li>
<li>jb1 - stock instance of jbrowse 1 v1.16.11</li>
<li>jb2 embedded - a create-react-app with @jbrowse/react-linear-genome-view</li>
</ul><p>Address of the bookmark: <a href="https://github.com/GMOD/jb2profile" rel="nofollow">https://github.com/GMOD/jb2profile</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</guid>
	<pubDate>Fri, 15 Mar 2024 04:59:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</link>
	<title><![CDATA[bacLIFE: an automated genome mining tool for identification of lifestyle associated genes]]></title>
	<description><![CDATA[<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated<span>&nbsp;</span><strong style="font-weight: var(--base-text-weight-semibold, 600);">lifestyle-associated genes (LAGs)</strong>, and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:</p>
<ul style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">
<li><strong style="font-weight: var(--base-text-weight-semibold, 600);">Clustering module</strong><span>&nbsp;</span>Predicts, clusters and annotates the genes of every input genome</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Lifestyle prediction</strong><span>&nbsp;</span>Employs a machine learning model to forecast bacterial lifestyle or other specified metadata</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Analitical module (Shiny app)</strong><span>&nbsp;</span>Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.</li>
</ul>
<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">You can find the complete wiki here [<a href="https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki">https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki</a>]</p><p>Address of the bookmark: <a href="https://github.com/Carrion-lab/bacLIFE" rel="nofollow">https://github.com/Carrion-lab/bacLIFE</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45278/the-day-we-began-reading-the-dna-of-the-world</guid>
	<pubDate>Sat, 05 Sep 2026 01:43:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45278/the-day-we-began-reading-the-dna-of-the-world</link>
	<title><![CDATA[The Day We Began Reading the DNA of the World]]></title>
	<description><![CDATA[<div>Picture waking up and knowing that in one lab, a scientist is reading the DNA of a whale. In another, a team is sequencing a rare plant. Meanwhile, researchers in India are decoding the genomes of different human populations.</div><div>&nbsp;</div><div>Many organisms. Many countries. All share one remarkable goal: to understand the genetic story of life.</div><div>DNA is nature's instruction manual, written with just four letters: A, T, C, and G. For years, scientists could only read small sections of this huge book. Now, new sequencing technology lets us read entire genomes like never before.</div><div>&nbsp;</div><div>One of the most ambitious projects is the Earth BioGenome Project (EBP). Its goal is to create reference genomes for about 1.5 million known eukaryotic species. This project links genome research across continents, species, and scientific fields.</div><div>&nbsp;</div><div>But this story goes beyond just animals and plants.</div><div>&nbsp;</div><div><strong>A New Chapter in Human Genomics</strong></div><div>&nbsp;</div><div>Large human genome projects are changing how we understand our own genetic diversity.</div><div>India's GenomeIndia project has sequenced thousands of people from different populations. This work is uncovering genetic variation that global databases have often missed.</div><div>&nbsp;</div><div>Similar population-scale effSimilar large-scale projects are happening worldwide. The All of Us Research Program in the United States, Europe's 1+ Million Genomes initiative, South Korea's national genomic data program, and projects in Saudi Arabia, Australia, and Singapore are all building huge genomic resources.collect DNA.</div><div>&nbsp;</div><div>The real goal is to link genomes with health, disease, and other biological information. This creates a base for more accurate research and, in time, more personalized medicine.</div><div>&nbsp;</div><div><strong>A Race Against Time</strong></div><div>&nbsp;</div><div>There is also an important twist.</div><div>&nbsp;</div><div>We are sequencing Earth's biodiversity even as many species face growing environmental threats.</div><div>A genome alone cannot save a species, but it does keep important information about its biology, evolution, and genetic diversity. This knowledge helps scientists understand risks, guide conservation, and find traits that could help agriculture or medicine.</div><div>&nbsp;</div><div>Projects like the Darwin Tree of Life, which studies species in Britain and Ireland, and the African BioGenome Project, which is growing genomics work across Africa, are key parts of this worldwide effort.</div><div>&nbsp;</div><div>Projects to Watch</div><ol>
<li>Earth BioGenome Project (EBP) - A global effort to sequence and annotate roughly 1.5 million known eukaryotic species and create a comprehensive reference library of Earth's biodiversity.</li>
<li>Vertebrate Genomes Project (VGP) - Focuses on producing high-quality reference genomes for vertebrate species, providing a major foundation for the wider Tree of Life.</li>
<li>Darwin Tree of Life - A UK and Ireland initiative aiming to sequence approximately 70,000 species, creating a detailed genomic map of regional biodiversity.</li>
<li>African BioGenome Project (AfricaBP) - A pan-African initiative focused on sequencing Africa's biodiversity while strengthening genomics and bioinformatics capacity across the continent.</li>
<li>European Reference Genome Atlas (ERGA) - A European effort to generate high-quality reference genomes for Europe's biodiversity and connect national sequencing efforts.</li>
<li>Canada BioGenome Project - Building reference genomes for Canadian biodiversity, supporting conservation, research and the study of ecosystems.</li>
<li>California Conservation Genomics Project - Uses genomics to understand and protect California's biodiversity and help inform conservation decisions.</li>
<li>Bat1K - An ambitious global effort to sequence the genomes of all known bat species, helping scientists study their evolution, longevity, immunity and unique biology.</li>
<li>Bird 10,000 (B10K) - A global project aiming to create genome resources covering the world's bird diversity and reconstruct avian evolutionary history.</li>
<li>Global Invertebrate Genomics Alliance (GIGA) - Builds genomic resources for the enormous and genetically diverse world of invertebrates.</li>
<li>GenomeIndia - India's national human-genome initiative, designed to capture the country's extraordinary population diversity and create a reference resource for Indian genomics.</li>
<li>All of Us Research Program - A US national research program combining genomic information with health and lifestyle data at population scale.</li>
<li>1+ Million Genomes / Genome of Europe - A European effort to enable secure, cross-border use of genomic and health data and develop genome-scale resources involving more than one million people.</li>
<li>Korea National Integrated Bio Big Data Project - South Korea's large-scale national effort combining genomic and health information from a very large population cohort.</li>
<li>Saudi Genome Program - A national genomics initiative focused on genetic diseases, population genomics and precision medicine in Saudi Arabia.</li>
<li>Australian Genomics / Genomics Health Futures Mission - Australia's growing investment in genomic medicine, rare disease research and population-scale health genomics.</li>
</ol><div><strong>The Library of Life</strong></div><div>&nbsp;</div><div>The fuThe future of genomics is not just about sequencing one genome at a time. Now, it is about creating a worldwide network of genomes&mdash;human and non-human, individual and species&mdash;linked by powerful databases and advanced computational tools.ine a library without shelves.</div><div>&nbsp;</div><div>In this library, the books are genomes. The pages are made of DNA. Scientists and machines are the readers. And this library is still being written. Each genome sequenced today adds a new page to the story of life and helps us better understand our place in it.</div>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42958/claus-peter-stelzer-lab</guid>
  <pubDate>Mon, 15 Mar 2021 15:24:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[Claus-Peter Stelzer Lab]]></title>
  <description><![CDATA[
<p>Interested in various topics at the intersection of ecology and evolution. In my research I use rotifers as model organisms for experimental studies at the individual and population level. Rotifers are ideally suited for this, because populations of thousands can be kept in small containers in the lab, while single individuals can still be handled conveniently. </p>

<p>More at https://www.uibk.ac.at/limno/personnel/stelzer/index.html.en#research</p>
]]></description>
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