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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30973?offset=320</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</guid>
	<pubDate>Tue, 29 Jul 2014 09:11:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</link>
	<title><![CDATA[The Genome 10K Project]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/B57xDIGtCT0" frameborder="0" allowfullscreen></iframe>https://genome10k.soe.ucsc.edu

The Genome 10K project aims to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species, approximately one for every vertebrate genus. The trajectory of cost reduction in DNA sequencing suggests that this project will be feasible within a few years. Capturing the genetic diversity of vertebrate species would create an unprecedented resource for the life sciences and for worldwide conservation efforts.

The growing Genome 10K Community of Scientists (G10KCOS), made up of leading scientists representing major zoos, museums, research centers, and universities around the world, is dedicated to coordinating efforts in tissue specimen collection that will lay the groundwork for a large-scale sequencing and analysis project.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26322/liftover</guid>
	<pubDate>Mon, 08 Feb 2016 15:45:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26322/liftover</link>
	<title><![CDATA[liftover]]></title>
	<description><![CDATA[<p><span>Convenient conversions between genome assemblie.&nbsp;The liftover package makes it easy to remap genomic coordinates to a different genome assembly. </span></p>
<p><span>More at https://github.com/aaronwolen/liftover<br></span></p>
<p><span>https://www.bioconductor.org/help/workflows/liftOver/</span></p><p>Address of the bookmark: <a href="https://github.com/aaronwolen/liftover" rel="nofollow">https://github.com/aaronwolen/liftover</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26752/rna-seq-de-novo-assembly-using-trinity</guid>
	<pubDate>Wed, 23 Mar 2016 05:53:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26752/rna-seq-de-novo-assembly-using-trinity</link>
	<title><![CDATA[RNA-Seq De novo Assembly Using Trinity]]></title>
	<description><![CDATA[<p>Trinity, developed at the <a href="http://www.broadinstitute.org">Broad Institute</a> and the <a href="http://www.cs.huji.ac.il">Hebrew University of Jerusalem</a>, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:</p>
<ul>
<li>
<p><em>Inchworm</em> assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.</p>
</li>
<li>
<p><em>Chrysalis</em> clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.</p>
</li>
<li>
<p><em>Butterfly</em> then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.</p>
</li>
</ul>
<p>More at https://github.com/trinityrnaseq/trinityrnaseq/wiki</p>
<p>......................................................................................................................................</p>
<p>Download Trinity <a href="https://github.com/trinityrnaseq/trinityrnaseq/releases">here</a>.</p>
<p>Build Trinity by typing 'make' in the base installation directory.</p>
<p>Assemble RNA-Seq data like so:</p>
<pre><code> Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G 
</code></pre>
<p>Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'</p><p>Address of the bookmark: <a href="https://github.com/trinityrnaseq/trinityrnaseq/wiki" rel="nofollow">https://github.com/trinityrnaseq/trinityrnaseq/wiki</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26911/raca-reference-assisted-chromosome-assembly</guid>
	<pubDate>Wed, 06 Apr 2016 09:29:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26911/raca-reference-assisted-chromosome-assembly</link>
	<title><![CDATA[RACA: Reference-Assisted Chromosome Assembly]]></title>
	<description><![CDATA[<p>Rreference-Assisted Chromosome Assembly (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.</p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/23307812</p>
<p>http://bioen-compbio.bioen.illinois.edu/RACA/</p><p>Address of the bookmark: <a href="http://bioen-compbio.bioen.illinois.edu/RACA/" rel="nofollow">http://bioen-compbio.bioen.illinois.edu/RACA/</a></p>]]></description>
	<dc:creator>Priya Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26999/discovar</guid>
	<pubDate>Mon, 18 Apr 2016 11:59:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26999/discovar</link>
	<title><![CDATA[DISCOVAR]]></title>
	<description><![CDATA[<p><strong>DISCOVAR</strong> is a new variant caller and <strong>DISCOVAR <em>de novo</em></strong> a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer &mdash; produced on MiSeq or HiSeq 2500 &mdash; and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.</p>
<p><strong>DISCOVAR</strong> can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.</p>
<p><strong>DISCOVAR <em>de novo</em></strong> can generate <em>de novo</em> assemblies for both large and small genomes. It currently does not call variants.</p>
<p>More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/discovar/blog/" rel="nofollow">https://www.broadinstitute.org/software/discovar/blog/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27806/blobology</guid>
	<pubDate>Mon, 13 Jun 2016 10:18:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27806/blobology</link>
	<title><![CDATA[Blobology]]></title>
	<description><![CDATA[<p><span>Tools for making blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step</span></p>
<p>Blaxter Lab, Institute of Evolutionary Biology, University of Edinburgh</p>
<p><span>Goal</span>: To create blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step.</p>
<p>This repository accompanies the paper:<br><span>Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots.</span>&nbsp;<em>Sujai Kumar, Martin Jones, Georgios Koutsovoulos, Michael Clarke, Mark Blaxter</em><br>(submitted 2013-10-01 to&nbsp;<em>Frontiers in Bioinformatics and Computational Biology special issue : Quality assessment and control of high-throughput sequencing data</em>).</p>
<p>It contains bash/perl/R scripts for running the analysis presented in the paper to create a preliminary assembly, and to create and collate GC content, read coverage and taxon annotation for the preliminary assembly, which can be visualised, such as Figure 2a from the paper showing TAGC plots/blobplots for&nbsp;<em>Caenorhabditis</em>&nbsp;sp. 5:&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/blaxterlab/blobology" rel="nofollow">https://github.com/blaxterlab/blobology</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30012/swalo</guid>
	<pubDate>Wed, 30 Nov 2016 05:06:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30012/swalo</link>
	<title><![CDATA[SWALO]]></title>
	<description><![CDATA[<p>SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.</p>
<p><a href="https://atifrahman.github.io/SWALO/swalo-0.9.7-beta.tar.gz"><strong>Download</strong></a></p>
<p><strong>Git repository of SWALO is at <a href="https://github.com/atifrahman/SWALO">https://github.com/atifrahman/SWALO</a>.</strong></p><p>Address of the bookmark: <a href="https://atifrahman.github.io/SWALO/" rel="nofollow">https://atifrahman.github.io/SWALO/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34216/meraculous-de-novo-genome-assembly-with-short-paired-end-reads</guid>
	<pubDate>Tue, 07 Nov 2017 04:36:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34216/meraculous-de-novo-genome-assembly-with-short-paired-end-reads</link>
	<title><![CDATA[Meraculous: De Novo Genome Assembly with Short Paired-End Reads]]></title>
	<description><![CDATA[<p><span>We describe a new algorithm, meraculous, for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast&nbsp;</span><em>Pichia stipitis</em><span>. More than 95% of the genome is recovered, with no errors; half the assembled sequence is in contigs longer than 101 kilobases and in scaffolds longer than 269 kilobases. Incorporating fosmid ends recovers entire chromosomes. Meraculous relies on an efficient and conservative traversal of the subgraph of the&nbsp;</span><em>k</em><span>-mer (deBruijn) graph of oligonucleotides with unique high quality extensions in the dataset, avoiding an explicit error correction step as used in other short-read assemblers. A novel memory-efficient hashing scheme is introduced. The resulting contigs are ordered and oriented using paired reads separated by &sim;280 bp or &sim;3.2 kbp, and many gaps between contigs can be closed using paired-end placements. Practical issues with the dataset are described, and prospects for assembling larger genomes are discussed.</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158087/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158087/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/43292/bioinformatics-scientist-production-bioinformatics-south-san-francisco-ca</guid>
  <pubDate>Thu, 19 Aug 2021 08:45:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA]]></title>
  <description><![CDATA[
<p>wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.</p>

<p>More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/43559/job-offer-for-a-postdoctoral-researcher-in-genomics-bioinformatics-2-years</guid>
  <pubDate>Fri, 22 Oct 2021 04:44:33 -0500</pubDate>
  <link></link>
  <title><![CDATA[Job offer for a postdoctoral researcher in genomics / bioinformatics (2 years)]]></title>
  <description><![CDATA[
<p>Ongoing research in the group of Karine Van Doninck involves topics at the core of<br />evolutionary biology, including the evolution of sex, genome maintenance,<br />recombination and extreme stress resistance on different eukaryotic systems,<br />including rotifers, amoeba and Corbicula clams. We are employing different tools<br />(including experimental ecology, population genetics, phylogeny, comparative<br />genomics, transcriptomics, bioinformatics, molecular and cellular biology) to study<br />evolutionary processes at the level of populations, both experimental and natural, and<br />genomes.</p>

<p>Offer<br />We offer a full-time contract for two years. The contract starts between October 2021<br />and December 2021. The position involves no or extremely light teaching load, if the<br />candidate is interested. Salaries are competitive at the European level. The recruited<br />person will benefit from the Belgian social insurance scheme (health care, etc.) without<br />additional expenses.</p>

<p>Profile<br />Applicants are expected to show outstanding commitment to research and must have<br />obtained a PhD by the start of the position. A strong expertise in genomics is required.<br />More specifically, solid competences in bioinformatics (e.g. scripting pipelines) and in<br />genome evolution are needed. Knowledge or interest regarding recombination,<br />metazoan evolution, phylogenomics and population genomics is an added-value.</p>

<p>Application<br />Applications should be submitted via email to karine.van.doninck@ulb.be. The<br />application package should contain the following documents:<br />- A curriculum vitae with the complete list of publications<br />- A cover letter mentioning why the candidate is interested in the position<br />- Minimum 2 recommendation letters<br />Interviews: Interviews will be conducted with the selected candidates. Selected<br />candidates could also be invited to give a seminar to MBE ULB.<br />For any additional information, please contact karine.van.doninck@ulb.be</p>
]]></description>
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