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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42923?offset=70</link>
	<atom:link href="https://bioinformaticsonline.com/related/42923?offset=70" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43766/genometools-the-versatile-open-source-genome-analysis-software</guid>
	<pubDate>Wed, 02 Feb 2022 04:00:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43766/genometools-the-versatile-open-source-genome-analysis-software</link>
	<title><![CDATA[GenomeTools: The versatile open source genome analysis software]]></title>
	<description><![CDATA[<p>The&nbsp;<em>GenomeTools</em>&nbsp;genome analysis system is a&nbsp;<a href="http://genometools.org/license.html">free</a>&nbsp;collection of bioinformatics&nbsp;<a href="http://genometools.org/tools.html">tools</a>&nbsp;(in the realm of genome informatics) combined into a single binary named&nbsp;<em>gt</em>. It is based on a C library named &ldquo;libgenometools&rdquo; which consists of several modules.</p>
<p><img src="http://genometools.org/images/annotation.png" alt="image" style="border: 0px;"></p>
<p>If you are interested in gene prediction, have a look at&nbsp;<a href="http://genomethreader.org/" title="GenomeThreader gene prediction        software"><em>GenomeThreader</em></a>.</p>
<p>http://genometools.org/pub/</p><p>Address of the bookmark: <a href="http://genometools.org/" rel="nofollow">http://genometools.org/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43661/maftools</guid>
	<pubDate>Fri, 17 Dec 2021 03:18:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43661/maftools</link>
	<title><![CDATA[maftools]]></title>
	<description><![CDATA[<p>With advances in Cancer Genomics, <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a> (MAF) is being widely accepted and used to store somatic variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">Resulting data</a> consisting of somatic variants are stored in the form of <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html</p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades</guid>
	<pubDate>Mon, 31 Jan 2022 07:18:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades</link>
	<title><![CDATA[Short-read assembly using Spades !]]></title>
	<description><![CDATA[<h2 id="short-read-assembly-a-comparison">If we only had Illumina reads, we could also assemble these using the tool Spades.</h2><p>You can try this here, or try it later on your own data.</p><h2 id="get-data">Get data</h2><p>We will use the same Illumina data as we used above:</p><ul>
<li>illumina_R1.fastq.gz: the Illumina forward reads</li>
<li>illumina_R2.fastq.gz: the Illumina reverse reads</li>
</ul><h2 id="assemble">Assemble</h2><p>Run Spades:</p><div><pre>spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina
</pre></div><ul>
<li><code>-1</code>&nbsp;is input file of forward reads</li>
<li><code>-2</code>&nbsp;is input file of reverse reads</li>
<li><code>--careful</code>&nbsp;minimizes mismatches and short indels</li>
<li><code>--cov-cutoff auto</code>&nbsp;computes the coverage threshold (rather than the default setting, &ldquo;off&rdquo;)</li>
<li><code>-o</code>&nbsp;is the output directory</li>
</ul><h2 id="results">Results</h2><p>Move into the output directory and look at the contigs:</p><div><pre>infoseq contigs.fasta</pre></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</guid>
	<pubDate>Fri, 25 Feb 2022 04:42:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</link>
	<title><![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]></title>
	<description><![CDATA[<p dir="auto">This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.</p>
<p dir="auto">Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:</p>
<p dir="auto"><a href="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" target="_blank"><img src="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" alt="smudgeexample" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/KamilSJaron/smudgeplot" rel="nofollow">https://github.com/KamilSJaron/smudgeplot</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43867/genomeqc-a-quality-assessment-tool-for-genome-assemblies-and-gene-structure-annotations</guid>
	<pubDate>Thu, 19 May 2022 04:29:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43867/genomeqc-a-quality-assessment-tool-for-genome-assemblies-and-gene-structure-annotations</link>
	<title><![CDATA[GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations]]></title>
	<description><![CDATA[<p><span>The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at&nbsp;</span><a href="https://genomeqc.maizegdb.org/">https://genomeqc.maizegdb.org/</a><span>&nbsp;under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository&nbsp;</span><a href="https://github.com/HuffordLab/GenomeQC">https://github.com/HuffordLab/GenomeQC</a><span>.</span></p>
<p>https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2</p><p>Address of the bookmark: <a href="https://github.com/HuffordLab/GenomeQC" rel="nofollow">https://github.com/HuffordLab/GenomeQC</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</guid>
	<pubDate>Mon, 24 Jul 2023 07:04:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</link>
	<title><![CDATA[Bioinformatics tools for genome assembly !]]></title>
	<description><![CDATA[<p>There are numerous genome assembly tools available, each with its strengths and weaknesses. Here is a list of some widely used genome assembly tools as of my last update in September 2021:</p><ol>
<li>
<p><span>SPAdes:</span> An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.</p>
</li>
<li>
<p><span>ABySS:</span> A parallelized assembler for large genomes that uses de Bruijn graphs.</p>
</li>
<li>
<p><span>Velvet:</span> Another de Bruijn graph-based assembler optimized for short-read sequencing data.</p>
</li>
<li>
<p><span>SOAPdenovo:</span> A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.</p>
</li>
<li>
<p><span>MaSuRCA:</span> A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.</p>
</li>
<li>
<p><span>Canu:</span> A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.</p>
</li>
<li>
<p><span>Flye:</span> A long-read assembler suitable for bacterial and small eukaryotic genomes.</p>
</li>
<li>
<p><span>SMARTdenovo:</span> An assembler designed for long reads, particularly suited for PacBio data.</p>
</li>
<li>
<p><span>SPAdes Long Read (SPAdesLR):</span> An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.</p>
</li>
<li>
<p><span>Minia:</span> An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.</p>
</li>
<li>
<p><span>Unicycler:</span> A hybrid assembler that combines short and long reads for circular bacterial genome assembly.</p>
</li>
<li>
<p><span>wtdbg2:</span> A de Bruijn graph assembler for long reads, efficient for very large genomes.</p>
</li>
<li>
<p><span>Shasta:</span> A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.</p>
</li>
<li>
<p><span>Sparc:</span> An assembler designed to handle noisy long reads from Nanopore sequencing.</p>
</li>
<li>
<p><span>CANA:</span> An assembler for metagenomic data, particularly for complex and diverse microbial communities.</p>
</li>
<li>
<p><span>Ra</span> Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.</p>
</li>
</ol><p>Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44549/quartet-a-telomere-to-telomere-toolkit-for-gap-free-genome-assembly-and-centromeric-repeat-identification</guid>
	<pubDate>Sat, 08 Jun 2024 15:54:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44549/quartet-a-telomere-to-telomere-toolkit-for-gap-free-genome-assembly-and-centromeric-repeat-identification</link>
	<title><![CDATA[quarTeT: a telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification.]]></title>
	<description><![CDATA[<p><span>quarTeT is a collection of tools for T2T genome assembly and basic analysis in automatic workflow.</span><br><br><span>Task include:</span></p>
<ul>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#AssemblyMapper">AssemblyMapper</a>&nbsp;: reference-guided genome assembly</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#GapFiller">GapFiller</a>&nbsp;: long-reads based gap filling</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#TeloExplorer">TeloExplorer</a>&nbsp;: telomere identification</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#CentroMiner">CentroMiner</a>&nbsp;: centromere candidate prediction</li>
</ul>
<p>https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false&nbsp;</p><p>Address of the bookmark: <a href="http://www.atcgn.com:8080/quarTeT/home.html" rel="nofollow">http://www.atcgn.com:8080/quarTeT/home.html</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/6458/bigre-lab</guid>
  <pubDate>Sun, 17 Nov 2013 10:35:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[BIGRE Lab]]></title>
  <description><![CDATA[
<p>The Laboratoire de Bioinformatique des Génomes et des Réseaux (Genome and Network Bioinformatics) is specialized in the conception, implementation, evaluation and application of bioinformatics approaches for the analysis of genome, transcriptome, proteome and metabolism.<br />Our main activities include</p>

<p>Analysis of regulatory sequences (RSAT project)<br />Classification and analysis of mobile genetic elements (ACLAME project).<br />Analysis of molecular interaction networks (NeAT project)<br />Inference of metabolic pathways from genomic and post-genomic data <br />(metabolic pathfinding, see also metabolic pathfinding in NeAT)<br />Critical assesment of protein interactions (CAPRI)</p>

<p>Lab Page http://www.bigre.ulb.ac.be/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4158/sorghum-genome-sequenced</guid>
	<pubDate>Sun, 01 Sep 2013 19:46:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4158/sorghum-genome-sequenced</link>
	<title><![CDATA[Sorghum genome Sequenced!!]]></title>
	<description><![CDATA[<p>Sorghum, a staple food for 500 million resource-poor people in marginal environments and a model for other important crops, sorghum holds vital genetic resources as humanity confronts the nexus of food crisis and climate change. The recent research provides an unmatched resource to respond to these challenges by identifying a large high-quality SNP and indel data set in diverse sorghum genotypes.</p><p>In addition to providing a broad sample of the diversity in S. bicolor, the genotypes included in this study are known to display agronomically important traits including stay-green drought resistance, insect resistance, grain size and grain quality.</p><p>Find more at&nbsp;http://www.nature.com/ncomms/2013/130827/ncomms3320/full/ncomms3320.html</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4961/genetics-epigenetics-and-disease</guid>
	<pubDate>Fri, 27 Sep 2013 11:32:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4961/genetics-epigenetics-and-disease</link>
	<title><![CDATA[Genetics, epigenetics and disease]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/SHpfkNRscOc" frameborder="0" allowfullscreen></iframe>Royal Society GlaxoSmithKline Prize Lecture given by Professor Adrian Bird CBE FMedSci FRS on Tuesday 22 January 2013.

Adrian Bird CBE FMedSci FRS is the Buchanan Chair of Genetics at the University of Edinburgh.

The human genome sequence has been available for more than a decade, but its significance is still not fully understood. While most human genes have been identified, there is much to learn about the DNA signals that control them. This lecture described an unusually short DNA sequence, just two base pairs long, CG, which occurs in several chemically different forms. Defects in signalling by CG are implicated in disease. For example, the autism spectrum disorder Rett syndrome is caused by loss of a protein that reads methylated CG and affects the activity of genes.

The Royal Society GlaxoSmithKline Prize Lecture is awarded for original contributions to medical and veterinary sciences published within ten years from the date of the award.]]></description>
	
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