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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36398?offset=140</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36583/eugi-a-novel-resource-for-studying-genomic-islands-to-facilitate-horizontal-gene-transfer-detection-in-eukaryotes</guid>
	<pubDate>Sat, 12 May 2018 07:26:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36583/eugi-a-novel-resource-for-studying-genomic-islands-to-facilitate-horizontal-gene-transfer-detection-in-eukaryotes</link>
	<title><![CDATA[EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes]]></title>
	<description><![CDATA[<p><span>SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.</span></p>
<p>https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8</p><p>Address of the bookmark: <a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8" rel="nofollow">https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</guid>
	<pubDate>Tue, 14 Jan 2020 06:47:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</link>
	<title><![CDATA[Shasta long read assembler]]></title>
	<description><![CDATA[<p>The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;flow cells.</p>
<p>Computational methods used by the Shasta assembler include:</p>
<ul>
<li>Using a&nbsp;<a href="https://en.wikipedia.org/wiki/Run-length_encoding">run-length</a>&nbsp;representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.</li>
<li>Using in some phases of the computation a representation of the read sequence based on&nbsp;<em>markers</em>, a fixed subset of short k-mers (k &asymp; 10).</li>
</ul>
<p>More at&nbsp;<a href="https://chanzuckerberg.github.io/shasta/index.html">https://chanzuckerberg.github.io/shasta/index.html</a></p><p>Address of the bookmark: <a href="https://github.com/chanzuckerberg/shasta" rel="nofollow">https://github.com/chanzuckerberg/shasta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</guid>
	<pubDate>Sat, 08 Aug 2020 08:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</link>
	<title><![CDATA[ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.]]></title>
	<description><![CDATA[<p>How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).</p><p>During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.</p><p>ENCODE&rsquo;s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.</p><p><span>The&nbsp;</span><a href="https://www.nature.com/articles/s41586-020-2493-4">Encyclopedia paper of ENCODE 3</a><span>, published in&nbsp;</span><em>Nature</em><span>, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate&nbsp;</span><em>cis</em><span>-regulatory elements (cCREs).</span></p><p>More at&nbsp;<a href="https://www.nature.com/immersive/d42859-020-00027-2/index.html">https://www.nature.com/immersive/d42859-020-00027-2/index.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly</guid>
	<pubDate>Tue, 17 Aug 2021 13:17:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly</link>
	<title><![CDATA[Bioinformatics tools for telomere to telomere assembly !]]></title>
	<description><![CDATA[<p>●&nbsp;<a href="https://github.com/arangrhie/merfin" target="_blank">Merfin</a>&nbsp;&ndash; k-mer-based assembly and variant calling evaluation for improved consensus accuracy (Arang Rhie)<br />●&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2020.11.11.378133v1" target="_blank">PanGenie</a>&nbsp;&ndash; algorithm that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation (Tobias Marschall)<br />●&nbsp;<a href="https://github.com/ConesaLab/SQANTI3" target="_blank">SQANTI3</a>&nbsp;&ndash; an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline (Roc&iacute;o Amor&iacute;n de Heged&uuml;s&nbsp;<a href="https://twitter.com/rocioadh" target="_blank">@rocioadh</a>)<br />●&nbsp;<a href="https://github.com/GenomeRIK/tama" target="_blank">tama</a>&nbsp;(Transcriptome Annotation by Modular Algorithms) &ndash; software designed for processing Iso-Seq data and other long-read transcriptome data (Richard Kuo&nbsp;<a href="https://twitter.com/GenomeRIK" target="_blank">@GenomeRIK</a>)<br />●&nbsp;<a href="https://github.com/PacificBiosciences/pbAA" target="_blank">pbaa</a>&nbsp;(PacBio Amplicon Analysis) &ndash; separates complex mixtures of amplicon targets from genomic samples to cluster and generate high-quality consensus sequences from HiFi reads (Zev Kronenberg&nbsp;<a href="https://twitter.com/zevkronenberg" target="_blank">@zevkronenberg</a>)<br />●&nbsp;<a href="https://github.com/yuanyuan929/bellerophon" target="_blank">bellerophon</a>&nbsp;&ndash; analyzes MHC typing and other low-complexity gene amplicon data; performs allele calling while detecting polymorphic sites within the sequences and removing potential chimeric sequence variants (Yuanyuan Cheng&nbsp;<a href="https://twitter.com/Yuanyuan929" target="_blank">@Yuanyuan929</a>)<br />●&nbsp;<a href="https://github.com/amwenger/svpack" target="_blank">svpack</a>&nbsp;&ndash; tools for filtering, comparing, and annotating structural variant (SV) calls in VCF format (Aaron Wenger)<br />●&nbsp;<a href="https://github.com/AntonBankevich/jumboDB" target="_blank">JumboDB</a>&nbsp;&ndash; tool for de Bruijn graph construction (Anton Bankevich&nbsp;<a href="https://twitter.com/AntonBankevich" target="_blank">@AntonBankevich</a>)<br />●&nbsp;<a href="https://github.com/ksahlin/ultra" target="_blank">uLTRA</a>&nbsp;&ndash; tool for splice alignment of long transcriptomic reads to a genome, guided by a database of exon annotations. (Kristoffer Sahlin&nbsp;<a href="https://twitter.com/krsahlin" target="_blank">@krsahlin</a>)<br />●&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2021.01.25.428044v1.full.pdf" target="_blank">LeafGo</a>&nbsp;&ndash; workflow to rapidly produce high-quality de novo plant genomes (Luca Ermini&nbsp;<a href="https://twitter.com/ermini_luca" target="_blank">@ermini_luca</a>)</p><p>Reference:</p><p>https://www.pacb.com/blog/young-investigators-share-stellar-science-career-advice-and-bioinformatics-tools-at-smrt-leiden-2021/</p><p>&nbsp;</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44288/upset-plots</guid>
	<pubDate>Fri, 24 Mar 2023 22:30:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44288/upset-plots</link>
	<title><![CDATA[Upset plots !]]></title>
	<description><![CDATA[<p>Upset plots are a type of visualization used to analyze the intersection of sets or categories. They are particularly useful for displaying data with multiple categories and analyzing their overlaps.</p>
<p>In an upset plot, each row represents a category or set, and each column represents a data point. The length of the bar for each category indicates the number of data points that belong to that category. The plot also shows the intersections between categories, represented by overlapping bars.</p>
<p>Upset plots are useful for visualizing complex data with multiple categories and intersections, and can help identify patterns and relationships between categories. They are often used in fields such as bioinformatics, where they can be used to analyze gene expression data or to compare the results of different experimental conditions.</p>
<p>https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions</p><p>Address of the bookmark: <a href="https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions" rel="nofollow">https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</guid>
	<pubDate>Tue, 18 Jun 2024 02:04:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</link>
	<title><![CDATA[BioKit: a set of tools dedicated to bioinformatics, data visualisation]]></title>
	<description><![CDATA[<p><span>BioKit is a set of tools dedicated to bioinformatics, data visualisation (</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.viz" title="biokit.viz"><code><span>biokit.viz</span></code></a><span>), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis (e.g.,&nbsp;</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.stats" title="biokit.stats"><code><span>biokit.stats</span></code></a><span>). Since R is quite common in bioinformatics, we also provide a convenient module to run R inside your Python scripts or shell (:mod:biokit.rtools module).</span></p><p>Address of the bookmark: <a href="https://biokit.readthedocs.io/en/latest/index.html" rel="nofollow">https://biokit.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/20328/research-article-publiaction</guid>
	<pubDate>Fri, 09 Jan 2015 18:50:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/20328/research-article-publiaction</link>
	<title><![CDATA[Research article- Publiaction]]></title>
	<description><![CDATA[<p>Dear All,</p><p>I am Lalit, Working as SRF In Haffkine Institute. I would like to publish research article related to protein modeling and docking which in cludes MD as final step.</p><p>I am currently working on protein modeling and docking area.&nbsp;</p><p>Those who are interested . Kindly contact me on this email id:samantlalit@gmail.com</p>]]></description>
	<dc:creator>LALIT SAMANT</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27479/biogps</guid>
	<pubDate>Mon, 23 May 2016 03:15:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27479/biogps</link>
	<title><![CDATA[BioGPS]]></title>
	<description><![CDATA[<p>A free&nbsp;<em>extensible</em>&nbsp;and&nbsp;<em>customizable</em>&nbsp;<strong>gene annotation portal</strong>, a complete resource for learning about&nbsp;<strong>gene and protein function</strong>.</p>
<p>http://biogps.org/#goto=welcome</p><p>Address of the bookmark: <a href="http://biogps.org/#goto=welcome" rel="nofollow">http://biogps.org/#goto=welcome</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</guid>
	<pubDate>Thu, 22 Jun 2017 07:58:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</link>
	<title><![CDATA[DarkHorse: a method for genome-wide prediction of horizontal gene transfer]]></title>
	<description><![CDATA[<p><span>A new approach to rapid, genome-wide identification and ranking of horizontal transfer candidate proteins is presented. The method is quantitative, reproducible, and computationally undemanding. It can be combined with genomic signature and/or phylogenetic tree-building procedures to improve accuracy and efficiency. The method is also useful for retrospective assessments of horizontal transfer prediction reliability, recognizing orthologous sequences that may have been previously overlooked or unavailable. These features are demonstrated in bacterial, archaeal, and eukaryotic examples.</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</guid>
	<pubDate>Sat, 02 Jun 2018 07:38:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</link>
	<title><![CDATA[GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence]]></title>
	<description><![CDATA[<p><span>GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/glean-gene/" rel="nofollow">https://sourceforge.net/projects/glean-gene/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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