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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36017?</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33482/tardis-toolkit-for-automated-and-rapid-discovery-of-structural-variants</guid>
	<pubDate>Fri, 09 Jun 2017 04:43:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33482/tardis-toolkit-for-automated-and-rapid-discovery-of-structural-variants</link>
	<title><![CDATA[TARDIS: Toolkit for automated and rapid discovery of structural variants]]></title>
	<description><![CDATA[<p>tardis</p>
<p>Toolkit for Automated and Rapid DIscovery of Structural variants</p>
<p>Requirements</p>
<p>zlib (http://www.zlib.net)<br>mrfast (https://github.com/BilkentCompGen/mrfast)<br>htslib (included as submodule; http://htslib.org/)<br>Fetching tardis</p>
<p>git clone https://github.com/BilkentCompGen/tardis.git --recursive</p>
<p>&nbsp;</p>
<p>https://github.com/BilkentCompGen/tardis</p><p>Address of the bookmark: <a href="https://github.com/BilkentCompGen/tardis" rel="nofollow">https://github.com/BilkentCompGen/tardis</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39630/vespa-very-large-scale-evolutionary-and-selective-pressure-analyses</guid>
	<pubDate>Sat, 22 Jun 2019 03:07:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39630/vespa-very-large-scale-evolutionary-and-selective-pressure-analyses</link>
	<title><![CDATA[VESPA: Very large-scale Evolutionary and Selective Pressure Analyses]]></title>
	<description><![CDATA[<p>find the resources we provide here useful in getting you set up to analyse and interpret your data.</p>
<p>To reference VESPA:&nbsp;<a href="https://peerj.com/preprints/1895/">https://peerj.com/preprints/1895/</a></p>
<p>Documentation is now hosted on&nbsp;<a href="http://vespa-evol.readthedocs.io/en/latest/">ReadTheDocs</a></p>
<p>&nbsp;</p>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/aewebb80/VESPA" rel="nofollow">https://github.com/aewebb80/VESPA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41582/flexidot-highly-customizable-ambiguity-aware-dotplots-for-visual-sequence-analyses</guid>
	<pubDate>Fri, 24 Apr 2020 08:39:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41582/flexidot-highly-customizable-ambiguity-aware-dotplots-for-visual-sequence-analyses</link>
	<title><![CDATA[flexidot: Highly customizable, ambiguity-aware dotplots for visual sequence analyses]]></title>
	<description><![CDATA[<p><span>FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of mismatches and ambiguous residues. The custom shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities to the images. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of a large sequence sets, allowing dotplot use for routine screening.</span></p>
<p><img src="https://github.com/molbio-dresden/flexidot/blob/master/images/Beetle_matrix_shading.png?raw=true" alt="image" style="border: 0px; border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/molbio-dresden/flexidot" rel="nofollow">https://github.com/molbio-dresden/flexidot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38752/hgtector-an-automated-method-facilitating-genome-wide-discovery-of-putative-horizontal-gene-transfers</guid>
	<pubDate>Mon, 21 Jan 2019 06:50:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38752/hgtector-an-automated-method-facilitating-genome-wide-discovery-of-putative-horizontal-gene-transfers</link>
	<title><![CDATA[HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers]]></title>
	<description><![CDATA[<p>A computational pipeline for genome-wide detection of putative horizontal gene transfer (HGT) events based on sequence homology search hit distribution statistics</p>
<p>Authors: Qiyun Zhu (<a href="mailto:qiyunzhu@gmail.com">qiyunzhu@gmail.com</a>), Katharina Dittmar (<a href="mailto:katharinad@gmail.com">katharinad@gmail.com</a>)</p>
<p>Affiliation: Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, USA</p>
<p>Zhu Q, Kosoy M, Dittmar K. HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers.&nbsp;<em style="font-size: 12.8px;">BMC Genomics</em>. 2014. 15:717.</p>
<p>Usage: Simply execute&nbsp;<span style="font-size: 12.8px;">perl HGTector.pl</span>, or, open&nbsp;<span style="font-size: 12.8px;">GUI.html</span>&nbsp;in a web browser to see a step-by-step wizard.</p>
<p>Download&nbsp;<a href="https://github.com/DittmarLab/HGTector/archive/0.2.2.zip">HGTector 0.2.2</a>.</p><p>Address of the bookmark: <a href="https://github.com/DittmarLab/HGTector" rel="nofollow">https://github.com/DittmarLab/HGTector</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</guid>
	<pubDate>Thu, 20 Jul 2017 08:02:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</link>
	<title><![CDATA[Mulan: MUltiple sequence Local AligNment and conservation visualization tool]]></title>
	<description><![CDATA[<p><span>Mulan performs multiple (2 or more) sequence alignments with an efficient and rapid "full local" alignment strategy that ensures a recapitulation of evolutionary sequence rearrangements (such as inversions and reshuffling) in any of the species. It combines&nbsp;</span><a href="http://www.bx.psu.edu/miller_lab/" target="_new"><em>refine</em>&nbsp;and&nbsp;<em>tba</em></a><span>&nbsp;tools to align either "draft" or "finished" quality sequences. Mulan provides a dynamic graphical interface to align and visualize conservation profiles for evolutionarily distant and closely related species.</span><br><span></span></p>
<p><span>Input formats, automated data upload from the&nbsp;</span><a href="http://genome.ucsc.edu/" target="_new">UCSC Genome Browser</a><span>, gene annotation, annotation of repetitive elements, and progress report were previously described in the&nbsp;</span><a href="https://zpicture.dcode.org/zpInstructions.html" target="_zp">zPicture instructions</a><span>&nbsp;and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.</span><span><br></span></p><p>Address of the bookmark: <a href="https://mulan.dcode.org/mulanInstructions.php" rel="nofollow">https://mulan.dcode.org/mulanInstructions.php</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33955/crocoblast-optimized-parallel-implementation-of-local-sequence-alignment-algorithms</guid>
	<pubDate>Tue, 25 Jul 2017 05:03:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33955/crocoblast-optimized-parallel-implementation-of-local-sequence-alignment-algorithms</link>
	<title><![CDATA[CrocoBLAST: Optimized parallel implementation of local sequence alignment algorithms]]></title>
	<description><![CDATA[<p><span>Local sequence alignment is a cornerstone of bioinformatics, allowing to compare the amino-acid sequences of different proteins, or the nucleotide sequences of different pieces of DNA. The Basic Local Alignment Search Tool (BLAST) has revolutionized the field of bioinformatics, and is currently implemented in all free and commercial bioinformatics packages. However, with the advent of Next Generation Sequencing (NGS) and the development of new sequencing techniques, the utility of traditional BLAST implementations is limited. CrocoBLAST combines the accuracy and general applicability of BLAST with computational efficiency, accessibility, and user experience, so that NGS data can be analyzed efficiently even when only modest computational resources are available.</span></p>
<p>https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST</p><p>Address of the bookmark: <a href="https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST" rel="nofollow">https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</guid>
	<pubDate>Mon, 21 Jan 2019 17:58:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</link>
	<title><![CDATA[SvABA: Genome-wide detection of structural variants and indels by local assembly]]></title>
	<description><![CDATA[<p><span>SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of&nbsp;</span><a href="https://github.com/jts/sga">SGA</a><span>&nbsp;(String Graph Assembler) by Jared Simpson, and&nbsp;</span><a href="https://github.com/lh3/bwa">BWA-MEM</a><span>&nbsp;by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using&nbsp;</span><a href="https://github.com/walaj/VariantBam">VariantBam</a><span>&nbsp;rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.</span></p><p>Address of the bookmark: <a href="https://github.com/walaj/svaba" rel="nofollow">https://github.com/walaj/svaba</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44470/phyloherb-phylogenomic-analysis-pipeline-for-herbarium-specimens</guid>
	<pubDate>Wed, 21 Feb 2024 06:15:13 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44470/phyloherb-phylogenomic-analysis-pipeline-for-herbarium-specimens</link>
	<title><![CDATA[PhyloHerb: Phylogenomic Analysis Pipeline for Herbarium Specimens]]></title>
	<description><![CDATA[<p><span>What is PhyloHerb</span><span>: PhyloHerb is a wrapper program to process&nbsp;</span><span>genome skimming</span><span>&nbsp;data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include&nbsp;</span><span>low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming</span><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/lmcai/PhyloHerb/" rel="nofollow">https://github.com/lmcai/PhyloHerb/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</guid>
	<pubDate>Wed, 21 Feb 2024 06:19:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</link>
	<title><![CDATA[pipesnake: bioinformatics best-practice analysis pipeline for phylogenomic reconstruction]]></title>
	<description><![CDATA[<p dir="auto"><span>ausarg/pipesnake</span>&nbsp;is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.</p>
<p dir="auto">The pipeline is built using&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The&nbsp;<a href="https://www.nextflow.io/docs/latest/dsl2.html">Nextflow DSL2</a>&nbsp;implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.</p><p>Address of the bookmark: <a href="https://github.com/AusARG/pipesnake" rel="nofollow">https://github.com/AusARG/pipesnake</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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