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	<title><![CDATA[BOL: LEGE's bookmarks]]></title>
	<link>https://bioinformaticsonline.com/bookmarks/owner/lege?offset=30</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44489/proksee</guid>
	<pubDate>Wed, 27 Mar 2024 11:11:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44489/proksee</link>
	<title><![CDATA[Proksee]]></title>
	<description><![CDATA[<p><span>Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.</span></p>
<fieldset><legend>Please Cite the Following</legend>
<div>Grant JR, Enns E, Marinier E, Mandal A, Herman EK, Chen C, Graham M, Van Domselaar G, and Stothard P</div>
<div><a href="https://pubmed.ncbi.nlm.nih.gov/37140037/">Proksee: in-depth characterization and visualization of bacterial genomes</a></div>
<div>Nucleic Acids Research, 2023, gkad326, https://doi.org/10.1093/nar/gkad326</div>
</fieldset><p>Address of the bookmark: <a href="https://proksee.ca/" rel="nofollow">https://proksee.ca/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</guid>
	<pubDate>Sun, 24 Mar 2024 20:06:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</link>
	<title><![CDATA[R Package for PCA Analysis]]></title>
	<description><![CDATA[<p><span>An R package for performing principal component analysis (PCA) of genomics data. The package performs PCA, generates the publication-ready plots, and identifies population-specific outlier individuals. The package can be accessed on GitHub:&nbsp;https://github.com/Devashish13/PopulationStructure</span></p><p>Address of the bookmark: <a href="https://rpubs.com/Devashish13/PCAGenomics" rel="nofollow">https://rpubs.com/Devashish13/PCAGenomics</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</guid>
	<pubDate>Tue, 05 Mar 2024 00:23:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</link>
	<title><![CDATA[doubletrouble: identify duplicated genes from whole-genome protein sequences and classify]]></title>
	<description><![CDATA[<p><span>doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.</span></p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/html/doubletrouble.html" rel="nofollow">https://bioconductor.org/packages/release/bioc/html/doubletrouble.html</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</guid>
	<pubDate>Wed, 21 Feb 2024 15:01:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</link>
	<title><![CDATA[OMArk: software for proteome (protein-coding gene repertoire) quality assessment]]></title>
	<description><![CDATA[<p><span>OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.</span></p><p>Address of the bookmark: <a href="https://github.com/DessimozLab/OMArk" rel="nofollow">https://github.com/DessimozLab/OMArk</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</guid>
	<pubDate>Wed, 21 Feb 2024 12:37:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</link>
	<title><![CDATA[CLAW: Chloroplast Long-read Assembly Workflow]]></title>
	<description><![CDATA[<p dir="auto">CLAW (Chloroplast Long-read Assembly Workflow) is an mostly-automated Snakemake-based workflow for the assembly of chloroplast genomes. CLAW uses chloroplast long-reads, which are baited out of larger read libraries (e.g., an Oxford Nanopore Technologies MinION read library derived from photosynthetic tissue), for assembly with Flye and/or Unicycler. CLAW was designed with the novice bioinformatician in mind - it is easy to install and easy to use, requiring only minimal user input.</p><p>Address of the bookmark: <a href="https://github.com/aaronphillips7493/CLAW" rel="nofollow">https://github.com/aaronphillips7493/CLAW</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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<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>
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	<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/44468/orthoflow-workflow-for-phylogenetic-inference-of-genome-scale-datasets-of-protein-coding-genes</guid>
	<pubDate>Wed, 21 Feb 2024 06:13:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44468/orthoflow-workflow-for-phylogenetic-inference-of-genome-scale-datasets-of-protein-coding-genes</link>
	<title><![CDATA[Orthoflow: workflow for phylogenetic inference of genome-scale datasets of protein-coding genes]]></title>
	<description><![CDATA[<p><span>Orthoflow is a workflow for phylogenetic inference of genome-scale datasets of protein-coding genes. Our goal was to make it straightforward to work from a combination of input sources including annotated contigs in Genbank format and FASTA files containing CDSs. It uses several state of the art inference methods for orthology inference, either based on HMM profiles or de novo inference of orthogroups. Through the use of OrthoSNAP, many additional ortholog alignments can be generated from multi-copy gene families. For phylogenetic inference, users can choose a supermatrix approach and/or gene tree inference followed by supertree reconstruction. Users can specify a range of alignment filtering settings to retain high-quality alignments for phylogenetic inference. The workflow produces a detailed report that, in addition to the phylogenetic results, includes a range of diagnostics to verify the quality of the results.</span></p><p>Address of the bookmark: <a href="https://github.com/rbturnbull/orthoflow" rel="nofollow">https://github.com/rbturnbull/orthoflow</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44445/ppanggolin-depicting-microbial-species-diversity-via-a-partitioned-pangenome-graph-of-linked-neighbors</guid>
	<pubDate>Thu, 01 Feb 2024 00:24:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44445/ppanggolin-depicting-microbial-species-diversity-via-a-partitioned-pangenome-graph-of-linked-neighbors</link>
	<title><![CDATA[PPanGGOLiN: Depicting microbial species diversity via a Partitioned PanGenome Graph Of Linked Neighbors]]></title>
	<description><![CDATA[<p dir="auto"><span>PPanGGOLiN</span>&nbsp;(<a href="https://doi.org/10.1371/journal.pcbi.1007732">Gautreau et al. 2020</a>) is a software suite used to create and manipulate prokaryotic pangenomes from a set of either genomic DNA sequences or provided genome annotations. It is designed to scale up to tens of thousands of genomes. It has the specificity to partition the pangenome using a statistical approach rather than using fixed thresholds which gives it the ability to work with low-quality data such as&nbsp;<em>Metagenomic Assembled Genomes (MAGs)</em>&nbsp;or&nbsp;<em>Single-cell Amplified Genomes (SAGs)</em>&nbsp;thus taking advantage of large scale environmental studies and letting users study the pangenome of uncultivable species.</p>
<p dir="auto">A complete documentation is available&nbsp;<a href="https://ppanggolin.readthedocs.io/">here</a>.</p>
<p dir="auto" style="text-align: center;"><a href="https://github.com/labgem/PPanGGOLiN/blob/master/docs/_static/logo.png" target="_blank"><img src="https://github.com/labgem/PPanGGOLiN/raw/master/docs/_static/logo.png" alt="logo" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/labgem/PPanGGOLiN" rel="nofollow">https://github.com/labgem/PPanGGOLiN</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</guid>
	<pubDate>Thu, 14 Dec 2023 12:33:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</link>
	<title><![CDATA[ReConPlot: an R package for the visualization and interpretation of genomic rearrangements]]></title>
	<description><![CDATA[<p>ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort.</p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false" rel="nofollow">https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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