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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38420?offset=130</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r</guid>
	<pubDate>Thu, 29 Aug 2019 01:38:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r</link>
	<title><![CDATA[Retrieving Taxonomic Information with R]]></title>
	<description><![CDATA[<p>This vignette will introduce users to the retrieval of taxonomic information with&nbsp;<code>myTAI</code>. The&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;relies on the powerful package&nbsp;<a href="https://github.com/ropensci/taxize">taxize</a>. Nevertheless, taxonomic information retrieval has been customized for the&nbsp;<code>myTAI</code>&nbsp;standard and for organism specific information retrieval.</p>
<p>Specifically, the&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;can be used to classify genomes according to phylogenetic classification into Phylostrata (Phylostratigraphy) or to retrieve species specific taxonomic information when performing Divergence Stratigraphy (see&nbsp;<a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Introduction.html">Introduction</a>&nbsp;for details).</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html" rel="nofollow">https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container</guid>
	<pubDate>Mon, 23 Dec 2019 09:56:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container</link>
	<title><![CDATA[‘dockr’: the R container]]></title>
	<description><![CDATA[<p><code>dockr</code> 0.8.6 is now available on CRAN. <code>dockr</code> is a minimal toolkit to build a lightweight Docker container image for your R package, in which the package itself is available. The Docker image seeks to mirror your R session as close as possible with respect to R specific dependencies. Both dependencies on CRAN R packages as well as local non-CRAN R packages will be included in the Docker container image.</p>
<p>If you want to know, how Docker works, and why you should consider using Docker, please take a look at the <a href="https://www.docker.com/why-docker" target="_blank">Docker website</a>.</p><p>Address of the bookmark: <a href="https://www.docker.com/why-docker" rel="nofollow">https://www.docker.com/why-docker</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41041/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-mfd</guid>
  <pubDate>Sat, 15 Feb 2020 06:13:35 -0600</pubDate>
  <link></link>
  <title><![CDATA[Post Doc Computational Biology, Bioinformatics - Network Biology &amp; Data Science, NGS (m/f/d)]]></title>
  <description><![CDATA[
<p>https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df</p>

<p>Requirements<br />Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus<br />Ability to numerically process complex and large data sets<br />Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)<br />Experience in analyzing next-generation sequencing data sets using network biology<br />Scientific publication record in applied bioinformatics<br />Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r</guid>
	<pubDate>Wed, 11 Nov 2020 23:06:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r</link>
	<title><![CDATA[CRBHits: From Conditional Reciprocal Best Hits to Codon Alignments and Ka/Ks in R]]></title>
	<description><![CDATA[<p>CRBHits is a coding sequence (CDS) analysis pipeline in R (R Core Team, 2019). It reimplements the Conditional Reciprocal Best Hit (CRBH) algorithm crb-blast and covers all necessary steps from sequence similarity searches, codon alignments to Ka/Ks calculations and synteny. The new R package targets ecology, population and evolutionary biologists working in the field of comparative genomics.</p><p>Address of the bookmark: <a href="https://gitlab.gwdg.de/mpievolbio-it/crbhits" rel="nofollow">https://gitlab.gwdg.de/mpievolbio-it/crbhits</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</guid>
	<pubDate>Tue, 28 May 2024 07:42:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</link>
	<title><![CDATA[Powerful books for learning data analysis with R]]></title>
	<description><![CDATA[<p><span>R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:</span></p>
<p><span>https://csgillespie.github.io/efficientR/</span></p>
<p><span>https://r-graphics.org/</span></p>
<p><span>https://rstudio-education.github.io/hopr/</span></p>
<p><span>https://r-pkgs.org/</span></p>
<p><span>https://r4ds.had.co.nz/</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://r-graphics.org/" rel="nofollow">https://r-graphics.org/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36616/srbreak-a-read-depth-and-split-read-framework-to-identify-breakpoints-of-different-events-inside-simple-copy-number-variable-regions</guid>
	<pubDate>Tue, 15 May 2018 04:42:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36616/srbreak-a-read-depth-and-split-read-framework-to-identify-breakpoints-of-different-events-inside-simple-copy-number-variable-regions</link>
	<title><![CDATA[SRBreak: A Read-Depth and Split-Read Framework to Identify Breakpoints of Different Events Inside Simple Copy-Number Variable Regions]]></title>
	<description><![CDATA[SRBreak is a read-depth and split-read package written in R for identifying copy-number variants in next-generation sequencing datasets.

Note: SBReak was designed to work for multiple samples. It can work for &gt;= 2 samples, but we suggest that users should use &gt;= 5 samples as in the work tested in our paper.<p>Address of the bookmark: <a href="https://github.com/hoangtn/SRBreak" rel="nofollow">https://github.com/hoangtn/SRBreak</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</guid>
	<pubDate>Tue, 07 Nov 2017 05:33:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</link>
	<title><![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]></title>
	<description><![CDATA[<div><p><span>kallisto</span></p></div><div><p>Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)</p><p>Software (C++)</p><p><a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a></p><p>Sailfish</p><p>Estimation of isoform abundances from reference sequences and RNA-seq data (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="http://www.cs.cmu.edu/~ckingsf/software/sailfish/">http://www.cs.cmu.edu/~ckingsf/software/sailfish/</a></p><p>Salmon</p><p>Quantification of the expression of transcripts using RNA-seq data (uses&nbsp;<em>k</em>-mers)</p><p><a href="https://combine-lab.github.io/salmon/">https://combine-lab.github.io/salmon/</a></p><p>RNA-Skim</p><p>RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses&nbsp;<em>sig</em>-mers, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C++)</p><p><a href="http://www.csbio.unc.edu/rs/">http://www.csbio.unc.edu/rs/</a></p><p>Variant calling</p><p>ChimeRScope</p><p>Fusion transcript prediction using gene&nbsp;<em>k</em>-mers profiles of the RNA-seq paired-end reads</p><p>Software (Java)</p><p><a href="https://github.com/ChimeRScope/ChimeRScope/wiki">https://github.com/ChimeRScope/ChimeRScope/wiki</a></p><p>FastGT</p><p>Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique&nbsp;<em>k</em>-mers</p><p>Software (C)</p><p><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></p><p>Phy-Mer</p><p>Reference-independent mitochondrial haplogroup classifier from NGS data (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/danielnavarrogomez/phy-mer">https://github.com/danielnavarrogomez/phy-mer</a></p><p>LAVA</p><p>Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (<em>k</em>-mer based)</p><p>Software (C)</p><p><a href="http://lava.csail.mit.edu/">http://lava.csail.mit.edu/</a></p><p>MICADo</p><p>Detection of mutations in targeted third-generation NGS data (can distinguish patients&rsquo; specific mutations; algorithm uses&nbsp;<em>k</em>-mers and is based on colored de Bruijn graphs)</p><p>Software (Python)</p><p><a href="http://github.com/cbib/MICADo">http://github.com/cbib/MICADo</a></p><p>General mapper</p><p>Minimap</p><p>Lightweight and fast read mapper and read overlap detector (uses the concept of &ldquo;minimazers&rdquo;, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C)</p><p><a href="https://github.com/lh3/minimap">https://github.com/lh3/minimap</a></p><p>Assembly</p><p>De novo genome assembly</p><p>MHAP</p><p>Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash</p><p>Software (Java)</p><p><a href="https://github.com/marbl/MHAP">https://github.com/marbl/MHAP</a></p><p>Miniasm</p><p>Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)</p><p>Software (C)</p><p><a href="https://github.com/lh3/miniasm">https://github.com/lh3/miniasm</a></p><p>LINKS</p><p>Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)</p><p>Software (Perl)</p><p><a href="https://github.com/warrenlr/LINKS/">https://github.com/warrenlr/LINKS/</a></p><p>Read clustering</p><p>afcluster</p><p>Clustering of reads from different genes and different species based on&nbsp;<em>k</em>-mer counts</p><p>Software (C++)</p><p><a href="https://github.com/luscinius/afcluster">https://github.com/luscinius/afcluster</a></p><p>QCluster</p><p>Clustering of reads with alignment-free measures (<em>k</em>-mer based) and quality values</p><p>Software (C++)</p><p><a href="http://www.dei.unipd.it/~ciompin/main/qcluster.html">http://www.dei.unipd.it/~ciompin/main/qcluster.html</a></p><p>Reads error correction</p><p>Lighter</p><p>Correction of sequencing errors in raw, whole genome sequencing reads (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="https://github.com/mourisl/Lighter">https://github.com/mourisl/Lighter</a></p><p>QuorUM</p><p>Error corrector for Illumina reads using k-mers</p><p>Software (C++)</p><p><a href="https://github.com/gmarcais/Quorum">https://github.com/gmarcais/Quorum</a></p><p>Trowel</p><p>Software (C++)</p><p><a href="https://sourceforge.net/projects/trowel-ec/">https://sourceforge.net/projects/trowel-ec/</a></p><p>Metagenomics</p><p>Assembly-free phylogenomics</p><p>AAF</p><p>Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/fanhuan/AAF">https://github.com/fanhuan/AAF</a></p><p>kSNP v3</p><p>Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on&nbsp;<em>k</em>-mer analysis)</p><p>Software (C)</p><p><a href="https://sourceforge.net/projects/ksnp/files/">https://sourceforge.net/projects/ksnp/files/</a></p><p>NGS-MC</p><p>Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2&nbsp;S&nbsp;under different Markov chain models (using&nbsp;<em>k</em>-words)</p><p>R package</p><p><a href="http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html">http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html</a></p><p>Species identification/taxonomic profiling</p><p>CLARK</p><p>Taxonomic classification of metagenomic reads to known bacterial genomes using&nbsp;<em>k</em>-mer search and LCA assignment</p><p>Software (C++)</p><p><a href="http://clark.cs.ucr.edu/">http://clark.cs.ucr.edu/</a></p><p>FOCUS</p><p>Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)</p><p>Web service Software (Python)</p><p><a href="http://edwards.sdsu.edu/FOCUS/">http://edwards.sdsu.edu/FOCUS/</a></p><p>GSM</p><p>Estimation of abundances of microbial genomes in metagenomic samples (<em>k</em>-mer based)</p><p>Software (Go)</p><p><a href="https://github.com/pdtrang/GSM">https://github.com/pdtrang/GSM</a></p><p>Mash</p><p>Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)</p><p>Software (C++)</p><p><a href="https://github.com/marbl/mash">https://github.com/marbl/mash</a></p><p>Kraken</p><p>Taxonomic assignment in metagenome analysis by exact&nbsp;<em>k</em>-mer search; LCA assignment of short reads based on a comprehensive sequence database</p><p>Software (C++)</p><p><a href="https://ccb.jhu.edu/software/kraken/">https://ccb.jhu.edu/software/kraken/</a></p><p>LMAT</p><p>Assignment of taxonomic labels to reads by&nbsp;<em>k</em>-mers searches in precomputed database</p><p>Software (C++/Python)</p><p><a href="https://sourceforge.net/projects/lmat/">https://sourceforge.net/projects/lmat/</a></p><p>stringMLST</p><p><em>k</em>-mer-based tool for MLST directly from the genome sequencing reads</p><p>Software (Python)</p><p><a href="http://jordan.biology.gatech.edu/page/software/stringMLST">http://jordan.biology.gatech.edu/page/software/stringMLST</a></p><p>Taxonomer</p><p><em>k</em>-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples</p><p>Web service</p><p><a href="http://taxonomer.iobio.io/">http://taxonomer.iobio.io/</a></p><p>Other</p><p>d2-tools</p><p>Word-based (<em>k</em>-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads</p><p>Software (Python/R)</p><p><a href="https://code.google.com/p/d2-tools/">https://code.google.com/p/d2-tools/</a></p><p>VirHostMatcher</p><p>Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)</p><p>Software (C++)</p><p><a href="https://github.com/jessieren/VirHostMatcher">https://github.com/jessieren/VirHostMatcher</a></p><p>MetaFast</p><p>Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray&ndash;Curtis dissimilarity measure</p><p>Software (Java)</p><p><a href="https://github.com/ctlab/metafast">https://github.com/ctlab/metafast</a></p></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</guid>
	<pubDate>Tue, 02 Oct 2018 17:57:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</link>
	<title><![CDATA[S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences]]></title>
	<description><![CDATA[<p><span>S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/lkalesinskas/splot" rel="nofollow">https://bitbucket.org/lkalesinskas/splot</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44555/ultra-ultra-locates-tandemly-repetitive-areas-effective-labeling-of-repetitive-genomic-sequence</guid>
	<pubDate>Sat, 08 Jun 2024 16:03:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44555/ultra-ultra-locates-tandemly-repetitive-areas-effective-labeling-of-repetitive-genomic-sequence</link>
	<title><![CDATA[ULTRA (ULTRA Locates Tandemly Repetitive Areas) : Effective Labeling of Repetitive Genomic Sequence]]></title>
	<description><![CDATA[<p dir="auto">ULTRA is a tool to find and annotate tandem repeats inside genomic sequence. It is able to find repeats of any length and of any period (up to a maximum period of 4000). It can find highly decayed repeats missed by other software, and it will also be able to find very large repeats in highly repetitive sequence, regardless of the size of sequence or length of repeats. ULTRA offers meaningful annotation scores and can produce annotation P-values at user request.</p>
<p dir="auto">More at&nbsp;https://www.biorxiv.org/content/10.1101/2024.06.03.597269v1</p><p>Address of the bookmark: <a href="https://github.com/TravisWheelerLab/ULTRA" rel="nofollow">https://github.com/TravisWheelerLab/ULTRA</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33983/web-apollo-a-web-based-genomic-annotation-editing-platform</guid>
	<pubDate>Fri, 28 Jul 2017 04:48:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33983/web-apollo-a-web-based-genomic-annotation-editing-platform</link>
	<title><![CDATA[Web Apollo: a web-based genomic annotation editing platform]]></title>
	<description><![CDATA[<p><span>Web Apollo is the first instantaneous, collaborative genomic annotation editor available on the web. One of the natural consequences following from current advances in sequencing technology is that there are more and more researchers sequencing new genomes. These researchers require tools to describe the functional features of their newly sequenced genomes. With Web Apollo researchers can use any of the common browsers (for example, Chrome or Firefox) to jointly analyze and precisely describe the features of a genome in real time, whether they are in the same room or working from opposite sides of the world.</span></p><p>Address of the bookmark: <a href="http://genomearchitect.github.io/" rel="nofollow">http://genomearchitect.github.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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