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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40351?offset=50</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35294/httdb-horizontally-transferred-transposable-elements-database</guid>
	<pubDate>Tue, 23 Jan 2018 12:07:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35294/httdb-horizontally-transferred-transposable-elements-database</link>
	<title><![CDATA[HTTDB - Horizontally transferred transposable elements database]]></title>
	<description><![CDATA[<p><span>Transposons or Transposable elements (TEs) are "mobile genes" capable of mobilization from one genomic location to another through non-homologous recombination. As this movement is mediated by its own proteins and does not contribute to the survival of the host that it inhabits, they are known as selfish genomic parasites. Despite their capacity for transposition inside genomes, they can frequently transpose the species boundaries and consequently migrate from one species to another. Such phenomenon is called Horizontal Transposons Transfer. HTT was first discovered by Daniels et al. (1984) when analysing a&nbsp;</span><em>P</em><span>&nbsp;element that was transferred from&nbsp;</span><em>Drosophila willistoni</em><span>&nbsp;to&nbsp;</span><em>D. melanogaster</em><span>. Since then, many more cases have been documented in the literature. Moreover, in the last years, such discoveries have been boosted by the unprecedented amount of new genomes available. Despite the recognition of HTT as a common phenomenon in recent years, it is still difficult to draw major conclusions about HTT patterns, such as where in the tree of life these cases are more frequently found. This is mainly due to the historical bias and lack of studies in many taxa. To date, there has been no easy way to visualise each TE or host species, and should be further analysed in order to provide a more comprehensive view of such phenomena. Based on these concerns, we developed the HTT database to keep an updated repository of HTT events in all eukaryotes, allowing not only TE specialists to add new events and search the database, but also non-specialists. Moreover, we expanded the database to include Horizontal-Virus Transfer also known as endogenization events which is characterized by the stable integration a viral genomic fragment into the host genome.</span></p>
<p><span>https://www.ncbi.nlm.nih.gov/pubmed/29315358</span></p><p>Address of the bookmark: <a href="http://lpa.saogabriel.unipampa.edu.br:8080/httdatabase/" rel="nofollow">http://lpa.saogabriel.unipampa.edu.br:8080/httdatabase/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</guid>
	<pubDate>Wed, 23 May 2018 03:24:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</link>
	<title><![CDATA[bpRNA: large-scale automated annotation and analysis of RNA secondary structure]]></title>
	<description><![CDATA[<p>bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.</p>
<p>The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.</p><p>Address of the bookmark: <a href="http://github.com/hendrixlab/bpRNA" rel="nofollow">http://github.com/hendrixlab/bpRNA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</guid>
	<pubDate>Mon, 17 Aug 2020 05:25:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</link>
	<title><![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]></title>
	<description><![CDATA[<p>SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:</p>
<ol>
<li>Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome</li>
<li>Co-assembly of a large number of metagenomes via merging of individual metagenomes</li>
<li>Includes binning and bin checking, for retrieving individual genomes</li>
<li>The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.</li>
<li>Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.</li>
<li>Metatranscriptomic support via mapping of cDNA reads against reference metagenomes</li>
</ol><p>Address of the bookmark: <a href="https://github.com/jtamames/SqueezeMeta" rel="nofollow">https://github.com/jtamames/SqueezeMeta</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</guid>
	<pubDate>Tue, 18 Feb 2020 03:22:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</link>
	<title><![CDATA[SeqMule: Automated human exome/genome variants detection]]></title>
	<description><![CDATA[<p>SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file.</p><p>Address of the bookmark: <a href="https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/" rel="nofollow">https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</guid>
	<pubDate>Tue, 28 Jan 2020 03:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</link>
	<title><![CDATA[Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search]]></title>
	<description><![CDATA[<p>Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only</p>
<p><span>Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles.&nbsp; Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.</span></p>
<p><a href="https://arxiv.org/abs/1809.06700">https://arxiv.org/abs/1809.06700</a></p>
<p><a href="https://github.com/redayounsi/2KP2P">https://github.com/redayounsi/2KP2P</a></p>
<blockquote>
<p>/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1</p>
<p>&nbsp;</p>
</blockquote><p>Address of the bookmark: <a href="https://github.com/redayounsi/2KP2P" rel="nofollow">https://github.com/redayounsi/2KP2P</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 03:21:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</link>
	<title><![CDATA[Kevler: Reference-free variant discovery in large eukaryotic genomes]]></title>
	<description><![CDATA[<p><span>Welcome to&nbsp;</span><span>kevlar</span><span>, software for predicting&nbsp;</span><em>de novo</em><span>&nbsp;genetic variants without mapping reads to a reference genome! kevlar's&nbsp;</span><em>k</em><span>-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://kevlar.readthedocs.io/en/latest/">https://kevlar.readthedocs.io/en/latest/</a></span></p>
<p><span><a href="https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf">https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf</a></span></p><p>Address of the bookmark: <a href="https://github.com/kevlar-dev/kevlar" rel="nofollow">https://github.com/kevlar-dev/kevlar</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42325/published-a-dataset-of-363-genomes-from-approximately-92-percent-of-bird-families</guid>
	<pubDate>Thu, 19 Nov 2020 07:04:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42325/published-a-dataset-of-363-genomes-from-approximately-92-percent-of-bird-families</link>
	<title><![CDATA[Published a dataset of 363 genomes from approximately 92 percent of bird families]]></title>
	<description><![CDATA[<div>A research team published a dataset of 363 genomes from approximately 92 percent of bird families and showed the significance of sampling dense organisms for biodiversity research. The study was jointly conducted by Chinese and international institutions and museums and was led by researchers from the Kunming Institute of Zoology (KIZ) of the Chinese Academy of Sciences (CAS). Total of 267 were newly published among the 363 sequenced genomes.&nbsp;They were mainly taken from samples of avian tissue kept in museums around the world, enabling researchers to sequence rare and endangered birds' genomes.</div><div>&nbsp;</div><div>Its descendants have adapted to a wide variety of ecological niches since the first bird formed more than 150 million years ago, giving rise to small, hovering hummingbirds, plunge-diving pelicans and showy paradise birds. More than 10,000 bird species live on the planet today - and now scientists are well on their way to capturing a full genetic image of that diversity.</div><div>&nbsp;</div><div>B10K is expanding its efforts to encompass the next stage of avian classification with 363 genomes complete. The team will sequence thousands of extra genomes in this process, attempting to represent each of the approximately 2,300 bird genera.</div><div>&nbsp;</div><div><img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41586-020-2873-9/MediaObjects/41586_2020_2873_Fig1_HTML.png?as=webp" alt="image" style="border: 0px;"></div><div>&nbsp;</div><div>The genomic resource is expected to provide new insights on evolutionary processes in cross-species comparative studies and assist in efforts to protect species, according to the research findings reported as a cover story in the journal Nature.</div><div>&nbsp;</div><div>Ref at&nbsp;Dense sampling of bird diversity increases power of comparative genomics&nbsp;https://www.nature.com/articles/s41586-020-2873-9</div>]]></description>
	<dc:creator>Jit</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>
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