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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34562?offset=50</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</guid>
	<pubDate>Fri, 19 Oct 2018 07:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</link>
	<title><![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]></title>
	<description><![CDATA[<p><span>new&nbsp;</span><em>de novo</em><span>&nbsp;assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.</span></p><p>Address of the bookmark: <a href="https://github.com/dhlbh/BASE" rel="nofollow">https://github.com/dhlbh/BASE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39867/gepard-allows-the-calculation-of-dotplots-even-for-large-sequences-like-chromosomes-or-bacterial-genomes</guid>
	<pubDate>Mon, 26 Aug 2019 11:38:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39867/gepard-allows-the-calculation-of-dotplots-even-for-large-sequences-like-chromosomes-or-bacterial-genomes</link>
	<title><![CDATA[Gepard: allows the calculation of dotplots even for large sequences like chromosomes or bacterial genomes]]></title>
	<description><![CDATA[<p>Gepard (German: "cheetah", Backronym for "GEnome PAir - Rapid Dotter") allows the calculation of dotplots even for large sequences like chromosomes or bacterial genomes. Reference: Krumsiek J, Arnold R, Rattei T. Gepard: A rapid and sensitive tool for creating dotplots on genome scale. Bioinformatics 2007; 23(8): 1026-8. PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/17309896" target="_blank">17309896</a></p>
<p><a href="http://cube.univie.ac.at/gepard">http://cube.univie.ac.at/gepard</a></p><p>Address of the bookmark: <a href="https://github.com/univieCUBE/gepard" rel="nofollow">https://github.com/univieCUBE/gepard</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 04:06:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</link>
	<title><![CDATA[mutatrix: a population genome simulator which generates simulated genomes.]]></title>
	<description><![CDATA[<p><span>genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms</span></p>
<p><span>More at&nbsp;<a href="https://github.com/ekg/mutatrix">https://github.com/ekg/mutatrix</a></span></p>
<pre>./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta</pre><p>Address of the bookmark: <a href="https://github.com/ekg/mutatrix" rel="nofollow">https://github.com/ekg/mutatrix</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42941/csa-a-high-throughput-chromosome-scale-assembly-pipeline-for-vertebrate-genomes</guid>
	<pubDate>Wed, 10 Mar 2021 06:13:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42941/csa-a-high-throughput-chromosome-scale-assembly-pipeline-for-vertebrate-genomes</link>
	<title><![CDATA[CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes]]></title>
	<description><![CDATA[<p>The pipeline can use information from scaffolded assemblies (for example from HiC or 10X Genomics), or even from diverged (~65-100 Mya) reference genomes for ordering the contigs and thus support the assembly process. This typically results in improved contig N50 when compared to current state of the art methods.</p>
<p><img src="https://github.com/HMPNK/CSA2.6/raw/master/Fig1.png" alt="image" style="border: 0px;"></p>
<p>For smaller vertebrate genomes (~1 Gbp) chromosome scale assemblies can be achieved within 12h on high-end Desktop computers (Intel i7, 12 CPU threads, 128 GB RAM). Larger mammalian genomes (~3Gbp) can be processed within 15-18 h on server equipment (Xeon, 96 CPU threads, 1TB RAM).</p><p>Address of the bookmark: <a href="https://github.com/HMPNK/CSA2.6" rel="nofollow">https://github.com/HMPNK/CSA2.6</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42357/irscope-an-online-program-to-visualize-the-junction-sites-of-chloroplast-genomes</guid>
	<pubDate>Wed, 25 Nov 2020 19:44:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42357/irscope-an-online-program-to-visualize-the-junction-sites-of-chloroplast-genomes</link>
	<title><![CDATA[IRscope: an online program to visualize the junction sites of chloroplast genomes]]></title>
	<description><![CDATA[<p><span>eMPRess, a software program for phylogenetic tree reconciliation under the duplication-transfer-loss model that systematically addresses the problems of choosing event costs and selecting representative solutions, enabling users to make more robust inferences.</span></p><p>Address of the bookmark: <a href="https://sites.google.com/g.hmc.edu/empress/home" rel="nofollow">https://sites.google.com/g.hmc.edu/empress/home</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44768/tritex-a-computational-pipeline-for-chromosome-scale-assembly-of-plant-genomes</guid>
	<pubDate>Fri, 14 Feb 2025 10:53:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44768/tritex-a-computational-pipeline-for-chromosome-scale-assembly-of-plant-genomes</link>
	<title><![CDATA[TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes]]></title>
	<description><![CDATA[<p><span>This is the documentation of TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes. It was developed in the research group Domestication Genomics at the Leibniz Institute of Plant Genetics and Crop Research (IPK) Gatersleben.</span></p><p>Address of the bookmark: <a href="https://tritexassembly.bitbucket.io/" rel="nofollow">https://tritexassembly.bitbucket.io/</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/36360/dendropy-a-python-library-for-phylogenetic-computing</guid>
	<pubDate>Mon, 23 Apr 2018 05:49:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36360/dendropy-a-python-library-for-phylogenetic-computing</link>
	<title><![CDATA[DendroPy: a Python library for phylogenetic computing]]></title>
	<description><![CDATA[<p>DendroPy is a Python library for phylogenetic computing. It provides classes and functions for the simulation, processing, and manipulation of phylogenetic trees and character matrices, and supports the reading and writing of phylogenetic data in a range of formats, such as NEXUS, NEWICK, NeXML, Phylip, FASTA, etc. Application scripts for performing some useful phylogenetic operations, such as data conversion and tree posterior distribution summarization, are also distributed and installed as part of the libary. DendroPy can thus function as a stand-alone library for phylogenetics, a component of more complex multi-library phyloinformatic pipelines, or as a scripting &ldquo;glue&rdquo; that assembles and drives such pipelines.</p>
<p>The primary home page for DendroPy, with detailed tutorials and documentation, is at:</p>
<blockquote><div><a href="http://dendropy.org/">http://dendropy.org/</a></div></blockquote>
<p>DendroPy is also hosted in the official Python repository:</p>
<blockquote><div><a href="http://packages.python.org/DendroPy/">http://packages.python.org/DendroPy/</a></div></blockquote>
<div id="requirements-and-installation">
<h2>Requirements and Installation</h2>
<p>DendroPy 4.x runs under Python 3 (all versions &gt; 3.1) and Python 2 (Python 2.7 only).</p>
<p>You can install DendroPy by running:</p>
<pre>&nbsp;</pre>
<p>More information is available here:</p>
<blockquote><div><a href="http://dendropy.org/downloading.html">http://dendropy.org/downloading.html</a></div></blockquote>
</div>
<div id="documentation">
<h2>Documentation</h2>
<p>Full documentation is available here:</p>
<blockquote><div><a href="http://dendropy.org/">http://dendropy.org/</a></div></blockquote>
<p>This includes:</p>
<blockquote>
<ul>
<li><a href="http://dendropy.org/primer/index.html">A comprehensive &ldquo;getting started&rdquo; primer</a>&nbsp;.</li>
<li><a href="http://dendropy.org/library/index.html">API documentation</a>&nbsp;.</li>
<li><a href="http://dendropy.org/schemas/index.html">Descriptions of data formats supported for reading/writing</a>&nbsp;.</li>
</ul>
</blockquote>
<p>and more.</p>
</div><p>Address of the bookmark: <a href="https://pypi.org/project/DendroPy/" rel="nofollow">https://pypi.org/project/DendroPy/</a></p>]]></description>
	<dc:creator>Seema Singh</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/26252/recombination-detection-tool</guid>
	<pubDate>Tue, 02 Feb 2016 10:11:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool</link>
	<title><![CDATA[Recombination detection tool]]></title>
	<description><![CDATA[<p>A program to detect recombination hotspots using population genetic data.</p>
<p>More at https://github.com/auton1/LDhot</p><p>Address of the bookmark: <a href="https://github.com/auton1/LDhot" rel="nofollow">https://github.com/auton1/LDhot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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