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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36616?offset=10</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36015/repeat-aware-repeat-aware-scaffolding-evaluation-framework-by-igor-mandric</guid>
	<pubDate>Wed, 21 Mar 2018 18:10:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36015/repeat-aware-repeat-aware-scaffolding-evaluation-framework-by-igor-mandric</link>
	<title><![CDATA[repeat-aware: Repeat aware scaffolding evaluation framework by Igor Mandric]]></title>
	<description><![CDATA[<p>Genome scaffolding is a classical challenging problem in bioinformatics. It refers to joining assembly contigs into chains (called scaffolds). The join between two contigs A and B is considered correct if:</p>
<ul>
<li>Their relative orientation is correct</li>
<li>Their relative order is correct</li>
<li>The gap estimate is similar to the true distance on the reference</li>
</ul>
<p>The problem of scaffolding validation is also a challenging one. One of the main issues which hinders from an adequate scaffolding evaluation are genome repeats. The previous standard for evaluation&nbsp;<a href="https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-3-r42">(Hunt et al.,&nbsp;<em>Genome Biology</em>, 2014)</a>&nbsp;did not take into account repeats. In this evaluation framework, repeats are taken into account.</p>
<p style="text-align: center;"><a href="https://camo.githubusercontent.com/9675b90205e5bc0dc0b6b84b321b00bc87d8d88e/687474703a2f2f616c616e2e63732e6773752e6564752f7265706561742d61776172652f6669677572652e706e67" target="_blank"><img src="https://camo.githubusercontent.com/9675b90205e5bc0dc0b6b84b321b00bc87d8d88e/687474703a2f2f616c616e2e63732e6773752e6564752f7265706561742d61776172652f6669677572652e706e67" width="75%" alt="image" style="border: 0px;"></a></p>
<p>The new evaluation framework considers the optimal assignment of contigs in the output scaffolding to contigs in the reference scaffolding in the sense of the number of correct links.</p>
<p>&nbsp;</p>
<p>https://github.com/mandricigor/repeat-aware</p><p>Address of the bookmark: <a href="https://github.com/mandricigor/repeat-aware" rel="nofollow">https://github.com/mandricigor/repeat-aware</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</guid>
	<pubDate>Thu, 31 Jan 2019 05:12:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</link>
	<title><![CDATA[nQuire: A statistical framework for ploidy estimation using NGS short-read data]]></title>
	<description><![CDATA[<p>nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.</p>
<p>For more background see also the publication at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2128-z">BMC Bioinformatics</a>.</p>
<p>https://github.com/clwgg/nQuire</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuire" rel="nofollow">https://github.com/clwgg/nQuire</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42201/rosettaantibodydesign-rabd-a-general-framework-for-computational-antibody-design</guid>
	<pubDate>Sun, 20 Sep 2020 06:03:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42201/rosettaantibodydesign-rabd-a-general-framework-for-computational-antibody-design</link>
	<title><![CDATA[RosettaAntibodyDesign (RAbD): A general framework for computational antibody design]]></title>
	<description><![CDATA[<p><strong>RosettaAntibodyDesign (RAbD)</strong>&nbsp;is a generalized framework for the design of antibodies, in which a user can easily tailor the run to their project needs.&nbsp;<strong>The algorithm is meant to sample the diverse sequence, structure, and binding space of an antibody-antigen complex.</strong>&nbsp;It can be used for a multitude of project types, from denovo design to redesigns that improve binding affinity, optimize stability, or manipulate function.</p>
<p>The framework is based on rigorous bioinformatic analysis and rooted very much on our&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pubmed/21035459">recent clustering</a>&nbsp;of antibody CDR regions. It uses the&nbsp;<strong>North/Dunbrack CDR definition</strong>&nbsp;as outlined in the North/Dunbrack clustering paper.</p>
<p>More at</p>
<p>https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign</p>
<p>https://bio-jade.readthedocs.io/en/latest/installation.html</p><p>Address of the bookmark: <a href="https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign" rel="nofollow">https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38804/grabb-selective-assembly-of-genomic-regions-a-new-niche-for-genomic-research</guid>
	<pubDate>Sat, 26 Jan 2019 18:58:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38804/grabb-selective-assembly-of-genomic-regions-a-new-niche-for-genomic-research</link>
	<title><![CDATA[GRAbB: Selective Assembly of Genomic Regions, a New Niche for Genomic Research]]></title>
	<description><![CDATA[<p><span>GRAbB is shown to be more efficient than MITObim in terms of speed, memory and disk usage. The other functionalities (handling multiple targets simultaneously and extracting homologous regions) of the new program are not matched by other programs. The program is available with explanatory documentation at&nbsp;</span><a href="https://github.com/b-brankovics/grabb">https://github.com/b-brankovics/grabb</a><span>. GRAbB has been tested on Ubuntu (12.04 and 14.04), Fedora (23), CentOS (7.1.1503) and Mac OS X (10.7). Furthermore, GRAbB is available as a docker repository: brankovics/grabb (</span><a href="https://hub.docker.com/r/brankovics/grabb/">https://hub.docker.com/r/brankovics/grabb/</a><span>).</span></p><p>Address of the bookmark: <a href="https://github.com/b-brankovics/grabb" rel="nofollow">https://github.com/b-brankovics/grabb</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29284/genebreak-a-tool-to-systematically-identify-genes-recurrently-affected-by-the-genomic-location-of-chromosomal-cna-associated-breaks-by-a-genome-wide-approach</guid>
	<pubDate>Sat, 01 Oct 2016 15:15:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29284/genebreak-a-tool-to-systematically-identify-genes-recurrently-affected-by-the-genomic-location-of-chromosomal-cna-associated-breaks-by-a-genome-wide-approach</link>
	<title><![CDATA[GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach]]></title>
	<description><![CDATA[<p>Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).</p>
<p> </p><p>Address of the bookmark: <a href="http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html" rel="nofollow">http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35108/mobyle-a-new-full-web-bioinformatics-framework</guid>
	<pubDate>Sun, 07 Jan 2018 19:33:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35108/mobyle-a-new-full-web-bioinformatics-framework</link>
	<title><![CDATA[Mobyle: a new full web bioinformatics framework]]></title>
	<description><![CDATA[<p><span>Mobyle, to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software.</span></p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/25/22/3005/179064" rel="nofollow">https://academic.oup.com/bioinformatics/article/25/22/3005/179064</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</guid>
	<pubDate>Thu, 04 Oct 2018 05:23:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</link>
	<title><![CDATA[nQuire: a statistical framework for ploidy estimation using next generation sequencing]]></title>
	<description><![CDATA[<p>nQuire provides a statistical framework to study organisms with intraspecific variation in ploidy. nQuire is likely to be useful in epidemiological studies of pathogens, artificial selection experiments, and for historical or ancient samples where intact nuclei are not preserved. It is implemented as a stand-alone Linux command line tool in the C programming language and is available at https://github.com/clwgg/nQuireunder the MIT license.</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuireunder" rel="nofollow">https://github.com/clwgg/nQuireunder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment</guid>
	<pubDate>Mon, 25 Mar 2019 12:46:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment</link>
	<title><![CDATA[ChIPulate: A Python3 framework to simulate read counts in a ChIP-seq experiment]]></title>
	<description><![CDATA[<p><span style="color: #202020; font-size: 13px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff; float: none;">ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength.<span> <br></span></span></p><p>Address of the bookmark: <a href="https://github.com/vishakad/chipulate" rel="nofollow">https://github.com/vishakad/chipulate</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</guid>
	<pubDate>Fri, 08 Mar 2024 23:36:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</link>
	<title><![CDATA[UniAligner: a parameter-free framework for fast sequence alignment]]></title>
	<description><![CDATA[<p>UniAligner (formerly, TandemAligner) is the first parameter-free algorithm for sequence alignment that introduces a sequence-dependent alignment scoring that automatically changes for any pair of compared sequences. Classical alignment approaches, such as the Smith-Waterman algorithm, that work well for most sequences, fail to construct biologically adequate alignments of extra-long tandem repeats (ETRs), such as human centromeres and immunoglobulin loci. This limitation was overlooked in the previous studies since the sequences of the centromeres and other ETRs across multiple genomes only became available recently.</p>
<p>More at https://www.nature.com/articles/s41592-023-01970-4</p><p>Address of the bookmark: <a href="https://github.com/seryrzu/unialigner" rel="nofollow">https://github.com/seryrzu/unialigner</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</guid>
	<pubDate>Sat, 02 Jun 2018 07:36:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</link>
	<title><![CDATA[Gblocks: eliminates poorly aligned positions and divergent regions of a DNA or protein alignment]]></title>
	<description><![CDATA[<p><a href="http://molevol.cmima.csic.es/castresana/Gblocks.html">Gblocks</a><span>&nbsp;eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. This server implements the most important features of the Gblocks program to make its use as simple as possible without loosing the functionality that it is necessary in most of the cases. Other options can be changed in the stand-alone program. You can see here an&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks_server/nad3.pir-gb.htm">example output file</a><span>&nbsp;showing the blocks selected from a protein alignment. Further information can be found in the&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks/Gblocks_documentation.html">online documentation</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="http://molevol.cmima.csic.es/castresana/Gblocks_server.html" rel="nofollow">http://molevol.cmima.csic.es/castresana/Gblocks_server.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

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