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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26303?offset=610</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26968/scalce</guid>
	<pubDate>Fri, 15 Apr 2016 05:09:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26968/scalce</link>
	<title><![CDATA[SCALCE]]></title>
	<description><![CDATA[<p><span>SCALCE (</span><code>/skeɪlz/</code><span>, a.k.a. boosting&nbsp;</span><span style="text-decoration: underline;">S</span><span>equence&nbsp;</span><span style="text-decoration: underline;">C</span><span>ompression&nbsp;</span><span style="text-decoration: underline;">A</span><span>lgorithms using&nbsp;</span><span style="text-decoration: underline;">L</span><span>ocally&nbsp;</span><span style="text-decoration: underline;">C</span><span>onsistent</span><span style="text-decoration: underline;">E</span><span>ncoding) is a tool for compressing FASTQ files. It is designed specifically for the Illumina-generated FASTQ files, but supports any valid FASTQ with consistent read lengths.&nbsp;</span></p>
<p><span>More at&nbsp;http://sfu-compbio.github.io/scalce/</span></p><p>Address of the bookmark: <a href="http://sfu-compbio.github.io/scalce/" rel="nofollow">http://sfu-compbio.github.io/scalce/</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27092/medea-comparative-genomic-visualization-with-adobe-flash</guid>
	<pubDate>Tue, 26 Apr 2016 12:15:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27092/medea-comparative-genomic-visualization-with-adobe-flash</link>
	<title><![CDATA[MEDEA: Comparative Genomic Visualization with Adobe Flash]]></title>
	<description><![CDATA[<p><span>As the number of sequence and annotated genomes grows larger, the need to understand, compare, and contrast the data becomes increasingly important. Using the power of the human visual system to detect trends and spot outliers is necessary in such large and complex data sets.</span></p>
<p><span>More at&nbsp;http://www.broadinstitute.org/annotation/medea/</span></p><p>Address of the bookmark: <a href="http://www.broadinstitute.org/annotation/medea/" rel="nofollow">http://www.broadinstitute.org/annotation/medea/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27261/segemehl</guid>
	<pubDate>Tue, 10 May 2016 08:10:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27261/segemehl</link>
	<title><![CDATA[segemehl]]></title>
	<description><![CDATA[<p><span>segemehl is a software to map short sequencer reads to reference genomes. Unlike other methods, segemehl is able to detect not only mismatches but also insertions and deletions. Furthermore, segemehl is not limited to a specific read length and is able to map&nbsp;primer- or polyadenylation contaminated reads correctly.&nbsp; segemehl implements a matching strategy based on enhanced suffix arrays (ESA).&nbsp;</span></p>
<p><span>More at&nbsp;http://www.bioinf.uni-leipzig.de/Software/segemehl/</span></p>
<p><span>Manual&nbsp;http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf</span></p><p>Address of the bookmark: <a href="http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html" rel="nofollow">http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</guid>
	<pubDate>Fri, 20 May 2016 18:53:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</link>
	<title><![CDATA[MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping]]></title>
	<description><![CDATA[<p><span>MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</guid>
	<pubDate>Thu, 16 Jun 2016 17:55:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</link>
	<title><![CDATA[CNIDARIA: fast, reference-free phylogenomic clustering]]></title>
	<description><![CDATA[<p>Motivation: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but these do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.</p>
<p>Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.</p>
<p>Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.</p>
<p>Contact: Saulo Aflitos - sauloal@gmail.com</p>
<p>Supplementary information: Supplementary data are available at Bioinformatics online.</p><p>Address of the bookmark: <a href="https://github.com/sauloal/cnidaria/wiki" rel="nofollow">https://github.com/sauloal/cnidaria/wiki</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26363/flo</guid>
	<pubDate>Wed, 10 Feb 2016 10:52:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26363/flo</link>
	<title><![CDATA[flo]]></title>
	<description><![CDATA[<p>flo - same species annotations lift over pipeline</p>
<p>Lift over is the process of transferring annotations from one genome assembly to another. Usually lift over is done because there is a new, improved genome assembly for the species and good quality annotations (maybe manually curated or experimentally verified) are available on the old assembly.</p>
<p>The idea is simple: align the new assembly with the old one (e.g., with BLAT), process the alignment data to define how a coordinate or coordinate range on the old assembly should be transformed to the new assembly (e.g., as a chain file), transform the coordinates (e.g., with liftOver).</p>
<p>&nbsp;</p>
<p>https://github.com/wurmlab/flo</p><p>Address of the bookmark: <a href="https://github.com/wurmlab/flo" rel="nofollow">https://github.com/wurmlab/flo</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</guid>
	<pubDate>Thu, 23 Jun 2016 07:26:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</link>
	<title><![CDATA[WgSim]]></title>
	<description><![CDATA[<p>Reads simulator</p>
<p>Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.<br><br>Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.<br><br></p><p>Address of the bookmark: <a href="https://github.com/lh3/wgsim" rel="nofollow">https://github.com/lh3/wgsim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28117/quin%E2%80%99s-web-server</guid>
	<pubDate>Mon, 27 Jun 2016 10:44:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28117/quin%E2%80%99s-web-server</link>
	<title><![CDATA[QuIN’s web server]]></title>
	<description><![CDATA[<p><span>Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.&nbsp;</span></p>
<p><strong>AVAILABILITY:</strong><span>&nbsp;QuIN&rsquo;s web server is available at&nbsp;</span><a href="http://quin.jax.org/">http://quin.jax.org</a><span>&nbsp;QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:</span><a href="https://github.com/UcarLab/QuIN/">https://github.com/UcarLab/QuIN/</a><span>.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809" rel="nofollow">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</guid>
	<pubDate>Mon, 29 May 2017 05:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</link>
	<title><![CDATA[Genome Annotation Transfer Utility (GATU)]]></title>
	<description><![CDATA[<p>Genome Annotation Transfer Utility (GATU) was designed to facilitate quick, efficient annotation of similar genomes using genomes that have already been annotated. For example, whenever a new strain of SARS coronavirus is sequenced, it is possible, using GATU, to automatically annotate the new strain using a previously-annotated strain of SARS CoV. This saves researchers from tedious manual annotation of these sequences.</p>
<p>The program utilizes tBLASTn and BLASTn algorithms to map genes from the reference genome (the annotated strain) to the new sequence (the unannotated strain). The goal is to annotate the majority of the new genome&rsquo;s genes in a single step. ORFs present in the target genome and absent from the reference genome are also identified; these ORFs can be further analyzed using BLAST, VGO and BBB. Afterwards, they can either be accepted for/rejected from annotation. GATU can handle multiple-exon genes as well as mature peptides. Although it was designed for use with viral genomes, GATU can also be used to help annotate larger genomes (ie. bacterial genomes).</p>
<p>The output is saved in GenBank, XML, or EMBL file format.</p><p>Address of the bookmark: <a href="https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/" rel="nofollow">https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</guid>
	<pubDate>Fri, 01 Jul 2016 12:09:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</link>
	<title><![CDATA[Genome Workbench 2.10.7]]></title>
	<description><![CDATA[<p>Genome Workbench 2.10.7 is here! New features include added support for local custom BLAST databases and improvements to Tree View.</p><p>For the full list of features, improvements and fixes, see the release notes:<a href="https://ncbi.nlm.nih.gov/tools/gbench/releasenotes" target="_blank">https://ncbi.nlm.nih.gov/tools/gbench/releasenotes</a></p><p>New Features</p><ul>
<li>BLAST Tool: added support for local custom BLAST databases</li>
<li>Graphical Sequence View: added log scaling option for graph tracks</li>
<li>Generic Table View:&nbsp;<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial17">new tutorial</a>&nbsp;added</li>
</ul><p>Bug Fixes and Improvements</p><ul>
<li>Project Tree View: Genomic Collections/Assemblies now show accessions, not just names</li>
<li>Tree View: layout updated to better accommodate nodes of different sizes</li>
<li>Table Import Dialog (MacOS): fixed issue with table visibility</li>
<li>Fixed bug where different molecules IDs in GenBank could resolve to the same sequence</li>
<li>Graphical Sequence View: fixed issue where sequence track was not shown for some sequences</li>
<li>Graphical Sequence View: fixed protein coloration methods</li>
<li>Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images</li>
<li>Create Gene Model tool: fixed scenario when gene model tool failed with local sequences</li>
<li>Search View: ORF Finder &ndash; fixed incorrect protein lengths</li>
<li>Fixed bug with not opening project file (.gbp) on a click</li>
<li>Fixed issues in GVF import</li>
<li>Fixed BLAST Search tool against NCBI databases not working</li>
<li>Fixed tblastn (protein BLAST) not working in standalone mode</li>
<li>Fixed GTF export failure</li>
</ul>]]></description>
	<dc:creator>Gudiya Pal</dc:creator>
</item>

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