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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35899?</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</guid>
	<pubDate>Mon, 20 Aug 2018 14:17:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</link>
	<title><![CDATA[CoLoRMap: Correcting Long Reads by Mapping short reads]]></title>
	<description><![CDATA[<p><span>Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap</span></p>
<p><span>ehaghshe@sfu.ca or cedric.chauve@sfu.ca</span></p><p>Address of the bookmark: <a href="https://github.com/sfu-compbio/colormap" rel="nofollow">https://github.com/sfu-compbio/colormap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26537/destruct</guid>
	<pubDate>Mon, 29 Feb 2016 17:34:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26537/destruct</link>
	<title><![CDATA[destruct]]></title>
	<description><![CDATA[<p>Destruct is a tool for joint prediction of rearrangement breakpoints from single or multiple tumour samples.</p>
<p>More at&nbsp;https://bitbucket.org/dranew/destruct</p><p>Address of the bookmark: <a href="https://bitbucket.org/dranew/destruct" rel="nofollow">https://bitbucket.org/dranew/destruct</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36921/breakpointer-using-local-mapping-artifacts-to-support-sequence-breakpoint-discovery-from-single-end-reads</guid>
	<pubDate>Tue, 12 Jun 2018 12:41:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36921/breakpointer-using-local-mapping-artifacts-to-support-sequence-breakpoint-discovery-from-single-end-reads</link>
	<title><![CDATA[Breakpointer: using local mapping artifacts to support sequence breakpoint discovery from single-end reads]]></title>
	<description><![CDATA[Breakpointer is a fast tool for locating sequence breakpoints from the alignment of single end reads (SE) produced by next generation sequencing (NGS). It adopts a heuristic method in searching for local mapping signatures created by insertion/deletions (indels) or more complex structural variants(SVs).<p>Address of the bookmark: <a href="https://github.com/ruping/Breakpointer" rel="nofollow">https://github.com/ruping/Breakpointer</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26456/the-mills-lab</guid>
  <pubDate>Wed, 24 Feb 2016 16:18:38 -0600</pubDate>
  <link></link>
  <title><![CDATA[The Mills lab]]></title>
  <description><![CDATA[
<p>The laboratory is focused on the discovery and analysis of structural variation (SVs) from genomic sequence data. As part of the 1000 Genomes Project and other endeavors, we have helped produce initial fine-scale maps using a variety of SV discovery approaches including: (i) paired-end mapping (or read pair analysis) based on abnormally mapped pairs of clone ends; (ii) read-depth analysis, which detects deletions and duplications through analysis of the read depth-of-coverage; (iii) split read analysis, which detects SVs by evaluating gapped sequence alignments; and (iv) sequence assembly, which enables the discovery of novel (non-reference) sequence insertions.</p>

<p>http://millslab.org/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44219/chromosome-breakpoint-a-breakup-to-remember</guid>
	<pubDate>Tue, 07 Mar 2023 13:31:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44219/chromosome-breakpoint-a-breakup-to-remember</link>
	<title><![CDATA[Chromosome breakpoint - a breakup to remember]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Chromosome breakpoint refers to the physical location where a chromosome is broken and rearranged. Chromosome breakage can occur spontaneously or be induced by environmental factors such as radiation, chemicals, or viruses. The rearrangement of genetic material resulting from a chromosome breakpoint can have important consequences, including the development of genetic diseases, chromosomal abnormalities, or cancer.</p><p>Chromosome breakpoints can occur in two ways: interstitial or terminal. Interstitial breakpoints occur within the chromosome, while terminal breakpoints occur at the end of the chromosome. Terminal breakpoints can lead to the loss of genetic material, whereas interstitial breakpoints can result in the duplication or deletion of genetic material.</p><p>Chromosome breakpoints can be detected using a variety of techniques, including cytogenetic analysis, fluorescence in situ hybridization (FISH), and molecular methods such as polymerase chain reaction (PCR) and next-generation sequencing (NGS). These techniques can also help identify the exact location of the breakpoint and the nature of the rearrangement, such as translocations, inversions, deletions, or duplications.</p><p>Translocations are one of the most common types of chromosome rearrangements caused by breakpoints. In a translocation, genetic material is exchanged between two different chromosomes, resulting in a balanced or unbalanced distribution of genetic material. Unbalanced translocations can cause genetic diseases or developmental abnormalities, while balanced translocations can be inherited without any apparent phenotypic effects.</p><p>Inversions occur when a chromosome segment is inverted, resulting in a change in the order of genetic material. Inversions can be pericentric, involving the centromere, or paracentric, not involving the centromere. Inversions can cause genetic diseases or phenotypic effects if they disrupt the function of essential genes or regulatory elements.</p><p>Deletions and duplications are caused by interstitial breakpoints that result in the loss or gain of genetic material. Deletions can cause genetic diseases or developmental abnormalities if they involve essential genes or regulatory elements. Duplications can also have phenotypic effects, depending on the location and size of the duplicated segment.</p><p>Chromosome breakpoints can also be involved in the formation of complex chromosomal rearrangements, such as ring chromosomes or dicentric chromosomes. These complex rearrangements can have important clinical implications, as they can cause genetic diseases or cancer.</p><p>In conclusion, chromosome breakpoints are important genetic events that can lead to the rearrangement of genetic material and have important clinical implications. The detection and characterization of chromosome breakpoints using cytogenetic, molecular, and genomic methods are essential for the diagnosis, prognosis, and treatment of genetic diseases and cancer. Further research is needed to understand the molecular mechanisms underlying chromosome breakage and to develop new therapies targeting these events.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</guid>
	<pubDate>Fri, 26 Aug 2016 06:00:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</link>
	<title><![CDATA[GeneValidator - Identify problems with predicted genes]]></title>
	<description><![CDATA[<p>GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.</p>
<p>If you would like to use GeneValidator on a few sequences, see our online&nbsp;<a href="http://genevalidator.sbcs.qmul.ac.uk/">GeneValidator Web App</a>&nbsp;-<a href="http://genevalidator.sbcs.qmul.ac.uk/">http://genevalidator.sbcs.qmul.ac.uk</a>.</p>
<p>If you use GeneValidator in your work, please cite us as follows:</p>
<blockquote>
<p><a href="http://bioinformatics.oxfordjournals.org/content/early/2016/02/26/bioinformatics.btw015">Dragan M<span>&Dagger;</span>, Moghul MI<span>&Dagger;</span>, Priyam A, Bustos C &amp; Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions.&nbsp;<em>Bioinformatics</em>, doi: 10.1093/bioinformatics/btw015</a>.</p>
<p>&nbsp;</p>
</blockquote>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/wurmlab/genevalidator" rel="nofollow">https://github.com/wurmlab/genevalidator</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35787/protein-subcellular-localization-prediction</guid>
	<pubDate>Thu, 01 Mar 2018 06:20:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35787/protein-subcellular-localization-prediction</link>
	<title><![CDATA[Protein Subcellular Localization Prediction]]></title>
	<description><![CDATA[<p>Assigning subcellular localization to a protein is an important step towards elucidating its interaction partners, function, and potential role(s) in the cellular machinery. Computational tools offer an attractive complement to time-consuming and laborious experimental methods.</p>
<p>http://abi.inf.uni-tuebingen.de/Services/YLoc/webloc.cgi</p><p>Address of the bookmark: <a href="https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization" rel="nofollow">https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</guid>
	<pubDate>Thu, 22 Jun 2017 07:58:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</link>
	<title><![CDATA[DarkHorse: a method for genome-wide prediction of horizontal gene transfer]]></title>
	<description><![CDATA[<p><span>A new approach to rapid, genome-wide identification and ranking of horizontal transfer candidate proteins is presented. The method is quantitative, reproducible, and computationally undemanding. It can be combined with genomic signature and/or phylogenetic tree-building procedures to improve accuracy and efficiency. The method is also useful for retrospective assessments of horizontal transfer prediction reliability, recognizing orthologous sequences that may have been previously overlooked or unavailable. These features are demonstrated in bacterial, archaeal, and eukaryotic examples.</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37669/strum-structure-based-prediction-of-protein-stability-changes-upon-single-point-mutation</guid>
	<pubDate>Mon, 10 Sep 2018 13:21:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37669/strum-structure-based-prediction-of-protein-stability-changes-upon-single-point-mutation</link>
	<title><![CDATA[STRUM: structure-based prediction of protein stability changes upon single-point mutation]]></title>
	<description><![CDATA[<p><span>STRUM is a method for predicting the fold stability change (&Delta;&Delta;G) of protein molecules upon single-point nsSNP mutations. STRUM adopts a gradient boosting regression approch to train the Gibbs free-energy changes on a variety of features at different levels of sequence and structure properties. The unique characteristics of STRUM is the combination of sequence profiles with low-resolution structure models from protein structure prediction, which helps enhance the robustness and accuracy of the method and make it applicable to various protein seqences, including those without experimental structures&nbsp;</span></p><p>Address of the bookmark: <a href="https://zhanglab.ccmb.med.umich.edu/STRUM/" rel="nofollow">https://zhanglab.ccmb.med.umich.edu/STRUM/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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