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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29407?offset=250</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</guid>
	<pubDate>Tue, 03 Jul 2018 04:14:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</link>
	<title><![CDATA[ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data]]></title>
	<description><![CDATA[ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.<p>Address of the bookmark: <a href="https://github.com/bcgsc/ChopStitch" rel="nofollow">https://github.com/bcgsc/ChopStitch</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Fri, 10 Aug 2018 18:41:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[<p>The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at&nbsp;<a href="https://github.com/wdecoster/nanopack" target="">https://github.com/wdecoster/nanopack</a>, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at&nbsp;<a href="http://nanoplot.bioinf.be/" target="">http://nanoplot.bioinf.be</a>&nbsp;and command line tools.</p>
<p>&nbsp;https://academic.oup.com/bioinformatics/article/34/15/2666/4934939</p><p>Address of the bookmark: <a href="https://github.com/wdecoster/nanoQC" rel="nofollow">https://github.com/wdecoster/nanoQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</guid>
	<pubDate>Fri, 07 Sep 2018 05:19:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</link>
	<title><![CDATA[P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads]]></title>
	<description><![CDATA[<p><span>P_RNA_scaffolder is a novel scaffolding tool using Pair-end RNA-seq to scaffold genome fragments. The method is suitable for most genomes. The program could utilize Illumina Paired-end RNA-sequencing reads from target speciesies. Our method provides another practical alternative to existing mate-pair_based approaches or other Protein-based approaches (for instance,&nbsp;</span><a href="http://www.fishbrowser.org/software/PEP_scaffolder/">PEP_scaffolder&nbsp;</a><span>) for scaffolding genome sequences. The most important feature of this method is to improve the completeness of gene regions and long-coding gene regions (for instance,&nbsp;</span><a href="http://circrna.org/">circRNA</a><span>).</span></p><p>Address of the bookmark: <a href="http://www.fishbrowser.org/software/P_RNA_scaffolder/#" rel="nofollow">http://www.fishbrowser.org/software/P_RNA_scaffolder/#</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data</guid>
	<pubDate>Fri, 04 Jan 2019 10:10:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data</link>
	<title><![CDATA[EXCAVATOR: detecting copy number variants from whole-exome sequencing data]]></title>
	<description><![CDATA[<p><span>EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at&nbsp;</span><span><a href="http://sourceforge.net/projects/excavatortool/" target="_blank"><span>http://sourceforge.net/projects/excavatortool/</span></a></span><span>.</span><br><br><br><span>EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.</span><br><span>EXCAVATOR has been published on Genome Biology (</span><a href="http://genomebiology.com/2013/14/10/R120/abstract" target="_blank">http://genomebiology.com/2013/14/10/R120/abstract<span></span></a><span>).</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/excavatortool/" rel="nofollow">https://sourceforge.net/projects/excavatortool/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/39827/prof-dr-med-andreas-ramming</guid>
  <pubDate>Wed, 07 Aug 2019 03:25:48 -0500</pubDate>
  <link></link>
  <title><![CDATA[Prof. Dr. med. Andreas Ramming]]></title>
  <description><![CDATA[
<p>In many autoimmune diseases, a misdirected immune response leads to chronic inflammation and subsequently to fibrotic and degenerative tissue remodeling. Therapeutic options are available for inflammatory joint diseases, but only about 40% of patients respond to these existing therapies on a permanent basis. In the remaining cases, these therapies miss their target from the beginning or later during the course of treatment failure. There are currently no causal therapies available for the treatment of fibrotic autoimmune diseases such as systemic sclerosis. Therefore, there is an urgent need to develop new therapeutic options for the treatment of fibrotic and synovitic autoimmune diseases. His group is therefore deal with the molecular mechanisms of these misdirected signaling pathways for the development of novel, targeted therapies</p>

<p>http://www.medizin3.uk-erlangen.de/forschung/arbeitsgruppen/matrixbiologie-entzuendliche-signalwege-in-arthritis-und-fibrose/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42271/mcclintock-meta-pipeline-to-identify-transposable-element-insertions-using-next-generation-sequencing-data</guid>
	<pubDate>Tue, 27 Oct 2020 00:21:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42271/mcclintock-meta-pipeline-to-identify-transposable-element-insertions-using-next-generation-sequencing-data</link>
	<title><![CDATA[McClintock: Meta-pipeline to identify transposable element insertions using next generation sequencing data]]></title>
	<description><![CDATA[<p><span>an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (</span><a href="https://github.com/bergmanlab/mcclintock">https://github.com/bergmanlab/mcclintock</a><span>), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote,&nbsp;</span><em>Saccharomyces cerevisiae</em><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/bergmanlab/mcclintock" rel="nofollow">https://github.com/bergmanlab/mcclintock</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44659/figeno-tool-for-plotting-sequencing-data-along-genomic-coordinates</guid>
	<pubDate>Tue, 17 Sep 2024 02:28:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44659/figeno-tool-for-plotting-sequencing-data-along-genomic-coordinates</link>
	<title><![CDATA[Figeno: Tool for plotting sequencing data along genomic coordinates.]]></title>
	<description><![CDATA[<p><span>Tool for plotting sequencing data along genomic coordinates.</span></p>
<div>
<pre><code>FIGENO is a
  FIGure
    GENerator
for GENOmics</code></pre>
</div>
<p dir="auto">With figeno, you can plot various types of sequencing data along genomic coordinates. Video overview:&nbsp;<a href="https://www.youtube.com/watch?v=h1cBeXoSYTA">https://www.youtube.com/watch?v=h1cBeXoSYTA</a>.</p>
<p dir="auto"><a href="https://github.com/CompEpigen/figeno/blob/main/docs/content/images/figeno.png" target="_blank"><img src="https://github.com/CompEpigen/figeno/raw/main/docs/content/images/figeno.png" alt="figeno" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/CompEpigen/figeno" rel="nofollow">https://github.com/CompEpigen/figeno</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/23586/calling-narrow-and-broad-peaks-from-chip-seq-data</guid>
	<pubDate>Tue, 04 Aug 2015 05:06:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/23586/calling-narrow-and-broad-peaks-from-chip-seq-data</link>
	<title><![CDATA[Calling narrow and broad peaks from ChIP-Seq data]]></title>
	<description><![CDATA[<p><span>Know about the state-of-the-art algorithms implemented in Strand NGS for detecting the binding sites of transcription factor (narrow peaks) and enriched regions of histone modification (broad peaks) from ChIP-Seq data.</span><br /><a href="http://www.strand-ngs.com/learn/white-papers#broad-peaks" target="_blank" title="Calling narrow and broad peaks from ChIP-Seq data in Strand NGS">Read the benchmarking study</a><span>&nbsp;on Calling narrow and broad peaks from ChIP-Seq data in Strand NGS by Rohit Gupta and Anita Sathyanarayanan. For more information, please&nbsp;</span><a href="http://www.strand-ngs.com/contact/sales" target="_blank" title="strand ngs contact">contact us</a></p>]]></description>
	<dc:creator>Strand</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/28940/webinar-on-implications-of-next-generation-sequencing-in-molecular-diagnosis-of-cancer-on-28-sep-2016</guid>
	<pubDate>Thu, 01 Sep 2016 01:52:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/28940/webinar-on-implications-of-next-generation-sequencing-in-molecular-diagnosis-of-cancer-on-28-sep-2016</link>
	<title><![CDATA[Webinar on Implications of Next Generation Sequencing in Molecular Diagnosis of Cancer on 28 Sep 2016]]></title>
	<description><![CDATA[<h3>Abstract:</h3><p>Genetic testing requires screening of the entire gene, which by conventional sequencing is time consuming and expensive. Next Generation Sequencing (NGS) based approaches increase the sensitivity of mutation detection, making it fast and cost-effective compared to the conventional tests performed in a reflex-testing mode. Strand NGS includes workflows with quality assessment and filter sections that do not require any manual intervention. Post-analytical workflows in Strand NGS allow users to execute sequence analysis with stringent filtering to eliminate false positive and low quality reads. This simplifies the analysis in large scale cohort settings, where every sample needs to be processed identically.</p><p>In this webinar we will discuss the implications of next generation sequencing based tests in multi-gene testing. We will also show how NGS based tests help to identify copy number variations, split read analysis and breakpoint identification. Finally, we will show a brief glimpse of Indian cohort data, where NGS based tests have shown improved mutation detection. In this webinar, we will present clinical case studies in on Hereditary Breast and Ovarian Cancer (HBOC) and Retinoblastoma patients to demonstrate how CNV analysis in Strand NGS enables researchers to detect and visualize copy number changes ranging from single exon to full gene.</p><h3>Speaker:</h3><p>Dr. Jaya Singh, Senior Scientist, Strand Life Sciences</p><p><strong>Date:</strong> <a href="http://www.strand-ngs.com/webinar_registration">28 September 2016</a></p><p><strong>Session1:</strong> <a href="http://www.strand-ngs.com/webinar_registration">2:30 PM IST</a></p><p><strong>Session2:</strong> <a href="http://www.strand-ngs.com/webinar_registration">10 PM IST</a></p><p><a href="http://www.strand-ngs.com/webinar_registration"><strong>Register here:</strong></a>&nbsp;http://www.strand-ngs.com/webinar_registration</p>]]></description>
	<dc:creator>Strand</dc:creator>
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