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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37233?offset=340</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34195/strand-life-sciences-announces-the-release-of-strand-ngs-v31-at-ashg-2017</guid>
	<pubDate>Mon, 23 Oct 2017 02:36:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34195/strand-life-sciences-announces-the-release-of-strand-ngs-v31-at-ashg-2017</link>
	<title><![CDATA[Strand Life Sciences announces the release of Strand NGS v3.1 at ASHG 2017]]></title>
	<description><![CDATA[<h1><a href="http://www.strand-ngs.com/strand-announce-strandngss-v31">Strand Life Sciences announces the release of Strand NGS v3.1 at ASHG 2017</a></h1>
<p><strong><em>ORLANDO, USA, Oct 17, 2017/ PRNewswire/</em></strong></p>
<p><em>Strand NGS now supports large scale RNA- and small-RNA-Seq and Unique Molecular Identifiers (UMIs) for DNA-, RNA-, and small-RNA-Seq.</em></p>
<p>Strand Life Sciences announced the latest version release of its bioinformatics flagship product, Strand NGS, at the Annual Meeting of the American Society of Human Genetics today. Two major themes in Strand NGS v3.1 address recent challenges in next generation sequencing (NGS).</p>
<p>The first theme is large-scale RNA-Seq data analysis. Current cross-cohort RNA- and small-RNA-Seq studies span tens of replicates and batches across hundreds of samples, sometimes conducted across several different institutions. For such studies, Strand NGS v3.1 includes confounding variable analysis to eliminate technical effects, including batch effects; the t-SNE plot; profile and heat-map plots of gene-body coverage; and several other notable visual enhancements.</p>
<p>The second new feature is support for Unique Molecular Identifiers, or UMIs, for DNA-, RNA- and small-RNA-Seq. UMI support in Strand NGS is end-to-end, spanning alignment to variant calling in DNA-Seq, and alignment to quantification in RNA- and small-RNA-Seq. The Bioo Scientific, Qiagen, and Rubicon UMI protocols are natively supported, and an intuitive interface allows the specification of custom UMI protocols.</p>
<p><em>&ldquo;For liquid biopsies and low-grade FFPE samples, UMI support in DNA-Seq enables the detection of somatic variants at low concentrations. In RNA-Seq, large-scale and UMI support can be used in single-cell-based studies that reveal tumor-cell heterogeneity, even at low concentrations&rdquo;, says<strong>&nbsp;Dr. Vamsi Veeramachaneni, Chief Scientific Officer, Strand Life Sciences.</strong></em></p>
<p><em>&ldquo;At Strand, we are continuously working towards improving the accuracy and efficiency of NGS data analysis. Customers can look forward to Strand NGS becoming available on the cloud in the near future&rdquo;, says&nbsp;<strong>Dr. Ramesh Hariharan, Chief Executive Officer, Strand Life Sciences.</strong></em></p>
<p>Visit Strand Life Sciences at ASHG booth #1017 to know more about Strand NGS v3.1 and other products and service offerings from Strand Life Sciences. Click here to access detailed agenda and v3.1&nbsp;<a href="http://www.strand-ngs.com/download/releasenotes">release notes</a>.</p>
<p><strong>About Strand Life Sciences</strong></p>
<p>Strand Life Sciences is a premier life science informatics innovation company. Founded in 2000, Strand is a leader in technology innovations for healthcare using genomics. By enhancing sequence-based diagnostics and clinical genomic data interpretation using a strong foundation of computational, scientific, and medical expertise, Strand is bringing individualized medicine to the world. To know more, visit&nbsp;<a href="http://www.strandls.com/" title="www.strandls.com">www.strandls.com</a></p><p>Address of the bookmark: <a href="http://www.strand-ngs.com/strand-announce-strandngss-v31" rel="nofollow">http://www.strand-ngs.com/strand-announce-strandngss-v31</a></p>]]></description>
	<dc:creator>Yeshodari</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</guid>
	<pubDate>Tue, 09 Jun 2020 05:21:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</link>
	<title><![CDATA[StringTie Transcript assembly and quantification for RNA-Seq]]></title>
	<description><![CDATA[<p><strong>StringTie</strong><span>&nbsp;is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional&nbsp;</span><em>de novo</em><span>&nbsp;assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like&nbsp;</span><a href="https://github.com/alyssafrazee/ballgown">Ballgown</a><span>,&nbsp;</span><a href="http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html">Cuffdiff</a><span>&nbsp;or other programs (DESeq2, edgeR, etc.).</span></p><p>Address of the bookmark: <a href="https://ccb.jhu.edu/software/stringtie/" rel="nofollow">https://ccb.jhu.edu/software/stringtie/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</guid>
	<pubDate>Fri, 13 Dec 2024 04:03:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</link>
	<title><![CDATA[Exploring RNA Sequence Analysis: Tools for Every Bioinformatician]]></title>
	<description><![CDATA[<p>RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.</p><h4><strong>1. RNA-Seq Analysis Pipelines</strong></h4><p>RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:</p><ul>
<li><strong>FASTQC</strong>: For quality control of raw RNA-seq reads.</li>
<li><strong>Trimmomatic</strong>: For trimming and filtering RNA-seq reads.</li>
<li><strong>HISAT2/STAR</strong>: High-performance aligners for RNA-seq reads.</li>
<li><strong>FeatureCounts</strong>: For quantifying gene expression.</li>
<li><strong>DESeq2/EdgeR</strong>: For differential expression analysis.</li>
</ul><h4><strong>2. Transcriptome Assembly and Annotation</strong></h4><p>For analyzing transcriptomes from non-model organisms or assembling novel transcripts:</p><ul>
<li><strong>Trinity</strong>: For de novo transcriptome assembly.</li>
<li><strong>StringTie</strong>: For transcript assembly and quantification from RNA-seq alignments.</li>
<li><strong>TransDecoder</strong>: To predict coding regions within assembled transcripts.</li>
<li><strong>TAU</strong>: Tools for annotating non-coding and coding RNAs.</li>
</ul><h4><strong>3. Exploring Non-Coding RNA (ncRNA)</strong></h4><p>Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:</p><ul>
<li><strong>Infernal</strong>: For identifying ncRNA sequences based on covariance models.</li>
<li><strong>Rfam</strong>: Database and tools for ncRNA families.</li>
<li><strong>miRDeep</strong>: For identifying microRNAs in RNA-seq datasets.</li>
</ul><h4><strong>4. RNA Structure and Motif Analysis</strong></h4><p>Structural biology of RNA helps in understanding its function:</p><ul>
<li><strong>RNAfold (ViennaRNA)</strong>: Predicts secondary structures from RNA sequences.</li>
<li><strong>RNAstructure</strong>: Tools for RNA secondary structure prediction and analysis.</li>
<li><strong>MEME Suite</strong>: For identifying motifs in RNA sequences.</li>
<li><strong>IntaRNA</strong>: For RNA-RNA interaction prediction.</li>
</ul><h4><strong>5. RNA Editing and Modifications</strong></h4><p>Epitranscriptomics is a growing field focusing on RNA modifications:</p><ul>
<li><strong>REDItools</strong>: For RNA editing analysis.</li>
<li><strong>m6Aboost</strong>: For identifying m6A modifications in RNA.</li>
</ul><h4><strong>6. Long-Read RNA Sequencing Analysis</strong></h4><p>Long-read technologies like Nanopore and PacBio are transforming RNA research:</p><ul>
<li><strong>FLAIR</strong>: For isoform-level analysis of long-read RNA-seq data.</li>
<li><strong>NanoMod</strong>: For detecting modifications in RNA from Nanopore sequencing.</li>
</ul><h4><strong>7. RNA-Protein Interactions</strong></h4><p>To study RNA-protein interactions and complexes:</p><ul>
<li><strong>RBPmap</strong>: For identifying RNA-binding protein motifs.</li>
<li><strong>PARalyzer</strong>: For analyzing PAR-CLIP data.</li>
</ul><h4><strong>8. Functional Enrichment Analysis</strong></h4><p>Understanding biological functions and pathways from RNA-seq data:</p><ul>
<li><strong>getENRICH</strong>: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).</li>
<li><strong>ClusterProfiler</strong>: For GO and KEGG pathway enrichment analysis.</li>
</ul><h4><strong>9. Visualization and Data Sharing</strong></h4><p>Presenting and sharing RNA sequence analysis results effectively:</p><ul>
<li><strong>IGV</strong>: Genome browser for visualizing RNA-seq alignments.</li>
<li><strong>Circos</strong>: Circular visualization of RNA-seq data.</li>
<li><strong>DashBio</strong>: A Python library for creating bioinformatics visualizations.</li>
</ul><h4><strong>Conclusion</strong></h4><p>The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you&rsquo;re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</guid>
	<pubDate>Thu, 22 Feb 2018 06:38:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</link>
	<title><![CDATA[Webinar on RNA-Seq Data Analysis on 28 Feb 2018]]></title>
	<description><![CDATA[<p>Strand NGS is a biologist friendly NGS analysis tool that allows biologists to analyze their data using a very intuitive workflow for the analysis and visualization of RNA-Seq data. This webinar will give an overview of the workflow which includes Transcriptome/ Genome alignment, Differential expression analysis, Splicing events and gene fusion detection. Strand NGS also supports novel discovery like identification of novel genes, exons and novel splice junctions.<br />We will highlight the use of Strand NGS features such as PCA, sample correlation, clustering, Venn diagrams, CVA, UMI support and elastic genome browser used in RNA-Seq workflow that supports large scale RNA-Seq data analysis too. The tool also supports biological contextualization on the set of interesting genes from the data by allowing downstream analysis such as GO and pathway analysis. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS.</p><p>Details:<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 1: </a>28 Feb 2018, 9 AM CET<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 2:</a> 28 Feb 2018, 8 AM PST<br />Register here: http://www.strand-ngs.com/webinar_registration</p><p><span style="font-size: 12.8px;">About Speaker:</span></p><p>Dr. Suman Kapoor, Manager- Application Science at Strand Life Sciences, has over 11 years experience in molecular biology, next-generation sequencing based testing, clinical genomics, and personalized medicine for disease management and prenatal testing. Dr. Suman holds a Ph.D in Molecular and Cell Biology from Indian Institute of Science, Bangalore. Prior to joining Strand NGS team, Suman has worked extensively on protein synthesis in eubacteria and has experience working in CAP and NABL accredited lab validating and interpreting NGS based diagnostic tests.</p>]]></description>
	<dc:creator>Strand</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12567/workshop-on-molecular-modeling-and-dynamics-simulation-analyses</guid>
  <pubDate>Fri, 04 Jul 2014 13:38:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Workshop On Molecular Modeling and Dynamics Simulation Analyses]]></title>
  <description><![CDATA[
<p>Workshop On Molecular Modeling and Dynamics Simulation Analyses</p>

<p>August1-2, 2014</p>

<p>Organised By</p>

<p>Centre of Excellence in Bioinformatics<br />Bioinformatics Infrastructure Facility<br />Department of Biochemistry<br />University of Lucknow<br />Lucknow-226007</p>

<p>Course Contents</p>

<p>Molecular Modeling<br /> Homology Modeling<br />Molecular Docking<br />Post-structural Analyses</p>

<p>Molecular Dynamics (MD)<br />Simulation<br />Linux Introduction<br />Gromacs Installation</p>

<p>MD Simulation of Protein ligand complex<br />Analyses of MD<br />Trajectories<br />Visualization of Dynamic<br />complexes</p>

<p>Important Dates</p>

<p>Registration Begins June 25, 2014<br />Registration Closes July 25, 2014</p>

<p>Brochure : www.lkouniv.ac.in/conference/Brochure_August,%202014.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41899/stay-at-home-revbayes-workshop</guid>
  <pubDate>Sat, 20 Jun 2020 11:53:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Stay-at-Home RevBayes Workshop]]></title>
  <description><![CDATA[
<p>Stay-at-Home RevBayes Workshop<br />Location: Anywhere (online-only event)<br />Dates: 7/13, 2020 to 8/12, 2020<br />Instructors: Joëlle Barido-Sottani, Walker Pett, Josh Justison, Wade Dismukes, Luiza Fabreti, Tracy Heath, Jeremy M. Brown, Rosana Zenil-Ferguson<br />Register: https://iastate.qualtrics.com/jfe/form/SV_02sCYRWbxYK9I5D</p>

<p>Description<br />This free online-only RevBayes workshop will provide an introduction to the theory and use of RevBayes, with a focus on (1) tree inference from molecular data, (2) analyses combining fossil and extant taxa, and (3) evaluating MCMC performance, with advanced topics including assessing model adequacy and macroevolutionary analyses. Additional topics may be added depending on the interests of selected participants. The format will be a combination of interactive video sessions (via Zoom or similar tools), real-time discussions over Slack, self-guided tutorials, and pre-recorded videos.</p>

<p>The initial session will resolve technical issues and present the basics of using RevBayes. Participants will then be expected to work through several tutorials on their own schedule, with the help of pre-recorded materials. A Slack forum will be open for questions and issues. The workshop will conclude with several online Q&amp;A sessions with the instructors. The dates for the interactive sessions are currently tentative and may be adjusted depending on the schedules of the participants and instructors.</p>

<p>We are hoping to identify up to 15 participants for this online course. While we hope we are able to accommodate everyone who applies, we realize that this may not be possible because of time-zones and availability. If the number of applicants exceeds our capacity, we hope to organize a second round of sessions later in the year. Participants will not be charged for the course, but we will request that they commit to completing the tutorials and attending a majority of interactive sessions.</p>

<p>To apply to this course, please go to the registration form and submit your application by July 6, 2020.</p>

<p>More at https://revbayes.github.io/workshops/online2020.html</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</guid>
	<pubDate>Fri, 03 Mar 2017 05:15:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</link>
	<title><![CDATA[MetaPred2CS]]></title>
	<description><![CDATA[<p style="text-align: justify;"><strong>MetaPred2CS Web server&nbsp;</strong>is a meta-predictor based on&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/17160063">Support Vector Machine (SVM)</a>&nbsp;that combines 6 individual sequence based protein-protein interaction prediction methods to predict&nbsp;<strong>prokaryotic two-component system&nbsp;</strong>protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11933068">in-silico two hybrid (i2h)</a>&nbsp;and&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11707606">mirror tree (MT)</a>&nbsp;methods and 4 genomics context based methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/15947018">phylogenetic profiling (PP)</a>,&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/10573422">gene fusion (GF)</a>,&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene neighbourhood (GN)</a>&nbsp;and and&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene operon methods (GO)</a>.</p>
<p>&nbsp;http://metapred2cs.ibers.aber.ac.uk/</p><p>Address of the bookmark: <a href="https://github.com/martinjvickers/MetaPred2CS" rel="nofollow">https://github.com/martinjvickers/MetaPred2CS</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</guid>
	<pubDate>Fri, 24 Feb 2017 08:56:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</link>
	<title><![CDATA[VAGUE:Velvet Assembler Graphical Front End]]></title>
	<description><![CDATA[<p>VAGUE is a vague acronym for "Velvet Assembler Graphical Front End", which means it is a GUI for the Velvet <em>de novo</em> assembler. The command line version of Velvet can be complicated for beginners to use, but VAGUE makes it clear and simple</p>
<p>More at&nbsp;http://www.vicbioinformatics.com/software.vague.shtml</p><p>Address of the bookmark: <a href="http://www.vicbioinformatics.com/software.vague.shtml" rel="nofollow">http://www.vicbioinformatics.com/software.vague.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</guid>
	<pubDate>Tue, 08 Nov 2016 07:34:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</link>
	<title><![CDATA[Statistics and probability]]></title>
	<description><![CDATA[<h3><span>Topics</span></h3>
<div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/displaying-describing-data">Displaying and describing data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data">Modeling distributions of data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data">Describing relationships in quantitative data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/designing-studies">Designing studies</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/probability-library">Probability</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library">Random variables</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library">Sampling distributions</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample">Confidence intervals (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample">Significance tests (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-confidence-intervals-two-samples">Significance tests and confidence intervals (two samples)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/inference-categorical-data-chi-square-tests">Inference for categorical data (chi-square tests)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/advanced-regression-inference-transforming">Advanced regression (inference and tran</a></div>
</div><p>Address of the bookmark: <a href="https://www.khanacademy.org/math/statistics-probability" rel="nofollow">https://www.khanacademy.org/math/statistics-probability</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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