<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30124?offset=280</link>
	<atom:link href="https://bioinformaticsonline.com/related/30124?offset=280" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r</guid>
	<pubDate>Mon, 09 Jul 2018 05:20:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r</link>
	<title><![CDATA[ASAR: Advanced metagenomic Sequence Analysis in R]]></title>
	<description><![CDATA[<p><span>An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.</span></p>
<p><span><span>To read the manual, please click the link&nbsp;</span><a href="https://askarbek-orakov.github.io/ASAR/">https://askarbek-orakov.github.io/ASAR/</a></span></p><p>Address of the bookmark: <a href="https://github.com/Askarbek-orakov/ASAR" rel="nofollow">https://github.com/Askarbek-orakov/ASAR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24297/bioinformatics-walkin-at-nii</guid>
  <pubDate>Fri, 04 Sep 2015 21:48:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics WalkIn at NII]]></title>
  <description><![CDATA[
<p>ADVERTISEMENT OF WALK-IN-INTERVIEW</p>

<p>NAME OF THE POST : Bioinformatician (Part time 3 days in a week) (One Position only)</p>

<p>DURATION : One Year</p>

<p>NAME OF THE PROJECT : Next generation sequencing facility</p>

<p>EDUCATIONAL QUALIFICATIONS : At least a Masters degree in Bioinformatics and Bachelors degree in any stream of life sciences</p>

<p>REQUIREMENTS :</p>

<p>Around 5 years of experience and proven track record in next generation sequence data analysis (supported by publications in peer-reviewed journals), ability to analyze transcriptomics, Chip-seq, and small RNA –seq data.</p>

<p>: Should have the ability to analyze raw primary data generated by Illumina next generation sequencing platforms and create / troubleshoot custom analysis Pipelines.</p>

<p>Should have ability to handle all downstream secondary and tertiary data analysis using commercially available as well as open source softwares (transcriptomics, ChIP-seq, small RNA-seq)</p>

<p>Apart from these, the applicant should have knowledge of the following: Programming: Perl and Python. Operating system:</p>

<p>Linux and Windows. NGS Analysis tools: Maq, BWA, Bowtie, SAM tools, BEDTools, MACS, Galaxy, FastQC, Bismark, MEDIPS, Tophat, Cufflinks, AvadisNGS, CLC Genomics Workbench, Galaxy, BaseSpace, Trinity Statistics: Microsoft Excel and R. Database: MySQL Genome Browser: UCSC, Ensemble, IGV, IGB Motif Analysis Tools: MEME Suite, Transfac and RSAT Functional Annotation Tools: DAVID, GeneCodis, Gene Cards Networking Tools: Cytoscape</p>

<p>EMOLUMENTS : The incumbent will be paid a fee of Rs. 2000/- per sitting/ per day.</p>

<p>SCIENTIST NAME : Dr. Arnab Mukhopadhyay,</p>

<p>Staff Scientific V Next generation sequencing facility</p>

<p>SCIENTIST’S E-MAIL ID : arnab@nii.ac.in</p>

<p>WALK IN INTERVIEW ON : 18th September, 2015</p>

<p>REGISTRATION OF CANDIDATES: 10.30 AM to 11.00 AM</p>

<p>PLEASE NOTE- 1. CANDIDATE MAY FILL UP APPLICATION IN THE PRECRIBED FORMAT ALONG WITH NECESSARY DOCUMENTS FOR VERIFICATION. 2. APPLICATIONS CONTAINING INCOMPLETE INFORMATION SHALL NOT BE ENTERTAINED. 3. DATE OF PASSING THE EXAMINATIONS MUST BE INDICATED CLEARLY. 4. ONLY REGISTERED CANDIDATES WILL BE INTERVIEWED. 5. NO TA/DA WILL BE PAID FOR ATTENDING THE INTERVIEW PRESCRIBED FORM 1. NAME 2. FATHER’S NAME 3. MOTHER’S NAME 4. DATE OF BIRTH 5. SEX (MALE/FEMALE) 6. CATEGORY (SC/ ST/ OBC/ PH) 7. ADDRESS a. (CORRSPONDENCE) b. (PERMANENT) 8. E MAIL, TELEPHONE NO. &amp; MOBILE No (if any) 9. ACADEMIC &amp; PROFESSIONAL QUALIFICATIONS NAME OF EXAMINATION PASSED WITH SUBJECTS YEAR OF PASSING BOARD/ UNIVERSITY PERCENTAGE/ DIVISION REMARKS 10. PAST EXPERIENCE &amp; PRESENT EMPLOYMENT, IF ANY 11. CANDIDATES SHOULD STATE CLEARLY WHETHER THEY HAVE BEEN AWARDED PH.D DEGREE OR THESIS HAS BEEN SUBMITTED. 12. HAVE YOU APPLIED FOR A POSITION EARLIER IN THE INSTITUTE? IF SO:- (1) THE DETAILS OF THE PROJECT AND PROJECT INVESTIGATOR (2) IF CALLED FOR INVERVIEW, RESULTS THEREOF</p>

<p>More at http://www1.nii.res.in/sites/default/files/walkininterview-18sept2015.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24762/postdoctoral-fellowship-in-bioinformatics-at-pesolelab</guid>
  <pubDate>Thu, 01 Oct 2015 07:20:48 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral Fellowship in Bioinformatics at pesolelab]]></title>
  <description><![CDATA[
<p>Job Description: Bioinformatics postdoc positions are available in the area of genomics with main focus on exome and RNAseq technologies by ultra high-throughput sequencing platforms. Successful applicants should have the following qualities:</p>

<p>1) demonstrated experience in Bioinformatics research,<br />2) programing experience (python and/or R, C and C++ are very welcome),<br />3) knowledge of Linux/Unix environment,<br />4) experience in handling deep-seq data,<br />5) highly motivated and hard working, and<br />6) interested to work with a multi-disciplinary team combining bioinformatics, genomics, computational biology approaches with experimental biology.</p>

<p>Our research interest covers different areas of bioinformatics and genomics in order to achieve a deeper understanding of gene and genome structure and function (please look at our PubMed publications for more details about our research http://www.ncbi.nlm.nih.gov/pubmed/?term=pesole+g).</p>

<p>Interested applicants should email the curriculum vitae to Prof. Graziano Pesole at graziano.pesole@uniba.it or Dr. Ernesto Picardi at Ernesto.picardi@uniba.it.</p>

<p>Start date: immediate</p>

<p>Duration: up to 24 months<br />Contact Person (Referent): Ernesto Picardi<br />Ref. E-Mail: ernesto.picardi@uniba.it<br />Tel: +390805443308<br />Fax: +390805443317</p>

<p>Group Web Page: http://www.pesolelab.it/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</guid>
	<pubDate>Tue, 26 Apr 2016 03:48:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</link>
	<title><![CDATA[HOMER:  Software for motif discovery and next-gen sequencing analysis]]></title>
	<description><![CDATA[<p><span>This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/computerSetup.html">Setting up your computing environment</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/retrieveFiles.html">Retrieving and storing sequencing files</a>&nbsp;(your own data or from public sources)</li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/fastqFiles.html">Checking sequence quality, trimming, general sequence manipulation</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/mapping.html">Mapping reads to a reference genome</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/samfiles.html">Manipulating SAM/BAM alignment files</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/genomeBrowsers.html">Visualizing data in a genome browser</a></li>
</ol>
<p><br>RNA-Seq</p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqCufflinks.html">De novo transcript discovery and differential analysis with Cufflinks</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqR.html">Differential expression analysis with R/Bioconductor</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/clustering.html">Clustering of large expression datasets (microarray or RNA-Seq)</a></li>
</ol>
<p><br><span>Microarray</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/affymetrix.html">Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor</a></li>
</ol>
<p><span>General Tips for Data Analysis</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/excelTips.html">Excel workarounds, adding gene annotation, X-Y plots tips, etc.</a></li>
</ol><p>Address of the bookmark: <a href="http://homer.salk.edu/homer/basicTutorial/" rel="nofollow">http://homer.salk.edu/homer/basicTutorial/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</guid>
	<pubDate>Fri, 13 May 2016 04:54:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</link>
	<title><![CDATA[cutadapt]]></title>
	<description><![CDATA[<p>Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.</p>
<p>Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3&rsquo; sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don&rsquo;t want them to be in your reads.</p>
<p>Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.</p>
<p>Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.</p>
<p>If you use cutadapt, please cite <a href="http://dx.doi.org/10.14806/ej.17.1.200">DOI:10.14806/ej.17.1.200</a> .</p><p>Address of the bookmark: <a href="https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart" rel="nofollow">https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27847/anvio</guid>
	<pubDate>Thu, 16 Jun 2016 18:15:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27847/anvio</link>
	<title><![CDATA[Anvio]]></title>
	<description><![CDATA[<p>In a nutshell</p>
<p>Anvi&rsquo;o is an analysis and visualization platform for &lsquo;omics data.</p>
<p>Please find the methods paper here: https://peerj.com/articles/1319/</p>
<p>Anvi&rsquo;o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper</p>
<p><span>An analysis and visualization platform for 'omics data</span><span>&nbsp;</span><span><a href="http://merenlab.org/projects/anvio">http://merenlab.org/projects/anvio</a></span></p>
<p><span>Paper&nbsp;https://peerj.com/articles/1839/</span></p><p>Address of the bookmark: <a href="https://github.com/meren/anvio" rel="nofollow">https://github.com/meren/anvio</a></p>]]></description>
	<dc:creator>Shruti Paniwala</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/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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</guid>
	<pubDate>Tue, 05 Jul 2016 10:02:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</link>
	<title><![CDATA[Bioinformatics tools and software]]></title>
	<description><![CDATA[<p><a href="http://drive5.com/usearch">USEARCH &gt;</a><br><span>Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST.</span>&nbsp;<a href="http://drive5.com/muscle">MUSCLE &gt;</a><br><span>Multiple sequence alignment. Faster and more accurate than CLUSTALW.</span></p>
<p>&nbsp;<a href="http://drive5.com/uparse">UPARSE &gt;</a><br><span>OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures.</span>&nbsp;<a href="http://drive5.com/uchime">UCHIME &gt;</a><br><span>Chimeric sequence detection.</span>&nbsp;<a href="http://drive5.com/piler">PILER &gt;</a><br><span>De novo genome repeat finder.</span>&nbsp;<a href="http://drive5.com/pilercr">PILER-CR &gt;</a><br><span>Detection of CRISPR repeats in bacterial genomes.</span>&nbsp;<a href="http://drive5.com/qscore">QSCORE &gt;</a><br><span>Compare two multiple alignments for benchmarking.</span>&nbsp;<a href="http://drive5.com/pals">PALS &gt;</a><br><span>Whole-genome alignment.</span>&nbsp;<a href="http://drive5.com/muscle/prefab.htm">PREFAB &gt;</a><br><span>Protein Reference Alignment Database.</span>&nbsp;<a href="http://drive5.com/bench">MSA benchmark collection &gt;</a><br><span>Selected multiple alignment benchmarks in a standardized FASTA format.</span></p><p>Address of the bookmark: <a href="http://drive5.com/software.html" rel="nofollow">http://drive5.com/software.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>