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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44518?offset=40</link>
	<atom:link href="https://bioinformaticsonline.com/related/44518?offset=40" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41905/research-associate-bioinformatics-in-iisc-recruitment-2020</guid>
  <pubDate>Tue, 23 Jun 2020 21:53:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatics in IISc Recruitment 2020]]></title>
  <description><![CDATA[
<p>Research Associate Bioinformatics in IISc Recruitment 2020</p>

<p>Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.</p>

<p>Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.</p>

<p>Desirable Qualifications (Research Associate/s): i)  Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment). </p>

<p>ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv)  Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.</p>

<p>The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.</p>

<p>Emoluments: As per DST, GOI norms and commensurate with experience.</p>

<p>More at https://www.iisc.ac.in/positions-open/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources</guid>
	<pubDate>Fri, 11 Nov 2022 06:30:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources</link>
	<title><![CDATA[Interesting Bioinformatics Resources !]]></title>
	<description><![CDATA[<p>1. a reproducible workflow.&nbsp;<a href="https://www.youtube.com/watch?v=s3JldKoA0zw">https://www.youtube.com/watch?v=s3JldKoA0zw</a>&nbsp;This two minute video will change your mind on reproducible research&nbsp;</p><p>2. Parallel sequencing lives, or what makes large sequencing projects successful&nbsp;<a href="https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false">https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false</a></p><p>3. Common-sense approaches to sharing tabular data alongside publication&nbsp;<a href="https://www.sciencedirect.com/science/article/pii/S2666389921002300">https://www.sciencedirect.com/science/article/pii/S2666389921002300</a></p><p>4. A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker&nbsp;<a href="https://psyarxiv.com/8xzqy/">https://psyarxiv.com/8xzqy/</a></p><p>5. Practical Computational Reproducibility in the Life Sciences&nbsp;<a href="https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6">https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6</a></p><p>6. A video by Dr.Keith A. Baggerly from MD Anderson [The Importance of Reproducible Research in High-Throughput Biology](<a href="https://www.youtube.com/watch?v=7gYIs7uYbMo">https://www.youtube.com/watch?v=7gYIs7uYbMo</a>) highly recommended.</p><p>7. Ten Simple Rules for Reproducible Computational Research&nbsp;<a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285</a>)</p><p>8. Good Enough Practices in Scientific Computing&nbsp;<a href="http://arxiv.org/abs/1609.00037">http://arxiv.org/abs/1609.00037</a>&nbsp;</p><p>9. Best Practices for Scientific Computing&nbsp;<a href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745">https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745</a></p><p>10. A Quick Guide to Organizing Computational Biology Projects&nbsp;<a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042</a>&nbsp; A must read for computational biologists!</p><p>11. Reproducibility of computational workflows is automated using continuous analysis&nbsp;<a href="https://www.nature.com/articles/nbt.3780">https://www.nature.com/articles/nbt.3780</a></p><p>12. Five selfish reasons to work reproducibly&nbsp;<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44718/mycology-research-resources-for-bioinformaticians-unlocking-the-fungal-kingdom</guid>
	<pubDate>Fri, 13 Dec 2024 11:21:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44718/mycology-research-resources-for-bioinformaticians-unlocking-the-fungal-kingdom</link>
	<title><![CDATA[Mycology Research Resources for Bioinformaticians: Unlocking the Fungal Kingdom]]></title>
	<description><![CDATA[<p>Mycology, the study of fungi, is a field that bridges ecology, medicine, and biotechnology. With advancements in bioinformatics, researchers now have unprecedented opportunities to explore the fungal kingdom at molecular, genetic, and ecological levels. From understanding pathogenic fungi to harnessing fungal enzymes for industrial applications, the potential is vast.</p><p>To fully leverage these opportunities, bioinformaticians require specialized tools and databases. This blog highlights essential resources for mycology research, focusing on databases, tools, and platforms tailored for fungal biology.</p><h4><strong>1. Fungal Databases</strong></h4><h5><strong>1.1. MycoCosm</strong></h5><p><strong>Website</strong>: <a target="_new">MycoCosm</a><br />Developed by the DOE Joint Genome Institute, MycoCosm is a comprehensive portal for fungal genomics. It offers genomic and transcriptomic data for a wide range of fungi, including saprobes, pathogens, and symbionts.</p><ul>
<li><strong>Key Features</strong>: Genome browsers, comparative genomics tools, and functional annotations.</li>
<li><strong>Best For</strong>: Large-scale studies on fungal evolution and ecology.</li>
</ul><h5><strong>1.2. FungiDB</strong></h5><p><strong>Website</strong>: <a href="https://fungidb.org/" target="_new">FungiDB</a><br />FungiDB is an integrated genomic resource for fungal pathogens and non-pathogens. It provides access to genome sequences, transcriptomic data, and functional annotations.</p><ul>
<li><strong>Key Features</strong>: Advanced search options, BLAST, and pathway analysis tools.</li>
<li><strong>Best For</strong>: Studying fungal pathogenesis and host-pathogen interactions.</li>
</ul><h5><strong>1.3. Index Fungorum</strong></h5><p><strong>Website</strong>: <a href="http://www.indexfungorum.org/" target="_new">Index Fungorum</a><br />This nomenclatural database provides information on the scientific names of fungi. It&rsquo;s an essential resource for taxonomists and researchers focused on fungal biodiversity.</p><ul>
<li><strong>Key Features</strong>: Taxonomic hierarchy and synonymy tracking.</li>
<li><strong>Best For</strong>: Identifying and classifying fungal species.</li>
</ul><h5><strong>1.4. UNITE</strong></h5><p><strong>Website</strong>: <a target="_new">UNITE</a><br />UNITE is a specialized database for fungal ITS (Internal Transcribed Spacer) sequences, often used in fungal identification and phylogenetics.</p><ul>
<li><strong>Key Features</strong>: Curated reference datasets and community annotations.</li>
<li><strong>Best For</strong>: Environmental mycology and microbial ecology studies.</li>
</ul><h4><strong>2. Analytical Tools</strong></h4><h5><strong>2.1. Funannotate</strong></h5><p><strong>Repository</strong>: <a href="https://github.com/nextgenusfs/funannotate" target="_new">GitHub - Funannotate</a><br />Funannotate is a genome annotation tool designed for fungi. It supports tasks like gene prediction, functional annotation, and orthology analysis.</p><ul>
<li><strong>Best For</strong>: Annotating newly sequenced fungal genomes.</li>
</ul><h5><strong>2.2. BUSCO (Benchmarking Universal Single-Copy Orthologs)</strong></h5><p><strong>Website</strong>: <a target="_new">BUSCO</a><br />BUSCO evaluates genome assembly and annotation completeness using orthologs. It includes a fungal-specific dataset.</p><ul>
<li><strong>Best For</strong>: Assessing the quality of fungal genome assemblies.</li>
</ul><h5><strong>2.3. Pathogen-Host Interactions Database (PHI-base)</strong></h5><p><strong>Website</strong>: <a href="http://www.phi-base.org/" target="_new">PHI-base</a><br />PHI-base is a manually curated resource containing information on pathogen-host interactions, including fungal pathogens.</p><ul>
<li><strong>Best For</strong>: Exploring virulence factors and host-pathogen relationships.</li>
</ul><h4><strong>3. Visualization Platforms</strong></h4><h5><strong>3.1. Cytoscape</strong></h5><p><strong>Website</strong>: <a href="https://cytoscape.org/" target="_new">Cytoscape</a><br />A powerful tool for visualizing molecular interaction networks, Cytoscape can be used to study protein-protein interactions, gene networks, and metabolic pathways in fungi.</p><ul>
<li><strong>Best For</strong>: Network biology and functional genomics.</li>
</ul><h5><strong>3.2. iTOL (Interactive Tree of Life)</strong></h5><p><strong>Website</strong>: <a target="_new">iTOL</a><br />iTOL is an interactive tool for visualizing phylogenetic trees.</p><ul>
<li><strong>Best For</strong>: Displaying fungal phylogenies and comparing evolutionary relationships.</li>
</ul><h4><strong>4. Community Resources</strong></h4><h5><strong>4.1. Mycological Society of America (MSA)</strong></h5><p><strong>Website</strong>: <a href="https://msafungi.org/" target="_new">MSA</a><br />The MSA promotes fungal research and provides access to resources, conferences, and publications.</p><ul>
<li><strong>Best For</strong>: Networking with fungal researchers and accessing recent studies.</li>
</ul><h5><strong>4.2. OpenFungi</strong></h5><p><strong>Website</strong>: <a href="https://openfungi.org/" target="_new">OpenFungi</a><br />OpenFungi is an open-source initiative providing fungal genomic and transcriptomic datasets for research and education.</p><ul>
<li><strong>Best For</strong>: Sharing and accessing public fungal datasets.</li>
</ul><h4><strong>5. Genomics Workflows</strong></h4><h5><strong>5.1. Galaxy</strong></h5><p><strong>Website</strong>: <a href="https://usegalaxy.org/" target="_new">Galaxy Project</a><br />Galaxy offers a web-based platform for reproducible bioinformatics workflows, including tools for fungal genome and transcriptome analysis.</p><ul>
<li><strong>Best For</strong>: User-friendly analysis pipelines without requiring coding skills.</li>
</ul><h5><strong>5.2. Snakemake</strong></h5><p><strong>Repository</strong>: <a target="_new">Snakemake</a><br />A flexible pipeline management tool that supports fungal data processing and analysis.</p><ul>
<li><strong>Best For</strong>: Custom workflows for large-scale fungal datasets.</li>
</ul><h4><strong>Conclusion</strong></h4><p>Fungal research is a rapidly growing field with vast implications for medicine, agriculture, and industry. For bioinformaticians, the availability of specialized resources&mdash;databases, tools, and community platforms&mdash;opens doors to innovative discoveries. Whether you are investigating fungal genomics, studying host-pathogen interactions, or exploring fungal biodiversity, the resources outlined above will empower your research journey.</p><p>Dive into these resources and help unravel the mysteries of the fungal kingdom!</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</guid>
	<pubDate>Sat, 20 Mar 2021 00:28:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</link>
	<title><![CDATA[List of bioinformatics packages for NGS analysis !]]></title>
	<description><![CDATA[<p>Package suites gather software packages and installation tools for specific languages or platforms. We have some for bioinformatics software.</p><ul>
<li><a href="https://github.com/Bioconductor">Bioconductor</a>&nbsp;&ndash; A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [&nbsp;<a href="https://link.springer.com/article/10.1186/gb-2004-5-10-r80">paper-2004</a>&nbsp;|&nbsp;<a href="https://www.bioconductor.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/biopython/biopython">Biopython</a>&nbsp;&ndash; Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the&nbsp;<a href="http://open-bio.org/">Open Bioinformatics Foundation</a>. Contains the very useful&nbsp;<a href="https://biopython.org/DIST/docs/api/Bio.Entrez-module.html">Entrez</a>&nbsp;package for API access to the NCBI databases. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/19304878">paper-2009</a>&nbsp;|&nbsp;<a href="https://biopython.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/bioconda">Bioconda</a>&nbsp;&ndash; A channel for the&nbsp;<a href="http://conda.pydata.org/docs/intro.html">conda package manager</a>&nbsp;specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with&nbsp;<code>conda install</code>) bioinformatics packages. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29967506">paper-2018</a>&nbsp;|&nbsp;<a href="https://bioconda.github.io/">web</a>&nbsp;]</li>
<li><a href="https://github.com/BioJulia">BioJulia</a>&nbsp;&ndash; Bioinformatics and computational biology infastructure for the Julia programming language. [&nbsp;<a href="https://biojulia.net/">web</a>&nbsp;]</li>
<li><a href="https://github.com/rust-bio/rust-bio">Rust-Bio</a>&nbsp;&ndash; Rust implementations of algorithms and data structures useful for bioinformatics. [&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/early/2015/10/06/bioinformatics.btv573.short?rss=1">paper-2016</a>&nbsp;]</li>
<li><a href="https://github.com/seqan/seqan3">SeqAn</a>&nbsp;&ndash; The modern C++ library for sequence analysis.</li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/40882/troyanskaya-lab</guid>
  <pubDate>Tue, 04 Feb 2020 06:40:36 -0600</pubDate>
  <link></link>
  <title><![CDATA[Troyanskaya Lab]]></title>
  <description><![CDATA[
<p>The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.</p>

<p>https://function.princeton.edu/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools</guid>
	<pubDate>Tue, 27 Oct 2020 19:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools</link>
	<title><![CDATA[Frequent parameters for bioinformatics tools !]]></title>
	<description><![CDATA[<div><div>Third party executable parameters and options.</div><div>&nbsp;</div><div>Trimmomatic</div><div>&nbsp;</div><div>&ldquo;ILLUMINACLIP:...:2:30:10&rdquo;</div><div>&ldquo;LEADING:15&rdquo;</div><div>&ldquo;TRAILING:15&rdquo;</div><div>&ldquo;SLIDINGWINDOW:4:20&rdquo;</div><div>&ldquo;MINLEN:20&rdquo;</div><div>&ldquo;TOPHRED33&rdquo;</div><div>&nbsp;</div><div>Filtlong</div><div>--min_length 500</div><div>--min_mean_q 85</div><div>--min_window_q 65</div><div>&nbsp;</div><div>FastQ Screen</div><div>--aligner bowtie2' (bwa for PacBio)</div><div>--subset 1000 (for PacBio)</div><div>&nbsp;</div><div>SPAdes</div><div>--careful</div><div>--disable-gzip-output</div><div>--cov-cutoff auto</div><div>--phred-offset 33</div><div>&nbsp;</div><div>HGAP</div><div>Pbalign.task_options.min_accuracy: 70</div><div>Pbalign.task_options.no_split_subreads: false</div><div>Genomic_consensus.task_options.min_confidence: 40</div><div>falcon_ns.task_options.HGAP_GenomeLength_str:</div><div>6000000</div><div>Pbcoretools.task_options.read_length: 0</div><div>Genomic_consensus.task_options.use_score: 0</div><div>Pbalign.task_options.min_length: 50</div><div>Pbalign.task_options.algorithm_options: --minMatch 12</div><div>--bestn 10 --minPctSimilarity 70.0</div><div>Pbalign.task_options.hit_policy: randombest</div><div>Pbcoretools.task_options.other_filters: rq &gt;= 0.7</div><div>Pbalign.task_options.concordant: false</div><div>Genomic_consensus.task_options.min_coverage: 5</div><div>falcon_ns.task_options.HGAP_SeedCoverage_str: 30</div><div>falcon_ns.task_options.HGAP_AggressiveAsm_bool: false</div><div>Genomic_consensus.task_options.algorithm: best</div><div>falcon_ns.task_options.HGAP_SeedLengthCutoff_str: -1</div><div>Genomic_consensus.task_options.diploid: false</div><div>&nbsp;</div><div>MeDuSa</div><div>-random 100</div><div>&nbsp;</div><div>Prokka</div><div>--usegenus</div><div>--force</div><div>--addgenes</div><div>--rfam</div><div>--rawproduct</div><div>&nbsp;</div><div>cmsearch (taxonomy, 16S)</div><div>--rfam</div><div>--noali</div><div>&nbsp;</div><div>blastn (taxonomy, 16S)</div><div>-evalue 1E-10</div><div>&nbsp;</div><div>blastn (MLST)</div><div>-ungapped</div></div><div><div>-dust no</div><div>-evalue 1E-20</div><div>-word_size 32</div><div>-culling_limit 2</div><div>-perc_identity 95</div><div>&nbsp;</div><div>blastp (VF)</div><div>-culling_limit 2</div><div>&nbsp;</div><div>RGI (ABR)</div><div>--input_type contig</div><div>&nbsp;</div><div>bowtie2 (mapping)</div><div>--sensitive</div><div>&nbsp;</div><div>minimap2 (mapping)</div><div>-a</div><div>-x map-ont</div><div>&nbsp;</div><div>samtools mpileup (SNP&nbsp;detection)</div><div>-uRI</div><div>&nbsp;</div><div>bcftools call (SNP detection)</div><div>--variants-only</div><div>--skip-variants indels</div><div>--output-type v</div><div>--ploidy 1</div><div>-c</div><div>&nbsp;</div><div>SNPsift filter (SNP detection)</div><div>"( QUAL &gt;= 30 ) &amp; (( na FILTER ) | (FILTER = 'PASS')) &amp;</div><div>( DP &gt;= 20 ) &amp; ( MQ &gt;= 20 )"</div><div>&nbsp;</div><div>SNPeff ann (SNP detection)</div><div>-nodownload</div><div>-no-intron</div><div>-no-downstream</div><div>-no SPLICE_SITE_REGION</div><div>-upDownStreamLen 250</div><div>&nbsp;</div><div>bcftools consensus</div><div>(phylogenetic tree)</div><div>--haplotype 1</div><div>&nbsp;</div><div>fasttreemp</div><div>-nt</div><div>-boot 100</div><div>&nbsp;</div><div>roary</div><div>-e</div><div>-n</div><div>-cd 100</div><div>-g 100000</div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab</guid>
	<pubDate>Mon, 06 Oct 2014 12:41:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab</link>
	<title><![CDATA[Software developed in pevsner lab]]></title>
	<description><![CDATA[<div>
<div id="block-system-main">
<div>
<div id="node-7">
<div>
<div>
<div>
<div>
<p><a href="http://pevsnerlab.kennedykrieger.org/dragon.htm">DRAGON</a>: Database Referencing of Array Genes Online</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/96">SNOMAD</a>: Standardization and Normalization of Microarray Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/70">SNPduo</a>: SNP Analysis Between Two Individuals</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/71">SNPtrio</a>: Analyzing and Visualizing and Inheritance Patterns in Trios</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/64">SNPscan</a>: Data Analysis and Visualization of SNP Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/64">pediSNP</a>: Analyze SNP Data From a Pedigree of Two Generations</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/73">kcoeff</a>: Calculate Cotterman Coefficients of SNP Genotype Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/113">triPOD:</a> Detects chromosomal abnormalities in parent-child trio-based microarray data</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div><p>Address of the bookmark: <a href="http://pevsnerlab.kennedykrieger.org/php/?q=software" rel="nofollow">http://pevsnerlab.kennedykrieger.org/php/?q=software</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/19090/deeptools</guid>
	<pubDate>Sat, 08 Nov 2014 15:02:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19090/deeptools</link>
	<title><![CDATA[deepTools]]></title>
	<description><![CDATA[<p>deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. To do so, deepTools contains useful modules to process the mapped reads data to create coverage files in standard bedGraph and bigWig file formats. By doing so, deepTools allows the creation of normalized coverage files or the comparison between two files (for example, treatment and control). Finally, using such normalized and standardized files, multiple visualizations can be created to identify enrichments with functional annotations of the genome.<br /><br />Publicaton: http://nar.oxfordjournals.org/content/early/2014/05/05/nar.gku365.full<br /><br />Source Code and Wiki: https://github.com/fidelram/deepTools/wiki<br /><br />Galaxy Tool Shed repository: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools<br /><br />and example Galaxy workflows: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools_workflows</p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
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  <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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26356/spines</guid>
	<pubDate>Tue, 09 Feb 2016 05:07:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26356/spines</link>
	<title><![CDATA[Spines]]></title>
	<description><![CDATA[<div id="content-header">
<h1>Spines</h1>
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<p><a href="http://www.broadinstitute.org/ftp/distribution/software/spines/"><em>Spines</em></a> is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages: <em>Satsuma,</em> a highly parallelized program for high-sensitivity, genome-wide synteny; <em>Papaya,</em> an all-purpose alignment tool for less diverged sequences; and <em>SLAP,</em> a context-sensitive local aligner for diverged sequences with large gaps.</p>
<p>Access <em>Spines</em> <a href="http://www.broadinstitute.org/ftp/distribution/software/spines/">here</a>.</p>
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<p>http://www.broadinstitute.org/science/programs/genome-biology/spines</p><p>Address of the bookmark: <a href="http://www.broadinstitute.org/science/programs/genome-biology/spines" rel="nofollow">http://www.broadinstitute.org/science/programs/genome-biology/spines</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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