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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27465?offset=830</link>
	<atom:link href="https://bioinformaticsonline.com/related/27465?offset=830" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36398/tools-for-protein-protein-docking</guid>
	<pubDate>Wed, 25 Apr 2018 05:15:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36398/tools-for-protein-protein-docking</link>
	<title><![CDATA[Tools for Protein-Protein Docking !]]></title>
	<description><![CDATA[<p>Predicting the structure of protein&ndash;protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Following are the tools to predict&nbsp;<span>the structure of protein&ndash;protein complexes:</span></p><p><a href="http://www.sbg.bio.ic.ac.uk/docking/index.html" target="_blank">3D-Dock Suite</a></p><p>Global rigid search: FFTShape complementarity and electrostatics</p><p>Re-scoring and clustering. Refinement of interface side-chains</p><p><a href="http://www.sbg.bio.ic.ac.uk/~3dgarden/" target="_blank">3D-Garden</a></p><p>Global rigid search in ensamble</p><p>Shape complementarity and Lennard&ndash;Jones potential</p><p>Side chain and backbone dihedral refinement</p><p><a href="http://www.sdsc.edu/CCMS/DOT/" target="_blank">DOT</a></p><p>Global rigid search: FFTShape complementarity, electrostatics and VDWNone</p><p><a href="http://users.unimi.it/~ddl/escherng/index.htm" target="_blank">Escher NG</a></p><p>Global rigid searchShape complementarity, hydrogen bonds and electrostatic</p><p>Integrated in&nbsp;<a href="http://users.unimi.it/~ddl/vega/download.htm" target="_blank">VEGA</a></p><p><a href="http://vakser.bioinformatics.ku.edu/resources/gramm/gramm1" target="_blank">GRAMM</a>&nbsp;</p><p>Global rigid search: FFT. smooth protein surface representation for soft docking</p><p>Shape complementarity and Lennard-Jones potential</p><p>Clustering of conformations</p><p><a href="http://vakser.bioinformatics.ku.edu/resources/gramm/grammx/" target="_blank">GRAMM-X</a>&nbsp;</p><p>Global rigid search: FFT. smooth protein surface representation for soft docking</p><p>Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters</p><p><a href="http://www.loria.fr/~ritchied/hex_server/" target="_blank">HEX</a></p><p>Global rigid search: Fourier correlation of spherical harmonics</p><p>Shape complementarity</p><p><a href="http://www.csd.abdn.ac.uk/hex/" target="_blank"></a><a href="http://haddock.chem.uu.nl/Haddock/haddock.php" target="_blank">HADDOCK</a></p><p>Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data.&nbsp;</p><p><a href="http://www.molsoft.com/docking.html">ICM</a></p><p>Global rigid search: Monte CarloEmpirical scoring function</p><p>Clustering and selection of conformations. Refinement of interface side-chains and re-scoring</p><p><a href="http://www.weizmann.ac.il/Chemical_Research_Support/molfit/" target="_blank">MolFit&nbsp;</a></p><p>Global rigid search: FFTShape complementarity</p><p>Clustering of good solutions, filtering using&nbsp;<em>a priori&nbsp;</em>information and small, local rigid rotations around selected conformations</p><p><a href="http://bioinfo3d.cs.tau.ac.il/PatchDock/" target="_blank">PatchDock</a></p><p>Global rigid searchShape complementarity and atomic desolvation energy</p><p>Clustering of conformations</p><p><a href="http://inb.bsc.es/gn6/PyDock" target="_blank">PyDock</a></p><p>Global rigid search:FFTShape complementarity</p><p>rescoring by binding electrostatics and desolvation energy</p><p><a href="http://bioinfo3d.cs.tau.ac.il/PatchDock/" target="_blank"></a><a href="http://rosettadock.graylab.jhu.edu/" target="_blank">RosettaDock</a></p><p>Local rigid search: Monte Carlo with low and high resolution structure representation levels</p><p>Different scoring parameters for the different resolutions&nbsp;</p><p><a href="http://zlab.bu.edu/zdock/" target="_blank">ZDOCK</a></p><p>Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.</p><p>Energy minimization and re-scoringFree for academics</p><p>&nbsp;</p><p>Point to note:</p><p>The proper treatment of flexibility in protein&ndash;protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.</p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11035/bioinformatics-jrfsrf-position-at-nii</guid>
  <pubDate>Sun, 25 May 2014 16:54:04 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF/SRF position at NII]]></title>
  <description><![CDATA[
<p>NATIONAL INSTITUTE OF IMMUNOLOGY, NEW DELHI-110067</p>

<p>Applications are invited for the position of Senior Research Fellow for the following time-bound sponsored project as per the details given below:</p>

<p>1. BTIS project on, “Bioinformatics Center-National Infrastructural Facility in the Area of Immunology” funded by DBT</p>

<p>Senior Research Fellow (P) (One Position only)</p>

<p>Dr. Debasisa Mohanty<br />Staff Scientist-VI<br />deb@nii.res.in</p>

<p>Qualifications: M.Sc in Biological Sciences or Biotechnology with at least 04 years of Research experience in Bioinformatics or computational Biology after the master’s degree is essential.</p>

<p>Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications &amp; experience</p>

<p>Rs. 18,000/- per month consolidated plus 30% HRA if Leading to Ph.D/NET/GATE Qualified otherwise Rs. 14,000/- per month + 30% HRA.</p>

<p>Job description: The candidate should be well versed in programming in PERL/C++/HTML/CGI, web server and portal development, computational analysis of<br />protein structure &amp; function, molecular dynamics simulations and use of high performance computing systems.</p>

<p>GENERAL TERMS AND CONDITIONS:-</p>

<p>1. The candidates selected for the above posts will be on contract for one year or duration of the project whichever is shorter, at a time.<br />2. No hostel/ housing facility will be provided.<br />3. Number of posts may vary and shall be need based. Advertisement is no commitment.<br />4. Applicants may clearly mention the category they belong to i.e. SC/ST/OBC/PH and attach documentary proof of the same.<br />5. No TA/DA will be paid for attending the interview, if called for.<br />6. Apart from sending application in the prescribed format given below, candidates should send complete Curriculum Vitae along with the names of three referees. Curriculum Vitae should contain details of the experimental expertise.</p>

<p>HOW TO APPLY Interested candidates may apply directly, STRICTLY IN THE PRESCRIBED FORMAT GIVEN BELOW, through e-mail, to the Investigator of the project, clearly indicating the name of the project along with their complete C.V., e-mail id, fax numbers, telephone numbers. Only Short listed candidates will be called for interview and they required to submit attested copies of all their certificates and a Demand Draft of Rs 100/- drawn on Canara Bank or Indian Bank payable at Delhi/New Delhi in favour of the Director, NII (SC / ST and PH candidates are exempted subject to submission of documentary proof), at the time of interview.</p>

<p>LAST DATE OF RECEIPT OF APPLICATIONS: 06th June, 2014</p>

<p>Advertisement</p>

<p>www1.nii.res.in/sites/default/files/projectappointment-Dr.Mohanty-6June2014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</guid>
	<pubDate>Tue, 08 May 2018 04:39:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</link>
	<title><![CDATA[EvidentialGene: tr2aacds, mRNA Transcript Assembly Software]]></title>
	<description><![CDATA[<p><span>EvidentialGene is a genome informatics project, "Evidence Directed Gene Construction for Eukaryotes", to construct high quality, accurate gene sets for animals and plants, developed by Don Gilbert at Indiana University, see</span><br><a href="http://arthropods.eugenes.org/EvidentialGene/" target="_blank">http://arthropods.eugenes.org/EvidentialGene/<span></span></a><br><br><span>Construction refers to the combination of classical gene prediction, and more recent gene assembly (de-novo and genome-assisted) methods. The basic Evigene methods involve using available best-of-breed gene prediction and assembly software, combining all evidence for genes, from expressed sequences, genome assembly sequences, related species protein sequences, and any other, to annotate and score gene constructions. Over-produced constructions are classified by gene evidence for best qualities per "locus", including genome-aligned and gene-transcript aligned (genome-free) locus identification. All software developed for EvidentialGene is publicly available. See project wiki/blog for notes.</span></p>
<p><span>Download&nbsp;</span></p>
<p>http://arthropods.eugenes.org/EvidentialGene/trassembly.html</p>
<p>https://sourceforge.net/p/evidentialgene/blog/</p><p>Address of the bookmark: <a href="http://arthropods.eugenes.org/EvidentialGene/trassembly.html" rel="nofollow">http://arthropods.eugenes.org/EvidentialGene/trassembly.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/13014/bioinformatics-jrf-vacancy-at-icgeb-new-delhi</guid>
  <pubDate>Wed, 23 Jul 2014 16:07:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF vacancy at ICGEB, New Delhi]]></title>
  <description><![CDATA[
<p>Junior Research Fellow for a DBT sponsored project entitled "Computational and experimental characterization of stage specific arginine methylation in P. falciparum proteome". </p>

<p>Candidates should have a 1st class MSc/MTech/BTech degree in Bioinformatics. Please send complete CV, quoting Application for RMETH-JRF-2014, by email to Dr. Dinesh Gupta: dinesh@icgeb.res.in</p>

<p>Closing date for applications: 6 August 2014</p>

<p>More at http://www.icgeb.org/tl_files/Vacancies/JRF.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37520/mmgenome-tools-for-extracting-individual-genomes-from-metagneomes</guid>
	<pubDate>Thu, 09 Aug 2018 17:41:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37520/mmgenome-tools-for-extracting-individual-genomes-from-metagneomes</link>
	<title><![CDATA[mmgenome: Tools for extracting individual genomes from metagneomes]]></title>
	<description><![CDATA[<p>The mmgenome toolbox enables reproducible extraction of individual genomes from metagenomes. It builds on the&nbsp;<a href="http://madsalbertsen.github.io/multi-metagenome/">multi-metagenome</a>&nbsp;concept, but wraps most of the process of extracting genomes in simple R functions. Thereby making the whole process of binning easy and at the same time reproducible through the Rmarkdown format.</p>
<p>The mmgenome R package also facilitates effortless integration with additional data sources and hence should not be seen as "yet another binning method", but rather a package to integrate different binning strategies.</p>
<p>All functions in the mmgenome R package has associated documentation, check it out in R by e.g.&nbsp;<code>?mmplot</code>.</p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/mmgenome" rel="nofollow">https://github.com/MadsAlbertsen/mmgenome</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11313/linux-sort-commands-for-bioinformatics</guid>
	<pubDate>Sat, 31 May 2014 15:41:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11313/linux-sort-commands-for-bioinformatics</link>
	<title><![CDATA[Linux Sort Commands for Bioinformatics]]></title>
	<description><![CDATA[<p>Almost all the scripting languages such as Perl, Python etc have built-in sort, but unfortunately none of them are as flexible as sort command. But one when it come to space efficiency GNU sort stands at the top. It can sort a 20Gb file with less than 2Gb memory. It is not trivial to implement so powerful a sort by yourself.</p><p>sort a space-delimited file based on its first column, then the second if the first is the same, and so on:<br />sort input.txt</p><p>sort a huge file (GNU sort ONLY):<br />sort -S 1500M -t $HOME/tmp input.txt &gt; sorted.txt</p><p>sort starting from the third column, skipping the first two columns:<br />sort +2 input.txt</p><p>sort the second column as numbers, descending order; if identical, sort the 3rd as strings, ascending order:<br />sort -k2,2nr -k3,3 input.txt</p><p>sort starting from the 4th character at column 2, as numbers:<br />sort -k2.4n input.txt</p><p>More Linxu sort command information<br /><br />If you have any sort commands you'd like to share, please add them to our comments section below. For more help, you can also type:<br /><br />man sort<br /><br />or<br /><br />sort --help<br /><br />on your Unix/Linux system.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources</guid>
	<pubDate>Tue, 28 Jun 2022 04:08:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources</link>
	<title><![CDATA[List of comparative genomics resources !]]></title>
	<description><![CDATA[<div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1096638041"><span>3D-GENOMICS -- A Database to Compare Structural and Functional Annotations of Proteins between Sequenced Genomes</span></a></div><p>Compare structural and functional annotations of proteins between sequenced genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1100640374"><span>ARED Organism -- expansion of ARED reveals AU-rich element cluster variations between human and mouse</span></a></div><p>View AREs in the human transcriptome and study the comparative genomics of AREs in model organisms.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1234973128"><span>ATGC -- Alignable Tight Genomic Clusters Database</span></a></div><p>Find information about orthologous genes in prokaryotes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174596104"><span>AnimalQTLdb -- a livestock QTL database tool set for positional QTL information mining and beyond</span></a></div><p>Search for publicly available QTL data on livestocks and animal species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL20110518150135"><span>BGDB -- Bovine Genome Database</span></a></div><p>Find information about bovine genomics data.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1229012662"><span>COMPARE -- a multi-organism system for cross-species data comparison and transfer of information</span></a></div><p>A multi-organism web-based resource system designed to easily retrieve, correlate and interpret data across species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1218141952"><span>CONDOR -- COnserved Non-coDing Orthologous Regions</span></a></div><p>A database resource of developmentally associated conserved non-coding elements.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1099057221"><span>CORG -- A database for COmparative Regulatory Genomics</span></a></div><p>Delineate conserved non-coding blocks from upstream regions of putative orthologous gene pairs from man, mouse, rat, fugu, Mus musculus, Danio rerio, and zebrafish.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1203608896"><span>COXPRESdb -- a database of coexpressed gene networks in mammals</span></a></div><p>Find coexpressed gene lists and networks in human and mouse.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1097763045"><span>CVTree -- A Phylogenetic Tree Reconstruction Tool Based on Whole Genomes</span></a></div><p>Construct phylogenetic tree of microorganisms based on oligopeptide content of their complete proteomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1232729680"><span>CleanEST -- the cleansed EST libraries database</span></a></div><p>A novel database server that classifies GenBank's dbEST (database of expressed gene sequences) libraries and removes contaminants.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1256926144"><span>CoCoa -- COefficient of COAncestry software</span></a></div><p>Find information about the ancestral relationship between genes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1227549154"><span>CoGemiR -- a comparative genomics microRNA database</span></a></div><p>Provides an overview of the genomic organization of microRNAs and extent of conservation during evolution in different metazoan species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1117678221"><span>Comparative Genometrics (CG) -- a database dedicated to biometric comparisons of whole genomes</span></a></div><p>Conduct comparative biometric analysis of chromosomes of different organisms.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1151007916"><span>DoTS -- Database Of Transcribed Sequences</span></a></div><p>Search for Indices of gene and transcripts in human and mouse.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174510065"><span>DroSpeGe -- rapid access database for new Drosophila species genomes</span></a></div><p>Search and compare 12 new and old Drosophila genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1098208414"><span>ECR Browser -- A Tool for Visualizing and Accessing Data from Comparisons of Multiple Vertebrate Genomes</span></a></div><p>Access to whole genome alignments of human, mouse, rat and fish sequences.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209738459"><span>EPGD -- Eukaryotic Paralog Group Database</span></a></div><p>Find eukaryotic paralog/paralogon information.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1232726869"><span>EVOG -- evolutionary visualizer for overlapping genes</span></a></div><p>Analyze the evolutionary process of overlapping genes when comparing different species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1227633714"><span>GNAT -- Inter-species gene mention normalization (ISGN)</span></a></div><p>The first publicly available system reported to handle inter-species gene mention normalization.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1229438992"><span>GenColors -- annotation and comparative genomics of prokaryotes made easy</span></a></div><p>A web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1151086258"><span>GeneNest gene indices</span></a></div><p>Visualize gene indices of human, mouse, Arabidopsis, Zebrafish, Drosophila and Sheep.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174489378"><span>GenomeTrafac -- a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs</span></a></div><p>Use comparative genomics approach to characterize gene models and identify putative cis-regulatory regions of RefSeq Gene Orthologs.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL20110518150753"><span>IKMC -- International Knockout Mouse Consortium web portal</span></a></div><p>Find information about mutated mouse genes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209411604"><span>IMG/M -- Integrated Microbial Genomes/Metagenomes</span></a></div><p>A data management and analysis system for metagenomes</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1234976694"><span>ISED -- Influenza sequence and epitope database.</span></a></div><p>Search for influenza sequence, vaccine, and drug resistance information.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL20140710115515"><span>LAMDHI: The Search for Animal Models Starts Here</span></a></div><p>LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal models for their research.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1228843803"><span>MANTIS -- a phylogenetic framework for multi-species genome comparisons</span></a></div><p>The missing link between multi-species full genome comparisons and functional analysis.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1099578148"><span>MBGD -- Microbial genome database for comparative analysis</span></a></div><p>Conduct comparative analysis of completely sequenced microbial genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1221077729"><span>MEGA -- Molecular Evolutionary Genetics Analysis</span></a></div><p>A biologist-centric software for evolutionary analysis of DNA and protein sequences.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174596756"><span>MamPol -- a database of nucleotide polymorphism in the Mammalia class</span></a></div><p>Conduct single nucleotide polymorphisms diversity measurements among homologous sequences from the Mammalia class.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1266437314"><span>MicrobesOnline -- Prokaryotic Genome Database</span></a></div><p>Find information about 1000s of microbial genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1208461006"><span>Narcisse -- a mirror view of conserved syntenies</span></a></div><p>A database dedicated to the study of genome conservation.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1219772764"><span>OMA -- the Orthologous MAtrix project</span></a></div><p>Explore orthologous relations across 352 complete genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209738741"><span>OPTIC -- orthologous and paralogous transcripts in clades</span></a></div><p>Browse complete genomes in several clades.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209573208"><span>OrthoDB -- the hierarchical catalog of eukaryotic orthologs</span></a></div><p>Find groups of orthologous genes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1221231200"><span>OrthoMaM -- orthologous mammalian markers</span></a></div><p>A database of orthologous genomic markers for placental mammal phylogenetics.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1100009979"><span>PEDANT -- Protein Extraction, Description and ANalysis Tool</span></a></div><p>Conduct genome wide functional and structural analysis.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174489475"><span>PReMod -- a database of genome-wide mammalian cis-regulatory module predictions</span></a></div><p>Conduct genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1151083092"><span>PhenomicDB -- Comparison of phenotypes of orthologous genes in human and model organisms</span></a></div><p>Compare phenotypes of a given gene or gene set in different model organisms.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1190899370"><span>Phylemon -- A suite of web tools for molecular evolution, phylogenetics and phylogenomics</span></a></div><p>Phylemon is a web server that integrates a selected suite of more than 20 different tools from the most popular stand-alone programs of phylogenetic and evolutionary analysis.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1232555615"><span>PhyloPat -- the phylogenetic pattern database</span></a></div><p>Use this database to see where in the evolution some phylogenetic lineages were started, and over which species they were contained.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174510223"><span>Pristionchus.org -- a genome-centric database of the nematode satellite species Pristionchus pacificus</span></a></div><p>Search for genomic information on nematode satellite species Pristionchus pacificus.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1236367352"><span>ProtClustDB -- NCBI Protein Clusters Database</span></a></div><p>Find information about related protein sequences.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209410278"><span>ProtozoaDB -- database of protozoan genomes</span></a></div><p>Database hosting genomics and post-genomics data from multiple protozoans.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1232554690"><span>Pseudofam -- the pseudogene families database</span></a></div><p>A database of pseudogene families based on the protein families from the Pfam database.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL20110518151439"><span>RIDM - RIKEN Integrated Database of Mammals</span></a></div><p>Find genomic information about mammals.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1272562567"><span>RegPrecise -- Regulon Prediction Database</span></a></div><p>Find information about predicted regulons in prokaryotic transcription regulation.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1272477473"><span>SALAD -- Surveyed contained motif ALignment diagram and the Associating Dendrogram</span></a></div><p>Perform systematic comparison of proteome data among species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1229010765"><span>SGN -- SOL Genomics Network</span></a></div><p>A comparative map viewer dedicated to the biology of the Solanaceae family.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1256669040"><span>ShotgunFunctionalizeR -- R-package for functional comparison of metagenomes</span></a></div><p>Analyze data from functional analysis on fragmented microbial genetic material.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1256238439"><span>SnoopCGH -- Comparative Genomic Hybridization software</span></a></div><p>Visualize and explore comparative genomic hybridization data sets.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1174489598"><span>SwissRegulon -- a database of genome-wide annotations of regulatory sites</span></a></div><p>Search for genome-wide annotations of regulatory sites in yeast and prokaryotes genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1229013521"><span>TaxonGap -- a visualization tool for intra- and inter-species variation among individual biomarkers</span></a></div><p>Compare and select individual biomarkers.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1106063477"><span>The Adaptive Evolution Database (TAED) -- a phylogeny based tool for comparative genomics</span></a></div><p>Search for information on adaptive evolution in gene families of higher plants and chordate.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1216742716"><span>The CGView Server -- a comparative genomics tool for circular genomes</span></a></div><p>Generate graphical maps of circular genomes that show sequence features, base composition plots, analysis results and sequence similarity plots.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1099663588"><span>The ERGO -- Genome analysis and discovery system</span></a></div><p>Conduct a comprehensive analysis of genes and genomes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1177611772"><span>The Macaque Genome: Interactive Poster and Teaching Resource</span></a></div><p>An interactive online poster presentation on the Macaque genome, including high-quality images, video clips, and Web resources</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1103816940"><span>The TIGR Gene Indices -- clustering and assembling EST and known genes and integration with eukaryotic genomes</span></a></div><p>Search for annotated genetic information of expressed sequence tags (ESTs) in different eukaryotic organisms.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1043767169"><span>UniGene</span></a></div><p>Find mapping and expression information for a unigene cluster (ESTs and full-length mRNA sequences organized into clusters that each represent a unique known or putative gene)</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1216738072"><span>Uprobe -- universal overgo hybridization-based probe retrieval and design</span></a></div><p>A public online resource for identifying or designing 'universal' overgo-hybridization probes from conserved sequences that can be used to efficiently screen one or more genomic libraries from a designated group of species.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1098205291"><span>VISTA -- Computational Tools for Comparative Genomics</span></a></div><p>Comprehensive suite of programs and databases for comparative analysis of genomic sequences.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL20110518144404"><span>cBARBEL -- Catfish Breeder and Researcher Bioinformatics Entry Location</span></a></div><p>Find information about ictalurid catfish.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1209738040"><span>eggNOG -- evolutionary genealogy of genes: Non-supervised Orthologous Groups</span></a></div><p>Discover orthologous groups of genes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1234370319"><span>metaTIGER -- a metabolic gene evolution resource</span></a></div><p>Find metabolic networks and phylogenomic information on a taxonomically diverse range of eukaryotes.</p></div><div><div><a href="https://www.hsls.pitt.edu/obrc/index.php?page=URL1138901833"><span>xBASE -- a collection of online databases for bacterial comparative genomics</span></a></div><p>Conduct bacterial comparative genomics.</p></div>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</guid>
	<pubDate>Mon, 02 Jun 2014 18:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</link>
	<title><![CDATA[Next generation sequencing in R or bioconductor environment]]></title>
	<description><![CDATA[<p>There are many R software and bioconductor packages for NGS data analysis, some of them are as follows</p><h3><a name="TOC-Biostrings" id="TOC-Biostrings"></a>Biostrings</h3><p>The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. <a href="http://bioconductor.org/packages/release/bioc/html/Biostrings.html">Documentation</a></p><div><div style="text-align: left;"><div style="color: #000000;"><h4><a name="TOC-IRanges-Overview" id="TOC-IRanges-Overview"></a>IRanges Overview</h4><p>IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. <a href="http://bioconductor.org/packages/release/bioc/html/IRanges.html">Documentation</a></p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-GenomicRanges-Overview" id="TOC-GenomicRanges-Overview"></a>GenomicRanges Overview</h4><p>The <em>GenomicRanges</em> package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the <em>IRanges</em> infrastructure and defines three major data containers - <em>GRanges, GRangesList</em> and <em>GappedAlignments</em> - which are supporting other important BioC-Seq packages including <em>ShortRead, Rsamtools, rtracklayer, GenomicFeatures</em> and <em>BSgenome</em>.&nbsp; Compared to the IRanges container, the GRanges/<em>GRangesList</em> classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p></div></div></div></div><h3><a name="TOC-Motif-Discovery" id="TOC-Motif-Discovery"></a>Motif Discovery</h3><h4><a name="TOC-cosmo" id="TOC-cosmo"></a>cosmo</h4><p>The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. <a href="http://bioconductor.org/packages/release/bioc/html/cosmo.html">Documentation</a></p></div><div>
<p><span></span><span></span></p>
<div style="color: #0000ff;"><h4><a name="TOC-BCRANK" id="TOC-BCRANK"></a>BCRANK</h4><p>BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/BCRANK.html"></a></p>
<p>rGADEM: <a href="http://bioconductor.org/packages/devel/bioc/html/rGADEM.html">Documentation</a></p><p>MotIV: <a href="http://bioconductor.org/packages/devel/bioc/html/MotIV.html">Documentation</a></p></div><h3><a name="TOC-ShortRead" id="TOC-ShortRead"></a>ShortRead</h3><p>The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. <a href="http://bioconductor.org/packages/release/bioc/html/ShortRead.html">Documentation</a></p><h3><a name="TOC-Rsamtools" id="TOC-Rsamtools"></a>Rsamtools</h3><p>Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. <a href="http://bioconductor.org/help/bioc-views/2.7/bioc/html/Rsamtools.html">Documentation</a></p>
<p><a href="http://samtools.sourceforge.net/">Samtools Website</a><br /> <a href="http://bio-bwa.sourceforge.net/">BWA (Burrows-Wheeler Alignment) Website</a><br /><span style="color: #0000ff;"></span></p>
<div style="color: #000000;">&nbsp;</div></div><div>
<p><span style="color: #000000;">Additional tools for SNP analysis:&nbsp;</span></p>
<p><a href="http://bioconductor.org/help/bioc-views/release/bioc/html/snpMatrix.html">snpMatrix</a></p><h3><a name="TOC-BSgenome" id="TOC-BSgenome"></a>BSgenome</h3><p>BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. <a href="http://bioconductor.org/packages/release/bioc/html/BSgenome.html">Documentation</a></p><h3><a name="TOC-rtracklayer" id="TOC-rtracklayer"></a>rtracklayer</h3><p>rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. <a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">Documentation</a></p><h3><a name="TOC-biomaRt" id="TOC-biomaRt"></a>biomaRt</h3><p>The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. <a href="http://bioconductor.org/packages/release/bioc/html/biomaRt.html">Documentation</a></p><h3><a name="TOC-ChIP-Seq-Analysis-Packages" id="TOC-ChIP-Seq-Analysis-Packages"></a>ChIP-Seq Analysis Packages</h3><p>Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: <a href="http://www.bioconductor.org/workshops/2009/SeattleJan09/ChIP-seq/">BioC ChIP-seq Case Study</a> and BioC <a href="http://www.bioconductor.org/help/course-materials/2009/SeattleNov09/ChIP-seq/">ChIP-Seq</a>.</p><h4><a name="TOC-chipseq" id="TOC-chipseq"></a>chipseq</h4><p>The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. <a href="http://bioconductor.org/packages/release/bioc/html/chipseq.html">Documentation</a></p><h4><a name="TOC-BayesPeak" id="TOC-BayesPeak"></a>BayesPeak</h4><p>BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. <a href="http://bioconductor.org/packages/release/bioc/html/BayesPeak.html">Documentation</a> [ <a href="http://www.biomedcentral.com/1471-2105/10/299">Publication</a> ]</p><h4><a name="TOC-PICS" id="TOC-PICS"></a>PICS</h4><p>The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">Documentation</a> [ <a href="http://www.hubmed.org/display.cgi?uids=20528864">Publication</a> ]</p><h4><a name="TOC-ChIPpeakAnno" id="TOC-ChIPpeakAnno"></a>ChIPpeakAnno</h4><p>The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. <a href="http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html">Documentation</a></p><h4><a name="TOC-Additional-ChIP-Seq-Packages" id="TOC-Additional-ChIP-Seq-Packages"></a>Additional ChIP-Seq Packages</h4><p>DiffBind: <a href="http://www.bioconductor.org/packages/release/bioc/html/DiffBind.html">Documentation</a></p><p>MOSAICS: <a href="http://bioconductor.org/packages/devel/bioc/html/mosaics.html">Documentation</a></p><p>iSeq: <a href="http://bioconductor.org/packages/release/bioc/html/iSeq.html">Documentation</a></p><p>ChIPseqR: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPseqR.html">Documentation</a></p><p>ChiPsim: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPsim.html">Documentation</a></p><p>CSAR: <a href="http://www.bioconductor.org/packages/devel/bioc/html/CSAR.html">Documentation</a></p><p>ChIP-Seq Pipeline: <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">PICS</a>, rGADEM and MotIV (<a href="http://www.rglab.org/pics-and-bioconductor/">developer web site</a>)</p><p>SPP: <a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/">ChIP-seq processing pipeline</a></p><p><a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/tutorial.html">SPP Tutorial</a></p><p><a href="http://liulab.dfci.harvard.edu/MACS/index.html">MACS</a></p><p><a href="http://gmdd.shgmo.org/Computational-Biology/ChIP-Seq/download/SIPeS">SIPeS</a></p><h3><a name="TOC-RNA-Seq-Analysis" id="TOC-RNA-Seq-Analysis"></a>RNA-Seq Analysis</h3><h4><a name="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-" id="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-"></a>Counting Reads that Overlap with Annotation Ranges&nbsp;</h4><p>The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are <span>countOverlaps <span style="color: #000000;"><span>and</span></span> summarizeOverlaps</span>. For their proper usage, it is important to read the corresponding <a href="http://www.bioconductor.org/packages/devel/bioc/vignettes/GenomicRanges/inst/doc/summarizeOverlaps.pdf">PDF manual</a>. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-DESeq" id="TOC-Differential-Gene-Expression-Analysis-with-DESeq"></a>Differential Gene Expression Analysis with DESeq</h4><p>The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns).&nbsp; Such a count table can be imported into R or generated from BAM alignment files using the <span>countOverlaps</span> function as introduced above. <a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-edgeR" id="TOC-Differential-Gene-Expression-Analysis-with-edgeR"></a>Differential Gene Expression Analysis with edgeR</h4><p>The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.&nbsp;</p>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">Documentation</a></p>
<p><span style="color: #000000;">A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG): <br /> </span></p><p><a href="http://bioconductor.org/packages/devel/bioc/html/easyRNASeq.html">easyRNASeq</a> (simplifies read counting per genome feature)</p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/DEXSeq.html">DEXSeq</a> (Inference of differential exon usage);&nbsp;<a href="http://www.bioconductor.org/packages/release/data/experiment/html/parathyroidSE.html">parathyroidSE</a> explains how to generate exon read counts in R</p><p><a href="http://bioconductor.org/packages/release/bioc/html/DEGseq.html">DEGseq</a></p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/baySeq.html">baySeq</a> (also see: <a href="http://www.bioconductor.org/packages/release/bioc/html/segmentSeq.html">segmentSeq</a>)</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a> (<a href="http://www.hubmed.org/display.cgi?uids=20167110">Bullard et al. 2010</a>)</p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-Detection-of-Alternative-Splice-Junctions" id="TOC-Detection-of-Alternative-Splice-Junctions"></a>Detection of Alternative Splice Junctions</h4>
<p><span style="color: #000000;">Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:</span></p>
<p><a href="http://woldlab.caltech.edu/rnaseq/">ERANGE<br /> </a><a href="http://tophat.cbcb.umd.edu/">TopHat</a></p><p><a href="http://biogibbs.stanford.edu/%7Ekinfai/SpliceMap/">SpliceMap</a></p><p><a href="http://solidsoftwaretools.com/gf/project/splitseek/">SplitSeek</a></p><h3><a name="TOC-DNA-Methylation-Data-Analysis" id="TOC-DNA-Methylation-Data-Analysis"></a>DNA-Methylation Data Analysis</h3><div><ul>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/help/course-materials/2012/BiocEurope2012/mattia_pelizzola_methylPipe.pdf">methylPipe</a></span></li>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/packages/devel/bioc/html/bsseq.html">bsseq</a></span></li>
<li><a href="http://www.bioconductor.org/packages/devel/bioc/html/BiSeq.html">BiSeq</a></li>
<li>Much more under <a href="http://www.bioconductor.org/packages/devel/BiocViews.html#___DNAMethylation">BiocViews</a></li>
</ul></div></div></div><h3><a name="TOC-HT-Seq-Data-Visualization" id="TOC-HT-Seq-Data-Visualization"></a>HT-Seq Data Visualization</h3>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/ggbio.html">ggbio</a>: ggplot2 extension for genomics data (<a href="http://tengfei.github.com/ggbio/">online manual</a>) <a href="http://www.bioconductor.org/packages/devel/bioc/html/Gviz.html">Gviz</a>:&nbsp;Plotting data and annotation information along genomic coordinates <a href="http://bioconductor.org/packages/release/bioc/html/HilbertVis.html">HilbertVis</a>: Hilbert genome plots</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/GenomeGraphs.html">GenomeGraphs</a>: Plotting genomic information from Ensembl</p><p><a href="http://www.hubmed.org/display.cgi?uids=18507856">TileQC</a>: Flow Cell Quality Visualization</p><p><a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">rtracklayer</a>: R interface to genome browsers</p><p><a href="http://genoplotr.r-forge.r-project.org/">genoPlotR</a>: Plotting maps of genes and genomes</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a>: Tools for storing, accessing, analyzing and visualizing genomic data.</p><p>&nbsp;</p><p>To install all packages</p><blockquote><p>source("http://bioconductor.org/biocLite.R")<br />biocLite()<br />biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))</p></blockquote></div>]]></description>
	<dc:creator>John Parker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</guid>
	<pubDate>Mon, 06 Nov 2023 03:09:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</link>
	<title><![CDATA[Bioinformatics Tools for Phylogeny !]]></title>
	<description><![CDATA[<p><span>Direct access to the individual tools available on this server.</span></p><table summary="list of individual tools">
<thead>
<tr><th>Multiple Alignment:</th><th>Phylogeny:</th><th>Tree viewers:</th><th>Utilities:</th></tr>
</thead>
<tbody>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=muscle">MUSCLE</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=phyml">PhyML</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=treedyn">TreeDyn</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=gblocks">Gblocks</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tcoffee">T-Coffee</a>&nbsp;/&nbsp;<a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=expresso">3DCoffee</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tnt">TNT</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawgram">Drawgram</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=jalview">Jalview</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=clustalw">ClustalW</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=bionj">BioNJ</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawtree">Drawtree</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=readseq">Readseq</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=probcons">ProbCons</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=mrbayes">MrBayes</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=atv">ATV (A Tree Viewer)</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/data_converter.cgi">Built-in converter</a></td>
</tr>
</tbody>
</table>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11494/postdoc-position-at-centre-mediterraneen-de-medecine-moleculaire-nice-france</guid>
  <pubDate>Wed, 04 Jun 2014 07:20:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoc position at Centre Méditerranéen de Médecine Moléculaire - Nice - France]]></title>
  <description><![CDATA[
<p>The research group of Dr. Michele Trabucchi at the Centre Méditerranéen de Médecine Moléculaire (C3M) at INSERM U1065 (University of Nice Sophia-Antipolis, France) is seeking candidates for a Postdoctoral fellow position to start on October 2014 for 3 years funded by FRM (Fondation pour la Recherche Médicale).<br />The broad interest of the lab is in understanding the expression control and function of small RNAs in activated myeloid cells (visit our webpage to check research interests and publications of the group : http://www.unice.fr/c3m/EN/Equipe10.html ). </p>

<p>The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.<br /> <br />Candidates should hold a Ph.D. degree and have strong background in bioinformatics.<br />The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.</p>

<p>Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )</p>

<p>Homepage: http://www.unice.fr/c3m/EN/Equipe10.html</p>
]]></description>
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