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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/21367?offset=710</link>
	<atom:link href="https://bioinformaticsonline.com/related/21367?offset=710" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/14011/dynamic-chromosome-breakpoints</guid>
	<pubDate>Wed, 13 Aug 2014 18:38:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/14011/dynamic-chromosome-breakpoints</link>
	<title><![CDATA[Dynamic chromosome breakpoints !!!]]></title>
	<description><![CDATA[<p>Cell division involves the distribution of identical genetic material, DNA, to two daughters&rsquo; cells. During this process, duplicated deoxyribonucleic acid (DNA) goes through a condensation and decondensation process. This is followed by nuclear envelope dissolution, mitotic spindle assembly, migration of the sister chromatid pairs to the metaphase plate, division and segregation of identical sets of chromosomes into daughter nuclei and nuclear envelope reformation.</p><p>The vital metaphase stage of cell division, when the sister chromatids migrated to the centre and lined up in a row, and pulled apart using attached microtubules in such a way that half the DNA ends up in each daughter cell. However, before the mitotic spindle‐mediated movement gets start and pulled DNA apart, the chromosomes are free to undergo <strong>recombination </strong>which involves the exchange of genetic material either between multiple chromosomes or between different regions of the same chromosome.</p><p><img src="http://www.sciencelearn.org.nz/var/sciencelearn/storage/images/contexts/uniquely-me/sci-media/images/chromosomes-crossing-over/464438-1-eng-NZ/Chromosomes-crossing-over.jpg" alt="image" width="504" height="342" style="border: 0px; border: 0px;"></p><p>During recombination, the precise breakage of each strand, exchange between the strands, and sealing of the resulting recombined molecules happens. The &ldquo;<strong>chromosomal breakpoints</strong>&rdquo; refers to these places where they break. Mostly, this process occurs with a high degree of accuracy at high frequency in both eukaryotic and prokaryotic cells. But occasionally this &ldquo;break and sealing/ break and reattach&rdquo; process goes wrong and the reattachment happens in the wrong place which usually create disaster (with few exceptions).These chromosome disaster or abnormalities involve the gain, loss or rearrangement of visible amounts of genetic material during cell division. These abnormalities are of two type, the first one is numerical abnormalities &nbsp;where severe disorders are caused by the loss or gain of whole chromosomes, which affect the copy number of hundreds or even thousands of genes. The second are structural abnormalities which can be unbalanced or balanced. The former are similar to numerical abnormalities in that genetic material is either gained or lost. The natural defects in chromosome segregation are linked to cancer and several genetic diseases (http://en.wikipedia.org/wiki/List_of_genetic_disorders). Therefore, the enzymes involved in regulating cell division are still the attractive drug targets for many diseases.</p><p>&nbsp;</p><p>&nbsp;</p><p><img src="http://upload.wikimedia.org/wikipedia/commons/4/4a/Chromosomal_translocations.svg" alt="image" width="424" height="331" style="border: 0px; border: 0px;"></p><p>&nbsp;</p><p>Apart from certain chromosome abnormalities, these &ldquo;crossing over&rdquo; of segments of maternal and paternal chromosomes to form hybrid chromosomes have some evolutionary importance and considered as a driver of genetic variation. Moreover, the chromosome breakage in evolution is considered to be non-random in nature(http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020014). In addition the study of breakpoint regions and non-breakpoint (stable) regions of chromosomes indicates both the regions evolved in distinctly different ways ( http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). These breakage may lead to genetic diseases or participate to chromosomal rearranmgnets and contributed in development of new species.</p><p>I will try to explain the genome hotspots/Evolutionary Breakpoint Regions(EBRs)/fragile regions/weak fragments/&nbsp; in my next blog.</p><p><strong>Software for recombination detection:</strong></p><p><strong>RAT</strong> http://cbr.jic.ac.uk/dicks/software/RAT/</p><p><strong>Breakpointer</strong> https://github.com/ruping/Breakpointer</p><p><strong>DRP</strong> http://web.cbio.uct.ac.za/~darren/rdp.html</p><p><strong>RB-finder</strong> http://www.ncbi.nlm.nih.gov/pubmed/18707535</p><p><strong>LDhat2.0</strong> http://ldhat.sourceforge.net/LDhat2.0/instructions.shtml</p><p><strong>Reference:</strong></p><p>http://www.nature.com/scitable/topicpage/genetic-recombination-514#</p><p>Image: Wikipedia , sciencelearn.org.nz</p><p><strong>Recommended Articles:</strong></p><p>http://www.friendshipcircle.org/blog/2012/05/22/13-chromosomal-disorders-youve-never-heard-of/</p><p>http://web.udl.es/usuaris/e4650869/docencia/segoncicle/genclin98/recursos_classe_%28pdf%29/revisionsPDF/chromosyndromes.pdf</p><p>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775595/table/T2/</p><p>http://learn.genetics.utah.edu/content/disorders/chromosomal/</p><p>http://www.ncert.nic.in/html/learning_basket/biology/cc&amp;cd.pdf</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/923/phylogenetic-for-bioinformatics</guid>
	<pubDate>Tue, 16 Jul 2013 03:50:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/923/phylogenetic-for-bioinformatics</link>
	<title><![CDATA[Phylogenetic for Bioinformatics]]></title>
	<description><![CDATA[<p>Biologists estimate that there are about 5 to 100 million species of organisms living on Earth today. Evidence from morphological, biochemical, and gene sequence data suggests that all organisms on Earth are genetically related, and the genealogical relationships of living things can be represented by a vast evolutionary tree, the Tree of Life. The Tree of Life then represents the phylogeny of organisms, i. e., the history of organismal lineages as they change through time.<br />Every living organism contains DNA, RNA, and proteins. Closely related organisms generally have a high degree of agreement in the molecular structure of these substances, while the molecules of organisms distantly related usually show a pattern of dissimilarity. Molecular phylogeny uses such data to build a "relationship tree" that shows the probable evolution of various organisms. Not until recent decades, however, has it been possible to isolate and identify these molecular structures.&nbsp;<br />phylogenetics is the study of evolutionary relatedness among various groups of organisms (for example, species or populations), which is discovered through molecular sequencing data and morphological data matrices. In other word, Phylogenetics, the science of phylogeny, is one part of the larger field of systematics, which also includes taxonomy. Taxonomy is the science of naming and classifying the diversity of organisms Molecular phylogeny is the use of the structure of molecules to gain information on an organism's evolutionary relationships. The result of a molecular phylogenetic analysis is expressed in a so-called phylogenetic tree.</p><p>The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms.&nbsp;<br />Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.<br />The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.<br /><br /><strong>For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.</strong></p><p>Phylogeny and Reconstructing Phylogenetic Trees:&nbsp;<a href="http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html"></a><a href="http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html">http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html</a><br />the CBRG and Department of Statistics Phylogeny tutorial:&nbsp;<a href="http://www.compbio.ox.ac.uk/tutorials/phylogeny/"></a><a href="http://www.compbio.ox.ac.uk/tutorials/phylogeny/">http://www.compbio.ox.ac.uk/tutorials/phylogeny/</a><br />TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:<a href="http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html"></a><a href="http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html">http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html</a></p><p>PHYLIP:&nbsp;<a href="http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html"></a><a href="http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html">http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html</a><br />An Introduction to Molecular Phylogeny:&nbsp;<a href="http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf"></a><a href="http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf">http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf</a></p><p>How to make a phylogenetic tree:&nbsp;<a href="http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Tree"></a><a href="http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Tree">http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Tree</a>tutorial.html<br />Phylogenetic Trees:&nbsp;<a href="http://cnx.org/content/m11052/latest/"></a><a href="http://cnx.org/content/m11052/latest/">http://cnx.org/content/m11052/latest/</a><br />Phylogeny by Ron Shamir:&nbsp;<a href="http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf"></a><a href="http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf">http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf</a><br />Introduction to Phylogeny:&nbsp;<a href="http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm"></a><a href="http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm">http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm</a><br />Lecturer notes on Phylogeny:&nbsp;<a href="http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf"></a><a href="http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf">http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf</a><br />Principles and Practice of Phylogenetic Systematics:<a href="http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm"></a><a href="http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm">http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm</a></p><p>Inferring phylogenetic trees:&nbsp;<a href="http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf"></a><a href="http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf">http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf</a></p><p><strong>Lecture Notes</strong></p><p>Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:<a href="http://academic.emporia.edu/mooredwi/nathist/chap1.htm"></a><a href="http://academic.emporia.edu/mooredwi/nathist/chap1.htm">http://academic.emporia.edu/mooredwi/nathist/chap1.htm</a></p><p>Algorithms for Phylogenetic Reconstructions:<a href="http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf"></a><a href="http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf">http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf</a></p><p>Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail :&nbsp;<a href="http://www.phylogeny.fr/version2_cgi/index.cgi"></a><a href="http://www.phylogeny.fr/version2_cgi/index.cgi">http://www.phylogeny.fr/version2_cgi/index.cgi</a></p><p>A Brief Tutorial on Phylogenetics<br /><a href="http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf"></a><a href="http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf">http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf</a></p><p>A Brief Tutorial on Phylogenetics Human Rabbit Chicken<br /><a href="http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf"></a><a href="http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf">http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf</a></p><p>Phylogenetic Tree Computation Tutorial Overview<br /><a href="http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf"></a><a href="http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf">http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf</a></p><p>MrBayes: A program for the Bayesian inference of phylogeny<br /><a href="http://golab.unl.edu/teaching/SBseminar/manual.pdf"></a><a href="http://golab.unl.edu/teaching/SBseminar/manual.pdf">http://golab.unl.edu/teaching/SBseminar/manual.pdf</a></p><p><strong>Web sites providing software for the construction of phylogenetic trees</strong></p><ul>
<li><a href="http://www.mbio.ncsu.edu/BioEdit/bioedit.html">BioEdit</a></li>
</ul><ul>
<li><a href="http://www.dinofish.com/">Coelocanth-Fish Out of Time</a></li>
</ul><ul>
<li><a href="http://cbrg.inf.ethz.ch/">Computational Biochemistry Research Group</a></li>
</ul><ul>
<li><a href="http://www.geocities.com/RainForest/Vines/8695/software.html">Digital Taxonomy</a></li>
</ul><ul>
<li><a href="http://www.cladistics.org/education/hennig86.html">Hennig 86</a></li>
</ul><ul>
<li><a href="http://www.bioinformaticssolutions.com/">Hyperclean</a>&nbsp;from Bioinformatics Solutions, Inc.</li>
</ul><ul>
<li><a href="http://www.mun.ca/biology/scarr/Directory.html">Memorial University of Newfoundland</a></li>
</ul><ul>
<li><a href="http://morphbank.ebc.uu.se/mrbayes/">Mr. Bayes</a></li>
</ul><ul>
<li><a href="http://www.cladistics.com/about_nona.htm">NONA</a></li>
</ul><ul>
<li><a href="http://evolve.zoo.ox.ac.uk/">Oxford University Evolutionary Biology Group</a></li>
</ul><ul>
<li><a href="http://flatpebble.nceas.ucsb.edu/public/">Paleobiology Database</a></li>
</ul><ul>
<li><a href="http://paup.csit.fsu.edu/index.html">PAUP</a></li>
</ul><ul>
<li><a href="http://evolution.genetics.washington.edu/phylip.html">Phylip Homepage</a></li>
</ul><ul>
<li><a href="http://research.amnh.org/scicomp/projects/poy.php">Poy</a></li>
</ul><ul>
<li><a href="http://www.sinauer.com/">Sinauer Associates</a></li>
</ul><ul>
<li><a href="http://www.cladistics.org/downloads/webtnt.html">TNT</a>-Tree Analysis Using New Technology</li>
</ul><ul>
<li><a href="http://www.treebase.org/treebase/index.html">Tree Base</a></li>
</ul><ul>
<li><a href="http://www.treefinder.de/">Treefinder</a></li>
</ul><ul>
<li><a href="http://www.tree-puzzle.de/">Tree-Puzzle</a></li>
</ul><ul>
<li><a href="http://taxonomy.zoology.gla.ac.uk/rod/treeview.html">Tree View</a>-Taxonomy and Systematics Group at Glasgow</li>
</ul><ul>
<li><a href="http://evolution.genetics.washington.edu/phylip/software.html">Washington University</a>-List of Phylogeny Software</li>
</ul>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4037/perl-and-bioperl-tutorials</guid>
	<pubDate>Wed, 28 Aug 2013 05:51:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4037/perl-and-bioperl-tutorials</link>
	<title><![CDATA[Perl and BioPerl Tutorials]]></title>
	<description><![CDATA[<p>This bookmark is created to store the useful Perl and BioPerl tutorial links at one place. Feel free to share and add more useful tutorial links here ....&nbsp;</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://cbb.sjtu.edu.cn/course/database/beginning.pdf" rel="nofollow">http://cbb.sjtu.edu.cn/course/database/beginning.pdf</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44200/dashboard-designing-tutorial</guid>
	<pubDate>Thu, 02 Mar 2023 06:48:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44200/dashboard-designing-tutorial</link>
	<title><![CDATA[Dashboard designing tutorial !]]></title>
	<description><![CDATA[<p>Dashboard Design Tutorial</p><p>Address of the bookmark: <a href="https://github.com/dthill196/SARS-2-Dashboard-Tutorial" rel="nofollow">https://github.com/dthill196/SARS-2-Dashboard-Tutorial</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</guid>
	<pubDate>Mon, 27 Nov 2017 16:24:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</link>
	<title><![CDATA[Single Cell RNAseq data analysis tutorial !!]]></title>
	<description><![CDATA[<ul>
<li>A major breakthrough (replaced microarrays) in the late 00&rsquo;s and has been widely used since</li>
<li>Measures the&nbsp;average expression level&nbsp;for each gene across a large population of input cells</li>
<li>Useful for comparative transcriptomics, e.g.&nbsp;samples of the same tissue from different species</li>
<li>Useful for quantifying expression signatures from ensembles, e.g.&nbsp;in disease studies</li>
<li>Insufficient&nbsp;for studying heterogeneous systems, e.g.&nbsp;early development studies, complex tissues (brain)</li>
<li>Does&nbsp;not&nbsp;provide insights into the stochastic nature of gene expression</li>
</ul><p>Following are the useful links:</p><p><a href="http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf" target="_blank">Single Cell RNAseq data analysis Tutorial</a></p><p><a href="https://f1000research.com/articles/5-2122/v2" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data</a></p><p><a href="https://www.bioconductor.org/help/workflows/simpleSingleCell/" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor</a></p><p>SCell: single-cell RNA-seq analysis software</p><p><a href="https://github.com/diazlab/SCell">https://github.com/diazlab/SCell</a></p><p>Beta-Poisson model for single-cell RNA-seq data analyses</p><p><a href="https://github.com/nghiavtr/BPSC">https://github.com/nghiavtr/BPSC</a></p><p>Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis</p><p><a href="https://research.cchmc.org/pbge/sincera.html">https://research.cchmc.org/pbge/sincera.html</a></p><p>SC3 &ndash; consensus clustering of single-cell RNA-Seq data</p><p><a href="http://biorxiv.org/content/early/2016/09/02/036558">http://biorxiv.org/content/early/2016/09/02/036558</a></p><p>Citrus: A toolkit for single cell sequencing analysis</p><p><a href="http://biorxiv.org/content/early/2016/09/14/045070">http://biorxiv.org/content/early/2016/09/14/045070</a></p><p>Single-Cell Resolution of Temporal Gene Expression during Heart Development</p><p><a href="http://www.cell.com/developmental-cell/fulltext/S1534-5807%2816%2930682-7">http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7</a></p><p>Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects</p><p><a href="http://biorxiv.org/content/early/2016/11/15/087775">http://biorxiv.org/content/early/2016/11/15/087775</a></p><p>Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes</p><p><a href="http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract">http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract</a></p><p>SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation</p><p><a href="http://biorxiv.org/content/early/2016/11/21/088856">http://biorxiv.org/content/early/2016/11/21/088856</a></p><p>SCOUP is a probabilistic model to analyze single-cell expression data during differentiation</p><p><a href="https://github.com/hmatsu1226/SCOUP">https://github.com/hmatsu1226/SCOUP</a></p><p>scLVM is a modelling framework for single-cell RNA-seq data</p><p><a href="https://github.com/PMBio/scLVM">https://github.com/PMBio/scLVM</a></p><p>Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories</p><p><a href="https://github.com/jw156605/SLICER">https://github.com/jw156605/SLICER</a></p><p>SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality</p><p><a href="http://www.morgridge.net/SinQC.html">http://www.morgridge.net/SinQC.html</a></p><p>TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis</p><p><a href="https://github.com/zji90/TSCAN">https://github.com/zji90/TSCAN</a></p><p>Visualization and cellular hierarchy inference of single-cell data using SPADE</p><p><a href="http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html">http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html</a></p><p>OEFinder: Identify ordering effect genes in single cell RNA-seq data</p><p><a href="https://github.com/lengning/OEFinder">https://github.com/lengning/OEFinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/39307/awk-for-beginners</guid>
	<pubDate>Fri, 26 Apr 2019 16:19:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/39307/awk-for-beginners</link>
	<title><![CDATA[AWK for beginners !]]></title>
	<description><![CDATA[<p>AWK is a standard tool on every POSIX-compliant UNIX system. It&rsquo;s like flex/lex, from the command-line, perfect for text-processing tasks and other scripting needs. It has a C-like syntax, but without mandatory semicolons (although, you should use them anyway, because they are required when you&rsquo;re writing one-liners, something AWK excels at), manual memory management, or static typing. It excels at text processing. You can call to it from a shell script, or you can use it as a stand-alone scripting language.</p><p>Why use AWK instead of Perl? Readability. AWK is easier to read than Perl. For simple text-processing scripts, particularly ones that read files line by line and split on delimiters, AWK is probably the right tool for the job.</p><div><pre><span>#!/usr/bin/awk -f</span>

<span># Comments are like this</span>


<span># AWK programs consist of a collection of patterns and actions.</span>
<span>pattern1</span> <span>{</span> <span>action</span><span>;</span> <span>}</span> <span># just like lex</span>
<span>pattern2</span> <span>{</span> <span>action</span><span>;</span> <span>}</span>

<span># There is an implied loop and AWK automatically reads and parses each</span>
<span># record of each file supplied. Each record is split by the FS delimiter,</span>
<span># which defaults to white-space (multiple spaces,tabs count as one)</span>
<span># You can assign FS either on the command line (-F C) or in your BEGIN</span>
<span># pattern</span>

<span># One of the special patterns is BEGIN. The BEGIN pattern is true</span>
<span># BEFORE any of the files are read. The END pattern is true after</span>
<span># an End-of-file from the last file (or standard-in if no files specified)</span>
<span># There is also an output field separator (OFS) that you can assign, which</span>
<span># defaults to a single space</span>

<span>BEGIN</span> <span>{</span>

    <span># BEGIN will run at the beginning of the program. It's where you put all</span>
    <span># the preliminary set-up code, before you process any text files. If you</span>
    <span># have no text files, then think of BEGIN as the main entry point.</span>

    <span># Variables are global. Just set them or use them, no need to declare..</span>
    <span>count</span> <span>=</span> <span>0</span><span>;</span>

    <span># Operators just like in C and friends</span>
    <span>a</span> <span>=</span> <span>count</span> <span>+</span> <span>1</span><span>;</span>
    <span>b</span> <span>=</span> <span>count</span> <span>-</span> <span>1</span><span>;</span>
    <span>c</span> <span>=</span> <span>count</span> <span>*</span> <span>1</span><span>;</span>
    <span>d</span> <span>=</span> <span>count</span> <span>/</span> <span>1</span><span>;</span> <span># integer division</span>
    <span>e</span> <span>=</span> <span>count</span> <span>%</span> <span>1</span><span>;</span> <span># modulus</span>
    <span>f</span> <span>=</span> <span>count</span> <span>^</span> <span>1</span><span>;</span> <span># exponentiation</span>

    <span>a</span> <span>+=</span> <span>1</span><span>;</span>
    <span>b</span> <span>-=</span> <span>1</span><span>;</span>
    <span>c</span> <span>*=</span> <span>1</span><span>;</span>
    <span>d</span> <span>/=</span> <span>1</span><span>;</span>
    <span>e</span> <span>%=</span> <span>1</span><span>;</span>
    <span>f</span> <span>^=</span> <span>1</span><span>;</span>

    <span># Incrementing and decrementing by one</span>
    <span>a</span><span>++</span><span>;</span>
    <span>b</span><span>--</span><span>;</span>

    <span># As a prefix operator, it returns the incremented value</span>
    <span>++</span><span>a</span><span>;</span>
    <span>--</span><span>b</span><span>;</span>

    <span># Notice, also, no punctuation such as semicolons to terminate statements</span>

    <span># Control statements</span>
    <span>if</span> <span>(</span><span>count</span> <span>==</span> <span>0</span><span>)</span>
        <span>print</span> <span>"Starting with count of 0"</span><span>;</span>
    <span>else</span>
        <span>print</span> <span>"Huh?"</span><span>;</span>

    <span># Or you could use the ternary operator</span>
    <span>print</span> <span>(</span><span>count</span> <span>==</span> <span>0</span><span>)</span> <span>?</span> <span>"Starting with count of 0"</span> <span>:</span> <span>"Huh?"</span><span>;</span>

    <span># Blocks consisting of multiple lines use braces</span>
    <span>while</span> <span>(</span><span>a</span> <span>&lt;</span> <span>10</span><span>)</span> <span>{</span>
        <span>print</span> <span>"String concatenation is done"</span> <span>" with a series"</span> <span>" of"</span>
            <span>" space-separated strings"</span><span>;</span>
        <span>print</span> <span>a</span><span>;</span>

        <span>a</span><span>++</span><span>;</span>
    <span>}</span>

    <span>for</span> <span>(</span><span>i</span> <span>=</span> <span>0</span><span>;</span> <span>i</span> <span>&lt;</span> <span>10</span><span>;</span> <span>i</span><span>++</span><span>)</span>
        <span>print</span> <span>"Good ol' for loop"</span><span>;</span>

    <span># As for comparisons, they're the standards:</span>
    <span># a &lt; b   # Less than</span>
    <span># a &lt;= b  # Less than or equal</span>
    <span># a != b  # Not equal</span>
    <span># a == b  # Equal</span>
    <span># a &gt; b   # Greater than</span>
    <span># a &gt;= b  # Greater than or equal</span>

    <span># Logical operators as well</span>
    <span># a &amp;&amp; b  # AND</span>
    <span># a || b  # OR</span>

    <span># In addition, there's the super useful regular expression match</span>
    <span>if</span> <span>(</span><span>"foo"</span> <span>~</span> <span>"^fo+$"</span><span>)</span>
        <span>print</span> <span>"Fooey!"</span><span>;</span>
    <span>if</span> <span>(</span><span>"boo"</span> <span>!~</span> <span>"^fo+$"</span><span>)</span>
        <span>print</span> <span>"Boo!"</span><span>;</span>

    <span># Arrays</span>
    <span>arr</span><span>[</span><span>0</span><span>]</span> <span>=</span> <span>"foo"</span><span>;</span>
    <span>arr</span><span>[</span><span>1</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>

    <span># You can also initialize an array with the built-in function split()</span>

    <span>n</span> <span>=</span> <span>split</span><span>(</span><span>"foo:bar:baz"</span><span>,</span> <span>arr</span><span>,</span> <span>":"</span><span>);</span>

    <span># You also have associative arrays (actually, they're all associative arrays)</span>
    <span>assoc</span><span>[</span><span>"foo"</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>
    <span>assoc</span><span>[</span><span>"bar"</span><span>]</span> <span>=</span> <span>"baz"</span><span>;</span>

    <span># And multi-dimensional arrays, with some limitations I won't mention here</span>
    <span>multidim</span><span>[</span><span>0</span><span>,</span><span>0</span><span>]</span> <span>=</span> <span>"foo"</span><span>;</span>
    <span>multidim</span><span>[</span><span>0</span><span>,</span><span>1</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>
    <span>multidim</span><span>[</span><span>1</span><span>,</span><span>0</span><span>]</span> <span>=</span> <span>"baz"</span><span>;</span>
    <span>multidim</span><span>[</span><span>1</span><span>,</span><span>1</span><span>]</span> <span>=</span> <span>"boo"</span><span>;</span>

    <span># You can test for array membership</span>
    <span>if</span> <span>(</span><span>"foo"</span> <span>in</span> <span>assoc</span><span>)</span>
        <span>print</span> <span>"Fooey!"</span><span>;</span>

    <span># You can also use the 'in' operator to traverse the keys of an array</span>
    <span>for</span> <span>(</span><span>key</span> <span>in</span> <span>assoc</span><span>)</span>
        <span>print</span> <span>assoc</span><span>[</span><span>key</span><span>];</span>

    <span># The command line is in a special array called ARGV</span>
    <span>for</span> <span>(</span><span>argnum</span> <span>in</span> <span>ARGV</span><span>)</span>
        <span>print</span> <span>ARGV</span><span>[</span><span>argnum</span><span>];</span>

    <span># You can remove elements of an array</span>
    <span># This is particularly useful to prevent AWK from assuming the arguments</span>
    <span># are files for it to process</span>
    <span>delete</span> <span>ARGV</span><span>[</span><span>1</span><span>];</span>

    <span># The number of command line arguments is in a variable called ARGC</span>
    <span>print</span> <span>ARGC</span><span>;</span>

    <span># AWK has several built-in functions. They fall into three categories. I'll</span>
    <span># demonstrate each of them in their own functions, defined later.</span>

    <span>return_value</span> <span>=</span> <span>arithmetic_functions</span><span>(</span><span>a</span><span>,</span> <span>b</span><span>,</span> <span>c</span><span>);</span>
    <span>string_functions</span><span>();</span>
    <span>io_functions</span><span>();</span>
<span>}</span>

<span># Here's how you define a function</span>
<span>function</span> <span>arithmetic_functions</span><span>(</span><span>a</span><span>,</span> <span>b</span><span>,</span> <span>c</span><span>,</span>     <span>d</span><span>)</span> <span>{</span>

    <span># Probably the most annoying part of AWK is that there are no local</span>
    <span># variables. Everything is global. For short scripts, this is fine, even</span>
    <span># useful, but for longer scripts, this can be a problem.</span>

    <span># There is a work-around (ahem, hack). Function arguments are local to the</span>
    <span># function, and AWK allows you to define more function arguments than it</span>
    <span># needs. So just stick local variable in the function declaration, like I</span>
    <span># did above. As a convention, stick in some extra whitespace to distinguish</span>
    <span># between actual function parameters and local variables. In this example,</span>
    <span># a, b, and c are actual parameters, while d is merely a local variable.</span>

    <span># Now, to demonstrate the arithmetic functions</span>

    <span># Most AWK implementations have some standard trig functions</span>
    <span>localvar</span> <span>=</span> <span>sin</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>cos</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>atan2</span><span>(</span><span>b</span><span>,</span> <span>a</span><span>);</span> <span># arc tangent of b / a</span>

    <span># And logarithmic stuff</span>
    <span>localvar</span> <span>=</span> <span>exp</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>log</span><span>(</span><span>a</span><span>);</span>

    <span># Square root</span>
    <span>localvar</span> <span>=</span> <span>sqrt</span><span>(</span><span>a</span><span>);</span>

    <span># Truncate floating point to integer</span>
    <span>localvar</span> <span>=</span> <span>int</span><span>(</span><span>5.34</span><span>);</span> <span># localvar =&gt; 5</span>

    <span># Random numbers</span>
    <span>srand</span><span>();</span> <span># Supply a seed as an argument. By default, it uses the time of day</span>
    <span>localvar</span> <span>=</span> <span>rand</span><span>();</span> <span># Random number between 0 and 1.</span>

    <span># Here's how to return a value</span>
    <span>return</span> <span>localvar</span><span>;</span>
<span>}</span>

<span>function</span> <span>string_functions</span><span>(</span>    <span>localvar</span><span>,</span> <span>arr</span><span>)</span> <span>{</span>

    <span># AWK, being a string-processing language, has several string-related</span>
    <span># functions, many of which rely heavily on regular expressions.</span>

    <span># Search and replace, first instance (sub) or all instances (gsub)</span>
    <span># Both return number of matches replaced</span>
    <span>localvar</span> <span>=</span> <span>"fooooobar"</span><span>;</span>
    <span>sub</span><span>(</span><span>"fo+"</span><span>,</span> <span>"Meet me at the "</span><span>,</span> <span>localvar</span><span>);</span> <span># localvar =&gt; "Meet me at the bar"</span>
    <span>gsub</span><span>(</span><span>"e+"</span><span>,</span> <span>"."</span><span>,</span> <span>localvar</span><span>);</span> <span># localvar =&gt; "m..t m. at th. bar"</span>

    <span># Search for a string that matches a regular expression</span>
    <span># index() does the same thing, but doesn't allow a regular expression</span>
    <span>match</span><span>(</span><span>localvar</span><span>,</span> <span>"t"</span><span>);</span> <span># =&gt; 4, since the 't' is the fourth character</span>

    <span># Split on a delimiter</span>
    <span>n</span> <span>=</span> <span>split</span><span>(</span><span>"foo-bar-baz"</span><span>,</span> <span>arr</span><span>,</span> <span>"-"</span><span>);</span> <span># a[1] = "foo"; a[2] = "bar"; a[3] = "baz"; n = 3</span>

    <span># Other useful stuff</span>
    <span>sprintf</span><span>(</span><span>"%s %d %d %d"</span><span>,</span> <span>"Testing"</span><span>,</span> <span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span> <span># =&gt; "Testing 1 2 3"</span>
    <span>substr</span><span>(</span><span>"foobar"</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span> <span># =&gt; "oob"</span>
    <span>substr</span><span>(</span><span>"foobar"</span><span>,</span> <span>4</span><span>);</span> <span># =&gt; "bar"</span>
    <span>length</span><span>(</span><span>"foo"</span><span>);</span> <span># =&gt; 3</span>
    <span>tolower</span><span>(</span><span>"FOO"</span><span>);</span> <span># =&gt; "foo"</span>
    <span>toupper</span><span>(</span><span>"foo"</span><span>);</span> <span># =&gt; "FOO"</span>
<span>}</span>

<span>function</span> <span>io_functions</span><span>(</span>    <span>localvar</span><span>)</span> <span>{</span>

    <span># You've already seen print</span>
    <span>print</span> <span>"Hello world"</span><span>;</span>

    <span># There's also printf</span>
    <span>printf</span><span>(</span><span>"%s %d %d %d\n"</span><span>,</span> <span>"Testing"</span><span>,</span> <span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span>

    <span># AWK doesn't have file handles, per se. It will automatically open a file</span>
    <span># handle for you when you use something that needs one. The string you used</span>
    <span># for this can be treated as a file handle, for purposes of I/O. This makes</span>
    <span># it feel sort of like shell scripting, but to get the same output, the string</span>
    <span># must match exactly, so use a variable:</span>

    <span>outfile</span> <span>=</span> <span>"/tmp/foobar.txt"</span><span>;</span>

    <span>print</span> <span>"foobar"</span> <span>&gt;</span> <span>outfile</span><span>;</span>

    <span># Now the string outfile is a file handle. You can close it:</span>
    <span>close</span><span>(</span><span>outfile</span><span>);</span>

    <span># Here's how you run something in the shell</span>
    <span>system</span><span>(</span><span>"echo foobar"</span><span>);</span> <span># =&gt; prints foobar</span>

    <span># Reads a line from standard input and stores in localvar</span>
    <span>getline</span> <span>localvar</span><span>;</span>

    <span># Reads a line from a pipe (again, use a string so you close it properly)</span>
    <span>cmd</span> <span>=</span> <span>"echo foobar"</span><span>;</span>
    <span>cmd</span> <span>|</span> <span>getline</span> <span>localvar</span><span>;</span> <span># localvar =&gt; "foobar"</span>
    <span>close</span><span>(</span><span>cmd</span><span>);</span>

    <span># Reads a line from a file and stores in localvar</span>
    <span>infile</span> <span>=</span> <span>"/tmp/foobar.txt"</span><span>;</span>
    <span>getline</span> <span>localvar</span> <span>&lt;</span> <span>infile</span><span>;</span> 
    <span>close</span><span>(</span><span>infile</span><span>);</span>
<span>}</span>

<span># As I said at the beginning, AWK programs consist of a collection of patterns</span>
<span># and actions. You've already seen the BEGIN pattern. Other</span>
<span># patterns are used only if you're processing lines from files or standard</span>
<span># input.</span>
<span>#</span>
<span># When you pass arguments to AWK, they are treated as file names to process.</span>
<span># It will process them all, in order. Think of it like an implicit for loop,</span>
<span># iterating over the lines in these files. these patterns and actions are like</span>
<span># switch statements inside the loop. </span>

<span>/^fo+bar$/</span> <span>{</span>

    <span># This action will execute for every line that matches the regular</span>
    <span># expression, /^fo+bar$/, and will be skipped for any line that fails to</span>
    <span># match it. Let's just print the line:</span>

    <span>print</span><span>;</span>

    <span># Whoa, no argument! That's because print has a default argument: $0.</span>
    <span># $0 is the name of the current line being processed. It is created</span>
    <span># automatically for you.</span>

    <span># You can probably guess there are other $ variables. Every line is</span>
    <span># implicitly split before every action is called, much like the shell</span>
    <span># does. And, like the shell, each field can be access with a dollar sign</span>

    <span># This will print the second and fourth fields in the line</span>
    <span>print</span> <span>$</span><span>2</span><span>,</span> <span>$</span><span>4</span><span>;</span>

    <span># AWK automatically defines many other variables to help you inspect and</span>
    <span># process each line. The most important one is NF</span>

    <span># Prints the number of fields on this line</span>
    <span>print</span> <span>NF</span><span>;</span>

    <span># Print the last field on this line</span>
    <span>print</span> <span>$</span><span>NF</span><span>;</span>
<span>}</span>

<span># Every pattern is actually a true/false test. The regular expression in the</span>
<span># last pattern is also a true/false test, but part of it was hidden. If you</span>
<span># don't give it a string to test, it will assume $0, the line that it's</span>
<span># currently processing. Thus, the complete version of it is this:</span>

<span>$</span><span>0</span> <span>~</span> <span>/^fo+bar$/</span> <span>{</span>
    <span>print</span> <span>"Equivalent to the last pattern"</span><span>;</span>
<span>}</span>

<span>a</span> <span>&gt;</span> <span>0</span> <span>{</span>
    <span># This will execute once for each line, as long as a is positive</span>
<span>}</span>

<span># You get the idea. Processing text files, reading in a line at a time, and</span>
<span># doing something with it, particularly splitting on a delimiter, is so common</span>
<span># in UNIX that AWK is a scripting language that does all of it for you, without</span>
<span># you needing to ask. All you have to do is write the patterns and actions</span>
<span># based on what you expect of the input, and what you want to do with it.</span>

<span># Here's a quick example of a simple script, the sort of thing AWK is perfect</span>
<span># for. It will read a name from standard input and then will print the average</span>
<span># age of everyone with that first name. Let's say you supply as an argument the</span>
<span># name of a this data file:</span>
<span>#</span>
<span># Bob Jones 32</span>
<span># Jane Doe 22</span>
<span># Steve Stevens 83</span>
<span># Bob Smith 29</span>
<span># Bob Barker 72</span>
<span>#</span>
<span># Here's the script:</span>

<span>BEGIN</span> <span>{</span>

    <span># First, ask the user for the name</span>
    <span>print</span> <span>"What name would you like the average age for?"</span><span>;</span>

    <span># Get a line from standard input, not from files on the command line</span>
    <span>getline</span> <span>name</span> <span>&lt;</span> <span>"/dev/stdin"</span><span>;</span>
<span>}</span>

<span># Now, match every line whose first field is the given name</span>
<span>$</span><span>1</span> <span>==</span> <span>name</span> <span>{</span>

    <span># Inside here, we have access to a number of useful variables, already</span>
    <span># pre-loaded for us:</span>
    <span># $0 is the entire line</span>
    <span># $3 is the third field, the age, which is what we're interested in here</span>
    <span># NF is the number of fields, which should be 3</span>
    <span># NR is the number of records (lines) seen so far</span>
    <span># FILENAME is the name of the file being processed</span>
    <span># FS is the field separator being used, which is " " here</span>
    <span># ...etc. There are plenty more, documented in the man page.</span>

    <span># Keep track of a running total and how many lines matched</span>
    <span>sum</span> <span>+=</span> <span>$</span><span>3</span><span>;</span>
    <span>nlines</span><span>++</span><span>;</span>
<span>}</span>

<span># Another special pattern is called END. It will run after processing all the</span>
<span># text files. Unlike BEGIN, it will only run if you've given it input to</span>
<span># process. It will run after all the files have been read and processed</span>
<span># according to the rules and actions you've provided. The purpose of it is</span>
<span># usually to output some kind of final report, or do something with the</span>
<span># aggregate of the data you've accumulated over the course of the script.</span>

<span>END</span> <span>{</span>
    <span>if</span> <span>(</span><span>nlines</span><span>)</span>
        <span>print</span> <span>"The average age for "</span> <span>name</span> <span>" is "</span> <span>sum</span> <span>/</span> <span>nlines</span><span>;</span>
<span>}</span>
</pre><p><span>&nbsp;</span></p></div>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43863/snakemake-tutorials</guid>
	<pubDate>Mon, 09 May 2022 05:20:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43863/snakemake-tutorials</link>
	<title><![CDATA[Snakemake Tutorials !]]></title>
	<description><![CDATA[<p>A lesson introducing the Snakemake workflow system for bioinformatics analysis.</p>
<blockquote>
<h2 id="prerequisites">Prerequisites<a href="https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/index.html#prerequisites"></a></h2>
<p>This is an intermediate lesson and assumes learners have already done some bioinformatics:</p>
<ul>
<li>Familiarity with the BASH command shell, including concepts like pipes, variables and loops.</li>
<li>Knowledge of bioinformatics fundamentals like the FASTQ file format and transcriptome sequencing, in order to understand the example workflow.</li>
</ul>
<p>No previous knowledge of Snakemake or workflow systems is required.</p>
<p>https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/index.html</p>
</blockquote><p>Address of the bookmark: <a href="https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/aio/index.html" rel="nofollow">https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/aio/index.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534</guid>
	<pubDate>Mon, 06 Oct 2014 12:51:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534</link>
	<title><![CDATA[Orange-Bioinformatics 2.5.34]]></title>
	<description><![CDATA[<p>Orange Bioinformatics extends <a href="http://orange.biolab.si/">Orange</a>, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.</p>
<p>Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.</p><p>Address of the bookmark: <a href="https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34" rel="nofollow">https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18380/jrfsrf-at-university-of-hyderabad</guid>
  <pubDate>Fri, 17 Oct 2014 01:55:44 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at University of Hyderabad]]></title>
  <description><![CDATA[
<p>Applications are invited for the following post of Junior Research Fellow (temporary position coterminous with the project) under DBT funded research project on ““Understanding the functions of α1β1γ1/α2β1γ1 selective AMPK Modulators in dissecting the pharmacological role of these isozymes in metabolic diseases”</p>

<p>Qualified and interested candidates can send their curriculum vitae by e-mail to hr@drils.org on or before 27th October 2014 mention in the subject line of the mail the following code: AMPK-Biology.</p>

<p>Selected candidates will be called for a personal interview to Dr. Reddy’s Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad. The selected candidate is expected to report within two weeks from the date of selection to start work on the project.</p>

<p>Junior Research Fellowship (Molecular Modeling/Biology) for two years and Senior Research fellowship for one year</p>

<p>Junior Research Fellowship: Rs. 15,600/- (consolidated) per month for first two years.<br />Senior Research Fellowship: Rs. 18,200/-(consolidated) per month for the 3rd year.</p>

<p>Duration: The duration of the fellowship is for three years. However, the performance of the candidate will be reviewed after the completion of every year and the fellowship will be renewed only upon satisfactory performance.</p>

<p>Responsibilities:</p>

<p>1) Literature search.<br />2) Design, plan and execute experiments under the supervision of the scientist.<br />3) Provide scientific support to the scientist in his/her research activities.<br />4) Book keeping and maintenance of stocks and consumables.</p>

<p>Essential Qualifications:</p>

<p>Required: M.Sc. in Microbiology/Biotechnology/Bioinformatics or any other related branch of basic Sciences from a recognized university/institute with a consistent academic record of minimum 60% aggregate in all qualifying examinations. The candidate should be NET qualified for lectureship. The candidate should be motivated to work with dedication.</p>

<p>Desirable: expertise/experience in both Molecular Modeling and Molecular Biology.</p>

<p>Experience: 0-2 years in the areas of Molecular Modeling and/or Molecular Biology and cell biology and Biochemistry.</p>

<p>Preferable: Relevant research experience as evident from thesis/dissertation/project work.</p>

<p>Advertisement: http://www.ilsresearch.org/userfiles/Junior%20REsearch%20Fellowship%20-%20AMPK(Biology).pdf</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18385/biinformamatics-lead-at-google-life-sciences</guid>
  <pubDate>Fri, 17 Oct 2014 02:24:55 -0500</pubDate>
  <link></link>
  <title><![CDATA[Biinformamatics Lead at Google Life Sciences]]></title>
  <description><![CDATA[
<p>Google Life Sciences is recruiting a technical lead with experience in bioinformatics and clinical bioinformatics, including for biomarker discovery projects such as the Baseline study.</p>

<p>Responsibilities</p>

<p>Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.<br />Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.<br />Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.<br />Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.<br />Create scientifically rigorous visualizations, communications, and presentations of results.</p>

<p>Reference @ https://www.google.com/about/careers/search#!t=jo&amp;jid=62095001</p>
]]></description>
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