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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/2534?offset=70</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/2573/most-commonly-used-awk-by-bioinformatician</guid>
	<pubDate>Mon, 19 Aug 2013 01:12:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/2573/most-commonly-used-awk-by-bioinformatician</link>
	<title><![CDATA[Most Commonly used Awk by Bioinformatician]]></title>
	<description><![CDATA[<p style="text-align: center;">&nbsp;</p><p>Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.</p><p>Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.</p><p>choose rows where column 3 is larger than column 5:</p><p>awk '$3&gt;$5' input.txt &gt; output.txt</p><p>extract column 2,4,5:</p><p>awk '{print $2,$4,$5}' input.txt &gt; output.txt</p><p>awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt</p><p>show rows between 20th and 80th:</p><p>awk 'NR&gt;=20&amp;&amp;NR&lt;=80' input.txt &gt; output.txt</p><p>calculate the average of column 2:</p><p>awk '{x+=$2}END{print x/NR}' input.txt</p><p>regex (egrep):</p><p>awk '/^test[0-9]+/' input.txt</p><p>calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:</p><p>awk '{print $0,$2+$3}' input.txt</p><p>awk '{$1=$2+$3;print}' input.txt</p><p>join two files on column 1:</p><p>awk 'BEGIN{while((getline&lt;"file1.txt")&gt;0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt &gt; output.txt</p><p>count number of occurrence of column 2 (uniq -c):</p><p>awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt</p><p>apply "uniq" on column 2, only printing the first occurrence (uniq):</p><p>awk '!($2 in l){print;l[$2]=1}' input.txt</p><p>count different words (wc):</p><p>awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt</p><p>deal with simple CSV:</p><p>awk -F, '{print $1,$2}'</p><p>substitution (sed is simpler in this case):</p><p>awk '{sub(/test/, "no", $0);print}' input.txt</p><p>&nbsp;</p><p>OK now here's where to read this stuff properly explained. roll</p><p>Two thorough tutorials:</p><p>http://www.gnu.org/software/gawk/manual/gawk.html</p><p>http://www.grymoire.com/Unix/Awk.html</p><p>A famous list of useful one-liners - though they're short, many are quite tricky:</p><p>http://www.pement.org/awk/awk1line.txt</p><p>And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!</p><p>http://www.catonmat.net/blog/awk-one-li &hellip; -part-one/</p><p>http://www.catonmat.net/blog/ten-awk-ti &hellip; -pitfalls/</p>]]></description>
	<dc:creator>Neel</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/38590/senior-bioinformatics-scientist-strand-life-sciences-bangalore-india</guid>
  <pubDate>Wed, 02 Jan 2019 09:23:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[Senior Bioinformatics Scientist @ Strand Life Sciences -- Bangalore, India]]></title>
  <description><![CDATA[
<p>RESPONSIBILITIES<br />The candidate is expected to work on a variety of projects related to analysis of data from NGS, Mass Spectrometry, Flow Cytometry and other related modalities. The position expects hands-on work and a strong eye for detail. The candidate will be able to contribute to impactful work spanning patient care, clinical research, and new assay and method development.<br />REQUIREMENTS<br />A PhD in a quantitative field (statistics, math, bioinformatics, computer science, physics or similar) and work experience or post-doc experience handling high throughout genomics data.<br />PREFERENCES<br />Experience in working in inter-disciplinary groups and ability to author research publications are additional desired qualities.<br />LOCALE<br />The position is in Bangalore and reports to the Chief Scientific Officer.<br />HOW TO APPLY<br />Write to ramesh[at]strandls.com.</p>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/4106/phd-at-national-institute-for-research-in-reproductive-health</guid>
  <pubDate>Fri, 30 Aug 2013 04:50:35 -0500</pubDate>
  <link></link>
  <title><![CDATA[PhD at National Institute for Research in Reproductive Health]]></title>
  <description><![CDATA[
<p>National Institute for Research in Reproductive Health</p>

<p>(Indian Council of Medical Research )<br />Jehangir Merwanji Street, Parel, Mumbai 400 012</p>

<p>Advertisement No. 1/NIRRH/Ph.D. 2013<br />Admission to Ph.D. Programme – 2013</p>

<p>National Institute for Research in Reproductive Health, Mumbai, a premier institute of the Indian Council of Medical Research, conducts basic, clinical and operational research in different areas of reproductive health. The thrust areas of research include: Fertility Regulation, Infertility and Reproductive Disorders, Reproductive Tract Infections, Maternal and Child Health, Osteoporosis, Genetic Disorders, Stem Cell Biology, Structural Biology, Bioinformatics and Reproductive Toxicology. Institute is affiliated to the University of Mumbai for the award of Ph.D. degree in Applied Biology, Biochemistry, Life Sciences and Biotechnology. The institute invites applications from young and bright students for enrollment in Ph.D. programme.</p>

<p>More at http://www.nirrh.res.in/announcements/phd_program_2013.htm</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/40226/bioinformatics-training-courses-at-rasa-lsi</guid>
	<pubDate>Wed, 06 Nov 2019 00:30:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/40226/bioinformatics-training-courses-at-rasa-lsi</link>
	<title><![CDATA[Bioinformatics Training Courses At RASA LSI]]></title>
	<description><![CDATA[<p>RASA conducts comprehensive Life Science skill development training courses in Pune, India for working professionals, researchers, students and job-seeker. The trainings are crafted meticulously, covering different modules of courses such as Bioinformatics course, In silico Drug Discovery course, Next Generation Sequence data analysis course, Molecular Biology &amp; Life&nbsp;science software development course wherein you learn from industry leaders&nbsp;how to apply these skills in life science &amp; have a command over software developing process &nbsp;by using various methodologies. We conduct in-class training and instructor-led live online classes worldwide, along with corporate and skill development training worldwide.</p><p>Workshops are conducted in regular intervals on Drug Designing, Protein Modeling and Simulation, Chemoinformatics, Bioinformatics etc.The workshops are highly beneficial for working professionals, students, researcher for enhancements of the skills in short duration.</p>]]></description>
	<dc:creator>RASA Life Sciences</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/2742/baumbach-lab</guid>
  <pubDate>Wed, 21 Aug 2013 10:56:35 -0500</pubDate>
  <link></link>
  <title><![CDATA[Baumbach Lab]]></title>
  <description><![CDATA[
<p>The Computational Biology research group was established in October 2012 at the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark (SDU). It emerged from the Computational Systems Biology group, founded in March 2010 at the Max Planck Institute for Informatics (MPII) and the Cluster of Excellence for Multimodel Computing and Interaction (MMCI) at Saarland University, Saarbrücken, Germany.<br />​<br />The group is headed by Prof. Dr. Jan Baumbach and currently hosts nine PhD students and one postdoctoral fellow at both, IMADA/SDU and MMCI/MPII.</p>

<p>More at &gt;&gt; http://www.baumbachlab.net/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</guid>
	<pubDate>Tue, 31 Dec 2019 19:33:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</link>
	<title><![CDATA[Machine learning training and courses in bioinformatics !]]></title>
	<description><![CDATA[<p>Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.</p>
<p>More at&nbsp;http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</p>
<p>More tutorial at&nbsp;</p>
<p><a href="http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm">http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm</a></p>
<p><a href="http://www.raetschlab.org/lectures/MLBioinformatics">http://www.raetschlab.org/lectures/MLBioinformatics</a></p>
<p><a href="http://www.raetschlab.org/lectures/bertinoro08">http://www.raetschlab.org/lectures/bertinoro08</a></p>
<p>Book at&nbsp;</p>
<p><a href="https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf">https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf</a></p><p>Address of the bookmark: <a href="http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/" rel="nofollow">http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2839/look-up-a-biological-numbers</guid>
	<pubDate>Fri, 23 Aug 2013 03:27:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2839/look-up-a-biological-numbers</link>
	<title><![CDATA[Look up a biological numbers]]></title>
	<description><![CDATA[<p><strong>Did you ever need to look up a number</strong><span>&nbsp;like the volume of a cell or the cellular concentration of ATP, only to find yourself spending much more time than you wanted on the Internet or flipping through textbooks - all without much success?&nbsp;</span><br><br><span>Well, it didn&rsquo;t happen only to you. It is often surprising how difficult it can be to find concrete biological numbers, even for properties that have been measured numerous times. To help solve this for one and all, BioNumbers (</span><strong>the database of key numbers in molecular biology</strong><span>) was created. Along with the numbers, you'll find the relevant&nbsp;</span><strong>references to the original literature</strong><span>, useful comments, and related numbers.&nbsp;</span></p>
<p><span><span>To cite BioNumbers please refer to: Milo et al. Nucl. Acids Res. (2010) 38: D750-D753. When using a specific entry from the database it is highly recommended that you also specify the BioNumbers 6 digit ID, e.g. "BNID 100986, Milo et al 2010".&nbsp;</span></span></p><p>Address of the bookmark: <a href="http://bionumbers.hms.harvard.edu/" rel="nofollow">http://bionumbers.hms.harvard.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41991/sequence-ontology-bioinformatics-analysis-soba-tool-to-provide-a-simple-statistical-and-graphical-summary-of-an-annotated-genome</guid>
	<pubDate>Wed, 22 Jul 2020 10:11:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41991/sequence-ontology-bioinformatics-analysis-soba-tool-to-provide-a-simple-statistical-and-graphical-summary-of-an-annotated-genome</link>
	<title><![CDATA[Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome]]></title>
	<description><![CDATA[<p><span>We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.</span></p>
<p><span>More at <a href="https://pubmed.ncbi.nlm.nih.gov/20494974/">https://pubmed.ncbi.nlm.nih.gov/20494974/</a></span></p><p>Address of the bookmark: <a href="http://www.sequenceontology.org/cgi-bin/soba.cgi" rel="nofollow">http://www.sequenceontology.org/cgi-bin/soba.cgi</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4835/chang-lab</guid>
  <pubDate>Tue, 24 Sep 2013 17:25:49 -0500</pubDate>
  <link></link>
  <title><![CDATA[Chang lab]]></title>
  <description><![CDATA[
<p>The Chang lab is focused on how the activities of hundreds or even thousands of genes (gene parties) are coordinated to achieve biological meaning. We have pioneered methods to predict, dissect, and control large-scale gene regulatory programs; these methods have provided insights into human development, cancer, and aging. A particular interest is how cells know and remember their locations in the body, particularly with the help of long noncoding RNAs.</p>

<p>More at http://changlab.stanford.edu/index.html</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/42172/sr-scientist-bioinformatics-vacancies-at-indian-institute-of-toxicology-research-india</guid>
  <pubDate>Tue, 01 Sep 2020 07:21:04 -0500</pubDate>
  <link></link>
  <title><![CDATA[Sr. Scientist Bioinformatics Vacancies at Indian Institute of Toxicology Research, India]]></title>
  <description><![CDATA[
<p>Start date of online Registration: Wednesday August 19, 2020 11:00 Hrs IST<br />Last date for Registration: Monday September 21, 2020 17:30 Hrs IST<br />Last date for submission of online application: Monday September 21, 2020 17:30 Hrs IST<br />Last date of Receipt of physical copy of application at CSIR-IITR: Tuesday October 05, 2020 17:30 Hrs IST</p>

<p>Pay Matrix Level-12<br />No. of Post-01<br />(UR)<br />Post – Sr. Scientist<br />Area Bioinformatics<br />Age limit : 37 years<br />PhD in Computational Biology/Bioinformatics with 2 years experience in desired area<br />Or<br />ME/M.Tech in Bioinformatics or Genome Informatics or Genetic Engineering with 3 years experience in desired area<br />Experience of understanding fundamental science behind Artificial Intelligence, machine learning, novel Artificial Intelligence algorithms and architectures, software engineering principles for Artificial Intelligence, natural language processing with proficiency in programming as evident by publications in SCI journals with high impact factor. To be part a group of scientists working in the area of genomics, running the central<br />bioinformatics facility, developing independent projects and providing bioinformatics support to the user scientists of the Institute.</p>

<p>More at </p>

<p>http://14.139.62.50/CSIR-IITR%20Scientist%20Recruitment%20Adv%202020.pdf</p>
]]></description>
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