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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28842?offset=990</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts</guid>
	<pubDate>Thu, 22 Jan 2015 22:29:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts</link>
	<title><![CDATA[Bioinformatics Scripts]]></title>
	<description><![CDATA[<p>Some of the useful bioinformatics scripts.</p>
<p>For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.</p>
<p>http://milkweedgenome.org/?q=scripts</p><p>Address of the bookmark: <a href="http://milkweedgenome.org/?q=scripts" rel="nofollow">http://milkweedgenome.org/?q=scripts</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats</guid>
	<pubDate>Thu, 09 Mar 2023 02:40:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats</link>
	<title><![CDATA[Common methods to discover tandem repeats]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Tandem repeats are DNA sequences that are repeated in a contiguous manner in the genome. These sequences are often used as genetic markers and are important in many areas of genetics and genomics research. Here are some methods for discovering tandem repeats in genomes:</p><ol>
<li>
<p>Tandem Repeat Finder: Tandem Repeat Finder is a software tool that identifies tandem repeats in DNA sequences. It is available for free download and can be used on both nucleotide and protein sequences. The tool uses a statistical algorithm to identify repeats based on their length, copy number, and overall composition.</p>
</li>
<li>
<p>RepeatMasker: RepeatMasker is another software tool that can identify tandem repeats in DNA sequences. It works by comparing the input sequence to a database of known repeats and then identifies any tandem repeats that match those in the database.</p>
</li>
<li>
<p>PCR-based methods: Polymerase chain reaction (PCR) can be used to amplify and detect tandem repeats in genomic DNA. PCR primers are designed to flank the tandem repeat region, and amplification of the target DNA fragment can be visualized on a gel. This method can be useful for detecting novel tandem repeats and for genotyping.</p>
</li>
<li>
<p>Southern blotting: Southern blotting is a classic method for detecting DNA fragments in a sample. It can be used to detect tandem repeats by digesting genomic DNA with a restriction enzyme, separating the fragments by gel electrophoresis, and then probing the blot with a tandem repeat-specific probe.</p>
</li>
</ol><p>Overall, a combination of these methods can be used to comprehensively identify tandem repeats in genomes.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/20504/chromevol</guid>
	<pubDate>Sun, 25 Jan 2015 00:33:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/20504/chromevol</link>
	<title><![CDATA[ChromEvol]]></title>
	<description><![CDATA[<p>Chromosome number is a remarkably dynamic feature of eukaryotic evolution. Chromosome numbers can change by a duplication of the whole genome (a process termed polyploidy), or by single chromosome changes (ascending dysploidy via, e.g., chromosome fission or descending dysploidy via, e.g., chromosome fusion).<br> Of the various mechanisms of chromosome number change, polyploidy has received significant attention because of the impact such an event may have on the organism.<br> ChromEvol implements a series of likelihood models for the evolution of chromosome numbers. By comparing the fit of the different models to biological data, it may be possible to gain insight regarding the pathways by which the evolution of chromosome number proceeds. For each model, the program estimates the rates for the possible transitions assumed by the model, infers the set of ancestral chromosome numbers, and estimates the location along the tree for which polyploidy events (and other chromosome number changes) occurred. For further methodological details, see the publications and manual on the Downloads page.</p>
<p>http://www.tau.ac.il/~itaymay/cp/chromEvol/about.html</p><p>Address of the bookmark: <a href="http://www.tau.ac.il/~itaymay/cp/chromEvol/downloads.html" rel="nofollow">http://www.tau.ac.il/~itaymay/cp/chromEvol/downloads.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/20672/jrfra-structuralcomputational-biology-at-icgeb</guid>
  <pubDate>Thu, 29 Jan 2015 11:52:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[JRF/RA Structural/Computational Biology at ICGEB]]></title>
  <description><![CDATA[
<p>Research Associate and JRF positions in the Structural and Computational Biology Group starting 1st March 2015. Collaborative projects include work on:</p>

<p>a) bioinformatics, systems and computational biology <br />b) malaria <br />c) drug discovery <br />d) genomics <br />e) microbiology <br />f) metabolic disorders <br />g) molecular medicine</p>

<p>Eligibility: Applicants must have one of the following :</p>

<p>1) INSPIRE award for undertakig either PhD or Postdoctoral research; <br />2) SPM award for PhD; <br />3) JRF for pursuing PhD from CSIR/DBT/ICMR</p>

<p>Interest and experience in Biochemistry/Bioinformatics/Biophysics/ Chemistry/Genomics/Molecular Biology/ is essential.</p>

<p>Submit curriculum vitae to sb.icgeb@gmail.com by 20 February 2015</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21538/senior-research-fellow-at-all-india-institute-of-medical-sciences-aiims-delhi-delhi-delhi</guid>
  <pubDate>Wed, 11 Mar 2015 03:06:10 -0500</pubDate>
  <link></link>
  <title><![CDATA[SENIOR RESEARCH FELLOW at All India Institute of Medical Sciences (AIIMS Delhi) - Delhi, Delhi]]></title>
  <description><![CDATA[
<p>Applications are invited from eligible candidates for the following temporary post in an ICMR funded Research Project entitle “An Investigation to find out reasons for Phenotypic Heterogeneity/Variability in 22q11.2 Microdeletion Syndrome” in Department of Reproductive Biology, AIIMS, New Delhi PI: Dr. Ashutosh Halder, Professor, Department of Reproductive Biology </p>

<p>Name of the post: Senior Research Fellow (SRF) <br />Duration: 2 year <br />Salary: Rs. 28000/- per month + 30% HRA <br />Eligibility: MSc (life sciences) with 2 years research experience, NET/GATE qualified <br />Desirable: Experience in the field of Genomics, Epigenomics &amp; Bioinformatics <br />SELECTION PROCEDURE FOR ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI) – SENIOR RESEARCH FELLOW POST: </p>

<p>Candidates can apply on or before 15/03/2015 <br />No Detailed information about the selection process is mentioned in the recruitment notification <br />HOW TO APPLY FOR SENIOR RESEARCH FELLOW VACANCY IN ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI): </p>

<p>Deadline: 15.03.15 Submit your C.V in Room No. 2099 (Molecular Cytogenetics Lab), 2nd floor, Reproductive Biology, All India Institute of Medical Sciences, New Delhi-110029 or Email CV to: ashutoshhalder@gmail.com Your CV should include the details of your work experience &amp; degrees along with two references with e-mail and contact number Only 10 shortlisted (on merit) candidates will be invited for interview. No TA/DA will be applicable for the same</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/20959/research-associate-and-jrf-positions-in-the-structural-and-computational-biology-group-at-icgeb</guid>
  <pubDate>Mon, 02 Feb 2015 23:00:37 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Associate and JRF positions in the Structural and Computational Biology Group at ICGEB]]></title>
  <description><![CDATA[
<p>Research Associate and JRF positions in the Structural and Computational Biology Group starting 1st March 2015. Collaborative projects include work on:</p>

<p>a) bioinformatics, systems and computational biology <br />b) malaria <br />c) drug discovery <br />d) genomics <br />e) microbiology <br />f) metabolic disorders <br />g) molecular medicine</p>

<p>Eligibility: Applicants must have one of the following :</p>

<p>1) INSPIRE award for undertakig either PhD or Postdoctoral research; <br />2) SPM award for PhD; <br />3) JRF for pursuing PhD from CSIR/DBT/ICMR</p>

<p>Interest and experience in Biochemistry/Bioinformatics/Biophysics/ Chemistry/Genomics/Molecular Biology/ is essential.</p>

<p>Submit curriculum vitae to sb.icgeb@gmail.com by 20 February 2015</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21065/ra-bioinformatics-at-north-eastern-hill-university</guid>
  <pubDate>Sat, 07 Feb 2015 06:06:05 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at North Eastern Hill University]]></title>
  <description><![CDATA[
<p>Bioinformatics Infrastructure Facility, Department of RDAP, NEHU vacancy of Research Associate</p>

<p>Name of the Post: Research Associate<br />No. of the Post: 01 One<br />Age Limit: Max. 35 years<br />Salary: Rs. 22000/- per month plus HRA</p>

<p>Required Job Profile:<br />Candidate must possess M.Sc. in bioinformatics or biotechnology from recognized university or institute.<br />Desired Job Profile;<br />Candidate having Ph.D. or pursuing Ph.D. in the related subject or equivalent published work in reputed peer reviewed journals or advance PG dipoma in bioinformatics course.</p>

<p>How to apply:<br />Eligible and interested candidates should need to send the bio-data and bring all related documents in original and set of attested copies of the same in the time of interview.</p>

<p>Last date: 16.02.2015<br />Refer to http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf</p>

<p>Summary <br />Employer Address:	Dr.B.K. Mishra Coordinator BIF, RDAP Department, North Eastern Hill University, Tura Campus, Tura, Meghalaya<br />Email:	drbkm1972@yahoo.co.in;birendramishra14@gmail.com<br />URL:	http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf<br />Phone:	03651-223107<br />Required Skills:	not mentioned / required for this post<br />Required Experience:	not mentioned / required for this job post<br />Required Education:	M.Sc. in bioinformatics or biotechnology from recognized university or institute.<br />Job Location:	Tura, Meghalaya, India</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/21241/pacman</guid>
	<pubDate>Mon, 16 Feb 2015 12:15:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/21241/pacman</link>
	<title><![CDATA[Pacman]]></title>
	<description><![CDATA[<p><span>The pacman package is an R package management tool that combines the functionality of base library related functions into intuitively named functions. This package is ideally added to .Rprofile to increase workflow by reducing time recalling obscurely named functions, reducing code and integrating functionality of base functions to simultaneously perform multiple actions.<br /><br />Function names in the pacman package follow the format of p_xxx where &lsquo;xxx&rsquo; is the task the function performs. For instance the p_load function allows the user to load one or more packages as a more generic substitute for the library or require functions and if the package isn&rsquo;t available locally it will install it for you.<br /><br /></span></p><p><strong>Installation</strong></p><p><span>To download the development version of pacman:</span></p><p><span>Download the </span><a href="https://github.com/trinker/pacman/zipball/master">zip ball</a><span> or </span><a href="https://github.com/trinker/pacman/tarball/master">tar ball</a><span>, decompress and run </span><code>R CMD INSTALL</code><span> on it, or use th</span><span>e </span><strong>devtools</strong><span> package to install the development version:</span></p><pre title="">## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")
</pre><p>Note: Windows users need <a href="http://www.murdoch-sutherland.com/Rtools/">Rtools</a> and <a href="http://CRAN.R-project.org/package=devtools">devtools</a> to install this way.</p><p>More at https://github.com/trinker/pacman</p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21435/ra-walk-in-interview-nbfgr-lucknow</guid>
  <pubDate>Tue, 24 Feb 2015 08:23:48 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA WALK-IN-INTERVIEW @ NBFGR, Lucknow]]></title>
  <description><![CDATA[
<p>F.No. 1(122)/2015-Admn. (CABin Project)<br />Research Associate/Young Professional/SRF Zoology job vacancies in National Bureau of Fish Genetic Resources (NBFGR)<br />Post Name: Research Associate (Computer Science/ Applications)                <br />Qualification: Ph.D. In Computer Science/Computer Applications or equivalent. Or Post-Graduation in Computer Science/ Computer Applications with 1st Division or 60% marks or equivalent overall grade point average with at least two years of research experience. Desirable: 1. Expertise and experience of working/ handling High Performance Computing (H PC) and genomic resource data. 2. Expertise on database management, data mining technologies/ softwares/tools. 3. Published Research papers	<br />No.of Post: 1<br />Pay Scale: Consolidated Rs.24,000/- p.m. + HRA (as admissible) for Ph.D. holders and consolidated `23,000/- + HRA (as admissible) for Master degree holder.	<br />Age:40 years</p>

<p>Young Professional II (Computer Science/Applications)	<br />Master degree in Computer Science/Computer Applications/B.Tech (Computer Science) or equivalent. <br />Desirable: 1. Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics/Data mining tools. 2. Experience in handling HPC, programming languages and database management packages.	<br />A consolidated salary of Rs.25,000/- per month.	<br />21 to 45 year</p>

<p>Young Professional II (Biotechnology/ Bioinformatics)	<br />Master degree in Bioinformatics/ Biotechnology/ B. Tech(Biotech) or equivalent. Desirable: 1. Knowledge of Computational Genomics/Proteomics/Bioinformatics. 2. Expertise in NGS data analysis and knowledge of allied software and tools.	<br />A consolidated salary of Rs.25,000/- per month.	</p>

<p>Senior Research Fellow	<br />1. Bachelors degree with Zoology, Fisheries and 2. Master's degree in Fishery science/ Zoology with Fisheries/ Biotechnology/ Life Sciences with specialization in Fisheries/ Molecular Biology. 3. 1 st Division or 60% marks or equivalent overall grade point average. <br />Desirable: Work experience in Fisheries, molecular research techniques, bioinformatics and Computer skills. NET qualified <br />Note: The project involves extensive exploration tours and sampling from water bodies all over India	<br />Rs.16,000/- p.m. for 1st &amp; 2nd year and `18,000/- p.m. for 3rd and subsequent years +HRA (as per rules)	35 years for male and 40 years for female candidate</p>

<p>How to apply</p>

<p>A walk-in-interview will be held on 04th March, 2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow. Eligible and desirous candidates fulfilling all the requirements may appear for the interview with duly filled in application giving full details of academic records and experience(s) along with attested photocopy as well as original copy of the relevant documents and a passport size photograph on the attached proforma.</p>

<p>http://www.nbfgr.res.in/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</guid>
	<pubDate>Tue, 24 Feb 2015 20:15:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</link>
	<title><![CDATA[A guide for complete R beginners :- Getting data into R]]></title>
	<description><![CDATA[<p>For a beginner this can be is the hardest part, it is also the most important to get right.</p><p>It is possible to create a vector by typing data directly into R using the combine function &lsquo;c&rsquo;</p><blockquote><p><strong>x </strong></p></blockquote><p>same as</p><blockquote><p><strong>x </strong></p></blockquote><p>creates the vector x with the numbers between 1 and 5.</p><p>You can see what is in an object at any time by typing its name;</p><blockquote><p><strong>x</strong></p></blockquote><p>will produce the output<strong> &lsquo;[1] 1 2 3 4 5&prime;</strong></p><p>Note that names need to be quoted</p><blockquote><p><strong>daysofweek </strong><strong>&larr; c(&lsquo;Monday&rsquo;, &lsquo;Tuesday&rsquo;, &lsquo;Wednesday&rsquo;, &lsquo;Thursday&rsquo;, &lsquo;Friday&rsquo;);</strong></p></blockquote><p>Usually however you want to input from a file. We have touched on the &lsquo;read.table&rsquo; function already.</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Now <strong>mydata</strong> is a data frame with multiple vectors</p><p>each vector can be identified by the default syntax</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$V1 mydata$V2 mydata$V3 </strong></p></blockquote><p>By default the function assumes certain things from the file</p><ul>
<li>The file is a plain text file (there are function to read excel files: <em>not covered here</em>)</li>
<li>columns are separated by any number of tabs or spaces</li>
<li>there is the same number of data points in each column</li>
<li>there is no header row (labels for the columns)</li>
<li>there is no column with names for the rows** [I&rsquo;ll explain].</li>
</ul><p><span style="text-decoration: underline;">If any of these are false, we need to tell that to the function</span></p><p>If it has a header column</p><blockquote><p><strong>mydata <em>header=T also works</em></strong></p></blockquote><p>Note that there is a comma between different parts of the functions arguments</p><p>If there is one less column in the header row, then R assumes that the 1<sup>st</sup> column of data after the header are the row names</p><p>Now the vectors (columns) are identified by their name</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$A mydata$B mydata$C </strong></p></blockquote><p># Summary about the whole data frame</p><blockquote><p><strong>summary(mydata)</strong></p></blockquote><p># Summary information of column A</p><blockquote><p><strong>summary(mydata$A) </strong></p></blockquote><p>We can shortcut having to type the data frame each time by attaching it</p><blockquote><p><strong>attach(mydata)</strong></p></blockquote><p># summary of column B as &lsquo;mydata&rsquo; is attached</p><blockquote><p><strong>summary(B)</strong></p></blockquote><p><span style="text-decoration: underline;">Two other important options for </span><em><span style="text-decoration: underline;">read.table</span></em></p><p>If is is separated only by tabs and has a header</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.</p><p>If you know that the file has uneven columns</p><blockquote><p><strong>mydata </strong></p></blockquote><p>This causes R to fill empty spaces in a columns with &lsquo;NA&rsquo; .</p><p>The last two examples will still work with our file and give the same result as with only headers=T</p><p><span style="text-decoration: underline;">Graphs</span></p><p>to get an idea of what R is capable of type</p><blockquote><p><strong>demo(graphics)</strong></p></blockquote><p>steps through the examples, and the code is printed to the screen</p><p>We will work with simpler examples that have immediate use to biologists.</p><p>Remember to get more information about the options to a function type &lsquo;?function&rsquo;</p><p><span style="text-decoration: underline;">Histogram of A</span><span style="text-decoration: underline;"></span></p><blockquote><p><strong>hist(mydata$A)</strong></p></blockquote><p>If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).</p><blockquote><p><strong>boxplot(mydata)</strong></p></blockquote><p>We can get rid of the need to type the data frame each time by using the <strong>attach</strong> function</p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>boxplot(mydata$A, mydata$B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>same as</p><blockquote><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Scatter plot</span></p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>plot(A,B) # or plot(mydata$A, mydata$B)</strong></p></blockquote><p><strong><span style="text-decoration: underline;">SAVING an image</span></strong></p><p>Windows users (Rgui) RIGHT click on image and select which you want.</p><p><span style="text-decoration: underline;">These instructions work for everyone.</span></p><p>You need to create a new device of the type of file you need, then send the data to that device</p><p>to save as a png file (easy to load into the likes of powerpoint, also great for web applications.</p><blockquote><p><strong>png(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>or to save as a pdf</p><blockquote><p><strong>pdf(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Note</span></p><ul>
<li>Nothing will appear on screen, the output is going to the file</li>
<li>Also it may not be saved immediately but will once the device (or R) is turned quit.</li>
</ul><p>To quit R type</p><p><strong>q() # </strong>If you save your session, next time you start R, you will have your data preloaded.</p><p>Or if you want to remain in R</p><blockquote><pre><strong>dev.off() #</strong>turns of the png (or pdf etc) device, thus forces the data to save</pre></blockquote>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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