BOL: Related items

PhD at INSTITUTE OF LIFE SCIENCES, Bhubaneswar

Mon, 30 May 2016 03:36:04 -0500

INSTITUTE OF LIFE SCIENCES

Bhubaneswar 751023

Advt No. 07/2016

Institute of Life Sciences (ILS), Bhubaneswar, an autonomous Institute of the Department of Biotechnology, Ministry of Science & Technology, Government of India engaged in advanced research invites applications from Indian nationals for the Ph.D. program. The main focus of the projects will be computational biology in the following areas.

S. No. Area of Research Principal investigator

1. Computational Cancer Biology Dr. Anshuman Dixit

2. Immunogenomics & Systems Biology Dr. Sunil Kumar Raghav

3. Chromatin remodeling and hematopoiesis Dr. Punit Prasad

Candidates are strongly encouraged to visit ILS webpage for detailed information, regarding the research activities of the above mentioned scientists.

Essential Qualifications:

(a) Eligibility: M.Sc., M.V.Sc., M.Pharm., M.S. Pharma. (with NET/GATE/GPAT/BINC/any other equivalent national level exam) or M.Tech with minimum of 60% marks (or equivalent grade point). Those awaiting final result may also apply.

Applications received after the last date will not be accepted. The envelope should clearly be superscribed with “Application for Ph.D. program (computational biology)”. Short-listed candidates selected for the interview will be published in the Institute website (www.ils.res.in).

Application Fees: Applicants except SC/ST candidates are required to send a non-refundable D.D. for Rs.100/- in favour of “Director, Institute of Life Sciences, Bhubaneswar” payable at Bhubaneswar along with duly filled-in application form by the date mentioned below. Director, ILS reserves the right to withdraw the procedure without assigning any reasons thereof.

Important dates: 

Last date of receiving applications: 24th June 2016 

Date of display of short-listed candidates and instructions on the Institute website: 30th June 2016 

Date of interview: The interview will be organized on 25th July 2016

Advertisement: https://www.ils.res.in/wp-content/uploads/2016/05/advt07-16.pdf

Awesome bioinformatics pipelines !

Jitendra Prajapati — Wed, 30 Mar 2016 21:50:41 -0500

A curated list of awesome pipeline toolkits ...

https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/pditommaso/awesome-pipeline

BBMap/BBTools package: Multipurpose tool designed for converting reads or other nucleotide data between different formats.

Jit — Mon, 13 Jun 2016 05:47:21 -0500

Reformatis a member of the BBMap/BBTools package. It is a multipurpose tool designed for converting reads or other nucleotide data between different formats. It supports, and can inter-convert:

fastq
fasta
fasta+qual
sam
scarf (an old Illumina format)
bam (if samtools is installed)
gzip
zip
ascii-33 (sanger)
ascii-64 (old Illumina)
paired files
interleaved files

It is multithreaded and can process data at over 500 megabytes per second, and can accept streams from standard in and write to standard out, allowing it to be easily dropped into the middle of a pipeline for format conversion. Reformat autodetects formats based on file extensions and content, making it very easy to use; and the autodetection can be overridden, allowing flexibility for people who don't like to follow naming conventions, or out-of-spec fastq files with qualities values like -17 or 120.

The program has been gradually expanded, and can now perform various other functions. None of these will break pairing, if the input is paired.

Quality trimming (either or both ends)
Quality filtering
Fixed-length trimming
Generation of histograms (base composition, quality, etc)
Subsampling (to a fraction of input reads, or an exact number of reads or bases)
Changing fasta line-wrapping length
Reverse-complementing (all reads or only read 2)
Adding /1 and /2 suffix to read names
GC-content filtering
Length-filtering
Testing for corrupted interleaved files

Reformat is compatible with any platform that supports Java 1.7 or higher. It also has a bash shellscript for simpler invocation. Typical usage examples:

Reformat fastq into fasta:
reformat.sh in=x.fq out=y.fa

Interleave paired reads:
reformat.sh in1=x1.fq in2=x2.fq out=y.fq

Note - you can actually use a shortcut if paired read files have the same name with a 1 and a 2. This is equivalent to the above command:
reformat.sh in=x#.fq out=y.fq

De-interleave reads:
reformat.sh in=x.fq out1=y1.fq out2=y2.fq

Verify that interleaving appears correct, assuming Illumina namimg conventions:
reformat.sh in=x.fq vint

Convert ASCII-33 to ASCII-64:
reformat.sh in=x.fq out=y.fq qin=33 qout=64

Quality-trim paired reads to Q10 on the left and right ends and discard reads shorter than 50bp after trimming:
reformat.sh in1=x1.fq in2=x2.fq out1=y1.fq out2=y2.fq outsingle=singletons.fq qtrim=rl trimq=10 minlength=50

Subsample 10% of the first 20000 pairs in an interleaved file:
reformat.sh in=x.fq out=y.fq reads=20000 samplerate=0.1 int=t
(in this case "int=t" overrides interleaving autodetection, to ensure reads are treated as pairs)

Pipe in a gzipped sam file and pipe out fasta:
reformat.sh in=stdin.sam.gz out=stdout.fa

Reverse-complement reads:
reformat.sh in=x.fq out=y.fq rcomp

For reformatting a file with very long sequences, Reformat will need more memory; just add the additional flag "-Xmx2g". For example, to change the line-wrapping length on the human genome (which has individual sequences over 200Mbp long) to 70 characters:
reformat.sh -Xmx2g in=HG19.fa.gz out=HG19_wrapped.fa.gz fastawrap=70

For additional functions, please run the shellscript with no arguments, or just read it with a text editor. If you have any questions, please post them in this thread.

For people using a non-bash terminal, you may need to type "bash reformat.sh" instead of just "reformat.sh".
For users of Windows or other platforms that do not support bash shellscripts, replace "reformat.sh" with "java -ea -Xmx200m /path/to/bbmap/current/ jgi.ReformatReads"
for example,
java -ea -Xmx200m C:\bbmap\current\ jgi.ReformatReads in=x.fq out=y.fa

Reformat can be downloaded with BBTools here:
https://sourceforge.net/projects/bbmap/

CSBB-v1.0

Neel — Wed, 29 Jun 2016 07:33:05 -0500

CSBB is a command line based bioinformatics suite to analyze biological data acquired through varied avenues of biological experiments. CSBB is implemented in Perl, while it also leverages the use of R and python in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX, MAC OS and Windows platforms.

Currently CSBB provides 13 modules focused on analytical tasks like performing upper-quantile normalization on expression data or convert genome wide gene expression to z-scores when comparing expression data from different platforms.

More at https://github.com/skygenomics/CSBB-v1.0

Address of the bookmark: https://github.com/skygenomics/CSBB-v1.0

Sushi: An R/Bioconductor package for visualizing genomic data

Jit — Wed, 31 Aug 2016 08:29:12 -0500

Sushi: An R/Bioconductor package for visualizing genomic data

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/Sushi/inst/doc/Sushi.pdf

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

R Graphical Cookbook by Winston Chang

Abhimanyu Singh — Fri, 04 Nov 2016 12:50:30 -0500

R Graphical Cookbook by Winston Chang

A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".

The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.

More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf

EXCAVATOR2tool

Bulbul — Wed, 30 Nov 2016 04:09:19 -0600

EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.
EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it’s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

IBL laboratory

Mon, 12 Aug 2013 02:02:29 -0500

The IBL laboratory focuses on the multi-disciplinary analyses of the global responses of model microorganisms, cyanobacteria (mainly Synechocystis PCC6803) and yeasts (mainly Saccharomyces cerevisae) to environmental stresses triggered by oxidative agents, heavy metals, or drastic changes in nutrients availability. The genome-wide responses studied with the "omics" techniques (transcriptomics, proteomics, metabolomics and genetics) generate a wealth of experimental data, which are processed, archived, integrated and represented as working models through bioinformatics and mathematics.

Link : http://www-dsv.cea.fr/en/instituts/institut-de-biologie-et-de-technologies-de-saclay-ibitec-s/unites-de-recherche/service-de-biologie-integrative-et-genetique-moleculaire-sbigem/laboratoire-de-biologie-integrative-lbi/presentation__1

Computational Proteomics : Lets remember the basics

Jitendra Narayan — Thu, 01 Aug 2013 17:24:20 -0500

I spend some of my valuable time in computational drug designing sector. I remember my initial proteomics days, playing with interactive protein visualization software and dreaming big. Fortunately or unfortunately, I switched to genomics and handling the genomic floods in Petabytes which is expected to be in Brontobytes in coming years. Did I mention Brontobytes ??? Let me call to my server personnel … it gonna tsunami !!!!!

Today, refreshing my old memories I decided to blog about the basic knowledge of biochemistry and computational proteomics skills, but after I found several article on internet saying exactly what I had wanted to say I thought I might as well just redirect BOL's blog readers there instead:

Here is the list of website and videos links which provide a good resource for you basic chemistry need:

http://tecreativ.blogspot.co.uk/2012/09/funny-shortcut-remember-periodic-table.html

This blog have some specific hindi word to remember entire periodic table. I really like

Group 14 (C Si Ge Sn Pb) -> Sentence “Chemistry Sir Gives Sanki Problems”

Sanki is a hindi word which mean crazy :P

I found this link useful as well http://www.wikihow.com/Memorise-the-Periodic-Table

The eagle genomics group provide an element of bioinformatics in periodic tables. Yes you got it, this is not periodic table rather bioinformatics tools with periodicals

http://elements.eaglegenomics.com/

You can also try this video links, which provide you an overview with tricks on periodic tables:

http://www.youtube.com/watch?v=fLSfgNxoVGk

http://www.youtube.com/user/periodicvideos

For drug design educational material, software, tools, databses, viewer, file format and many more stuff at one place http://www.allfordrugs.com/drug-design/ I highly recommend you all computational drug designer to bookmark this page for future studies as well.

I just remember one of my mini project in which I use my flash knowledge (flash .. oh ya flash) to explain amino acids in interactive and user friendly manner. I can’t provide It right now, but promise you to provide a link in near future. I hope that you will enjoy my flashy creative skills :).

Moreover, I found some of very interesting tricks to remember all amino acids chemical formulae on youtube at

http://www.youtube.com/watch?v=gqrWb0fmzQ&list=PL6132651E70BB5575

http://www.youtube.com/watch?v=C2GfoGXfySQ&list=PL6132651E70BB5575

Key points for computer added drug designers?
1. A shortage of biochemistry skills means that you absolutely nowhere in understanding the key concept and do research.
2. Keep handy with complex mathematical formula, before merely running tools or software.
3. Dig it better and deeper guys .. design it.