No.of Posts:01 (SC)

Pay Scale:

Pay Band of Rs.15600-39100 + Rs.6000/- GP +NPA @ 25% of Basic Pay +Learning Resource Allowance @ Rs.20,000/-P.A.+ Conveyance Allowance @ Rs. 1650/-P.M.+ Academic Allowance @ Rs.2500/- P.M. and other admissible allowances.

Qualifications:

Area of Specialization:-

Bioinformatics/Computational/Biology/Genomics/ Proteomics/ Structural Biology

1. Postgraduate qualification, e.g. Master’s Degree in Biotechnology/Bioinformatics/ Biophysics.

2. A Doctorate Degree of recognized University/Institute in a basic or allied Medical Science subject e.g. Medical Biotechnology/Biophysics. Bioinformatics/X-ray Crystallography/

Immunology/Structural Biology etc

Experience:

1.Minimum three years teaching and/or research experience in a recognized medical/research Institution in an allied medical subject after obtaining doctorate degree and preferably in Medical

Molecular Biology/ Biophysics/Structural Biology/Genomics and Clinical Proteomics/Computational Biology.

2. Minimum two publication with atleast one in international journal and atleast one as first author

Desirable:-

Consistently excellent scholastic/academic record, demonstrated ability to write grant proposal/(s) successfully, Post Doctoral training in a frontier area of medical Bioinformatics Research and of direct relevance to clinical diagnosis or patient care (preferably from a recognized top-ranking medical institution abroad)

Send your applications to O/O, Deputy Registrar, Recruitment & Establishment Cell, University of Health Sciences, Rohtak by 08.7.2014

For more details,please visit website:http://pgimsrohtak.nic.in/2014%20AP%20Advt.pdf

➜ bloomfilter git:(master) ✗ swig -Wall -c++ -perl5 BloomFilter.i
➜ bloomfilter git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3
BloomFilter_wrap.cxx:1892:30: fatal error: ../BloomFilter.hpp: No such file or directory
compilation terminated.
➜ bloomfilter git:(master) ✗ cd swig
➜ swig git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3
In file included from BloomFilter_wrap.cxx:1877:0:
../BloomFilter.hpp: In member function ‘void BloomFilter::loadHeader(FILE*)’:
../BloomFilter.hpp:141:59: warning: ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]
fread(&header, sizeof(struct FileHeader), 1, file);
^
➜ swig git:(master) ✗ g++ -Wall -shared BloomFilter_wrap.o -o BloomFilter.so -O3
➜ swig git:(master) ✗ cd ..
➜ bloomfilter git:(master) ✗ cd ..
➜ lib git:(master) ✗ cd ..
➜ bin git:(master) ✗ ./LINKS
Usage: ./LINKS [v1.8.6]
-f sequences to scaffold (Multi-FASTA format, required)
-s file-of-filenames, full path to long sequence reads or MPET pairs [see below] (Multi-FASTA/fastq format, required)
-m MPET reads (default -m 1 = yes, default = no, optional)
! DO NOT SET IF NOT USING MPET. WHEN SET, LINKS WILL EXPECT A SPECIAL FORMAT UNDER -s
! Paired MPET reads in their original outward orientation <- -> must be separated by ":"
>template_name
ACGACACTATGCATAAGCAGACGAGCAGCGACGCAGCACG:ATATATAGCGCACGACGCAGCACAGCAGCAGACGAC
-d distance between k-mer pairs (ie. target distances to re-scaffold on. default -d 4000, optional)
Multiple distances are separated by comma. eg. -d 500,1000,2000,3000
-k k-mer value (default -k 15, optional)
-t step of sliding window when extracting k-mer pairs from long reads (default -t 2, optional)
Multiple steps are separated by comma. eg. -t 10,5
-o offset position for extracting k-mer pairs (default -o 0, optional)
-e error (%) allowed on -d distance e.g. -e 0.1 == distance +/- 10% (default -e 0.1, optional)
-l minimum number of links (k-mer pairs) to compute scaffold (default -l 5, optional)
-a maximum link ratio between two best contig pairs (default -a 0.3, optional)
*higher values lead to least accurate scaffolding*
-z minimum contig length to consider for scaffolding (default -z 500, optional)
-b base name for your output files (optional)
-r Bloom filter input file for sequences supplied in -s (optional, if none provided will output to .bloom)
NOTE: BLOOM FILTER MUST BE DERIVED FROM THE SAME FILE SUPPLIED IN -f WITH SAME -k VALUE
IF YOU DO NOT SUPPLY A BLOOM FILTER, ONE WILL BE CREATED (.bloom)
-p Bloom filter false positive rate (default -p 0.001, optional; increase to prevent memory allocation errors)
-x Turn off Bloom filter functionality (-x 1 = yes, default = no, optional)
-v Runs in verbose mode (-v 1 = yes, default = no, optional)

Error: Missing mandatory options -f and -s.

ERROR fixed

perl: symbol lookup error: /home/urbe/Tools/LINKS_new/bin/./lib/bloomfilter/swig/BloomFilter.so: undefined symbol: Perl_Gthr_key_ptr

1.Stupid for biologist: Biology is so complex that it will make bioinformatician feel stupid. There are no any universal fixed rules; it can surprise you any time. So be nice to biologists who ask questions and resolve your biological puzzles. Sometime you will have no idea what the hell you were doing either.

2.Puzzling why: Do not hesitate to ask question. Especially. at the beginning of project you will have to ask a lot of questions. Instead of puzzling it out at end check out and clear your doubt even for a single error. It may can leads to wrong conclusion.

3.Running marathon: The most of the biological software’s documentation is always incomplete. In other word they are no more than 95 percent complete. Sometime a single problem can halt your entire project for months. Compilation and running the pipelines in tedious because almost all are interdependent and need proper configuration. I face the same kind of problem with Evolver :( …

4.Folders missing: The pipelines generate lots of data, and we keep them in several folders for future use. But sometime we delete them by mistake and move to recovery…

5.Digging deeper: Digging deeper is fruitful, but some time it can be catastrophic. You may get frustrated or direction less. So keep a biologist with you for rescue …. Sometime an expert computer programmer to handle your server. Remember, the server will always go down when you need it the most.

The most common frustrating  common line: Why do we do this again?

The Bioinformatics and Systems Biology Unit of Université de Liège (Belgium) is looking for a highly motivated master student with programming skills for a PhD thesis project (4 years, fully funded) with the goal of designing computational tools that use literature, genomic and structural data in order to infer regulatory and metabolic networks.

Applicants are invited to send their resume and a recommendation letter to Prof. Patrick Meyer (more details at www.biosys.ulg.ac.be )

For more information : www.biosys.ulg.ac.be

java -Xms2G -Xmx4G -jar GenomeFinisher.jar  \
    -i target_contigs.fasta  \
    -ds alternative_assemblies.fasta -ref reference.fasta  \
    -o outputDirectory

Address of the bookmark: http://gfinisher.sourceforge.net

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of over 97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact: akiyama@cs.titech.ac.jp

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short

Finally, I find the underlying R code in a file created by GrapheR. For more details read also the package vignette, which is available in English, French and German!

Address of the bookmark: http://mitos.bioinf.uni-leipzig.de/index.py

More at http://pythonhosted.org/pybedtools/

A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/