Download

• Program, version 1.01 (July 10, 2012, documentation updated in August 2014)
• Examples with results (featured ancestors: boreoeutherian, amniote, yeasts, Burkholderia, monocots); please refer to the documentation of the distribution above.

Address of the bookmark: http://paleogenomics.irmacs.sfu.ca/ANGES/

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AGORA is used to generate ancestral genomes for the Genomicus online server for gene order comparison, and has been in constant use in the group since.

Address of the bookmark: https://github.com/DyogenIBENS/Agora

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

Address of the bookmark: https://github.com/AusARG/pipesnake

Research Area:
Analysis of Next Generation sequence data in cancer
Methods for analysis of structural variation in cancer genomes
Next Generation sequencing in malaria
Computational comparative genomics
Sensitive genomic sequence search techniques using hidden Markov models
Tasmanian devil facial tumour disease

Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html

Awesome viddeo explaining the way in which the antibody, HuLuc 63, appears to induce anti-tumor effects by binding to a protein that is only expressed on the surface of myeloma cells. This initiates antibody-dependent cellular cytotoxicity activity that kills myeloma cells and leaves healthy cells intact.

Yau Group develops statistical and computational methods for the analysis of genomic datasets with a particular interest in cancer sequencing applications and the use of Bayesian Statistics.

Yau Group are currently have projects in somatic mutation analysis of heterogeneous cancers, data fusion or integration techniques and single cell genomics.

More @ http://www.well.ox.ac.uk/~cyau/index.html

No. ACTREC/Advt./ 72 /2013

WALK IN INTERVIEW

1. JRF*
Genome-wide RNAi screen with human pooled tyrosine kinase shRNA libraries in head and neck squamous call carcinoma (HNSCC) cell lines
DBT A/C No. 3071, Dr. Amit Dutt

2. JRF
IRB Project ACTREC Funds
Dr. Amit Dutt

3. RA
Defining the cancer genome of Head and Neck Squamous Cell Carcinoma (HNSCC) with SNP arrays and next generation sequencing technology
A/C No. 2895, Dr. Amit Dutt

Duration of the Project: One year from the date of appointment, or as and when project terminates.

Consolidated Salary: RA : Rs. 40,000/- p.m.
JRF* (DBT): Rs. 20,800/- p.m.
JRF: Rs. 16,000/- p.m.
Date & Time: 6th November, 2013, at 10.00 a.m.

Venue: Conference Room

Minimum Qualifications and Experience:

RA: The ideal applicant should have a PhD in a relevant field. He/she should have a strong computational biology background, with demonstrated experience in coding using Perl, Python, Java or C++. He/she should be familiar with working in unix enviromnent, devising computational algorithms for data analysis, statistical data analysis in R and matlab and database programming using MySQL. Hands on experience in analyzing high throughput data would be an added advantage.

JRF* (DBT project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Valid UGC-CSIR/DBT/ICMR JRF qualification and laboratory experience in molecular biology. Previous experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team would be desirable.

JRF (ACTREC project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Minimum 2 yrs experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team is essential.

*M.Sc. degree obtained after a one year course will not be considered.

Candidates fulfilling above requirements should send their application by e-mail to
‘careers.duttlab@gmail.com. in the format given below so as to reach on or before
4th November, 2013.

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http://www.actrec.gov.in/data%20files/2013/AD-RA-JR-TECHN-6-NOV.pdf