BOL: Related items

Roderic Guigó Lab

Mon, 01 Sep 2014 17:13:00 -0500

Research in our group focuses on the investigation of the signals involved in gene specification in genomic sequences (promoter elements, splice sites, translation initiation sites, etc…). We are interested both in the mechanism of their recognition and processing, and in their evolution. In addition, but related to this basic component of our research, our group is also involved in the development of software for gene prediction and annotation in genomic sequences. Our group also actively participates in the analysis of many eukaryotic genomes and it in involved in the NIH-funded ENCODE project. Furthermore we are members of two large cancer-studies consortia (chronic lymphocytic leukemia "CLL" and Breast Cancer -Hospital del Mar/CRG/Roche-).

More at http://big.crg.cat/computational_biology_of_rna_processing

ArrayGen Bioinformatics Genomics Group

Sun, 28 Sep 2014 14:09:55 -0500

ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html

Comparative Genomics in Ensembl

Wed, 21 Jan 2015 08:31:11 -0600

The Ensembl browser provides viewable whole-genome alignments, homologues and phylogenetic gene trees, protein families, and ancestral sequences. Learn how to view and export these data in this video.

Alessandra Carbone Lab

Tue, 26 May 2015 08:54:34 -0500

Our group works on various problems connected with the functioning and evolution of biological systems. We use mathematical tools, coming from statistics and combinatorics, algorithmic tools and molecular physics tools to study basic principles of cellular functioning starting from genomic data. We run several projects in parallel, all aiming at understanding the basic principles of evolution and co-evolution of molecular structures in the cell. They are intimately linked to each other.

Our main research themes are:

Domain annotation and metagenomics
Transcriptomics and sequence analysis
Protein evolution and interactions
Protein conformational dynamics

More at http://www.lcqb.upmc.fr/AnalGenom/home.html

Rosenberg lab

Wed, 27 May 2015 17:52:24 -0500

Research. Research in the lab focuses on mathematical, statistical, and computational problems in evolutionary biology and human genetics. Long-term interests of the lab include topics such as:

Human genetic variation
Inference of human evolutionary history from genetic markers
Statistical analysis of population-genetic data
Mathematical models of gene genealogies
Theoretical population genetics
Combinatorics of evolutionary trees
The relationship between gene trees and species trees
The role of human evolutionary genetics in the search for genes that contribute to disease-susceptibility
More at https://web.stanford.edu/group/rosenberglab/index.html

CrossMap

Jitendra Narayan — Mon, 08 Feb 2016 15:47:00 -0600

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.

More at http://crossmap.sourceforge.net/

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

MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping

Neel — Fri, 20 May 2016 18:53:49 -0500

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581

PRISM

Jit — Sat, 10 Dec 2016 15:19:40 -0600

PRISM is a software for split read (reads which span across a structrual variant -- SV ) mapping and SV calling from the mapping result. PRISM is able to detect small insertions and abitrary size deletions, inversions and tandom duplications with the direction of discordant read pairs. PRISM_CTX is a tool for detecting inter-chromosome trans-location events.

PRISM and PRISM_CTX were originally designed and written by Michael Brudno and Yue Jiang, The original PRISM publication can be found here.

The authors may be contacted via e-mail at: prism at cs.toronto.edu.

Additional information is available in the PRISM README file and PRISM_CTX README file.

http://compbio.cs.toronto.edu/prism/

Address of the bookmark: http://compbio.cs.toronto.edu/prism/

Visiting Scientist - Computational Genomics

Mon, 17 Jul 2017 07:20:18 -0500

ICRISAT is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. ICRISAT and its partners help empower those living in the semi-arid tropics, especially smallholder farmers, to overcome poverty, hunger, malnutrition and a degraded environment through more efficient and profitable agriculture.

ICRISAT is headquartered in Patancheru near Hyderabad, India, with two regional hubs and five country offices in sub-Saharan Africa. ICRISAT, established in 1972, is a member of the CGIAR Consortium. For more details, see www.icrisat.org.

Job Responsibilities:
Planning and execution of different NGS/genomics data analysis
Apply, maintain, and support cutting-edge pipelines for the analysis and interpretation of NGS data
Analyze large-scale genomic datasets generated internally and through collaboration with others
Genome wide analysis- LD analysis, hapmap, genetic map construction and qtl mapping
Expression analysis based on RNA-seq, gene ontology and metabolic pathway data
Sequence level analysis like gene family analysis, orthology/paralogy etc.
Familiarity with genomic and biological information databases
Compilation and interpretation of results and writing reports
Experience working independently and in a team environment
Requirements:

PhD or M.Sc with 2-3 years experience from reputed institute in the area of life science or similar Knowledge in Next Generation Sequencing (NGS) data analysis. Should be familiar with various sequencing platforms. Sound knowledge of genomics and molecular biology is must. Proficient in any one of the programming/scripting in languages: Python, Perl, PHP, R, Shell Scripting. Must be experienced in working on Linux and CLI environment. Ability to work as team as well as independently with minimal support. Fluency in spoken and written English is essential.

NGS techniques like sequence alignment, variant calling based on whole genome re-sequencing and genotyping-by-sequencing (GBS), RNA-Seq/transcriptome analysis
Knowledge of various standard NGS related tool
Ability to solve complex problems in advanced genomics research areas
Ease and interest in working with people from diverse backgrounds
Excellent oral/written communication and interpersonal/networking skills
General:
This position is contract for a period of two years.

How to apply:
Applicants should apply on or before 27-July-2017, with latest Curriculum Vitae, and the names and contact information of three references that are knowledgeable about your professional qualifications and work experience. All applications will be acknowledged, however only short listed candidates will be contacted
Please CLICK HERE to submit your application.
ICRISAT is an equal opportunity employer

http://icrisat.careersitemanager.com/job-listings-Visiting-Scientist-Computational-Genomics-ICRISAT-Hyderabad-Secunderabad-2-to-4-years-060717000278