We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.

Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf

For more information visit: http://www.snubi.org/TBC2013/

http://www.interpretomics.co/

Currently, BETSY consists of modules required for the microarray and next-generation sequencing data [4] such as expression analysis, classification, peak calling, and visualization.

Address of the bookmark: https://github.com/jefftc/changlab

A qualified candidate should hold a Ph.D. in a relevant field including
Agronomy, Statistics, Bioinformatics, Plant Breeding, Plant Genetics or
Quantitative Genetics. For the position in Biometry and Bioinformatics, the
applicants capable of teaching fundamental statistics/bioinformatics
courses or with experience in crop science are preferable; for Plant
Breeding and Genetics, the applicants capable of teaching fundamental plant
breeding courses, with experience in crop breeding, or training in
quantitative genetics are preferred.

The application package should include two letters of reference and five
printed copies of the following documents (1) curriculum vitae, (2)
publication list, (3) undergraduate and graduate transcripts if applying
for the Assistant Professorship, (4) a photocopy of the Ph.D. diploma, (5)
teaching plan and course outline or syllabus (6) research proposal, (7) a
cover letter indicating the rank to apply, and one representative original
research article which was published by the applicant being the 1st or
corresponding author in an SCI peer-reviewed journal within 5 years (after
August 1, 2016); a copy of doctoral dissertation can be the representative
article if applying for the Assistant Professorship; (8) reprints of the
selected publications published within 7 years (after August 1, 2014).

The application package should mail to the Chair, Dr. Li-yu Daisy Liu
(lyliu@ntu.edu.tw), in the Department of Agronomy, National Taiwan
University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan, before
December 15, 2020 for full consideration.

Models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer. Application to breast, ovarian, colorectal, pancreatic and skin cancer.

Statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects; integration of genomic information across technologies; cross-study validation of genomics results.

Statistical methods for comparative effectiveness research: comprehensive models for lifetime history of chronic disease outcomes; Bayesian meta-analysis; Bayesian causal inference; decision analysis.

Bayesian modeling and computation: multilevel models; decision theoretic approaches to inference; sequential experimental design and their application to adaptive and multistage studies in clinical and epidemiological research.

http://bcb.dfci.harvard.edu/~gp/index.html

http://scholar.google.com/citations?user=OlpYP3UAAAAJ&hl=en

https://grupsderecerca.uab.cat/cacereslab/