PANTHER version 10: expanded protein families and functions, and analysis tools.

PANTHER (Protein Analysis THrough Evolutionary Relationships, http://pantherdb.org) is a widely used online resource for comprehensive protein evolutionary and functional classification, and includes tools for large-scale biological data analysis. Recent development has been focused in three main areas: genome coverage, functional information ('annotation') coverage and accuracy, and improved genomic data analysis tools.

Tobacco and alcohol in relation to male breast cancer: an analysis of the male breast cancer pooling project consortium.

Background: The etiology of male breast cancer is poorly understood, partly due to its relative rarity. Although tobacco and alcohol exposures are known carcinogens, their association with male breast cancer risk remains ill-defined.

Methods: The Male Breast Cancer Pooling Project consortium provided 2,378 cases and 51,959 controls for analysis from 10 case-control and 10 cohort studies.

Anthropometric and hormonal risk factors for male breast cancer: male breast cancer pooling project results.

Background: The etiology of male breast cancer is poorly understood, partly because of its relative rarity. Although genetic factors are involved, less is known regarding the role of anthropometric and hormonally related risk factors.

Methods: In the Male Breast Cancer Pooling Project, a consortium of 11 case-control and 10 cohort investigations involving 2405 case patients (n = 1190 from case-control and n = 1215 from cohort studies) and 52013 control subjects, individual participant data were harmonized and pooled.

Large-scale gene function analysis with the PANTHER classification system.

The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments.

Information systems for cancer research.

The last decade has seen a massive growth in data for cancer research, with high-throughput technologies joining clinical trials as major drivers of informatics needs. These data provide opportunities for developing new cancer treatments, but also major challenges for informatics, and we summarize the systems needed and potential issues arising in addressing these challenges. Integrating these data into the research enterprise will require investments in (1) data capture and management, (2) data analysis, (3) data integration standards, (4) visualization tools, and (5) methods for integration with other enterprise systems.

Comprehensive association testing of common genetic variation in DNA repair pathway genes in relationship with breast cancer risk in multiple populations.

Genetic association studies of multiple populations investigate a wider range of risk alleles than studies of a single ethnic group. In this study, we developed a multiethnic tagging strategy, exploiting differences in linkage disequilibrium (LD) structure between populations, to comprehensively capture common genetic variation across 60 genes spanning multiple DNA repair pathways, in five racial/ethnic populations. Over 2600 SNPs were genotyped in each population and single- and multi-marker predictors of common alleles were selected to capture the LD patterns specific to each group.