RAX2: a genome-wide detection method of condition-associated transcription variation.

Most mammalian genes have mRNA variants due to alternative promoter usage, alternative splicing, and alternative cleavage and polyadenylation. Expression of alternative RNA isoforms has been found to be associated with tumorigenesis, proliferation and differentiation. Detection of condition-associated transcription variation requires association methods. Traditional association methods such as Pearson chi-square test and Fisher Exact test are single test methods and do not work on count data with replicates. Although the Cochran Mantel Haenszel (CMH) approach can handle replicated count data, our simulations showed that multiple CMH tests still had very low power. To identify condition-associated variation of transcription, we here proposed a ranking analysis of chi-squares (RAX2) for large-scale association analysis. RAX2 is a nonparametric method and has accurate and conservative estimation of FDR profile. Simulations demonstrated that RAX2 performs well in finding condition-associated transcription variants. We applied RAX2 to primary T-cell transcriptomic data and identified 1610 (16.3%) tags associated in transcription with immune stimulation at FDR < 0.05. Most of these tags also had differential expression. Analysis of two and three tags within genes revealed that under immune stimulation short RNA isoforms were preferably used.

Finding the missing honey bee genes: lessons learned from a genome upgrade.

BACKGROUND: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. RESULTS: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. CONCLUSIONS: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.

An integrated genome-wide approach to discover tumor-specific antigens as potential immunologic and clinical targets in cancer.

Tumor-specific antigens (TSA) are central elements in the immune control of cancers. To systematically explore the TSA genome, we developed a computational technology called heterogeneous expression profile analysis (HEPA), which can identify genes relatively uniquely expressed in cancer cells in contrast to normal somatic tissues. Rating human genes by their HEPA score enriched for clinically useful TSA genes, nominating candidate targets whose tumor-specific expression was verified by reverse transcription PCR (RT-PCR). Coupled with HEPA, we designed a novel assay termed protein A/G-based reverse serological evaluation (PARSE) for quick detection of serum autoantibodies against an array of putative TSA genes. Remarkably, highly tumor-specific autoantibody responses against seven candidate targets were detected in 4% to 11% of patients, resulting in distinctive autoantibody signatures in lung and stomach cancers. Interrogation of a larger cohort of 149 patients and 123 healthy individuals validated the predictive value of the autoantibody signature for lung cancer. Together, our results establish an integrated technology to uncover a cancer-specific antigen genome offering a reservoir of novel immunologic and clinical targets.