MOTIVATION: For many complex traits/diseases, it is believed that rare variants account for some of the missing heritability that cannot be explained by common variants. Sequencing a large number of samples through DNA pooling is a cost-effective strategy to discover rare variants and to investigate their associations with phenotypes. Overlapping pool designs provide further benefit because such approaches can potentially identify variant carriers, which is important for downstream applications of association analysis of rare variants. However, existing algorithms for analysing sequence data from overlapping pools are limited. RESULTS: We propose a complete data analysis framework for overlapping pool designs, with novelties in all three major steps: variant pool and variant locus identification, variant allele frequency estimation and variant sample decoding. The framework can be used in combination with any design matrix. We have investigated its performance based on two different overlapping designs and have compared it with three state-of-the-art methods, by simulating targeted sequencing and by pooling real sequence data. Results on both datasets show that our algorithm has made significant improvements over existing ones. In conclusion, successful discovery of rare variants and identification of variant carriers using overlapping pool strategies critically depend on many steps, from generation of design matrixes to decoding algorithms. The proposed framework in combination with the design matrixes generated based on the Chinese remainder theorem achieves best overall results. AVAILABILITY: Source code of the program, termed VIP for Variant Identification by Pooling, is available at http://cbc.case.edu/VIP.

A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and it may provide much needed information for effective diagnosis, prognosis, and optimal therapy. However, despite considerable effort in studying cancer progressions, their molecular and genetic basis remains largely unknown. Microarray experiments can systematically assay gene expressions across genome, therefore they have been widely used to gain insights on cancer progressions. In general, expression data may be obtained from different stages of the same samples. More often, data were obtained from individuals at different stages. Existing methods such as the Student's t-test and clustering approaches focus on identification of differentially expressed genes in different stages, but they are not suitable for capturing real progression signatures across all progression stages. We propose an alternative approach, namely a multicategory logit model, to identify novel genes that show significant correlations across multiple stages. We have applied the approach on a real data set concerning prostate cancer progression and obtained a set of genes that show consistency trends across multiple stages. Further analysis based on Gene Ontology (GO) annotations, protein-protein interaction networks and KEGG pathways databases, as well as literature search demonstrates that our candidate list not only includes some well-known prostate cancer related genes such as MYC and AMACR, but also consists of novel genes (e.g. CKS2) that have been confirmed by very recent independent studies. Our results illustrate that ordinal analysis of cancer progression data has the potential to obtain a set of promising candidate genes. Such a list can be further prioritized by combining other existing biomedical knowledge to identify therapeutic targets and/or biomarkers of cancer progressions.