MOTIVATION: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. RESULTS: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level 'outputs') are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. AVAILABILITY: Source code available at http://sbenz.github.com/Paradigm,. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Taking advantage of the complete genome sequences of several mammals, we developed a novel method to detect losses of well-established genes in the human genome through syntenic mapping of gene structures between the human, mouse, and dog genomes. Unlike most previous genomic methods for pseudogene identification, this analysis is able to differentiate losses of well-established genes from pseudogenes formed shortly after segmental duplication or generated via retrotransposition. Therefore, it enables us to find genes that were inactivated long after their birth, which were likely to have evolved nonredundant biological functions before being inactivated. The method was used to look for gene losses along the human lineage during the approximately 75 million years (My) since the common ancestor of primates and rodents (the euarchontoglire crown group). We identified 26 losses of well-established genes in the human genome that were all lost at least 50 My after their birth. Many of them were previously characterized pseudogenes in the human genome, such as GULO and UOX. Our methodology is highly effective at identifying losses of single-copy genes of ancient origin, allowing us to find a few well-known pseudogenes in the human genome missed by previous high-throughput genome-wide studies. In addition to confirming previously known gene losses, we identified 16 previously uncharacterized human pseudogenes that are definitive losses of long-established genes. Among them is ACYL3, an ancient enzyme present in archaea, bacteria, and eukaryotes, but lost approximately 6 to 8 Mya in the ancestor of humans and chimps. Although losses of well-established genes do not equate to adaptive gene losses, they are a useful proxy to use when searching for such genetic changes. This is especially true for adaptive losses that occurred more than 250,000 years ago, since any genetic evidence of the selective sweep indicative of such an event has been erased.

PROBLEM STATEMENT: We have studied the relationships among SWISS-PROT, TrEMBL, and GenBank with two goals. First is to determine whether users can reliably identify those proteins in SWISS-PROT whose functions were determined experimentally, as opposed to proteins whose functions were predicted computationally. If this information was present in reasonable quantities, it would allow researchers to decrease the propagation of incorrect function predictions during sequence annotation, and to assemble training sets for developing the next generation of sequence-analysis algorithms. Second is to assess the consistency between translated GenBank sequences and sequences in SWISS-PROT and TrEMBL. RESULTS: (1) Contrary to claims by the SWISS-PROT authors, we conclude that SWISS-PROT does not identify a significant number of experimentally characterized proteins. (2) SWISS-PROT is more incomplete than we expected in that version 38.0 from July 1999 lacks many proteins from the full genomes of important organisms that were sequenced years earlier. (3) Even if we combine SWISS-PROT and TrEMBL, some sequences from the full genomes are missing from the combined dataset. (4) In many cases, translated GenBank genes do not exactly match the corresponding SWISS-PROT sequences, for reasons that include missing or removed methionines, differing translation start positions, individual amino-acid differences, and inclusion of sequence data from multiple sequencing projects. For example, results show that for Escherichia coli, 80.6% of the proteins in the GenBank entry for the complete genome have identical sequence matches with SWISS-PROT/TrEMBL sequences, 13.4% have exact substring matches, and matches for 4.1% can be found using BLAST search; the remaining 2.0% of E.coli protein sequences (most of which are ORFs) have no clear matches to SWISS-PROT/TrEMBL. Although many of these differences can be explained by the complexity of the DB, and by the curation processes used to create it, the scale of the differences is notable.