Comparison of whole genome assemblies of the human genome

Bioinformatic processing to identify single nucleotide polymorphism that potentially affect Ape1 function

Inactivation of DNA damage response mechanisms is associated with several disease syndromes, including cancer, aging and neurodegeneration. A major corrective pathway for alkylation or oxidative DNA damage is base excision repair (BER). As part of an effort to identify variation in DNA repair genes, we used the expressed sequence tag (EST) database to identify amino acid variation in Ape1, an essential gene in the BER repair pathway. Nucleotide substitutions were considered valid only if the amino acid changes were observed in at least two independent EST sequencing runs (i.e. two independent EST reports). In total eighty amino acid variants were identified for the Ape1 gene. Using software tools SIFT and PolyPhen, which predict impacts of amino acid substitutions on protein structure and function, twenty-six variants were predicted by both algorithms to be deleterious to protein function. Majority of these intolerant mutations such as V206C and F240S, lie within the core of the protein and may affect the stability and folding of Ape1, or in the case of N212H, N212K, and Y171N, are close to the enzyme's active site and could drastically affect its function. A few of the intolerant mutations, i.e., G178V and E217R, are surface residues and are far from the active site, and as such, the predicted effect on Ape1 stability or function is not evident. These variants are reagents for further protein function studies and molecular epidemiology studies of cancer susceptibility.

Bioinformatics: databasing and gene annotation

"Omics" experiments amass large amounts of data requiring integration of several data sources for data interpretation. For instance, microarray, metabolomic, and proteomic experiments may at most yield a list of active genes, metabolites, or proteins, respectively. More generally, the experiments yield active features that represent subsequences of the gene, a chemical shift within a complex mixture, or peptides, respectively. Thus, in the best-case scenario, the investigator is left to identify the functional significance, but more likely the investigator must first identify the larger context of the feature (e.g., which gene, metabolite, or protein is being represented by the feature). To completely annotate function, several different databases are required, including sequence, genome, gene function, protein, and protein interaction databases. Because of the limited coverage of some microarrays or experiments, biological data repositories may be consulted, in the case of microarrays, to complement results. Many of the data sources and databases available for gene function characterization, including tools from the National Center for Biotechnology Information, Gene Ontology, and UniProt, are discussed.