Detection of protein families in large databases is one of the principal research objectives in structural and functional genomics. Protein family classification can significantly contribute to the delineation of functional diversity of homologous proteins, the prediction of function based on domain architecture or the presence of sequence motifs as well as comparative genomics, providing valuable evolutionary insights. We present a novel approach called TRIBE-MCL for rapid and accurate clustering of protein sequences into families. The method relies on the Markov cluster (MCL) algorithm for the assignment of proteins into families based on precomputed sequence similarity information. This novel approach does not suffer from the problems that normally hinder other protein sequence clustering algorithms, such as the presence of multi-domain proteins, promiscuous domains and fragmented proteins. The method has been rigorously tested and validated on a number of very large databases, including SwissProt, InterPro, SCOP and the draft human genome. Our results indicate that the method is ideally suited to the rapid and accurate detection of protein families on a large scale. The method has been used to detect and categorise protein families within the draft human genome and the resulting families have been used to annotate a large proportion of human proteins.

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing.

Somatic hypermutation (SHM) features in a series of 1967 immunoglobulin heavy chain gene (IGH) rearrangements obtained from patients with chronic lymphocytic leukemia (CLL) were examined and compared with IGH sequences from non-CLL B cells available in public databases. SHM analysis was performed for all 1290 CLL sequences in this cohort with less than 100% identity to germ line. At the cohort level, SHM patterns were typical of a canonical SHM process. However, important differences emerged from the analysis of certain subgroups of CLL sequences defined by: (1) IGHV gene usage, (2) presence of stereotyped heavy chain complementarity-determining region 3 (HCDR3) sequences, and (3) mutational load. Recurrent, “stereotyped” amino acid changes occurred across the entire IGHV region in CLL subsets carrying stereotyped HCDR3 sequences, especially those expressing the IGHV3-21 and IGHV4-34 genes. These mutations are underrepresented among non-CLL sequences and thus can be considered as CLL-biased. Furthermore, it was shown that even a low level of mutations may be functionally relevant, given that stereotyped amino acid changes can be found in subsets of minimally mutated cases. The precise targeting and distinctive features of somatic hypermutation (SHM) in selected subgroups of CLL patients provide further evidence for selection by specific antigenic element(s).