Journal of Bioinformatics and Computational Biology

A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.

Biomolecule sequences and structures of land, air and water species are determined rapidly and the data entries are unevenly distributed for different organisms. It frequently leads to the BLAST results of homologous search containing undesirable entries from organisms living in different environments. To reduce irrelevant searching results, a separate database for comparative genomics is urgently required. A comprehensive bioinformatics tool set and an integrated database, named Bioinformatics tools for Marine and Freshwater Genomics (BiMFG), are constructed for comparative analyses among model species and underwater species. Novel matching techniques based on conserved motifs and/or secondary structure elements are designed for efficiently and effectively retrieving and aligning remote sequences through cross-species comparisons. It is especially helpful when sequences under analysis possess low similarities and unresolved structural information. In addition, the system provides core techniques of multiple sequence alignment, multiple second structure profile alignment and iteratively refined multiple structural alignments for biodiversity analysis and verification in marine and freshwater biology. The BiMFG web server is freely available for use at .

The AFL genes (ABI3/VP1, FUS3 and LEC2) belong to the plant-specific B3 superfamily, playing important roles in regulating seed development and maturation. It is unclear, however, whether these genes appeared at the same time as the origin of seed plants and if all these genes are necessary and sufficient for seed development for all seed plants. By conducting a genome-wide comparative analysis of the putative AFL genes in various plant species, we found that the ABI3 homologous genes existed in all land plant genomes, but the FUS3 homologous were present only in seed plant genomes and the LEC2-like sequences only in dicot genomes. Phylogenetic analysis indicated that the AFL genes had undergone successive rounds of gene duplication and subsequent diversification during land plant evolution, resulting in the stepwise origin of the ABI3, FUS3 and LEC2 genes. Comparison of gene structure of the AFL genes revealed a trend of decreasing in the number of conserved domains from ABI3 to FUS3 and LEC2.

In this paper, we present a method based on local density and random walks (LDRW) for core-attachment complexes detection in protein-protein interaction (PPI) networks whether they are weighted or not. Our LDRW method consists of two stages. Firstly, it finds all the protein-complex cores based on local density of subnetwork. Then it uses random walks with restarts for finding the attachment proteins of each detected core to form complexes. We evaluate the effectiveness of our method using two different yeast PPI networks and validate the biological significance of the predicted protein complexes using known complexes in the Munich Information Center for Protein Sequence (MIPS) and Gene Ontology (GO) databases. We also perform a comprehensive comparison between our method and other existing methods. The results show that our method can find more protein complexes with high biological significance and obtains a significant improvement. Furthermore, our method is able to identify biologically significant overlapped protein complexes.

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects.

Systematic studies have revealed that single gene deletions often display little phenotypic effects under laboratory conditions and that in many cases gene dispensability depends on the experimental conditions. To elucidate the environmental dependency of genes, we analyzed the effects of gene deletions by Phenotype MicroArray™ (PM), a system for quantitative screening of thousands of phenotypes in a high-throughput manner. Here, we proposed a new statistical approach to minimize error inherent in measurements of low respiration rates and find which mutants showed significant phenotypic changes in comparison to the wild-type. We show analyzing results from comprehensive PM assays of 298 single-gene knockout mutants in the Keio collection and two additional mutants under 1,920 different conditions. We focused on isozymes of these genes as simple duplications and analyzed correlations between phenotype changes and protein expression levels. Our results revealed divergence of the environmental dependency of the gene among the knockout genes and have also given some insights into possibilities of alternative pathways and availabilities of information on protein synthesis patterns to classify or predict functions of target genes from systematic phenotype screening.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common hereditary enzymatic disorder of red blood cells in humans due to mutations in the G6PD gene. The G6PD enzyme catalyzes the first step in the pentose phosphate pathway to protect cells against oxidative stress. Mutations in the G6PD gene will cause functional variants with various biochemical and clinical phenotypes. So far, about 160 mutations along with more than 400 biochemical variants have been described. G6PD-MutDB is a disease-specific resource of G6PD deficiency, collecting and integrating G6PD mutations with biochemical and clinical phenotypes. Data of G6PD deficiency is manually extracted from published papers, focusing primarily on variants with identified mutation and well-described quantitative phenotypes. G6PD-MutDB implements an approach, CNSHA predictor, to help identify a potential chronic non-spherocytic hemolytic anemia (CNSHA) phenotype of an unknown mutation. G6PD-MutDB is believed to facilitate analysis of relationship between molecular mutation and functional phenotype of G6PD deficiency owing to convenient data resource and useful tools. This database is available from .

MLH1 and MSH2 mutations underlie 90% of hereditary nonpolyposis colorectal cancer (HNPCC) mutations. The International Society of Gastrointestinal Hereditary Tumors (InSiGHT) has established an international database of mutations associated with HNPCC. Based on the InSiGHT database and the original references that reported the mutations, we analyzed the distributions of MLH1 and MSH2 mutations in yellow race and white race respectively and compared them subsequently. We found: (1) the distributions of mutation individuals in exon 1, 17 and 19 of MLH1 gene and in exon 2 of MSH2 gene showed significant differences between the two race groups (p < 0.05); (2) the distributions of mutation types in exon 2, 7 and 18 of MLH1 and exon 10 and 16 of MSH2 showed significant differences (p < 0.05); and (3) three mutations (c.649C > T, c.1625A > T and c.1721T > C) in MLH1 and five mutations (c.23C > T, c.187dupG, c.505A > G, c.1168C > T and c.2211-6T > C) in MSH2 have much higher frequency in yellow race than those in white race. Furthermore, three mutations (c.1453G > C, c.1742C > T and c.1758dupC) in MLH1 and two mutations (c.1255C > A and c.1886A > G) in MSH2 were only found in yellow race, which implies that specific mutations in yellow race need more attention when screening mutations in these two genes.

Effective identification of disease-causing gene locations can have significant impact on patient management decisions that will ultimately increase survival rates and improve the overall quality of health care. Linkage disequilibrium mapping is the process of finding disease gene locations through comparisons of haplotype frequencies between disease chromosomes and normal chromosomes. This work presents a new method for linkage disequilibrium mapping. The main advantage of the proposed algorithm, called LinkageTracker, is its consistency in producing good predictive accuracy under different conditions, including extreme conditions where the occurrence of disease samples with the mutation of interest is very low and there is presence of error or noise. We compared our method with some leading methods in linkage disequilibrium mapping such as HapMiner, Blade, GeneRecon, and Haplotype Pattern Mining (HPM). Experimental results show that for a substantial class of problems, our method has good predictive accuracy while taking reasonably short processing time. Furthermore, LinkageTracker does not require any population ancestry information about the disease and the genealogy of the haplotypes. Therefore, it is useful for linkage disequilibrium mapping when the users do not have such information about their datasets.

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

Due to its unprecedented high-resolution and detailed information, RNA-seq technology based on next-generation high-throughput sequencing significantly boosts the ability to study transcriptomes. The estimation of genes' transcript abundance levels or gene expression levels has always been an important question in research on the transcriptional regulation and gene functions. On the basis of the concept of Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes (UI-based) and summing up inferred isoform abundance (isoform-based) are the two current strategies to estimate gene expression levels, but produce different estimations. In this paper, we made the first attempt to compare the two strategies' performances through a series of simulation studies. Our results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. We applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.