Journal of Bioinformatics and Computational Biology

NodD transcription factor is a regulatory protein of nitrogen fixing bacteria, which activates expression of nod genes participating in nodulation during interaction with its symbiont legumes. It's DNA binding motifs have been characterized and reported in Sinorhizobium meliloti and this pattern information has been used in our theoretical analyses to detect its novel regulated genes in genomes of S. meliloti and Mesorhizobium loti. M. loti, a symbiont to model legume plant Lotus japonicus, showed presence of these regulatory motifs in upstream sequences of nod and other functionally related genes. The methodology involved comparative potential weight matrix construction through GIBBS SAMPLER (RSAT) and MEME tools, using information of conserved upstream sequences of nine genes including nod and neighboring genes of both genomes possessing nod-box like motif. The resultant DNA consensus sequence had highly conserved nod-box like 17 bp long motif consensus sequence pattern for binding of detected NodD transcription factors as analyzed by homologous clustering method and therefore the genome wide predictions were considered highly accurate since confirmed by operon delineation method and the described methodology can be used in other nitrogen fixing bacteria to pursue the study in detail.

Due to the large volume of protein sequence data, computational methods to determine the structure class and the fold class of a protein sequence have become essential. Several techniques based on sequence similarity, Neural Networks, Support Vector Machines (SVMs), etc. have been applied. Since most of these classifiers use binary classifiers for multi-classification, there may be ^Nc₂ classifiers required. This paper presents a framework using the Tree-Augmented Bayesian Networks (TAN) which performs multi-classification based on the theory of learning Bayesian Networks and using improved feature vector representation of (Ding et al., 2001).⁴ In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, hydrophobicity) for each class, which helps to further understand the complexity in protein structure.

The experiments on the datasets used in three prominent recent works show that our approach is more accurate than other discriminative methods. The framework is implemented on the BAYESPROT web server and it is available at . More detailed results are also available on the above website.

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

Protein structure alignment plays a key role in protein structure prediction and fold family classification. An efficient method for multiple protein structure alignment in a mathematical manner is presented, based on deterministic annealing technique. The alignment problem is mapped onto a nonlinear continuous optimization problem (NCOP) with common consensus chain, matching assignment matrices and atomic coordinates as variables. At each step in the annealing procedure, the NCOP is decomposed into as many subproblems as the number of protein chains, each of which is actually an independent pairwise structure alignment between a protein chain and the consensus chain and hence can be efficiently solved by the parallel computation technique. The proposed method is robust with respect to choice of iteration parameters for a wide range of proteins, and performs well in both multiple and pairwise structure alignment cases, compared with existing alignment methods.

A molecular interaction library modeling favorable non-bonded interactions between atoms and molecular fragments is considered. In this paper, we represent the structure of the interaction library by a network diagram, which demonstrates that the underlying prediction model obtained for a molecular fragment is multi-layered. We clustered the molecular fragments into four groups by analyzing the pairwise distances between the molecular fragments. The distances are represented as an unrooted tree, in which the molecular fragments fall into four groups according to their function. For each fragment group, we modeled a group-specific a priori distribution with a Dirichlet distribution. The group-specific Dirichlet distributions enable us to derive a large population of similar molecular fragments that vary only in their contact preferences. Bayes' theorem then leads to a population distribution of the posterior probability vectors referred to as a "Dickey–Savage"-density. Two known methods for approximating multivariate integrals are applied to obtain marginal distributions of the Dickey–Savage density. The results of the numerical integration methods are compared with the simulated marginal distributions. By studying interactions between the protein structure of cyclohydrolase and its ligand guanosine-5′-triphosphate, we show that the marginal distributions of the posterior probabilities are more informative than the corresponding point estimates.

Both cDNA microarray and spectroscopic data provide indirect information about the chemical compounds present in the biological tissue under consideration. In this paper simple univariate and bivariate measures are used to investigate correlations between both types of high dimensional analyses.

A large dataset of 42 hemp samples on which 3456 cDNA clones and 351 NIR wavelengths have been measured, was analyzed using graphical representations. For this purpose we propose clustered correlation and clustered discrimination images. Large, tissue-related differences are seen to dominate the cDNA-NIR correlation structure but smaller, more difficult to detect, variety-related differences can be found at specific cDNA clone/NIR wavelength combinations.

Given a raw protein sequence, knowing its subcellular location is an important step toward understanding its function and designing further experiments. A novel method is proposed for the prediction of protein subcellular locations from sequences. For four categories of eukaryotic proteins the overall predictive accuracy is 82.0%, 2.6% higher than that by using SVM approach. For three subcellular locations of prokaryotic proteins, an overall accuracy of 89.9% is obtained. In accordance with the architecture of cells, a hierarchical prediction approach is designed. Based on amino acid composition extracellular proteins and intracellular proteins can be identified with accuracy of 97%.

The validation of sequences is essential to perform accurate phylogeny and structure/function analysis. However among the thousands of protein sequences available in the public databases, most have been predicted in silico and have not systematically undergone a quality verification. It has recently become evident that they often contain sequence errors. To address the problem of automatic protein quality control, we have developed vALId, an interactive web interfaced software. Taking advantage of high quality multiple alignments of complete protein sequences (MACS), vALId first warns about the presence of suspicious insertions, deletions (indels) and divergent segments, and second, proposes corrections based on transcripts and genome contigs. In a first evaluation test, hundreds of indels and divergent segments were randomly generated in a manually refined MACS. The sensitivity (Sn) and specificity (Sp) of indel detection were excellent (0.96) while the mean Sn(0.49) and Sp(0.56) of divergent segment delineation depended on the percent identity between sequence neighbors. In a second test, 6195 sequences in 100 MACS corresponding to different functional and structural protein families were analyzed. 65% of the sequences were in silico predictions and 44% of eukaryote predicted proteins were partially incorrect with at least one suspicious indel or divergent segment.

The response of genes to cell stimuli is often measured by microarrays. However, studying the function of these genes rarely elucidate as to how the stimuli activate or suppress these genes. To understand the mechanisms of cell stimulation, we describe a computational method for analyzing mammalian promoters of early response genes to detect the transcription factors activated by cell stimulation. We first analyzed promoters of the response genes, for transcription factor binding sites conserved between human and mouse. We then applied hypergeometric statistics in conjunction with Bonferroni correction to identify the top transcription factors whose binding sites were significantly over-represented among these promoters. In five data sets with early response genes, a significantly larger than expected number of genes had binding sites in their promoters for transcription factors previously known to be involved in response to the stimulus, while data sets with measurements at longer time points (24 hours) failed to show such over-representation. Because the end points of signal transduction pathways are transcription factors, this methodology is useful for exploring signaling pathways activated by various stimuli through microarray studies.

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise.

To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.

A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.

The NAD biosynthetic pathway and NAD transformations in E. coli and S. typhi are well characterized. Using comparative genomics methods we describe the NadR regulon in other Enterobacteriaceae, identity new candidate regulon members and demonstrate that even a very simple regulon covering an essential methabolic pathway could be different in closely related genomes.