Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica.

OBJECTIVE: Elizabethkingia meningoseptica is a multidrug resistance strain which primarily causes meningitis in neonates and immunocompromised patients. Being a nosocomial infection causing agent, less information is available in literature, specifically, about its genomic makeup and associated features. An attempt is made to study them through bioinformatics tools with respect to compositions, embedded periodicities, open reading frames, origin of replication, phylogeny, orthologous gene clusters analysis and pathways. RESULTS: Complete DNA and protein sequence pertaining to E. meningoseptica were thoroughly analyzed as part of the study. E. meningoseptica G4076 genome showed 7593 ORFs it is GC rich. Fourier based analysis showed the presence of typical three base periodicity at the genome level. Putative origin of replication has been identified. Phylogenetically, E. meningoseptica is relatively closer to E. anophelis compared to other Elizabethkingia species. A total of 2606 COGs were shared by all five Elizabethkingia species. Out of 3391 annotated proteins, we could identify 18 unique ones involved in metabolic pathway of E. meningoseptica and this can be an initiation point for drug designing and development. Our study is novel in the aspect in characterizing and analyzing the whole genome data of E. meningoseptica.

Riboswitch detection using profile hidden Markov models.

BACKGROUND: Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM). Our method exploits the high degree of sequence conservation that characterizes the aptamer domain. RESULTS: Our method can detect riboswitches in genomic databases rapidly and accurately. Its sensitivity is comparable to the method based on the Covariance Model (CM). For six out of ten riboswitch classes, our method detects more than 99.5% of the candidates identified by the much slower CM method while being several hundred times faster. For three riboswitch classes, our method detects 97-99% of the candidates relative to the CM method. Our method works very well for those classes of riboswitches that are characterized by distinct and conserved sequence motifs. CONCLUSION: Riboswitches play a crucial role in controlling the expression of several prokaryotic genes involved in metabolism and transport processes. As more and more new classes of riboswitches are being discovered, it is important to understand the patterns of their intra and inter genomic distribution. Understanding such patterns will enable us to better understand the evolutionary history of these genetic regulatory elements. However, a complete picture of the distribution pattern of riboswitches will emerge only after accurate identification of riboswitches across genomes. We believe that the riboswitch detection method developed in this paper will aid in that process. The significant advantage in terms of speed, of our pHMM-based approach over the method based on CM allows us to scan entire databases (rather than 5'UTRs only) in a relatively short period of time in order to accurately identify riboswitch candidates.

Computational analysis of plant RNA Pol-II promoters.

Plant promoters have not yet been thoroughly analyzed in terms of their structural and sequence dependent properties like curvature, periodicity and information content and our present study is an attempt in that direction. Results were compared with E. coli and yeast data to get some insight into the promoter organization. Promoters having the TATA box (TATA(+)) and those lacking the same (TATA(-)) were also analyzed separately. It was found that plant promoters have marked differences for all these properties when compared to E. coli and yeast. Bias for A+T was observed in promoters of all the three groups. Compared to E. coli and yeast, plant promoters showed intermediate values for A+T content as well as curvature. Analysis showed that curvature of core promoters is more pronounced than non-promoters. Information theoretic analysis of plant promoters reveal high information content at certain consensus regions such as -30 (TATA box) and +1 transcription start site (TSS); and have moderate values at other positions as well. This factor was taken into account while developing weight matrices. For certain threshold values, these weight matrices could pick up all true positives, and reduce false positives to a great extent in a test set. A new multi-parameterized prediction strategy has been proposed that uses a combination of sequence composition, curvature and position weight matrices for identification of plant promoters. This strategy was tested and validated with experimentally known promoter sequences. Our study is novel in using in silico approaches to study the sequence dependent properties of plant RNA Pol-II promoters and their prediction, and important as there is no dedicated promoter search tool for plants.

Study of DNA binding sites using the Rényi parametric entropy measure.

Shannon's definition of uncertainty or surprisal has been applied extensively to measure the information content of aligned DNA sequences and characterizing DNA binding sites. In contrast to Shannon's uncertainty, this study investigates the applicability and suitability of a parametric uncertainty measure due to Rényi. It is observed that this measure also provides results in agreement with Shannon's measure, pointing to its utility in analysing DNA binding site region. For facilitating the comparison between these uncertainty measures, a dimensionless quantity called "redundancy" has been employed. It is found that Rényi's measure at low parameter values possess a better delineating feature of binding sites (of binding regions) than Shannon's measure. The critical value of the parameter is chosen with an outlier criterion.