Achuthsankar S. Nair

Biclustering of gene expression data using reactive greedy randomized adaptive search procedure

BackgroundBiclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.ResultsWe performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach.ConclusionThe Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.

Programming languages for synthetic biology

In the backdrop of accelerated efforts for creating synthetic organisms, the nature and scope of an ideal programming language for scripting synthetic organism in-silico has been receiving increasing attention. A few programming languages for synthetic biology capable of defining, constructing, networking, editing and delivering genome scale models of cellular processes have been recently attempted. All these represent important points in a spectrum of possibilities. This paper introduces Kera, a state of the art programming language for synthetic biology which is arguably ahead of similar languages or tools such as GEC, Antimony and GenoCAD. Kera is a full-fledged object oriented programming language which is tempered by biopart rule library named Samhita which captures the knowledge regarding the interaction of genome components and catalytic molecules. Prominent feature of the language are demonstrated through a toy example and the road map for the future development of Kera is also presented.

Protein location prediction using atomic composition and global features of the amino acid sequence

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectively used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.

Studies on neutralizing effect of Ophiorrhiza mungos root extract against Daboia russelii venom

ETHNOPHARMACOLOGICAL RELEVANCE The folklore or traditional therapy in southern India widely utilizes a plethora of local herbs to treat the patients challenged with snake venom. Despite the widespread implementation of antisera therapy, the local population of the country still relies on this centurys old medicinal formulas mainly due to the cost effectiveness, lesser side effects and also its cultural acceptability. The present study aims to validate the neutralizing ability of one such traditionally acclaimed antidote Ophiorrhiza mungos root extract against Russells viper (Daboia russelii) venom in the early developing chick embryos. MATERIALS AND METHODS The disc impregnated with venom, root extract or the combination of both was placed on the yolk sac membrane preferably over the anterior blood vessel of 6th day chick embryo. The neutralization/inhibition of venom-induced lethality or hemorrhage was achieved by incubating venom and extract before being applied to the embryo. The membrane stabilizing properties of root extract was estimated by HRBC lysis method. The preliminary phytochemical analysis was done to assess the phyto constituents in the root extract. RESULTS The LD50 of Russells viper venom in 6th day chick embryo was found to be 3 μg/μl. The neutralising effect of root extract was achieved by pre-incubating venom with various concentrations of extract and at the concentration of 10 μg/μl, 100% recovery of embryos was observed after 6h of incubation. Higher concentration of root extract showed remarkable results by completely abolishing traces of hemorrhagic lesions induced by viper venom. CONCLUSIONS The above observations confirmed that the root extract of Ophiorrhiza mungos possess potent anti snake venom neutralizing compounds, which inhibit the activity of viper venom. The chick embryo, a new insensate model used in the present study is significant in venom research as it reduces the ruthless suffering of higher mammalian experimental models.

Sequence signatures of allosteric proteins towards rational design

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence–based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.

Isolation and characterization of a novel chlorpyrifos degrading flavobacterium species EMBS0145 by 16S rRNA gene sequencing

Indiscriminate application of pesticides like chlorpyrifos, diazinon, or malathion contaminate the soil in addition has being unsafe often it has raised severe health concerns. Conversely, microorganisms like Trichoderma, Aspergillus and Bacteria like Rhizobium Bacillus, Azotobacter, Flavobacterium etc have evolved that are endowed with degradation of pesticides aforementioned to non-toxic products. The current study pitches into identification of a novel species of Flavobacterium bacteria capable to degrade the Organophosphorous pesticides. The bacterium was isolated from agricultural soil collected from Guntur District, Andhra Pradesh, India. The samples were serially diluted and the aliquots were incubated for a suitable time following which the suspected colony was subjected to 16S rDNA sequencing. The sequence thus obtained was aligned pairwise against Flavobacterium species, which resulted in identification of novel specie of Flavobacterium later named as EMBS0145, the sequence of which was deposited in in GenBank with accession number JN794045.

The elusive short gene – an ensemble method for recognition for prokaryotic genome

Accurate prediction of short protein coding DNA from genome sequence information remains an unsolved problem in DNA sequence analysis. Popular gene finding tools show drastic reduction in accuracy while attempting to predict genes of length less than 400 nt, a length we define as short. This study performs a quantitative evaluation of a set of selected coding measures in terms of their discriminative power in recognizing short genes in prokaryotic genomes. By performing Fast Correlation Based Feature Selection (FCBF) technique, we identified a subset of coding measures with high discriminative power. Using the measures identified thus, we present a novel approach for short genes recognition. A short-gene predictor employing AdaBoost.M1 in conjunction with random forests as the base classifier gives 92.74% accuracy, 94.77% sensitivity and 90.06% specificity on short genes.

Composition, Transition and Distribution (CTD) — A dynamic feature for predictions based on hierarchical structure of cellular sorting

Subcellular location of protein is crucial for the dynamic life of cells as it is an important step towards elucidating its function. It is widely recognized that the information from the amino acid sequence can serve as vital pointers in predicting location of proteins. We introduce a new feature vector for predicting proteins targeted to various compartments in the hierarchical structure of cellular sorting pathway from protein sequence. Features are based on the overall Composition, Transition and Distribution (CTD) of amino acid attributes such as hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure and solvent accessibility of the protein sequences. Classification of protein locations in cellular sorting pathway is achieved through Support Vector Machine. Our method gives an accuracy of 92% in human and 95% in fungi with non redundant test set at root level.

Biclustering of gene expression data using Greedy Randomized Adaptive Search Procedure

Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. The biclustering approach thus focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. In [1], Cheng and Church have introduced an initial measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we propose a new method to detect significant biclusters from large microarray datasets. Our method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using a multi-start metaheuristics - Greedy Randomized Adaptive Search Procedure (GRASP). It is an iterative search procedure where each iteration consists of a construction phase followed by a local search procedure. The construction phase of GRASP is essentially a randomized greedy algorithm. Repeated application of the construction procedure yields diverse starting solutions for the local search. Our experiment shows that the GRASP algorithm is efficient and is able to discover coherent biclusters.

Dynamic Web Pre-fetching Technique for Latency Reduction

This paper reports a dynamic pre-fetching technique in which Web caching and pre-fetching techniques are integrated. In this technique, number of subsequent links to be pre-fetched depends on the users interest in accessing the documents, cache contents, current bandwidth usage and maximum capacity of the existing network. Preference lists are used for maintaining users preferences. A hash table is used for storing the list of accessed URLs and its weight information. Intelligent agent monitors the bandwidth usage and helps the prediction engine to decide the number of Web pages to be pre-fetched. The simulation result shows that dynamic pre-fetching technique provides better utilization of bandwidth and reduces latency. Using dynamic pre-fetching technique, cache hit ratio is increased to 40%-75% and latency is reduced to 20%-63%.