Birla Institute of Scientific Research

Jaipur, India

Birla Institute of Scientific Research

Jaipur, India
SEARCH FILTERS
Time filter
Source Type

Lalwani S.,Birla Institute of Scientific Research | Kumar R.,Malaviya National Institute of Technology, Jaipur | Deep K.,Indian Institute of Technology Roorkee
Swarm and Evolutionary Computation | Year: 2017

This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments. © 2017 Elsevier B.V.


Lalwani S.,Birla Institute of Scientific Research | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta N.,Birla Institute of Scientific Research
Computing and Informatics | Year: 2016

This paper proposes two-level particle swarm optimization (TL-PSO), an efficient PSO variant that addresses two levels of optimization problem. Level one works on optimizing dimension for entire swarm, whereas level two works for optimizing each particle's position. The issue addressed here is one of the most challenging multiple sequence alignment (MSA) problem. TL-PSO deals with the arduous task of determination of exact sequence length with most suitable gap positions in MSA. The two levels considered here are: to obtain optimal sequence length in level one and to attain optimum gap positions for maximal alignment score in level two. The performance of TL-PSO has been assessed through a comparative study with two kinds of benchmark dataset of DNA and RNA. The efficiency of the proposed approach is evaluated with four popular scoring schemes at specific parameters. TL-PSO alignments are compared with four PSO variants, i.e. S-PSO, M-PSO, ED-MPSO and CPSO-Sjt, and two leading alignment software, i.e. ClustalW and T-Coffee, at different alignment scores. Hence obtained results prove the competence of TL-PSO at accuracy aspects and conclude better score scheme.


Saxena T.,Birla Institute of Scientific Research | Kaushik P.,University of Rajasthan | Krishna Mohan M.,Birla Institute of Scientific Research
Diagnostic Microbiology and Infectious Disease | Year: 2015

Escherichia coli O157:H7 is a zoonotic pathogen with its ability to cause human illness ranging from diarrheal disease to fatal hemolytic uremic syndrome. E. coli O157:H7 had been associated with waterborne outbreaks resulting in high morbidity and mortality worldwide. Therefore, it is important to investigate the prevalence of E. coli O157:H7 in water sources especially used for drinking and to develop the diagnostic methods for its early detection. The review describes traditional cultural methods, immunological techniques, and polymerase chain reaction (PCR)-based methods for detection of this bacterium in water sources. The current PCR-based techniques such as real-time PCR are more specific and sensitive and require less detection time (<3 hours). These methods can be applied for regular water monitoring and proper management of water sources to prevent waterborne diseases due to E. coli O157:H7. © 2015 Elsevier Inc.


Dadheech P.K.,Government of Rajasthan | Dadheech P.K.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | Abed R.M.M.,Sultan Qaboos University | Mahmoud H.,Kuwait University | And 2 more authors.
Phycologia | Year: 2012

Four new cyanobacterial strains isolated from biological desert crusts in Thar Desert, India were characterized using a polyphasic approach. The strains were designated to two mophotypes, but all strains exhibited identical 16S ribosomal RNA (rRNA) gene sequences. On the basis of 16S rRNA phylogenetic reconstruction, the strains belonged to the Oscillatoriales order and formed a coherent cluster in the phylogenetic tree, with more than 5% sequence divergence to the closest relative belonging to a species of Microcoleus Desmazières ex Gomont. Our strains were different from the genus Microcoleus in phenotypic characters such as organization of thallus, trichome width, cell shape, gas vesicle, thylakoid arrangement and habitat. Although the strains shared some morphological similarities with members of Phormidiaceae, they differed in thylakoid pattern and 16S rRNA gene sequences. The type strain (PD2001/TDC17T) grew up to 45°C with optimum growth between 30 and 35°C. The sharp decrease in growth of that strain at 1% salinity indicates its sensitivity to salts. Phylogenetic, morphological, ultrastuctural and physiological analyses demonstrated that the investigated strains represented a novel cyanobacterial genus, for which the name Desertifilum tharense gen. et sp. nov. is proposed, with PD2001/TDC17T (5 CCAP 1420/4T 5 BISR/CYANO/61T) as the type strain.


Yadav H.,Birla Institute of Scientific Research | Gothwal R.K.,Birla Institute of Scientific Research | Nigam V.K.,Birla Institute of Technology | Sinha-Roy S.,Birla Institute of Scientific Research | Ghosh P.,Birla Institute of Scientific Research
Biocatalysis and Agricultural Biotechnology | Year: 2013

In the present study we isolated and characterized thermo-tolerant phosphate solubilizing bacteria (PSB) having high ferric phosphate (Fe-P) and aluminum phosphate (Al-P) solubilizing abilities for the first time from rock phosphate mines of Jhamarkotra. Optimization for phosphate (P) solubilization by the isolate BISR-HY65 was performed. Different insoluble P sources viz. hydroxyapatite (H-Ap), Al-P and Fe-P along with rock phosphate (RP) from two different mines of India were used to characterize phosphate solubilizing (PSE) abilities. Optimum conditions found were: temperature 50 °C, pH 7.5, xylose as carbon source, ammonium oxalate as nitrogen source and potassium sulfate as potassium source. Phosphate solubilization was found to be associated with the release of organic acids in culture. HPLC analysis of the culture broth at 96. h of incubation detected four known acids (citric, gluconic, malic and formic acid) along with three unknown acids. Molecular characterization showed our strain to be of Brevibacillus sp. © 2013 Elsevier Ltd.


Lalwani S.,Birla Institute of Scientific Research | Lalwani S.,Malaviya National Institute of Technology, Jaipur | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta N.,Malaviya National Institute of Technology, Jaipur
Swarm and Evolutionary Computation | Year: 2016

This paper introduces a set-basedtwo-level particle swarm optimization algorithm (TL-PSOfold) with multiple swarmsfor finding secondary structure of RNA with prediction accuracy. First objectiveis concerned with maximizing number of stacked loops at hydrogen bond, whereas, second objective deals with minimum free energy (MFE) at standard nearest neighbor database (NNDB). First level of the algorithm works on theen tire search space for the best solution of each swarm, where as, these cond level worksatthe gbest solution of each swarm.The set based PSO approach has been applied at both levels to represent and up date these to for dered pairs of the folded RNA sequence. Improved weight parameters chemes with mutation operators are implemented for better convergence and to over come the stagnation problem. Bi-objectives nature of TL-PSO fold enables the algorithm to achieve maximum matching pairs as well as optimum structure a trespective levels. The performance of TL-PSO fold is compared with a family of PSO based a lgorithmsi. e. Helix PSO v1, Helix PSO v2, PSO fold, Set PSO,IPSO, FPSO, popular secondary structure prediction software RNA fold, mfold and other metaheuristics RNA-Predict, SARNA-Predict at the criteria of sensitivity, specificity and F-measure. Simulation results for TL-PSO fold show that it yield shigher prediction accuracy than all the compared approaches. The claimis supported by the non-parametric statistical significance testing using Kruskala-Wallistest followed by post-hoc analysis. © 2015 Elsevier B.V. All rights reserved.


Ghosh P.,Birla Institute of Scientific Research
Proceedings of the Indian National Science Academy | Year: 2015

The second generation biofuel technologies are evolving rapidly to provide solutions for the partial replacement of fossil fuels. Both bioethanol and biodiesel have great potential in India. Both the technologies, however, have to overcome various bottlenecks before they become commercial technologies. In this regard, several critical questions, besides science and technology, need to be resolved. This will require new ways of thinking about agriculture, energy infrastructure and rural economic development. © Printed in India.


Bagaria B.,Sms Medical College And Hospital | Sood S.,Sms Medical College And Hospital | Sharma R.,Sms Medical College And Hospital | Lalwani S.,Birla Institute of Scientific Research
Cancer Biology and Medicine | Year: 2013

Objective: To determine the clinical serum levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), individually and in combination, for the diagnosis of 50 healthy subjects and 150 cases of esophageal, gastric, and colon cancers. Methods: The sensitivities of the two markers were compared individually and in combination, with specificity set at 100%. Receiver operating characteristic (ROC) curves were plotted. Results: Serum CEA levels were significantly higher in cancer patients than in the control group. The sensitivity of CEA was determined: in esophageal cancer, sensitivity=28%, negative predictive value (NPV)=61.72%, and AUC=0.742 (SE=0.05), with a significance level of P<0.0001; in gastric cancer, sensitivity=30%, NPV=58.82%, and AUC=0.734 (SE=0.05), with a significance level of P<0.0001; in colon cancer, sensitivity=74%, NPV=79.36%, and AUC=0.856 (SE=0.04), with a significance level of P<0.0001. The sensitivity of CA19-9 was also evaluated: in esophageal cancer, sensitivity=18%, NPV=54.94%, and AUC=0.573 (SE=0.05), with a significance level of P=0.2054. In gastric cancer, sensitivity=42%, NPV=63.29%, and AUC=0.679 (SE=0.05), with a significance level of P<0.0011. In colon cancer, sensitivity=26%, NPV=57.47%, and AUC=0.580 (SE=0.05), with a significance level of P=0.1670. The following were the sensitivities of CEA/CA19-9 combined: in esophageal cancer, sensitivity=42%, NPV=63.29%, SE=0.078 (95% CI: 0.0159-0.322); gastric cancer, sensitivity=58%, NPV=70.42%, SE=0.072 (95% CI: -0.0866-0.198); and colon cancer, sensitivity=72%, NPV=78.12%, SE=0.070 (95% CI: 0.137-0.415). Conclusion: CEA exhibited the highest sensitivity for colon cancer, and CA19-9 exhibited the highest sensitivity for gastric cancer. Combined analysis indicated an increase in diagnostic sensitivity in esophageal and gastric cancer compared with that in colon cancer. Copyright © 2013 by Cancer Biology & Medicine.


Meena S.,Birla Institute of Scientific Research | Gothwal R.K.,Birla Institute of Scientific Research | Krishna Mohan M.,Birla Institute of Scientific Research | Ghosh P.,Birla Institute of Scientific Research
Extremophiles | Year: 2014

A strain of Brevibacillus formosus, capable of producing a high level of chitinase, was isolated and characterized for the first time from the Great Indian Desert soils. The production of extracellularly secreted chitinase was analyzed for its biocontrol potential and optimized by varying media pH, temperature, incubation period, substrate concentrations, carbon and nitrogen sources, etc. A twofold increase in chitinase production (798 IU/mL) was achieved in optimized media containing (g l-1) chitin 2.0, malt extract 1.5, glycerol 1.0, ammonium nitrate 0.3 %, T-20 (0.1 %) and media pH 7.0 at 37 °C. The produced enzyme was purified using a three-step purification procedure involving ultra-filtration, ammonium sulphate precipitation and adsorption chromatography. The estimated molecular weight of the purified enzyme was 37.6 kDa. The enzyme was found thermostable at higher temperatures and showed a t 1/2 of more than 5 h at 100 °C. Our results show that the chitinase produced by B. formosus BISR-1 is thermostable at higher temperatures. © 2014 Springer Japan.


Lalwani S.,Birla Institute of Scientific Research | Lalwani S.,Malaviya National Institute of Technology, Jaipur | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta N.,Malaviya National Institute of Technology, Jaipur
Swarm and Evolutionary Computation | Year: 2015

A two-level particle swarm optimization (TL-PSO) algorithm is proposed for training stochastic context-sensitive hidden Markov model (cs-HMM), that addresses a thrust area of bioinformatics i.e. structural alignment of pseudoknotted non-coding RNAs (ncRNAs). Due to the well-conserved sequences and corresponding secondary structures of ncRNAs, the structural information becomes imperative for performing their alignments. Proposed approach is unique in the sense: it is the first idea so far which works on optimization of the model length; also it is the first swarm intelligence technique that is proposed for training csHMM. The two-level strategy with training and cross training sets helps in increasing the diversity of the particles so as to avoid trapping in local optima, yields more accurate estimation parameters, preserves the structure of the model and provides the best compression from the model. TL-PSO yields a trained stochastic model with position-dependent probabilities that achieves high prediction ratios than the compared non-stochastic scoring matrix based csHMM approaches. TL-PSO is also tested solely for sequence alignment of proteins, by training the conventional HMMs. TLPSO-HMM produces an effective framework for sequence alignment in terms of alignment quality and prediction accuracy than the competitive state-of-the-art and family of PSO based algorithms. Conjointly, TLPSO-csHMM finds higher prediction measures than competitive RNA structural alignment techniques for pseudoknotted and non-pseudoknotted RNA structures of diverse complexities. © 2014 Elsevier B.V. All rights reserved.

Loading Birla Institute of Scientific Research collaborators
Loading Birla Institute of Scientific Research collaborators