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Metagenomic Surveys of Gut Microbiota

Genomics, Proteomics & Bioinformatics 2015年第3期13卷 148-158页

作者： Rahul Shubhra Mandal sudipto saha Santasabuj Das Biomedical Informatics Centre National Institute of Cholera and Enteric Diseases Bioinformatics Centre Bose Institute Division of Clinical Medicine National Institute of Cholera and Enteric Diseases

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational tax- onomic unit （OTU） levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ulti- mately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interac- tion among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe-microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe-microbe inter- action networks.

关键词： Disease Sequencing 16S rRNA Operational taxonomic unit Microbial interactionnetwork

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VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

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Genomics, Proteomics & Bioinformatics 2006年第1期4卷 42-47页

作者： sudipto saha G.P.S. Raghava Institute of Microbial Technology Chandigarh India.

In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins （255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors）. First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins （http：//***/raghava/vicmpred/）.

关键词： virulence factor cellular process information molecule tetrapeptide VICMpred gram-negative bacteria

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VGIchan:Prediction and Classification of Voltage-Gated Ion Channels

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Genomics, Proteomics & Bioinformatics 2006年第4期4卷 253-258页

作者： sudipto saha Jyoti Zack Balvinder Singh G.P.S.Raghava Institute of Microbial Technology Chandigarh 160036India.

This study describes methods for predicting and classifying voltage-gated ion channels. Firstly, a standard support vector machine （SVM） method was developed for predicting ion channels by using amino acid composition and dipeptide composition, with an accuracy of 82.89% and 85.56%, respectively. The accuracy of this SVM method was improved from 85.56% to 89.11% when combined with PSIBLAST similarity search. Then we developed an SVM method for classifying ion channels （potassium, sodium, calcium, and chloride） by using dipeptide composition and achieved an overall accuracy of 96.89%. We further achieved a classification accuracy of 97.78% by using a hybrid method that combines dipeptidebased SVM and hidden Markov model methods. A web server VGIchan has been developed for predicting and classifying voltage-gated ion channels using the above approaches. VGIchan is freely available at ***/raghava/vgichan/.

关键词： ion channels prediction VGIchan SVM HMM

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CFOA Based Band Pass and Band Stop Ladder Filter—A New Configuration

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Circuits and Systems 2016年第1期7卷 29-42页

作者： Praween K. Sinha Neelam Sharma Simran Agarwal sudipto saha Electronics and Communication Department Maharaja Agrasen Institute of Technology New Delhi India

A new technique for the conversion of ladder based filter into CFOA based filter has been proposed. The technique uses signal flow graph and converts the existing LC ladder based filter into band pass & band stop configurations. The design of band pass and band stop filter has been realized using the proposed technique. The proposed configuration is implemented using CFOA as an active device and all the capacitors are grounded. CFOA based circuits have greater linearity, high dynamic rate, high slew rate and high signal bandwidth. Simulation has been carried out using simulation software P Spice (v10.1). The simulation results have been demonstrated and discussed.

关键词： CFOA-Current Feedback Operational Amplifier Ladder Filter Signal Flow Graph Current Mode Voltage Mode Band Pass Filter Band Stop Filter

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