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Acharya K.K.,Institute of Bioinformatics and Applied Biotechnology IBAB | Acharya K.K.,Shodhaka Life science Pvt. Ltd. | Chandrashekar D.S.,Institute of Bioinformatics and Applied Biotechnology IBAB | Chitturi N.,Institute of Bioinformatics and Applied Biotechnology IBAB | And 8 more authors.
BMC Genomics | Year: 2010

Background: In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time.Results: The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a 'consensus' expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/experimental conditions and/or cell-types.Conclusions: Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a 'reliability-score', which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: http://resource.ibab.ac.in/MGEx-Tdb/. © 2010 Acharya et al; licensee BioMed Central Ltd. Source


Kouser,DoS in Computer Science | Rangarajan L.,DoS in Computer Science | Chandrashekar D.S.,Institute of Bioinformatics and Applied Biotechnology IBAB | Chandrashekar D.S.,Manipal University India | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

With the massive amount of biological sequence data being generated by current technologies there is an urgent need to come out with faster sequence comparison methods. Most of the existing sequence comparison methods are alignment based which are proven to be very computationally complex when compared to the alignment free methods. In this paper, we have proposed alignment free methods for analysis of promoter sequences. Promoter sequences play a crucial role in gene regulation. After extracting the promoter sequence, matrices of motif frequency with position information (Position Specific Motif Matrix (PSMM)) is constructed, this is further taken for promoter analysis. These designed Frequency Based (FD) algorithms are tested on three different promoter datasets obtained from NCBI and UCSC repositories. The results show high similarity values for promoters with similar functionality and low values otherwise. © Springer International Publishing Switzerland 2015. Source


Roy D.,Vellore Institute of Technology | Kumar V.,Vellore Institute of Technology | Kumar V.,Shodhaka Life science Pvt. Ltd. | Acharya K.K.,Shodhaka Life science Pvt. Ltd. | Thirumurugan K.,Vellore Institute of Technology
Applied Biochemistry and Biotechnology | Year: 2014

Human maltase glucoamylase (MGAM) is a potent molecular target for controlling post prandial glucose surplus in type 2 diabetes. Binding of small molecules from Syzygium sp. with α-glucosidase inhibitory potential in MGAM has been investigated in silico. Our results suggest that myricetin was the most potent inhibitor with high binding affinity for both N- and C-terminals of MGAM. Molecular dynamics revealed that myricetin interacts in its stretched conformation through water-mediated interactions with C-terminal of MGAM and by normal hydrogen bonding with the N-terminal. W1369 of the extended 21 amino acid residue helical loop of C-terminal plays a major role in myricetin binding. Owing to its additional sugar sites, overall binding of small molecules favours C-terminal MGAM. © 2013 Springer Science+Business Media New York. Source


Chitturi N.,Institute of Bioinformatics and Applied Biotechnology IBAB | Chitturi N.,International Institute of Information Technology, Hyderabad | Balagannavar G.,Institute of Bioinformatics and Applied Biotechnology IBAB | Chandrashekar D.S.,Institute of Bioinformatics and Applied Biotechnology IBAB | And 5 more authors.
BMC Genomics | Year: 2013

Background: Standard 3′ Affymetrix gene expression arrays have contributed a significantly higher volume of existing gene expression data than other microarray platforms. These arrays were designed to identify differentially expressed genes, but not their alternatively spliced transcript forms. No resource can currently identify expression pattern of specific mRNA forms using these microarray data, even though it is possible to do this.Results: We report a web server for expression profiling of alternatively spliced transcripts using microarray data sets from 31 standard 3′ Affymetrix arrays for human, mouse and rat species. The tool has been experimentally validated for mRNAs transcribed or not-detected in a human disease condition (non-obstructive azoospermia, a male infertility condition). About 4000 gene expression datasets were downloaded from a public repository. 'Good probes' with complete coverage and identity to latest reference transcript sequences were first identified. Using them, 'Transcript specific probe-clusters' were derived for each platform and used to identify expression status of possible transcripts. The web server can lead the user to datasets corresponding to specific tissues, conditions via identifiers of the microarray studies or hybridizations, keywords, official gene symbols or reference transcript identifiers. It can identify, in the tissues and conditions of interest, about 40% of known transcripts as 'transcribed', 'not-detected' or 'differentially regulated'. Corresponding additional information for probes, genes, transcripts and proteins can be viewed too. We identified the expression of transcripts in a specific clinical condition and validated a few of these transcripts by experiments (using reverse transcription followed by polymerase chain reaction). The experimental observations indicated higher agreements with the web server results, than contradictions. The tool is accessible at http://resource.ibab.ac.in/TIPMaP.Conclusion: The newly developed online tool forms a reliable means for identification of alternatively spliced transcript-isoforms that may be differentially expressed in various tissues, cell types or physiological conditions. Thus, by making better use of existing data, TIPMaP avoids the dependence on precious tissue-samples, in experiments with a goal to establish expression profiles of alternative splice forms - at least in some cases. © 2013 Chitturi et al.; licensee BioMed Central Ltd. Source


Chandrashekar D.S.,Institute of Bioinformatics and Applied Biotechnology IBAB | Chandrashekar D.S.,Manipal University India | Dey P.,Institute of Bioinformatics and Applied Biotechnology IBAB | Dey P.,Manipal University India | And 2 more authors.
PLoS ONE | Year: 2015

Background Genome-wide repeat sequences, such as LINEs, SINEs and LTRs share a considerable part of the mammalian nuclear genomes. These repeat elements seem to be important for multiple functions including the regulation of transcription initiation, alternative splicing and DNA methylation. But it is not possible to study all repeats and, hence, it would help to short-list before exploring their potential functional significance via experimental studies and/or detailed in silico analyses. Result We developed the 'Genomic Repeat Element Analyzer for Mammals' (GREAM) for analysis, screening and selection of potentially important mammalian genomic repeats. This web-server offers many novel utilities. For example, this is the only tool that can reveal a categorized list of specific types of transposons, retro-transposons and other genome-wide repetitive elements that are statistically over-/under-represented in regions around a set of genes, such as those expressed differentially in a disease condition. The output displays the position and frequency of identified elements within the specified regions. In addition, GREAM offers two other types of analyses of genomic repeat sequences: A) enrichment within chromosomal region(s) of interest, and b) comparative distribution across the neighborhood of orthologous genes. GREAM successfully short-listed a repeat element (MER20) known to contain functional motifs. In other case studies, we could use GREAM to short-list repetitive elements in the azoospermia factor a (AZFa) region of the human Y chromosome and those around the genes associated with rat liver injury. GREAM could also identify five over-represented repeats around some of the human and mouse transcription factor coding genes that had conserved expression patterns across the two species. Conclusion GREAM has been developed to provide an impetus to research on the role of repetitive sequences in mammalian genomes by offering easy selection of more interesting repeats in various contexts/regions. GREAM is freely available at http://resource.ibab.ac.in/GREAM/. © 2015 Chandrashekar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source

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