Publications by authors named "Shankaracharya"

16 Publications

  • Page 1 of 1

Pathogenic Huntingtin Repeat Expansions in Patients with Frontotemporal Dementia and Amyotrophic Lateral Sclerosis.

Neuron 2021 02 26;109(3):448-460.e4. Epub 2020 Nov 26.

Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia 25125, Italy; MAC Memory Clinic, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia 25125, Italy.

We examined the role of repeat expansions in the pathogenesis of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) by analyzing whole-genome sequence data from 2,442 FTD/ALS patients, 2,599 Lewy body dementia (LBD) patients, and 3,158 neurologically healthy subjects. Pathogenic expansions (range, 40-64 CAG repeats) in the huntingtin (HTT) gene were found in three (0.12%) patients diagnosed with pure FTD/ALS syndromes but were not present in the LBD or healthy cohorts. We replicated our findings in an independent collection of 3,674 FTD/ALS patients. Postmortem evaluations of two patients revealed the classical TDP-43 pathology of FTD/ALS, as well as huntingtin-positive, ubiquitin-positive aggregates in the frontal cortex. The neostriatal atrophy that pathologically defines Huntington's disease was absent in both cases. Our findings reveal an etiological relationship between HTT repeat expansions and FTD/ALS syndromes and indicate that genetic screening of FTD/ALS patients for HTT repeat expansions should be considered.
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http://dx.doi.org/10.1016/j.neuron.2020.11.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864894PMC
February 2021

Differential Expression in Blood-Brain Barrier Is Responsible for Strain Specific Central Nervous System Transduction Profile of AAV-PHP.B.

Hum Gene Ther 2020 01 13;31(1-2):90-102. Epub 2019 Dec 13.

Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts.

Adeno-associated virus (AAV) gene therapy for neurological diseases was revolutionized by the discovery that AAV9 crosses the blood-brain barrier (BBB) after systemic administration. Transformative results have been documented in various inherited diseases, but overall neuronal transduction efficiency is relatively low. The recent development of AAV-PHP.B with ∼60-fold higher efficiency than AAV9 in transducing the adult mouse brain was the major first step toward acquiring the ability to deliver genes to the majority of cells in the central nervous system (CNS). However, little is known about the mechanism utilized by AAV to cross the BBB, and how it may diverge across species. In this study, we show that AAV-PHP.B is ineffective for systemic CNS gene transfer in the inbred strains BALB/cJ, BALB/cByJ, A/J, NOD/ShiLtJ, NZO/HILtJ, C3H/HeJ, and CBA/J mice, but it is highly potent in C57BL/6J, FVB/NJ, DBA/2J, 129S1/SvImJ, and AKR/J mice and also the outbred strain CD-1. We used the power of classical genetics to uncover the molecular mechanisms AAV-PHP.B engages to transduce CNS at high efficiency, and by quantitative trait locus mapping we identify a 6 Mb region in chromosome 15 with an logarithm of the odds (LOD) score ∼20, including single nucleotide polymorphisms in the coding region of 9 different genes. Comparison of the publicly available data on the genome sequence of 16 different mouse strains, combined with RNA-seq data analysis of brain microcapillary endothelia, led us to conclude that the expression level of is likely the determining factor for differential efficacy of AAV-PHP.B in transducing the CNS across different mouse strains.
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http://dx.doi.org/10.1089/hum.2019.186DOI Listing
January 2020

ALPK1 missense pathogenic variant in five families leads to ROSAH syndrome, an ocular multisystem autosomal dominant disorder.

Genet Med 2019 09 10;21(9):2103-2115. Epub 2019 Apr 10.

Drs. Farley, Polo and Ho, Colonial Heights, VA, USA.

Purpose: To identify the molecular cause in five unrelated families with a distinct autosomal dominant ocular systemic disorder we called ROSAH syndrome due to clinical features of retinal dystrophy, optic nerve edema, splenomegaly, anhidrosis, and migraine headache.

Methods: Independent discovery exome and genome sequencing in families 1, 2, and 3, and confirmation in families 4 and 5. Expression of wild-type messenger RNA and protein in human and mouse tissues and cell lines. Ciliary assays in fibroblasts from affected and unaffected family members.

Results: We found the heterozygous missense variant in the ɑ-kinase gene, ALPK1, (c.710C>T, [p.Thr237Met]), segregated with disease in all five families. All patients shared the ROSAH phenotype with additional low-grade ocular inflammation, pancytopenia, recurrent infections, and mild renal impairment in some. ALPK1 was notably expressed in retina, retinal pigment epithelium, and optic nerve, with immunofluorescence indicating localization to the basal body of the connecting cilium of the photoreceptors, and presence in the sweat glands. Immunocytofluorescence revealed expression at the centrioles and spindle poles during metaphase, and at the base of the primary cilium. Affected family member fibroblasts demonstrated defective ciliogenesis.

Conclusion: Heterozygosity for ALPK1, p.Thr237Met leads to ROSAH syndrome, an autosomal dominant ocular systemic disorder.
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http://dx.doi.org/10.1038/s41436-019-0476-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752478PMC
September 2019

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene.

Neuron 2018 03;97(6):1268-1283.e6

Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.
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http://dx.doi.org/10.1016/j.neuron.2018.02.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867896PMC
March 2018

Rare mutations in RINT1 predispose carriers to breast and Lynch syndrome-spectrum cancers.

Cancer Discov 2014 Jul 2;4(7):804-15. Epub 2014 May 2.

Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; and.

Unlabelled: Approximately half of the familial aggregation of breast cancer remains unexplained. A multiple-case breast cancer family exome-sequencing study identified three likely pathogenic mutations in RINT1 (NM_021930.4) not present in public sequencing databases: RINT1 c.343C>T (p.Q115X), c.1132_1134del (p.M378del), and c.1207G>T (p.D403Y). On the basis of this finding, a population-based case-control mutation-screening study was conducted that identified 29 carriers of rare (minor allele frequency < 0.5%), likely pathogenic variants: 23 in 1,313 early-onset breast cancer cases and six in 1,123 frequency-matched controls [OR, 3.24; 95% confidence interval (CI), 1.29-8.17; P = 0.013]. RINT1 mutation screening of probands from 798 multiple-case breast cancer families identified four additional carriers of rare genetic variants. Analysis of the incidence of first primary cancers in families of women carrying RINT1 mutations estimated that carriers were at increased risk of Lynch syndrome-spectrum cancers [standardized incidence ratio (SIR), 3.35; 95% CI, 1.7-6.0; P = 0.005], particularly for relatives diagnosed with cancer under the age of 60 years (SIR, 10.9; 95% CI, 4.7-21; P = 0.0003).

Significance: The work described in this study adds RINT1 to the growing list of genes in which rare sequence variants are associated with intermediate levels of breast cancer risk. Given that RINT1 is also associated with a spectrum of cancers with mismatch repair defects, these findings have clinical applications and raise interesting biological questions.
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http://dx.doi.org/10.1158/2159-8290.CD-14-0212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4234633PMC
July 2014

A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data.

Nat Biotechnol 2014 Jul 18;32(7):663-9. Epub 2014 May 18.

Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.
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http://dx.doi.org/10.1038/nbt.2895DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4157619PMC
July 2014

Relationship estimation from whole-genome sequence data.

PLoS Genet 2014 Jan 30;10(1):e1004144. Epub 2014 Jan 30.

Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.
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http://dx.doi.org/10.1371/journal.pgen.1004144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907355PMC
January 2014

Meropenem: a potent drug against superbug as unveiled through bioinformatics approaches.

Int J Bioinform Res Appl 2013 ;9(2):109-20

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi, India.

Global spread of multi-drug resistant bacteria like Klebsiella pneumoniae and Escherichia coli have raised the alarm for researchers and doctors throughout the world. This new mechanism of resistance and the ability of ndm-1 gene to be transferred between the species may end the era of antibiotics treatment. Carbapenems are reliable drugs against many multi-resistant gram-negative pathogens. A 3-D homology model of NDM-1 was built and analysed for elucidation of functional site and binding interactions. This study revealed that meropenem has good interaction with the active sites of the receptor that could retard the spread of antibiotic resistant bacteria.
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http://dx.doi.org/10.1504/IJBRA.2013.052444DOI Listing
March 2014

Computational intelligence-based diagnosis tool for the detection of prediabetes and type 2 diabetes in India.

Rev Diabet Stud 2012 10;9(1):55-62. Epub 2012 May 10.

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, India.

Background: The incidence of diabetes is increasing rapidly across the globe. India has the highest proportion of diabetic patients, earning it the doubtful distinction of the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. To date, early identification of prediabetes or type 2 diabetes has proven problematic, such that there is an urgent requirement for tools enabling easy, quick, and accurate diagnosis.

Aim: To develop an easy, quick, and precise tool for diagnosing early diabetes based on machine learning algorithms.

Methods: The dataset used in this study was based on the health profiles of diabetic and non-diabetic patients from hospitals in India. A novel machine learning algorithm, termed "mixture of expert", was used for the determination of a patient's diabetic state. Out of a total of 1415 subjects, 1104 were used to train the mixture of expert system. The remaining 311 data sets were reserved for validation of the algorithm. Mixture of expert was implemented in matlab to train the data for the development of the model. The model with the minimum mean square error was selected and used for the validation of the results.

Results: Different combinations and numbers of hidden nodes and expectation maximization (EM) iterations were used to optimize the accuracy of the algorithm. The overall best accuracy of 99.36% was achieved with an iteration of 150 and 20 hidden nodes. Sensitivity, specificity, and total classification accuracy were calculated as 99.5%, 99.07%, and 99.36%, respectively. Furthermore, a graphical user interface was developed in java script such that the user can readily enter the variables and easily use the algorithm as a tool.

Conclusions: This study describes a highly precise machine learning prediction tool for identifying prediabetic, diabetic, and non-diabetic individuals with high accuracy. The tool could be used for large scale screening in hopsitals or diabetes prevention programs.
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http://dx.doi.org/10.1900/RDS.2012.9.55DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3448174PMC
January 2013

SWIFT MODELLER v2.0: a platform-independent GUI for homology modeling.

J Mol Model 2012 Jul 9;18(7):3021-3. Epub 2011 Dec 9.

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi, 835215 Jharkhand, India.

SWIFT MODELLER v2.0 is a platform-independent Java-based graphical user interface to MODELLER. It provides an interactive homology modeling solution by automating the formatting, scripting, and data extraction processes, meaning that the user only needs to paste in the protein target sequence as input. SWIFT MODELLER v2.0 takes a step-by-step approach where the flow of the software screens depicts steps in the homology modeling protocol. Ramachandran plots and DOPE profile graphs are sketched and displayed for in-depth model analysis, along with an embedded Jmol viewer for 3D visualization of the constructed model. SWIFT MODELLER v2.0 is functional on all Linux-based and Microsoft Windows operating systems for which MODELLER has been developed. The software is available as freeware at http://www.bitmesra.ac.in/swift-modeller/swift.htm .
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http://dx.doi.org/10.1007/s00894-011-1319-6DOI Listing
July 2012

Java-based diabetes type 2 prediction tool for better diagnosis.

Diabetes Technol Ther 2012 Mar 7;14(3):251-6. Epub 2011 Nov 7.

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi, India.

Background: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence.

Methods: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm.

Results: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format.

Conclusions: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.
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http://dx.doi.org/10.1089/dia.2011.0202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3284698PMC
March 2012

Homology modeling and function prediction of hABH1, involving in repair of alkylation damaged DNA.

Interdiscip Sci 2011 Sep 29;3(3):175-81. Epub 2011 Sep 29.

Department of Biotechnology, Birla Institute of Technology, Ranchi, 835215, India.

Inhibition of DNA repair mechanism through alkylating agents in tumor cells is an important method for cancer treatment. Alkylation damage repair gene AlkB was first reported in E. coli. In human and other mammals eight distinguishing homologs of AlkB were detected and are known as hABH1 to hABH8. Crystal structures of hABH2 and hABH3 elucidated the role of human AlkB homologs involved in DNA and RNA repair pathways. No crystal structure of hABH1 is available for the detailed study on the nature and function of the molecule. In the present work we performed homology modeling and different tertiary structure based study on human AlkB homolog hABH1. hABH1.B99990005.pdb, out of five models generated using the program modeler 9v7 and validated with Ramachandran plot showed that 97.9% residues were in the favored and additional allowed region and less residues in disallowed region, which is the best among all models. Functions of the selected model were studied in terms of cation binding, transition metal ion binding and metal ion binding function with oxidoreductase activity. Two functional sites and one conserved cluster were detected in the protein. Ligand binding residue prediction showed four ligand clusters with 17 ligands in cluster 1. In this cluster seven Fe(2+) heterogen counts were detected. Most significantly, predicted iron-binding motif in hABH1 was found as His231-X-Asp233-XnHis287 which corresponds to His131-XAsp133-Xn-His187 in AlkB of E. coli homologue. This shows the similar pattern of aspartic acid and histidine residues in the functional part of the protein both in human and E. coli. These results can be used further to design inhibitors aiding chemotherapy and cancer related diseases.
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http://dx.doi.org/10.1007/s12539-011-0087-4DOI Listing
September 2011

A molecular docking study of anticancer drug paclitaxel and its analogues.

Indian J Biochem Biophys 2011 Apr;48(2):101-5

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, India.

Present study was aimed at finding a better alternative to paclitaxel, an anticancer chemotherapeutic drug. Two targets, tubulin beta-1 chain and apoptosis regulator Bcl-2 protein (202F) were used in the study. Of these, structure of tubulin beta-1 chain is not known and that of Bcl-2 was taken from protein data bank with ID 202F. Tertiary structure model of tubulin beta-1 chain was predicted and validated. The validated 3D structure of tubulin beta-1 chain and Bcl-2 protein was taken to study their interaction with paclitaxel. Molecular docking of paclitaxel and its analogues was performed with these targets separately. Results showed that out of 84 analogues taken from PubChem, CID_44322802 had glide score of -9.62, as compared to -5.86 of paclitaxel with tubulin beta-1 chain. It was also observed that CID_9919057 had glide score of -9.0, as compared to -8.24 of paclitaxel with Bcl-2 protein. However, further experimental and clinical verification is needed to establish these analogues as drug.
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April 2011

SWIFT MODELLER: a Java based GUI for molecular modeling.

J Mol Model 2011 Oct 22;17(10):2601-7. Epub 2011 Jan 22.

Department of Biotechnology, Birla Institute of Technology, Mesra, 835215 Jharkhand, India.

MODELLER is command line argument based software which requires tedious formatting of inputs and writing of Python scripts which most people are not comfortable with. Also the visualization of output becomes cumbersome due to verbose files. This makes the whole software protocol very complex and requires extensive study of MODELLER manuals and tutorials. Here we describe SWIFT MODELLER, a GUI that automates formatting, scripting and data extraction processes and present it in an interactive way making MODELLER much easier to use than before. The screens in SWIFT MODELLER are designed keeping homology modeling in mind and their flow is a depiction of its steps. It eliminates the formatting of inputs, scripting processes and analysis of verbose output files through automation and makes pasting of the target sequence as the only prerequisite. Jmol (3D structure visualization tool) has been integrated into the GUI which opens and demonstrates the protein data bank files created by the MODELLER software. All files required and created by the software are saved in a folder named after the work instance's date and time of execution. SWIFT MODELLER lowers the skill level required for the software through automation of many of the steps in the original software protocol, thus saving an enormous amount of time per instance and making MODELLER very easy to work with.
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http://dx.doi.org/10.1007/s00894-011-0960-4DOI Listing
October 2011

Computational intelligence in early diabetes diagnosis: a review.

Rev Diabet Stud 2010 10;7(4):252-62. Epub 2011 Feb 10.

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi, India.

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.
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http://dx.doi.org/10.1900/RDS.2010.7.252DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143540PMC
October 2011

In silico analysis of motifs in promoters of differentially expressed genes in rice (Oryza sativa L.) under anoxia.

Int J Bioinform Res Appl 2009 ;5(5):525-47

Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.

The aim of this study was to characterise the molecular mechanisms of transcriptional regulation of Differentially Expressed Genes (DEGs) in rice coleoptiles under anoxia by identifying motifs that are common in the promoter region of co-regulated genes. Un-changed DEGs (<2 fold and >-2), up-regulated DEGs (>or=2 fold) and down-regulated DEGs (
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http://dx.doi.org/10.1504/IJBRA.2009.028681DOI Listing
January 2010