Bioinformatics Center, IMTECH

From DrugPedia: A Wikipedia for Drug discovery

Revision as of 09:29, 19 August 2008

Bioinformatics Center or BIC at Institutte of Microbial Technology IMTECH, Chandigarh.

IMTECH

History

Department of Biotechnology (DBT), Government of India established Biotechnology Information System (BTIS) in 1987, first of its kind in the world. The aim of BTIS network is to create an infrastructure that enables it to harness biotechnology through the application of Bioinformatics. Initially, nine Distributed Information Centres (DICs) were established all over the India with the objectives to create infrastructure for information dissemination in the field of biotechnology. Bioinformatics Centre (BIC) at the Institute of Microbial Technology (IMTECH), Chandigarh is one of the DICs supported by DBT under BTIS programme.

The major objective of BIC at IMTECH is to create infrastructure in the field of protein modeling and engineering. In its initial phase, BIC developed software to create and manage biological databases. One of the major strengths of this centre is that it was always ahead in establishing modern technologies such as

• Network was established with other DICs in 1990, via NICNET
• Established and maintained EMAIL facility in 1990 under ERNET project
• High speed workstation (DIGITAL Alpha Chips) were installed in 1994
• Local Area Network was established in 1994
• Internet & Intranet in was setup at IMTECH 1997

Over a period of time since its establishment in 1987, Bioinformatics centre at IMTECH has shown consistent growth in the different areas of bioinformatics whether in carrying out peer reviewed research or providing services world wide. BIC has developed a number of web servers based on peer reviewed research carried out at the bioinformatics centre. These servers are getting more than 25,000 hits per day. BIC staff has published more than 60 research papers in reputed international journals, approximately 30 Copyright software and more than 30 abstract/poster/papers presented in national and international conferences.

Research work

Computer-aided Protein Structure Prediction

One of the major strengths of this centre is its infrastructure in protein modeling, which forms the base for designing drugs in-silico. This centre has been working for last more than 15 years to create/maintain state of art infrastructure for protein modeling. BIC has not only installed existing software but also developed new and improved tools for protein modeling.

BIC staff is not only expert in using protein modeling techniques but has also developed a number of tools for predicting structure of proteins from its amino acid sequence. BIC at IMTECH is only centre from Asia, which has been participating in the international competitions (like CASP, CAFASP and EVA) on protein structure prediction successfully. A few web servers in this area have been developed at IMTECH and are presented below.

Immunoinformatics: Subunit Vaccine design

The infectious diseases are major cause of premature deaths (around 20 millions per year). These diseases are major burden on human kind, particularly in developing countries in term of economy. The existing vaccines are not adequate and efficient to combat the infectious diseases. There is a need to develop vaccine particularly subunit vaccine against infectious diseases especially against M. Tuberculosis and HIV. A large number of pathogenic organisms has been sequenced or is in the advance stage of sequencing. Thus, it is important to develop bioinformatics tools for designing subunit vaccine at a large scale.

BIC has initiated a mission in 1999 to develop tools and databases for searching antigenic regions which can serve as vaccine candidates. A few web servers in this area have been developed at IMTECH and are presented below.

Genome Annotation

Presently, genomes of more than 1500 organisms have been sequenced or are in the advanced stage of sequencing. This has posed a major challenge for bioinformaticians to annotate these genomes for predicting the genes and the repeat regions. A few web servers in this area have been developed at IMTECH and are presented below.

Functional Annotation of Proteins

The protein sequence databases are growing exponentially due to progress in sequence techniques. The major problem is functional annotation as most of the proteins obtained from most of the genomes do not provide any information about the function of protein. A few web servers in this area have been developed at IMTECH and are presented below.

Databases

Major Achievements

• From India, it is the only centre which has been competing successfully at the international level under CASP or CAFASP programmes- Olympics of proteins secondary structure prediction and fold prediction, respectively.

• Centre has developed databases like MHCBN (a curated database of MHC binders and non-binders), BCIPEP (A database of immunodomiant B cell epitopes), Public Domain software in biology (PDRB) and Free software for General Purpose (FSGP). MHCBN and BCIPEP have been hosted at the web site of European Bioinformatics Institute, UK also. Centre is also maintaining mirror sites for biological databases and software.

• More than 60 web servers in the areas of functional proteomics, genomics, immunoinformatics etc. have been launched from the web site http://www.imtech.res.in. These servers are based on peer reviewed research being carried out.

• Online software, VaxiPred has been launched for the commercial users in collaboration with industry partner, BioMantra, a division of eLquest India, New Delhi.

• The centre has proven expertise in developing databases, analysis of information and in developing prediction software. The experts at the centre are apt in structure prediction of proteins along with modeling and simulation of biomloecules and handling their interactions with ligands or other biomolecules using docking software tools.

• Centre has signed Memorandum of Understanding with industries like eLquest India, New Delhi, TCS, Hyderabad, BIGTECH and Opportunia, New Delhi. As part of this, centre has provided two weeks training to a few personnel of Asia Privacy, Korea.

• Centre is also involved in education & training by conducting workshops and training programmes regularly in the area of Bioinformatics for students and faculty members.

Services Offered

Training

Bioinformatics Centre (BIC) conducts a workshop at national level annually in order to train researchers, students from academic and industrial sectors. A number of project assistants, working in BIC, are learning, getting trained while developing bioinformatics tools. In addition we also provide training to the industry and academic institutes on the basis of their request e.g. we have trained a group from Asia Privacy, a private company from Korea and selected faculty of DOEACC, centres of northern India.

Contract and Collaborative Research

Staff of BIC has contributed significantly in the field of Bioinformatics as evident from the development of servers and published research papers. These are also an outcome of collaborative research with wet lab scientists of IMTECH as well as other institutions. Staff of BIC has contributed indigenously developed algorithms to the Biosuite, developed by Tata Consultancy Services, Hyderabad under NMITLI programme of CSIR, New Delhi.

Consultancy

Staff of BIC has also provided consultancy to private companies like eLquest, New Delhi and ITTL, New Delhi in the field of Bioinformatics.

Infrastructure

Hardware

BIC is equipped with state-of-art computers ranging from desktop to workstations to server. The servers at IMTECH includes:

• SUN Servers (Models 420R, V240 & T1000)
• SGI server (origin) & workstations (fuel, O2)
• Xeon IBM Servers
• Apple G5 Cluster
• DELL and SUN workstations
• Itanium HP workstations
• SUN, Apple, Tandberg Storage

BIC also has a number of desktops and laptops.

Networks

• 2MBPS (1:1) Internet Bandwidth from VSNL
• Member of GARUDA, a national grid based on fibre optics
• Local Area Network

 * Hybrid topology with Optical Fiber backbone; 
 * DSLAM and ADSL technology for intranet via telephone lines; Wireless network 

Software

1. Operating Systems: MS-DOS 6.22, Microsoft Windows for work group 3.11, Windows 95, 98, Me, Windows NT, 2000 Professional, XP, Red Hat Linux 6.2, 9.0, RedHat Linux Enterprise Edition Advanced Server, Digital Unix 4.0E, SGI IRIX, Solaris 10.0.

2. Artificial intelligence: SNNS, Support Vector Machine, KNN, Bayes

3. Sequence and Structure Analysis: BLAST, FASTA, Ssearch, Align, Lalign/Palign, CLUSTAL-W, Doolittle package, PHYLIP, DSSP, CE, SSAP

4. Molecular Modeling and docking: AMBER, GROMOS, CHARMM, BiomedCache, X-LPOR, BABEL, MODELER, TINKER, Hex, AutoDock, DS Modeling, Insight II.

5. Molecular graphics: Rasmol, Midas Plus, Swiss PDB viewer, MOLMOL, MOLSCRIPT, RASTER3D.

6. Database: Oracle, PostgreSQL, MS –Access

7. Softwares for Statistics: Statistica, SPSS, R, MATLAB

8. Graphics: Corel Draw, PhotoShop

9. Document publishing: Acrobat Suite of software, MS Word; Adobe Page Maker, Reference Manager, Star Office, MS Publisher.

10. Hindi publishing: Akshar for windows, LeapOffice 2000.

11. Presentations: MS PowerPoint, Adobe Acrobat 7.0

12. Web designing: Adobe Illustrator, Front Page, Page Maker, MS Publisher, Macromedia Dreamweaver.

13. Anti-virus software: Norton AntiVirus, MacAfee Virus Scan, Norton Utilities.

14. Software for spread sheet: MS-Excel

Publications

Research Articles

1. Kumar, M., Thakur, V. and Raghava, G. P. S. (2007) COPid: composition based protein identification. In Silico Biology(In Press)

2. Lata, S. and Raghava, G. P. S.(2007) CytoPred: a server for prediction and classification of cytokines. Protein Engineering, Design and Selection (In Press)

3. Vivona, S., Gardy J.L., Ramachandran, S., Brinkman, F.S.L., Raghava, G. P. S., Flower, D.R. and Filippini, F. (2007) Computer aided biotechnology: from immunoinformatics to reverse vaccinology Trends in Biotechnology (In Press)

4. Muthukrishnan S., Garg A. and Raghava, G. P. S. (2007) OxyPred: Prediction and Classification of Oxygen-Binding Proteins Genomics, Proteomics & Bioinformatics (In Press)

5. Kumar, M., Gromiha, M.M. and Raghava, G. P. S. (2007) Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins: Structure, Function and Bioinformatics. (In Press)

6. Pashov A., Monzavi-Karbassi B., Raghava, G. P. S. and Kieber-Emmons, T. (2007) Peptide mimotopes as prototypic templates of broad-spectrum surrogates of carbohydrate antigens for cancer vaccination CRITICAL REVIEWS IN IMMUNOLOGY 27 (3): 247-270

7. Saha, S. and Raghava, G. P. S. (2007) Prediction of allergenic proteins and mapping of IgE epitopes in allergens Nature Protocols 10.1038/nprot.2007.505 (Online)

8. Singh, H. and Raghava, G. P. S. (2007) Prediction and mapping of promiscuous MHC class II binders in an antigen sequence Nature Protocols 10.1038/nprot.2007.502 (Online)

9. Lata, S., Sharma, B.K. and Raghava, G. P. S. (2007) Analysis and prediction of antibacterial peptides Nature Protocols 10.1038/nprot.2007.503 (Online)

10. Kumar, M. , Verma, R. and Raghava, G. P. S. (2007) Mitpred2: An improved method for predicting mitochondrial proteins using SVM and HMM Nature Protocols 10.1038/nprot.2007.488 (Online)

11. Kumar M., Gromiha M.M. and Raghava, G. P. S. (2007) Identification of DNA-binding proteins using support vector machines and evolutionary profiles BMC Bioinformatics 8:463

12. Kaur, H., Garg, A. and Raghava, G. P. S. (2007) PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett. 14:626-30

13. Rashid M., Saha S. and Raghava, G. P. S. (2007) Support Vector Machine-based Method for Predicting Subcellular Localization of Mycobacterial Proteins Using Evolutionary Information and Motifs BMC Bioinformatics 8: 337

14. Raghava, G. P. S. (2007) Prediction of subcellular localization of proteins using machine learning techniques and evolutionary information Amino Acids 33(3): X-XI

15. Lata, S., Sharma, B.K. and Raghava, G. P. S. (2007) Analysis and prediction of antibacterial peptides BMC Bioinformatics 2007, 8:263

16. Mishra, N., Kumar, M. and Raghava, G. P. S. (2007) Support vector machine based method for predicting Glutathione S-transferases proteins. Protein Pept Lett. 6:575-80

17. Vidyasagar et al. ...... Raghava, G. P. S. ........ (2007) BioSuite: A comprehensive bioinformatics software package (A unique industry-academia collaboration). CURRENT SCIENCE 92 (1): 29-38

18. Saha, S. and Raghava, G. P. S. (2007) Prediction of bacterial proteins. In Silico Biology 7: 0028

19. Saha, S. and Raghava, G. P. S. (2007) Prediction of neurotoxins based on their function and source. In Silico Biology 7: 0025

20. Saha, S., Zack, J., Singh, B. and Raghava, G. P. S. (2007) VGIchan: Prediction and classification of voltage-gated ion channels. Genomics Proteomics & Bioinformatics 4:253-8

21. Greenbaum et al. ...... Raghava, G. P. S. ...... (2007) Towards a consensus on datasets and evaluation metrics for developing B cell epitope prediction tools. Journal Molecular Recognition 20:75-82

22. Bhasin, M. and Raghava, G. P. S. (2007) A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J. Biosci. 32:31-42 [PDF]

23. Kaundal, R., Kapoor, A.S. and Raghava, G. P. S. (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7: 485

24. Saha, S. and Raghava, G. P. S. (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.PROTEINS:Structure, Function, and Bioinformatics 65:42-9.

25. Raghava, G. P. S. and Barton, G.J. (2006) Quantification of the variation in percentage identity for protein sequence alignments. BMC Bioinformatics 7: 415

26. Saha, S. and Raghava, G. P. S. (2006) AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Research 34:W202-9.

27. Kim, J.K., Raghava, G. P. S., Bang, S. and Choi, S. (2006) Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine Pattern Recognition Letters 27: 996-1001.

28. Saha, S. and Raghava, G. P. S. (2006) VICMpred: SVM-based method for the prediction of functional proteins of gram-negative bacteria using amino acid patterns and composition. Genomics Proteomics & Bioinformatics 4:42-7.

29. Kumar, M. , Verma, R. and Raghava, G. P. S. (2006) Prediction of mitochondrial proteins using support vector machine and hidden markov model . J. Biol. Chem. 281: 5357 - 5363.

30. Kaur, H. and Raghava, G.P.S. (2006) Prediction of CÉø-H...O and CÉø-H...π interactions in proteins using recurrent neural network. In-Silico Biology 6:11

31. Singh, M.K., Srivastava, S., Raghava, G. P. S. and Varshney, G.C. (2006) HaptenDB: A comprehensive database of haptens, carrier proteins and anti-hapten antibodies. Bioinformatics 22:253-5.

32. Saha, S., Bhasin, M. and Raghava, G. P. S. (2005) BCIPEP: A database of B-cell epitopes. BMC Genomics 6:79.

33. Garg, A., Kaur, H. and Raghava, G. P. S. (2005) Real value prediction of Solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins: Structure, Function, and Bioinformatics 61(2): 318-324.

34. The Indian Genome Variation Consortium (2005) The Indian Genome Variation database (IGVdb): a project overview. Human Genetics, Sep 2005: 1-11.

35. Bhasin, M. and Raghava, G. P. S. (2005)Â Pcleavage: A SVM based Method for Prediction of Consitutive and Immuno proteasome Cleavage Sites in Antigenic Sequences . Nucleic Acids Research 33:W202-7.

36. Kumar, M., Bhasin, M., Natt, N.K. and Raghava, G. P. S. (2005) BhairPred: A webserver for Prediction of Beta-hairpins in proteins from Multiple Alignment Information Using ANN and SVM Techniques . Nucleic Acids Research 33:W154-9.

37. Bhasin, M., Garg A., and Raghava, G. P. S. (2005)Â PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21: 2522-4.

38. Raghava, G. P. S. and Han, J. H. (2005) Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein. BMC Bioinformatics 6:59.

39. Garg,A., Bhasin,M and Raghava, G. P. S. (2005) SVM-based method for subcellular localization of human proteins using amino acid compositions, their order and similarity search. Journal of Biological Chemistry 280:14427-32.

40. Saha, S., Bhasin, M. and Raghava, G. P. S. (2005)Â Bcipep . Nucleic Acids Reasearch (Online; http://www3.oup.co.uk/nar/database/summary/642/ )

41. Singh M.K., Srivastava S., Raghava, G. P. S. and Varshney G.C (2004) HaptenDB: A database of hapten molecules. Nucleic Acids Reasearch (Online; http://www3.oup.co.uk/nar/database/summary/682/ )

42. Bhasin, M. and Raghava, G. P. S. (2005) MHCBN: A comprehensive database of MHC/TAP binders/non-binders and T-cell epitopes. Nucleic Acids Reasearch (Online; http://www3.oup.co.uk/nar/database/summary/702/ ).

43. Issac, B. and Raghava, G. P. S. (2005) FASTA servers for sequence similarity search. Proteomics Handbook. Edited by John M. Walker and published by Humana press. Chapter 48: 503-526.

44. Bhasin, M. and Raghava, G. P. S. (2005) GPCRsclass : A web tool for classification of amine type of G-protein coupled Receptors. Nucleic Acids Research 33:W143-7.

45. Bhasin, M. and Raghava, G. P. S. (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22:3195-204.

46. Kaur,H. and Raghava, G. P. S. (2004) A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 20:2751-8.

47. Kaur, H. and Raghava, G. P. S. (2004) Prediction of Alpha-turns in proteins using PSI-BLAST profiles and secondary structure information. Proteins: Structure, Function, and Genetics 55:83-90

48. Bhasin, M. and Raghava, G. P. S. (2004) SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics 12:421.

49. Bhasin, M. and Raghava, G. P. S. (2004) Analysis and Prediction of affinity of TAP binding peptides using Cascade SVM. Protein Science 13:596-607.

50. Sharma, D., Issac, B., Raghava, G. P. S. (2004) Ramaswamy, R. Spectral Repeat Finder (SRF): Identification of repetitive sequences using fourier transformation. Bioinformatics 20: 1405-1412.

51. Natt, N.K., Kaur, H. and Raghava, G. P. S. (2004) Prediction of Transmembrane regions of beta-barrel proteins using ANN and SVM based method.Proteins: Structure, Function, and Bioinformatics 56:11-8.

52. Bhasin, M. and Raghava, G. P. S. (2004) ESLpred: SVM Based Method for Subcellular Localization of Eukaryotic Proteins using Dipeptide Composition and PSI-BLAST. Nucleic Acids Reasearch 32:W414-9.

53. Kaur, H. and Raghava, G. P. S. (2004) Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins. FEBS Letters 564:47-57.

54. Bhasin, M. and Raghava, G. P. S. (2004) Classification of nuclear receptors based on amino acid composition and dipeptide composition. Journal of Biological Chemistry 279: 23262-6.

55. Bhasin, M. and Raghava, G. P. S. (2004) GPCRpred: An SVM Based Method for Prediction of families and subfamilies of G-protein coupled receptors Nucleic Acids Reasearch 32:W383-9.

56. Issac, B. and Raghava, G. P. S. (2004) EGPred: Prediction of Eukaryotic genes using ab initio methods after combining with sequence similarity approaches Genome Research 14:1756-66.

57. Saha, S. and Raghava, G. P. S. (2004) BcePred: Prediction of continuous B-Cell epitopes in antigenic sequences using physico-chemical properties. ICARIS 2004: 197-204

58. Singh, B (2004) PepBuild: A web server for building structure data for a peptide/protein. Nucleic Acid Res. 32:W559-W561

59. Luthra-Guptasarma, M. & B. Singh (2004) HLA-B27 lacking associated beta2-microglobulin rearranges to auto-display or cross-display residues 169-181: a novel molecular mechanism for spondyloarthropathies. FEBS Lett., 575:1-8.

60. Bhasin M., Singh H., Raghava, G. P. S. (2003) MHCBN: Update 2002. Nucleic Acid Res. (Online)

61. Kaur, H. and Raghava, G. P. S.(2003) Prediction of Beta-turns in proteins from multiple alignment using neural network. Protein Sci 12:627-34

62. Bhasin, M., Singh, H. and Raghava, G. P. S. (2003) MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665

63. Singh, H. and Raghava, G. P. S. (2003) ProPred1: Prediction of promiscuous MHC class-I binding sites. Bioinformatics, 19: 1009-14

64. Kaur, H. and Raghava, G. P. S. (2003) A neural network based method for prediction of gama-turns in proteins from multiple sequence alignment. Protein Science; 12:923-929.

65. Kaur, H. and Raghava, G. P. S. (2003) BTEVAL: A server for evaluation of beta-turn prediction methods. Journal of Bioinformatics and Computational Biology 1(3):495-504

66. Raghava, G. P. S. Solanki, R.J., Soni, V. and Agrawal, P. (2003) Fingerprinting methods for phylogenetic classification and indentification of microorganisms based on variation in 16S rRNA gene sequences. Page 373-82, Chapter 44, BioComputing: Computer Tools for Biologists. Edited By Staurt M. Brown

67. Sarin,J., Raghava, G. P. S. and Chakraborti, P. K. (2003) Intrinsic contributions of polar amino acid residues towards thermal stability of an ABC-ATPase of mesophilic origin. Protein Science 12:2118-2120

68. Bhasin, M., and Raghava, G. P. S. (2003) Prediction of promiscuous and high affinity mutated MHC binders. Hybrid Hybridomics, 22(4):229-34.

69. Raghava, G. P. S., Searle, S.M., Audley, P.C., Barber J.D. and Barton G.J. (2003) OXBench: Evaluation of protein multiple sequence alignment BMC Bioinformatics 4:47

70. Issac, B., Singh H., Kaur, H. and Raghava, G. P. S. (2002) Locating probable genes using fourier transform. Bioinformatics 18:196-7

71. Bhasin M., Singh H., Raghava, G. P. S. (2002) MHCBN Nucleic Acid Res. ( Online)

72. Kaur, H. and Raghava, G. P. S. (2002) BetaTPred: Prediction of Beta-turns in a protein using statistical algorithms. Bioinformatics 18:498-9

73. Issac, B. and Raghava, G. P. S. (2002) GWFASTA: A server for FASTA search in Eukaryotic and Microbial genomes. Biotechniques 33:548-56

74. Kaur, H. and Raghava, G. P. S.. (2002) An Evaluation of Beta-Turn Prediction Methods. Bioinformatics 18:1508-14

75. Singh, H. and Raghava, G. P. S. (2002) Detection of Orientation of MHC Class II Binding Peptides Using Bioinformatics Tools Biotech Software and Internet Report, 3:146.

76. Raghava, G. P. S. (2001) PDSB: public domain software in biology. Biotech Software and Internet Report 2:154-156.

77. Raghava, G. P. S. and Agrewala, J. N. (2001) A web based method for computing endpoint titer and concentration of antibody/antigen. Biotech Software and Internet Report, 2:196-7.

78. Singh, H. and Raghava, G. P. S. (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17: 1236-7

79. Raghava, G. P. S. (2001) PDWSB: public domain web servers in biology. Biotech Software and Internet Report, 2:152-3

80. Raghava, G. P. S. (2001) A web server for computing size of DNA/Protein fragment using graphical method. Biotech Software and Internet Report, 2:198-200

81. Raghava, G. P. S. et al., Fingerprinting methods for phylogenetic classification and indentification of microorganisms based on variation in 16S rRNA gene sequences. Biotechniques 29:108-115.

82. Raghava, G. P. S. Procalss: A computer program for predicting the protein structural classes. J. Biosciences 24, 176

83. Nihalani, D., Raghava, G.P.S and Sahni, G (1997). Mapping of the plasminogen binding site of streptokinase with short synthetic peptides. Protein Science, 6:1284-92.

84. Raghava, G. P. S. (1995) DNAOPT : A computer program to aid optimization of gel conditions of DNA gel electrophoresis and SDS-PAGE. Biotechniques 18: 274-81.

85. Raghava, G. P. S., Goel, A., Singh, A. M., and Varshney, G. (1994) A simple microassay for computing the hemolytic potency of drugs. Biotechniques it 17: 1148-53.

86. Raghava, G. P. S.. (1994) Improved estimation of DNA fragment lengths from gel electrophoresis. Biotechniques 17: 100-104

87. Raghava, G. P. S.. and Agrewala, J.N. (1994) Method for determining the affinity of monoclonal antibody using non-competitive ELISA : A computer program. Journal of Immunoassay 15: 115-128.

88. Raghava, G. P. S.. and Sahni, G. (1994) GMAP: a multipurpose computer program to aid synthetic gene design, cassette mutagenesis and introduction of potential restriction sites into DNA sequences. Biotechniques 16: 1116-1123.

89. Agrewala, J.N., Raghava, G. P. S., Mishra, G.C. (1993) Measurement and computation of murine interleukine-4 and interfron-gamma by exploiting the unique abilities of these lymphokines to induce the secretion of IgG1 and IgG2a. Journal of Immunoassay 14, 83-97.

90. Raghava, G. P. S., Joshi, A.K. and Agrewala, J.N. (1992) Calculation of antibody and antigen concentrations from ELISA data using a graphical method. J. Immunol. Methods 153, 263-264.

91. Tripathy, S.C., Balasubramanian, R., Raghava, G. P. S., Chatterjee, J.K. (1988) Microprocessor based active and reactive power measuremet. Journal of the Institution of Engineers (India): Electrical Engineering Division 69 pt 2 , 73-77.

See also

• IMTECH
• Dr. G. P. S. Raghava

External Links

IMTECH

BIC

Dr. GPS Raghava

Dr. B. Singh

Reprints