Welcome to Prof. Gajendra P.S. Raghava's Group

Curriculam Vitae of Gajendra P.S. Raghava

PDF version of Biodata
Nomination Paper for Election as Fellow

 Curriculum Vitae

Gajendra P. S. Raghava

 

PERSONAL DETAILS:

­­

Name: Gajendra P. S. Raghava

Current Position: Professor

Date of Birth: 25th May 1963

Organization/Institute: Indraprastha Institute of Information Technology,

Web Site http://webs.iiitd.edu.in/raghava/ 

Google Scholar: https://scholar.google.co.in/citations?user=XK5GUiYAAAAJ&hl=en

 

Email: ragahva@iiitd.ac.in                                       Mobile No: 91-9915656900

 

 

EDUCATION QUALIFICATION & PROFESSIONAL EXPERIENCE        

 

Academic Qualification (Bachelor’s Degree onwards)

_____________________________________________________________________________     

Degree                                       Institution                                                   Year                           Percentage

_____________________________________________________________________________

Ph. D.                        IMTECH/Panjab Univ. Chandigarh                       1996                          N/A

M. Tech.                  Indian Institute of Technology, Delhi                 1986                          7.41 CGPA

M. Sc.                       Meerut University, Meerut                                   1984                          68.9%

B. Sc.                         Meerut University, Meerut                                   1982                          66.4%

_____________________________________________________________________________

 

Positions held in chronological order

Period

 

From                To

Place of Employment

Designation

Scale of Pay

1986              1991

1991              1996

1996              2002

2002             2007

2008              2013

2013              2017

2017             Cont.

IMTECH, Chandiagrh

------- do ---------

------- do ---------

------- do ---------

------- do ---------

------- do ---------

IIIT, Delhi

Scientist-B

Scientist-C

Scientist-E1

Scientist-EII

Scientist-F

Chief Scientist

Professor

Rs 700-1400

Rs 3000-4500

Rs 12000-16000

PBIV GP 8700

PB IV GP 8900

PB IV GP 10000

PB IV GP 10500

 

 

Significant foreign assignments

Period of visit

From                                  To

Institute/ country visited

Purpose of visit

 August 1996

 July 1998

 Oxford University, Oxford, UK

Worked as Post-Doctoral fellow

 Sept. 2002

 March 2003

 UAMS, Little Rock, USA

 To establish bioinformatics infrastructure at UAMS

March 2004

August 2004

POSTECH, South Korea

Worked as visiting professor

 March 2006

 Sept. 2006

 UAMS, Little Rock, USA

Advanced infrastructure for research in bioinformatics.

 

 

 

MAJOR PROJECTS/FUNDINGS

(Major grants/project in last 10 years)

 

Bioinformatics Centre on Protein Modelling/Engineering (Coordinator: G. P. S. Raghava): A continuous project of Department of Biotechnology, Govt. of India, to provide bioinformatics services at national level. The Bioinformatics Center at Institute of Microbial Technology, Chandigarh was established in 1987 with specialization in the area of Protein Modeling and Protein Engineering. This is one of the core facilities in the field of biotechnology providing access to the latest information of the worlds databases in the fields of Protein Modeling and Protein Engineering. Dr Raghava head this project from 1994 to 2016 and got funding of around Rs 5 crores from DBT over the years.

 

Genomics and Informatics Solutions for integrating Biology(GENESIS): This was a mega network project of CSIR under 12th five-year plan (2012-2017) where Raghava is nodal officer. In this project 15 CSIR labs and nearly 55 scientists are participated in this project. GENESIS is an interdisciplinary project which aims to integrate computational scientists and biologists across CSIR to understand complex biological problems, mathematically model biological systems, compile and mine experimental data, discover drug/vaccine candidates and finally support translation of leads to medicine. Following site may provide more information about this project GENESIS. This project total budget was around Rs 50 crores.

J. C. Bose national fellowship: Raghava got this prestigious fellowship for 2010-2015 and 2015-2020 from department of science and technology (DST). Aim of this fellowship/project is to understand biological interactions particularly interaction network of proteins.  Total for two tenure of this fellowship is more than 1.5 cores.  

Advanced Centre for Protein Informatics, Science, Engineering & Technology: This is a facility creation project of CSIR, coordinated by CSIR-IMTECH (Nodal Officer: G P S Raghava) under 11th five-year plan (2007-11). The proposal is to set up a one-stop Centre for expertise, consultancy, and facilities, in the area of protein science and engineering, and protein biotechnology. This project has been completed successfully with world class infrastructure for Protein Informatics, Science, Engineering & Technology. Raghava got total fund of around 40 crores under this project. 

 

 

RESEARCH CONTRIBUTION      

 

Significant contributions to science and/or technology

Raghava contributed significantly in the field of bioinformatics and chemoinformatics particularly in the field of computer-aided drug and vaccine design. In contrast to traditional researchers where a scientist contributes to a particular problem or field; He contributed to multiple problems/fields important for translational medicine. His group developed more than 200 web servers, databases and software packages, which is highest contribution by a single group in the world. Following are major contributions.

·      Potential Drug Targets:  His group developed software for annotating genomes at nucleotide as well as at protein level, in order to identify potential drug targets. Highly accurate and novel methods have been developed for predicting genes and Spectral Repeats in genomes. These methods have been published in the top journals in the field of bioinformatics (e.g. Genome Research; Bioinformatics) and genomics. Raghava’s group developed number of novel methods for classifying and predicting receptors (G-protein coupled  receptor (GPCR), nuclear receptors),  toxins and virulent proteins.  His group developed method for predicting secondary structure (regular as well as irregular), super secondary structure (e.g. beta-hairpins, beta-barrels) and tertiary structure (ab initio methods for bioactive peptides). The performance of their best secondary structure prediction method was ranked within the top methods in the world, according to the community wide competitions like CASP, CAFASP and EVA. Raghava’s group is developing computation resources for drug discovery (CRDD) an in-silico modules of Open Source Drug Discovery(OSDD) where his group is collecting, compiling and developing computational resources for designing therapeutic molecules. His group is responsible for promoting open source in the field of chemoinformatics and pharmacoinformatics. In addition to development of software for designing small molecules, group also developed for designing cell penetrating, tumor homing, antibacterial peptides etc.

·      Computer-Aided Vaccine Design: Since 2001, his group is developing methods for predicting various component required for understand immune system and for designing subunit vaccines. Six primary/reference immunological databases (MHCBN, BCIpep, PRRDB, AntigenDB, HaptenDB, PolysacDB) have been developed by his group. In order to predict CTL epitopes with high accuracy, algorithams were developed for each component of its pathway , it includes i) Propred1 for  prediction of binders for 47 MHC class I alleles; ii) TAPpred for predict TAP binding peptides; iii) Pcleavage for predicting protease clevage sites. First time group developed method to predict conformational B-cell epitopes in antigen sequence. Overall more than 20 databases and web servers have been developed in the field of immunoinformatics.   

·      Experimental Validation of Predictions: His group is known for developing in silico tools which are heavily used by scientific community. Recently group also integrated theortical and experimental science to solve real life problems. His group discovered novel cell penetrating peptides using this integrative approach, which have better efficacy than existing peptides. These peptides also have capability to deliver proteins and  peptides inside human cells and skin layer.  These peptides have been patented and published.  In addition his group sequenced whole genome of number of ogansims  and annotated important pathways with experimental validation.  

 

 

 

Research in term of Impact factor

Raghava’s group has developed more than 250 in silico products (web servers and databases), each product is based on novel algorithm or data. Most of publications based on these in silico products are highly cited. Students and scientific community in the field of education, vaccine and drug discovery heavily use these services.  Following is summary of these publications that includes name of journal, journals impact factor, number of papers published by group and total impact (impact x papers)

 

Name of Journal

Impact

Factor*

No. of

Papers

Total

Impact

Comment

Science

34.6

1

34.7

 

*Impact factor is either latest if paper published long time back or maximum impact factor if multiple papers published in different years.

 

It excludes impact factor of papers published in new journals or protocols

Genome Research

14.6

1

14.6

Trends in Biotech.

11.9

1

11.9

Briefings in Bioinformatics

9.6

1

9.6

Nucleic Acids Res.

9.1

16

145.6

Journal Biol. Chemistry

6.4

3

19.2

Bioinformatics

6.0

11

66.0

BMC Bioinformatics

5.4

23

124.2

Scientific Reports (Nature)

5.6

22

123.2

Biology Direct

4.7

7

32.9

BMC Genomics

4.4

5

22.0

Plos One

4.4

21

92.4

Proteins

4.4

7

30.8

Protein Science

4.1

5

20.5

Front. Microbiol.

4.1

2

8.2

Other journals

~3.0

80

240

Total Impact factor (around) of papers

~1000

 

            Citation information 

Total citations on all papers                                  ~15200

Maximum citation of a single paper                           840

Papers with more than 200 citations                             16

Papers with more than 100 citations                             45

Papers with more than 10 citations (g-index)             191

Average Citation per paper                                         ~40

h-index                                                                          67

 

TEACHING AND HUMAN RESOURCE DEVELOPMENT (HRD)

 

Raghava works at two organization (CSIR-IMTECH and IIIT Delhi) during the current position of professor. Following is brief description of contribution at two organizations.

 

CSIR-IMTECH

·      Long term training: More than 80 students have been trained (PA, RA, Summer trainees) that includes 36 Ph.D students (24 completed). 

·      Short Training: More than 700 students got short term training as workshop/conference participants. Two international & more than 10 national workshop/training/conference were organized.

·      Bioinformatics Course: Taught around 200 pre-phd students over the years; full one session.  In addition, we are organizing small training programs for faculty and student of IMTECH from last 20 years.

·      Virtual skill development: In addition to direct training, we are providing training to users via our online computational resources. All tutorials/ documents/presentations related to bioinformatics are available from our sites. Under GPSR package we provide PERL code required to write core script in the field of computational biology. All over the world students and young faculties are using theses source codes for learning as well as for developing their own software packages.

·      Specialized Trainings: A customized training was organized for employees of a private company from South Korea in the year of 2002, for which we received Rs 4.55 lakhs. We also organized training for Department of Electronics (DOE) in year 2003, on PERL in Bioinformatics for which we received Rs 2.80 lakhs. 

 

IIIT Delhi

Since 2017, Raghava is working at IIIT Delhi, he taught two courses ("Machine Learning for Biomedical Applications" and "Big Data Mining in Healthcare"). Both courses are very popular among students particularly in B.Tech/M.Tech computer science students, in last semester more than 100 students join the course. Based on feedback of students, I am getting "Teaching Excellence Award" every year at IIIT Delhi. In addition, I got " Outstanding Educator Award" based on nomination by graduating BTech and MTech batches. A number of workshop/conferences organized to trained students outside IIIT Delhi. Number of M.Tech/B.Tech students completed their thesis, IP, IS and Capstone project in last two years.

 

SERVICE CONTRIBUTIONS

 

Institute service

·      Number of workshop/conferences organized at department of computational biology at IIIT Delhi.

·      Serving as Head of Department and contributing to all department activities

·      Member/chairman of number of committees at IIIT Delhi including chairman of space committee.

·      Contributed towards for initiating B. Tech in Computer Science and Bioscience program at IIIT Delhi in year 2018.  

    

Service outside the institute / Professional Service

·      Role as Editor: Serving editor in reputed journals like Section Editor of Translational Medicine, Academic Editor of Plos One, Associate Editor of BMC Bioinformatics.

·      Role as Reviewer: Reviewed number of manuscript for reputed journals.

·      Number of Ph.D. thesis has been evaluated for reputed universities.

·      Number of invited lectures have been delivered in workshops/conferences.  

·      Serving as a member of Task Force on “Theoretical and computational Biology” of DBT.

Service for Society

      Portal for Health Informatics (PHI): A web portal “Portal for Health Informatics (PHI)” has been developed to compile contribution of Indian Researchers/Academicians in the field of health informatics (See http://webs.iiitd.edu.in/  ).  This web portal maintained wide range of servers, databases and software developed in the field of bioinformatics, chemoinformatics, immunoinformatics, clinical bioinformatics, health informatics, genomics, etc. The main purpose of this web portal is to provide help to biologist working in the field of vaccine development, drug designing, etc. The servers help biologist’s in identifying potential vaccine candidates and hence save time and money. Overall aim of the web portal is to provide scientific computation and resources required in the healthcare sector. Indian researchers had contributed significantly in the field of informatics, particularly in the field of biomedicine. In order to provide visibility to web servers/software developed by Indian scientific community, we provide a link to these resources. This web portal is heavily used by the scientific community, getting thousands of hits per day.

      Group Web Server at IIITD: This was the first major project for group to install/launch web servers developed by group in the last two decades at IMTECH, Chandigarh. We successfully install and set more than 200 web servers at IIIT at Delhi. All web servers including new web servers developed at IIIT Delhi are working fine and heavily used by the scientific community. Scientific community especially experimental researcher are using our web server for predicting and validating vaccine and drug targets ( http://webs.iiitd.edu.in/raghava/  ).

 

HONORS & AWARDS

Ø  Fellow of National Academies

·      Fellow of National Academy of Sciences, India

·      Fellow of Indian Academy of Sciences, Banglore

Ø  Major National Awards

1.     National Bioscience Award for Carrier Development 2006, by DBT

2.     Shanti Swarup Bhatnagar Award 2008 in Biological Sciences

3.     NASI-Reliance Industries Platinum Jubilee Award (2009)

4.     JC Bose National Fellowship, 2010-15 & 2015-2020 by DST, India

5.     Lakshmipat Singhania-IIM Lucknow National Leadership Awards 2011 (Young Leader in category of Science and Technology)

6.     One paper listed in top 70 highly cited papers (ranked 18) published by CSIR scientists in last 70 years

7.     Sun Pharma Research Award 2018 by Sun Pharma Science Foundation

8.     Scientist Award 2019 by Organisation of Pharmaceutical Producers of India

Ø  Major International recognition/awards

  1. Listed in "The World's Most Influential Scientific Minds" by Thompson Reuters. This list contain 3200 individuals who published the greatest number of highly cited papers in one of 21 broad  fields, 2002-2012. Highly cited papers rank in top 1% by citations for their field and year of publication. Seven Indian scientist have been listed in above list. http://sciencewatch.com/sites/sw/files/sw-article/media/worlds-most-influential-scientific-minds-2014.pdf
  2. Thomson Reuters Research Excellence - India Research Front Awards 2009

Annexure I

List of Patents

1.     Raghava GPS, Gautam A, Nandanwar HS (2015): Cell Penetrating Peptide for Biomolecule Delivery. Patent WO/2015/075747-A1 .

2.     Raghava GPS, Gautam A (2018): Chemically Modified Cell-penetrating peptide for Intracellular Delivery of Nucleic Acids.  WO/2018/173077.

3.     Sharma DK, Gupa A, Raghava GPS, Gautam A, Kumari M (2019) Potent Peptide Inhibitors of Protein Aggregation. WO/2019/058389


Annexure II

Research Publications of Raghava

     1.         Bhalla, S., Kaur, H., Kaur, R., Sharma, S., & Raghava, G. P. S. (2020). Expression based biomarkers and models to classify early and late-stage samples of  Papillary Thyroid Carcinoma. PloS One, 15(4), e0231629. https://doi.org/10.1371/journal.pone.0231629

     2.         Dhall, A., Patiyal, S., Kaur, H., Bhalla, S., Arora, C., & Raghava, G. P. S. (2020). Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics. Frontiers in Genetics, 11.

     3.         Kaur, D., Arora, C., & Raghava, G. P. S. (2020). A hybrid model for predicting pattern recognition receptors using evolutionary information. Frontiers in Immunology, 11, 71.

     4.         Kaur, H., Bhalla, S., Kaur, D., & Raghava, G. P. S. (2020). CancerLivER: a database of liver cancer gene expression resources and biomarkers. Database, 2020.

     5.         Kumar, R., Lathwal, A., Kumar, V., Patiyal, S., Raghav, P. K., & Raghava, G. P. S. (2020). CancerEnD: A database of cancer associated enhancers. Genomics. https://doi.org/10.1016/j.ygeno.2020.04.028

     6.         Kumar, V., Kumar, R., Agrawal, P., Patiyal, S., & Raghava, G. P. S. (2020). A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure. Frontiers in Pharmacology, 11, 54.

     7.         Lathwal, A., Kumar, R., & Raghava, G. P. S. (2020). Computer-aided designing of oncolytic viruses for overcoming translational  challenges of cancer immunotherapy. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2020.04.008

     8.         Mason, M. J., Schinke, C., Eng, C. L. P., Towfic, F., Gruber, F., Dervan, A., White, B. S., Pratapa, A., Guan, Y., Chen, H., & others. (2020). Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease. Leukemia, 1–9.

     9.         Patiyal, S., Agrawal, P., Kumar, V., Dhall, A., Kumar, R., Mishra, G., & Raghava, G. P. S. (2020). NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence. Protein Science, 29(1), 201–210.

  10.         Agrawal, P., Kumar, S., Singh, A., Raghava, G. P. S., & Singh, I. K. (2019). NeuroPIpred: a tool to predict, design and scan insect neuropeptides. Scientific Reports, 9(1), 1–12.

  11.         Agrawal, P., Mishra, G., & Raghava, G. P. S. (2019). SAMbinder: A Web Server for Predicting S-Adenosyl-L-Methionine Binding Residues of a  Protein From Its Amino Acid Sequence. Frontiers in Pharmacology, 10, 1690. https://doi.org/10.3389/fphar.2019.01690

  12.         Agrawal, P., Patiyal, S., Kumar, R., Kumar, V., Singh, H., Raghav, P. K., & Raghava, G. P. S. (2019). ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank. Database, 2019.

  13.         Agrawal, P., Singh, H., Srivastava, H. K., Singh, S., Kishore, G., & Raghava, G. P. S. (2019). Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics, 19(13), 426.

  14.         Akhter, S., Kaur, H., Agrawal, P., & Raghava, G. P. S. (2019). RareLSD: a manually curated database of lysosomal enzymes associated with rare diseases. Database, 2019.

  15.         Bhalla, S., Kaur, H., Dhall, A., & Raghava, G. P. S. (2019). Prediction and analysis of skin cancer progression using genomics profiles of patients. Scientific Reports, 9(1), 1–16.

  16.         Brown, P., Tan, A.-C., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., Almeida, G. M. F., Cernava, T., Sorzano, C. O., Yeung, A. W. K., & others. (2019). Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database, 2019.

  17.         Kaur, D., Patiyal, S., Sharma, N., Usmani, S. S., & Raghava, G. P. S. (2019). PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. Database, 2019.

  18.         Kaur, H., Bhalla, S., & Raghava, G. P. S. (2019). Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PloS One, 14(9).

  19.         Kumar, R., Nagpal, G., Kumar, V., Usmani, S. S., Agrawal, P., & Raghava, G. P. S. (2019). HumCFS: A database of fragile sites in human chromosomes. BMC Genomics, 19(9), 985.

  20.         Kumar, R., Patiyal, S., Kumar, V., Nagpal, G., & Raghava, G. P. S. (2019). In Silico Analysis of Gene Expression Change Associated with Copy Number of Enhancers in Pancreatic Adenocarcinoma. International Journal of Molecular Sciences, 20(14), 3582.

  21.         Lathwal, A., Arora, C., & Raghava, G. P. S. (2019). Prediction of risk scores for colorectal cancer patients from the concentration of proteins involved in mitochondrial apoptotic pathway. PloS One, 14(9).

  22.         Raghav, P. K., Kumar, R., Kumar, V., & Raghava, G. P. S. (2019). Docking-based approach for identification of mutations that disrupt binding between Bcl-2 and Bax proteins: Inducing apoptosis in cancer cells. Molecular Genetics & Genomic Medicine, 7(11), e910.

  23.         Usmani, S. S., Agrawal, P., Sehgal, M., Patel, P. K., & Raghava, G. P. S. (2019). ImmunoSPdb: an archive of immunosuppressive peptides. Database, 2019.

  24.         Agrawal, P., Raghav, P. K., Bhalla, S., Sharma, N., & Raghava, G. P. S. (2018). Overview of free software developed for designing drugs based on protein-small molecules interaction. Current Topics in Medicinal Chemistry.

  25.         Agrawal, P., Bhalla, S., Chaudhary, K., Kumar, R., Sharma, M., & Raghava, G. P. S. (2018). In silico approach for prediction of antifungal peptides. Frontiers in Microbiology, 9, 323.

  26.         Agrawal, P., & Raghava, G. P. S. (2018). Prediction of Antimicrobial Potential of a Chemically Modified Peptide from its Tertiary Structure. Frontiers in Microbiology, 9, 2551.

  27.         Kumar, R., Kaur, R., Bhondekar, A. P., & Raghava, G. P. S. (2018). Human Opinion Inspired Feature Selection Strategy for Predicting the Pleasantness of a Molecule. In Advanced Computational and Communication Paradigms (pp. 197–205). Springer.

  28.         Kumar, V., Agrawal, P., Kumar, R., Bhalla, S., Usmani, S. S., Varshney, G. C., & Raghava, G. P. S. (2018). Prediction of cell-penetrating potential of modified peptides containing natural and chemically modified residues. Frontiers in Microbiology, 9, 725.

  29.         Mathur, D., Mehta, A., Firmal, P., Bedi, G., Sood, C., Gautam, A., & Raghava, G. P. S. (2018). TopicalPdb: A database of topically delivered peptides. PloS One, 13(2).

  30.         Mathur, D., Singh, S., Mehta, A., Agrawal, P., & Raghava, G. P. S. (2018). In silico approaches for predicting the half-life of natural and modified peptides in blood. PloS One, 13(6).

  31.         Nagpal, G., Chaudhary, K., Agrawal, P., & Raghava, G. P. S. (2018). Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants. Journal of Translational Medicine, 16(1), 181.

  32.         Nagpal, G., Usmani, S. S., & Raghava, G. P. S. (2018). A web resource for designing subunit vaccine against major pathogenic species of bacteria. Frontiers in Immunology, 9, 2280.

  33.         Usmani, S. S., Bhalla, S., & Raghava, G. P. S. (2018). Prediction of antitubercular peptides from sequence information using ensemble classifier and hybrid features. Frontiers in Pharmacology, 9(AUG). https://doi.org/10.3389/fphar.2018.00954

  34.         Usmani, S. S., Kumar, R., Bhalla, S., Kumar, V., & Raghava, G. P. S. (2018). In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. Advances in Protein Chemistry and Structural Biology.

  35.         Usmani, S. S., Kumar, R., Kumar, V., Singh, S., & Raghava, G. P. S. (2018). AntiTbPdb: a knowledgebase of anti-tubercular peptides. Database, 2018.

  36.         Baindara, P., Gautam, A., Raghava, G. P. S., & Korpole, S. (2017). Anticancer properties of a defensin like class IId bacteriocin Laterosporulin10. Scientific Reports, 7, 46541.

  37.         Bhalla, S., Chaudhary, K., Kumar, R., Sehgal, M., Kaur, H., Sharma, S., & Raghava, G. P. S. (2017). Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer. Scientific Reports, 7, 44997.

  38.         Bhalla, S., Sharma, S., & Raghava, G. P. S. (2017). Challenges in Prediction of different Cancer Stages using Gene Expression Profile of Cancer Patients. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 602.

  39.         Bhalla, S., Verma, R., Kaur, H., Kumar, R., Usmani, S. S., Sharma, S., & Raghava, G. P. S. (2017). CancerPDF: A repository of cancer-associated peptidome found in human biofluids. Scientific Reports, 7(1), 1511.

  40.         Dhanda, S. K., Usmani, S. S., Agrawal, P., Nagpal, G., Gautam, A., & Raghava, G. P. S. (2017). Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Briefings in Bioinformatics, 18(3), 467–478.

  41.         Keller, A., Gerkin, R. C., Guan, Y., Dhurandhar, A., Turu, G., Szalai, B., Mainland, J. D., Ihara, Y., Yu, C. W., Wolfinger, R., & others. (2017). Predicting human olfactory perception from chemical features of odor molecules. Science, eaal2014.

  42.         Nagpal, G., Chaudhary, K., Dhanda, S. K., & Raghava, G. P. S. (2017). Computational Prediction of the Immunomodulatory Potential of RNA Sequences. In RNA Nanostructures (pp. 75–90). Humana Press, New York, NY.

  43.         Nagpal, G., Usmani, S. S., Dhanda, S. K., Kaur, H., Singh, S., Sharma, M., & Raghava, G. P. S. (2017). Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Scientific Reports, 7, 42851.

  44.         Pahil, S., Taneja, N., Ansari, H. R., & Raghava, G. P. S. (2017). In silico analysis to identify vaccine candidates common to multiple serotypes of Shigella and evaluation of their immunogenicity. PloS One, 12(8).

  45.         Usmani, S. S., Bedi, G., Samuel, J. S., Singh, S., Kalra, S., Kumar, P., Ahuja, A. A., Sharma, M., Gautam, A., & Raghava, G. P. S. (2017). THPdb: database of FDA-approved peptide and protein therapeutics. PloS One, 12(7).

  46.         Agrawal, P., Bhalla, S., Usmani, S. S., Singh, S., Chaudhary, K., Raghava, G. P. S., & Gautam, A. (2016). CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides. Nucleic Acids Research, 44(D1), D1098--D1103.

  47.         Chaudhary, K., Kumar, R., Singh, S., Tuknait, A., Gautam, A., Mathur, D., Anand, P., Varshney, G. C., & Raghava, G. P. S. (2016). A Web Server and Mobile App for Computing Hemolytic Potency of Peptides. Scientific Reports, 6, 22843.

  48.         Chaudhary, K., Nagpal, G., Dhanda, S. K., & Raghava, G. P. S. (2016). Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Scientific Reports, 6, 20678.

  49.         Dhanda, S. K., Chaudhary, K., Gupta, S., Brahmachari, S. K., & Raghava, G. P. S. (2016). A web-based resource for designing therapeutics against Ebola Virus. Scientific Reports, 6, 24782.

  50.         Dhanda, S. K., Vir, P., Singla, D., Gupta, S., Kumar, S., & Raghava, G. P. S. (2016). A web-based platform for designing vaccines against existing and emerging strains of Mycobacterium tuberculosis. PloS One, 11(4).

  51.         Gautam, A., Chaudhary, K., Kumar, R., Gupta, S., Singh, H., & Raghava, G. P. S. (2016). Managing Drug Resistance in Cancer: Role of Cancer Informatics. Cancer Drug Resistance: Overviews and Methods, 299–312.

  52.         Gautam, A., Nanda, J. S., Samuel, J. S., Kumari, M., Priyanka, P., Bedi, G., Nath, S. K., Mittal, G., Khatri, N., & Raghava, G. P. S. (2016). Topical Delivery of Protein and Peptide Using Novel Cell Penetrating Peptide IMT-P8. Scientific Reports, 6, 26278.

  53.         Gupta, A. K., Kaur, K., Rajput, A., Dhanda, S. K., Sehgal, M., Khan, M. S., Monga, I., Dar, S. A., Singh, S., Nagpal, G., & others. (2016). ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis. Scientific Reports, 6, 32713.

  54.         Gupta, S., Chaudhary, K., Dhanda, S. K., Kumar, R., Kumar, S., Sehgal, M., Nagpal, G., & Raghava, G. P. S. (2016). A platform for designing genome-based personalized immunotherapy or vaccine against cancer. PloS One, 11(11).

  55.         Gupta, S., Chaudhary, K., Kumar, R., Gautam, A., Nanda, J. S., Dhanda, S. K., Brahmachari, S. K., & Raghava, G. P. S. (2016). Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine. Scientific Reports, 6, 23857.

  56.         Kumar, R., & Raghava, G. P. S. (2016). ApoCanD: Database of human apoptotic proteins in the context of cancer. Scientific Reports, 6, 20797.

  57.         Kumar, R., Kaur, R., Bhondekar, A. P., & Raghava, G. P. S. (2016). SMELL AND LANGUAGE: DATACENTRIC APPROACH TO PREDICTING SMELL OF A MOLECULE. Journal of Digital Olfaction Society, 4(1).

  58.         Mathur, D., Prakash, S., Anand, P., Kaur, H., Agrawal, P., Mehta, A., Kumar, R., Singh, S., & Raghava, G. P. S. (2016). PEPlife: A Repository of the Half-life of Peptides. Scientific Reports, 6, 36617.

  59.         Nanda, J. S., Kumar, R., & Raghava, G. P. S. (2016). dbEM: A database of epigenetic modifiers curated from cancerous and normal genomes. Scientific Reports, 6, 19340.

  60.         Nupur, L. N. U., Vats, A., Dhanda, S. K., Raghava, G. P. S., Pinnaka, A. K., & Kumar, A. (2016). ProCarDB: a database of bacterial carotenoids. BMC Microbiology, 16(1), 96.

  61.         Randhawa, H. K., Gautam, A., Sharma, M., Bhatia, R., Varshney, G. C., Raghava, G. P. S., & Nandanwar, H. (2016). Cell-penetrating peptide and antibiotic combination therapy: a potential alternative to combat drug resistance in methicillin-resistant Staphylococcus aureus. Applied Microbiology and Biotechnology, 100(9), 4073–4083.

  62.         Singh, H., Kumar, R., Singh, S., Chaudhary, K., Gautam, A., & Raghava, G. P. S. (2016). Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer, 16(1), 77.

  63.         Singh, H., & Raghava, G. P. S. (2016). BLAST-based structural annotation of protein residues using Protein Data Bank. Biology Direct, 11(1), 1–13.

  64.         Singh, H., Srivastava, H. K., & Raghava, G. P. S. (2016). A web server for analysis, comparison and prediction of protein ligand binding sites. Biology Direct, 11(1), 14.

  65.         Singh, S., Chaudhary, K., Dhanda, S. K., Bhalla, S., Usmani, S. S., Gautam, A., Tuknait, A., Agrawal, P., Mathur, D., & Raghava, G. P. S. (2016). SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Research, 44(D1), D1119--D1126.

  66.         Bhatia, R., Gautam, A., Gautam, S. K., Mehta, D., Kumar, V., Raghava, G. P. S., & Varshney, G. C. (2015). Assessment of SYBR Green I Dye-Based Fluorescence Assay for Screening Antimalarial Activity of Cationic Peptides and DNA Intercalating Agents. Antimicrobial Agents and Chemotherapy, 59(5), 2886–2889.

  67.         Dhar, J., Chakrabarti, P., Saini, H., Raghava, G. P. S., & Kishore, R. (2015). $\omega{\$}-Turn: A novel $\beta{\$}-turn mimic in globular proteins stabilized by main-chain to side-chain C H···O interaction. Proteins: Structure, Function, and Bioinformatics, 83(2), 203–214.

  68.         Gautam, A., Chaudhary, K., Kumar, R., & Raghava, G. P. S. (2015). Computer-aided virtual screening and designing of cell-penetrating peptides. In Cell-Penetrating Peptides (pp. 59–69). Humana Press, New York, NY.

  69.         Gautam, A., Sharma, M., Vir, P., Chaudhary, K., Kapoor, P., Kumar, R., Nath, S. K., & Raghava, G. P. S. (2015). Identification and characterization of novel protein-derived arginine-rich cell-penetrating peptides. European Journal of Pharmaceutics and Biopharmaceutics, 89, 93–106.

  70.         Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., & Raghava, G. P. S. (2015). Peptide Toxicity Prediction. In Computational Peptidology (pp. 143–157). Springer New York.

  71.         Kumar, R., Singh Chauhan, J., & Pal Singh Raghava, G. (2015). In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP. Current Cancer Drug Targets, 15(9), 836–846.

  72.         Kumar, R., Chaudhary, K., Chauhan, J. S., Nagpal, G., Kumar, R., Sharma, M., & Raghava, G. P. S. (2015). An in silico platform for predicting, screening and designing of antihypertensive peptides. Scientific Reports, 5, 12512.

  73.         Kumar, R., Chaudhary, K., Sharma, M., Nagpal, G., Chauhan, J. S., Singh, S., Gautam, A., & Raghava, G. P. S. (2015). AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides. Nucleic Acids Research, 43(D1), D956--D962.

  74.         Nagpal, G., Gupta, S., Chaudhary, K., Dhanda, S. K., Prakash, S., & Raghava, G. P. S. (2015). VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Scientific Reports, 5, 12478.

  75.         Panwar, B., & Raghava, G. P. S. (2015). Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides. Genomics, 105(4), 197–203.

  76.         Singh, H., Gupta, S., Gautam, A., & Raghava, G. P. S. (2015). Designing B-Cell Epitopes for Immunotherapy and Subunit Vaccines. Peptide Antibodies: Methods and Protocols, 327–340.

  77.         Singh, H., Singh, S., & Raghava, G. P. S. (2015). In silico platform for predicting and initiating $\beta{\$}-turns in a protein at desired locations. Proteins: Structure, Function, and Bioinformatics, 83(5), 910–921.

  78.         Singh, H., Singh, S., Singla, D., Agarwal, S. M., & Raghava, G. P. S. (2015). QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biology Direct, 10(1), 10.

  79.         Singh, S., Singh, H., Tuknait, A., Chaudhary, K., Singh, B., Kumaran, S., & Raghava, G. P. S. (2015). PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues. Biology Direct, 10(1), 1–19.

  80.         Tyagi, A., Tuknait, A., Anand, P., Gupta, S., Sharma, M., Mathur, D., Joshi, A., Singh, S., Gautam, A., & Raghava, G. P. S. (2015). CancerPPD: a database of anticancer peptides and proteins. Nucleic Acids Research, 43(D1), D837--D843.

  81.         Ahmad, S., Gupta, S., Kumar, R., Varshney, G. C., & Raghava, G. P. S. (2014). Herceptin resistance database for understanding mechanism of resistance in breast cancer patients. Scientific Reports, 4, 4483.

  82.         Chauhan, J. S., Dhanda, S. K., Singla, D., Agarwal, S. M., Raghava, G. P. S., Consortium, O. S. D. D., & others. (2014). QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR. PLOS ONE, 9(7), e101079.

  83.         Gautam, A., Kapoor, P., Chaudhary, K., Kumar, R., Raghava, G. P. S., Consortium, S. D. D., & others. (2014). Tumor Homing Peptides as Molecular Probes for Cancer Therapeutics, Diagnostics and Theranostics. Current Medicinal Chemistry, 21(21), 2367–2391.

  84.         Kumar, R., Chaudhary, K., Singla, D., Gautam, A., & Raghava, G. P. S. (2014). Designing of promiscuous inhibitors against pancreatic cancer cell lines. Scientific Reports, 4, 4668.

  85.         Mehta, D., Anand, P., Kumar, V., Joshi, A., Mathur, D., Singh, S., Tuknait, A., Chaudhary, K., Gautam, S. K., Gautam, A., & others. (2014). ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures. Database, 2014, bau051.

  86.         Mishra, N. K., Singla, D., Agarwal, S., & Raghava, G. P. S. (2014). ToxiPred: A Server for Prediction of Aqueous Toxicity of Small Chemical Molecules in T. Pyriformis. Journal of Translational Toxicology, 1(1), 21–27.

  87.         Nagpal, G., Sharma, M., Kumar, S., Chaudhary, K., Gupta, S., Gautam, A., & Raghava, G. P. S. (2014). PCMdb: pancreatic cancer methylation database. Scientific Reports, 4, 4197.

  88.         Panwar, B., Arora, A., & Raghava, G. P. S. (2014). Prediction and classification of ncRNAs using structural information. BMC Genomics, 15(1), 127.

  89.         Panwar, B., & Raghava, G. P. S. (2014). Prediction of uridine modifications in tRNA sequences. BMC Bioinformatics, 15(1), 326.

  90.         S Yadav, I., Singh, H., Khan, I., Chaudhury, A., Raghava, G. P. S., M Agarwal, S., & others. (2014). EGFRIndb: Epidermal Growth Factor Receptor Inhibitor Database. Anti-Cancer Agents in Medicinal Chemistry (Formerly Current Medicinal Chemistry-Anti-Cancer Agents), 14(7), 928–935.

  91.         Sharma, A., Singla, D., Rashid, M., & Raghava, G. P. (2014). Designing of peptides with desired half-life in intestine-like environment. BMC Bioinformatics, 15(1), 282.

  92.         Singh, H., Singh, S., & Raghava, G. P. S. (2014). Evaluation of Protein Dihedral Angle Prediction Methods. PloS One, 9(8), e105667.

  93.         Ahmed, F., Kaundal, R., & Raghava, G. P. S. (2013). PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors. BMC Bioinformatics, 14(Suppl 14), S9.

  94.         Ansari, H. R., Flower, D. R., & Raghava, G. (2013). Vaccine Antigen Databases. Encyclopedia of Systems Biology, 2331–2335.

  95.         Ansari, H. R., & Raghava, G. P. S. (2013). In Silico Models for B-Cell Epitope Recognition and Signaling. In In Silico Models for Drug Discovery (pp. 129–138). Humana Press.

  96.         Bala, M., Kumar, S., Raghava, G. P. S., & Mayilraj, S. (2013). Draft genome sequence of Rhodococcus qingshengii strain BKS 20-40. Genome Announcements, 1(2), e00128--13.

  97.         Bhartiya, D., Pal, K., Ghosh, S., Kapoor, S., Jalali, S., Panwar, B., Jain, S., Sati, S., Sengupta, S., Sachidanandan, C., & others. (2013). lncRNome: a comprehensive knowledgebase of human long noncoding RNAs. Database, 2013, bat034.

  98.         Chauhan, J. S., Rao, A., & Raghava, G. P. S. (2013). In silico platform for prediction of N-, O-and C-glycosites in eukaryotic protein sequences. PloS One, 8(6).

  99.         Dhanda, S. K., Gupta, S., Vir, P., & Raghava, G. P. S. (2013). Prediction of IL4 inducing peptides. Clinical and Developmental Immunology, 2013.

100.         Gautam, A., Chaudhary, K., Kumar, R., Sharma, A., Kapoor, P., Tyagi, A., Raghava, G. P., & others. (2013). In silico approaches for designing highly effective cell penetrating peptides. J Transl Med, 11(1), 74.

101.         Gautam, A., Chaudhary, K., Singh, S., Joshi, A., Anand, P., Tuknait, A., Mathur, D., Varshney, G. C., & Raghava, G. P. S. (2013). Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Research, gkt1008.

102.         Gupta, S., Ansari, H. R., Gautam, A., Raghava, G. P., Consortium, O. S. D. D., & others. (2013). Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biology Direct, 8(1), 27.

103.         Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Raghava, G. P. S., Consortium, O. S. D. D., & others. (2013). In Silico Approach for Predicting Toxicity of Peptides and Proteins. PloS One, 8(9), e73957.

104.         Iquebal, M. A., Dhanda, S. K., Arora, V., Dixit, S. P., Raghava, G. P. S., Rai, A., Kumar, D., & others. (2013). Development of a model webserver for breed identification using microsatellite DNA marker. BMC Genetics, 14(1), 118.

105.         Kaur, N., Kumar, S., Bala, M., Raghava, G. P. S., & Mayilraj, S. (2013). Draft genome sequence of Amycolatopsis decaplanina strain DSM 44594T. Genome Announcements, 1(2), e00138--13.

106.         Kumar, R., Chaudhary, K., Gupta, S., Singh, H., Kumar, S., Gautam, A., Kapoor, P., & Raghava, G. P. S. (2013). CancerDR: cancer drug resistance database. Scientific Reports, 3, 1445.

107.         Kumar, R., Raghava, G. P. S., & Abrams, W. R. (2013). Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence. PloS One, 8(4).

108.         Kumar, S., Kaur, C., Kimura, K., Takeo, M., Raghava, G. P. S., & Mayilraj, S. (2013). Draft genome sequence of the type species of the genus Citrobacter, Citrobacter freundii MTCC 1658. Genome Announcements, 1(1), e00120--12.

109.         Kumar, S., Kaur, N., Singh, N. K., Raghava, G. P. S., & Mayilraj, S. (2013). Draft genome sequence of Streptomyces gancidicus strain BKS 13-15. Genome Announcements, 1(2), e00150--13.

110.         Kumar, S., Vikram, S., & Raghava, G. P. S. (2013). Genome annotation of Burkholderia sp. SJ98 with special focus on chemotaxis genes. PloS One, 8(8), e70624.

111.         Mangal, M., Sagar, P., Singh, H., Raghava, G. P. S., & Agarwal, S. M. (2013). NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Research, 41(D1), D1124--D1129.

112.         Monu, B., Shailesh, K., Raghava, G. P. S., Shanmugam, M., & others. (2013). Draft genome sequence of Rhodococcus ruber strain BKS 20-38. Genome Announcements, 1(2).

113.         Panwar, B., Gupta, S., & Raghava, G. P. S. (2013). Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information. BMC Bioinformatics, 14(1), 44.

114.         Raghava, G. P. S., Mondal, A. K., Singla, D., Dhanda, S. K., Singla, D., Mondal, A. K., & Raghava, G. P. S. (2013). DrugMint: a webserver for predicting and designing of drug-like molecules. Biology Direct, 8(1), 1–12.

115.         Sandeep Kumar, D., Pooja, V., & Gajendra, R. (2013). Designing of interferon-gamma inducing MHC class-II binders. Biology Direct, 8(1), 30.

116.         Shailesh, K., Monu, B., Raghava, G. P. S., Shanmugam, M., & others. (2013). Draft genome sequence of Rhodococcus triatomae strain BKS 15-14. Genome Announcements, 1(2).

117.         Sharma, A., Kapoor, P., Gautam, A., Chaudhary, K., Kumar, R., Chauhan, J. S., Tyagi, A., & Raghava, G. P. S. (2013). Computational approach for designing tumor homing peptides. Scientific Reports, 3, 1607.

118.         Singh, H., Ansari, H. R., & Raghava, G. P. S. (2013). Improved method for linear B-cell epitope prediction using Antigen’s primary sequence. PloS One, 8(5), e62216.

119.         Singh, N. K., Kumar, S., Raghava, G. P. S., & Mayilraj, S. (2013). Draft genome sequence of Acinetobacter baumannii strain MSP4-16. Genome Announcements, 1(2), e00137--13.

120.         Singh, S. V., Kumar, N., Singh, S. N., Bhattacharya, T., Sohal, J. S., Singh, P. K., Singh, A. V., Singh, B., Chaubey, K. K., Gupta, S., & others. (2013). Genome sequence of the “Indian Bison Type” biotype of Mycobacterium avium subsp. paratuberculosis strain S5. Genome Announcements, 1(1), e00005--13.

121.         Singla, D., Dhanda, S. K., Chauhan, J. S., Bhardwaj, A., Brahmachari, S. K., Raghava, G. P. S., & others. (2013). Open Source Software and Web Services for Designing Therapeutic Molecules. Current Topics in Medicinal Chemistry, 13(10), 1172–1191.

122.         Singla, D., Tewari, R., Kumar, A., & Raghava, G. P. S. (2013). Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv). Chemistry Central Journal, 7(1), 49.

123.         Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., & Raghava, G. P. S. (2013). In silico models for designing and discovering novel anticancer peptides. Scientific Reports, 3, 2984.

124.         Vikram, S., Kumar, S., Vaidya, B., Pinnaka, A. K., & Raghava, G. P. S. (2013). Draft genome sequence of the 2-chloro-4-nitrophenol-degrading bacterium Arthrobacter sp. strain SJCon. Genome Announcements, 1(2), e00058--13.

125.         Vikram, S., Pandey, J., Kumar, S., & Raghava, G. P. S. (2013). Genes Involved in Degradation of para-Nitrophenol Are Differentially Arranged in Form of Non-Contiguous Gene Clusters in Burkholderia sp. strain SJ98. PloS One, 8(12), e84766.

126.         Aithal, A., Sharma, A., Joshi, S., Raghava, G. P. S., & Varshney, G. C. (2012). PolysacDB: A Database of Microbial Polysaccharide Antigens and Their Antibodies. PloS One, 7(4), e34613.

127.         Ansari, H. R., Flower, D. R., & Raghava, G. P. S. (2012). On the Development of Vaccine Antigen Databases: Progress, Opportunity, and Challenge. In Immunomic Discovery of Adjuvants and Candidate Subunit Vaccines (p. 117). Springer.

128.         Bhat, A. H., Mondal, H., Chauhan, J. S., Raghava, G. P. S., Methi, A., & Rao, A. (2012). ProGlycProt: a repository of experimentally characterized prokaryotic glycoproteins. Nucleic Acids Research, 40(D1), D388--D393.

129.         Chauhan, J. S., Bhat, A. H., Raghava, G. P. S., & Rao, A. (2012). GlycoPP: a webserver for prediction of N-and O-glycosites in prokaryotic protein sequences. PloS One, 7(7).

130.         Gautam, A., Singh, H., Tyagi, A., Chaudhary, K., Kumar, R., Kapoor, P., & Raghava, G. P. S. (2012). CPPsite: a curated database of cell penetrating peptides. Database, 2012, bas015.

131.         Kapoor, P., Singh, H., Gautam, A., Chaudhary, K., Kumar, R., & Raghava, G. P. S. (2012). TumorHoPe: a database of tumor homing peptides. PloS One, 7(4).

132.         Kumar, S., Kushwaha, H., Bachhawat, A. K., Raghava, G. P. S., & Ganesan, K. (2012). Genome sequence of the oleaginous red yeast Rhodosporidium toruloides MTCC 457. Eukaryotic Cell, 11(8), 1083–1084.

133.         Kumar, S., Randhawa, A., Ganesan, K., Raghava, G. P. S., & Mondal, A. K. (2012). Draft Genome Sequence of Salt-Tolerant Yeast Debaryomyces hansenii var. hansenii MTCC 234. Eukaryotic Cell, 11(7), 961–962.

134.         Kumar, S., Subramanian, S., Raghava, G. P. S., & Pinnaka, A. K. (2012). Genome sequence of the marine bacterium Marinilabilia salmonicolor JCM 21150T. Journal of Bacteriology, 194(14), 3746.

135.         Kumar, S., Vikram, S., & Raghava, G. P. S. (2012). Genome sequence of the nitroaromatic compound-degrading Bacterium Burkholderia sp. strain SJ98. Journal of Bacteriology, 194(12), 3286.

136.         Kumar, S., Vikram, S., Subramanian, S., Raghava, G. P. S., & Pinnaka, A. K. (2012). Genome Sequence of the Halotolerant Bacterium Imtechella halotolerans K1T. Journal of Bacteriology, 194(14), 3731.

137.         Raghava, G. P. S., Pinnaka, A. K., Kumar, S., Vikram, S., & Subramanian, S. (2012). Genome Sequence of the Halotolerant. J. Bacteriol, 194(14), 3731.

138.         Singh, H., Chauhan, J. S., Gromiha, M. M., Raghava, G. P. S., & others. (2012). ccPDB: compilation and creation of data sets from Protein Data Bank. Nucleic Acids Research, 40(D1), D486--D489.

139.         Vikram, S., Kumar, S., Subramanian, S., & Raghava, G. P. S. (2012). Draft genome sequence of the nitrophenol-degrading actinomycete Rhodococcus imtechensis RKJ300. Journal of Bacteriology, 194(13), 3543.

140.         Vikram, S., Pandey, J., Bhalla, N., Pandey, G., Ghosh, A., Khan, F., Jain, R. K., & Raghava, G. P. S. (2012). Branching of the p-nitrophenol (PNP) degradation pathway in burkholderia sp. Strain SJ98: Evidences from genetic characterization of PNP gene cluster. AMB Express, 2(1), 1–10.

141.         Agarwal, S., Kumar Mishra, N., Singh, H., & Raghava, G. P. S. (2011). Identification of mannose interacting residues using local composition. PloS One, 6(9), e24039_1--e24039_9.

142.         Ahmed, F., & Raghava, G. P. S. (2011). Designing of Highly Effective Complementary and Mismatch siRNAs for Silencing a Gene. PLoS ONE, 6(8), e23443.

143.         Bhardwaj, A., Scaria, V., Raghava, G. P., Lynn, A. M., Chandra, N., Banerjee, S., Raghunandanan, M. V, Pandey, V., Taneja, B., Yadav, J., & others. (2011). Open Source Drug Discovery C, Brahmachari SK. Open source drug discovery—a new paradigm of collaborative research in tuberculosis drug development. Tuberculosis, 91(5), 479–486.

144.         Kumar Mishra, N., Raghava, P. S., & others. (2011). Prediction of Specificity and Cross-Reactivity of Kinase Inhibitors. Letters in Drug Design &# 38; Discovery, 8(3), 223–228.

145.         Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2011). SVM based prediction of RNA-binding proteins using binding residues and evolutionary information. Journal of Molecular Recognition, 24(2), 303–313.

146.         Kumar, R., Panwar, B., Chauhan, J. S., & Raghava, G. P. S. (2011). Analysis and prediction of cancerlectins using evolutionary and domain information. BMC Research Notes, 4(1), 237.

147.         Panwar, B., & Raghava, G. P. S. (2011). Predicting sub-cellular localization of tRNA synthetases from their primary structures. Amino Acids, 1–11.

148.         Singla, D., Anurag, M., Dash, D., & Raghava, G. P. S. (2011). A web server for predicting inhibitors against bacterial target GlmU protein. BMC Pharmacology, 11(1), 5.

149.         Tyagi, A., Ahmed, F., Thakur, N., Sharma, A., Raghava, G. P. S., & Kumar, M. (2011). HIVsirDB: a database of HIV inhibiting siRNAs. PLoS One, 6(10), e25917_1--e25917_6.

150.         Zhang, G. L., Ansari, H. R., Bradley, P., Cawley, G. C., Hertz, T., Hu, X., Jojic, N., Kim, Y., Kohlbacher, O., Lund, O., & others. (2011). Machine learning competition in immunology--Prediction of HLA class I binding peptides. Journal of Immunological Methods, 374(1), 1–4.

151.         Agarwal, S. M., Raghav, D., Singh, H., & Raghava, G. P. S. (2010). CCDB: a curated database of genes involved in cervix cancer. Nucleic Acids Research, 39(suppl_1), D975--D979.

152.         Anastas, P., Bejatolah, M.-K., Gajendra, P. S., & others. (2010). Bridging Innate and Adaptive Antitumor Immunity Targeting Glycans. Journal of Biomedicine and Biotechnology, 2010.

153.         Ansari, H. R., & Raghava, G. P. S. (2010). Identification of NAD interacting residues in proteins. BMC Bioinformatics, 11(1), 160.

154.         Ansari, H. R., Flower, D. R., & Raghava, G. P. S. (2010). AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Research, 38(suppl_1), D847--D853.

155.         Ansari, H. R., & Raghava, G. P. S. (2010). Identification of conformational B-cell Epitopes in an antigen from its primary sequence. Immunome Research, 6(1), 6.

156.         Ansari, H., Flower, D., & Raghava, G. (2010). AntigenDB. Nucleic Acids Research, 38(20103804).

157.         Chauhan, J. S., Mishra, N. K., & Raghava, G. P. S. (2010). Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information. BMC Bioinformatics, 11(1), 301.

158.         Garg, A., Tewari, R., & Raghava, G. P. S. (2010). Virtual screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis. BMC Bioinformatics, 11(Suppl 1), S53.

159.         Garg, A., Tewari, R., & Raghava, G. P. S. (2010). KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials. BMC Bioinformatics, 11(1), 125.

160.         Lata, S., Mishra, N. K., & Raghava, G. P. S. (2010). AntiBP2: improved version of antibacterial peptide prediction. BMC Bioinformatics, 11(Suppl 1), S19.

161.         Mishra, N. K., Agarwal, S., & Raghava, G. P. S. (2010). Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacology, 10(1), 8.

162.         Mishra, N. K., & Raghava, G. P. S. (2010). Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information. BMC Bioinformatics, 11(Suppl 1), S48.

163.         Panwar, B., & Raghava, G. P. S. (2010). Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains. BMC Genomics, 11(1), 507.

164.         Rashid, M., Ramasamy, S., & Raghava, G. P. S. (2010). A Simple Approach for Predicting Protein-Protein Interactions. Current Protein and Peptide Science, 11(7), 589–600.

165.         Singla, D., Sharma, A., Kaur, J., Panwar, B., & Raghava, G. P. S. (2010). BIAdb: A curated database of benzylisoquinoline alkaloids. BMC Pharmacology, 10(1), 4.

166.         Verma, R., Varshney, G. C., & Raghava, G. P. S. (2010). Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile. Amino Acids, 39(1), 101–110.

167.         Ahmed, F., Ansari, H. R., & Raghava, G. P. S. (2009). Prediction of guide strand of microRNAs from its sequence and secondary structure. BMC Bioinformatics, 10(1), 105.

168.         Ahmed, F., Kumar, M., & Raghava, G. P. S. (2009). Prediction of polyadenylation signals in human DNA sequences using nucleotide frequencies. In Silico Biology, 9(3), 135–148.

169.         Arora, P. K., Kumar, M., Chauhan, A., Raghava, G. P. S., & Jain, R. K. (2009). OxDBase: a database of oxygenases involved in biodegradation. BMC Research Notes, 2(1), 67.

170.         Chaudhary, N., Mahajan, L., Madan, T., Kumar, A., Raghava, G. P. S., Katti, S. B., Haq, W., & Sarma, P. U. (2009). Prophylactic and Therapeutic Potential of Asp f1 Epitopes in Na{\"\i}ve and Sensitized BALB/c Mice. Immune Network, 9(5), 179.

171.         Chauhan, J. S., Mishra, N. K., & Raghava, G. P. S. (2009). Identification of ATP binding residues of a protein from its primary sequence. BMC Bioinformatics, 10(1), 434.

172.         Kaundal, R., & Raghava, G. P. S. (2009). RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information. Proteomics, 9(9), 2324–2342.

173.         Kumar, M., & Raghava, G. P. S. (2009). Prediction of nuclear proteins using SVM and HMM models. BMC Bioinformatics, 10(1), 22.

174.         Lata, S., Bhasin, M., & Raghava, G. P. S. (2009). MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes. BMC Research Notes, 2(1), 61.

175.         Lata, S., & Raghava, G. P. S. (2009). Databases and Web-Based Tools for Innate Immunity. In Bioinformatics for Immunomics (pp. 67–76). Springer New York.

176.         Lata, S., & Raghava, G. P. S. (2009). Prediction and classification of chemokines and their receptors. Protein Engineering Design and Selection, gzp016.

177.         Singla, D., Raghava, G. P., Kumar, M., Sharma, A., & Rashid, M. (2009). Hmrbase: a database of hormones and their receptors. BMC Genomics, 10(1).

178.         Garg, A., & Raghava, G. P. S. (2008). ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics, 9(1), 503.

179.         Garg, A., & Raghava, G. P. S. (2008). A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search. In Silico Biology, 8(2), 129–140.

180.         Kalita, M. K., Nandal, U. K., Pattnaik, A., Sivalingam, A., Ramasamy, G., Kumar, M., Raghava, G. P. S., & Gupta, D. (2008). CyclinPred: a SVM-based method for predicting cyclin protein sequences. PloS One, 3(7), e2605_1--e2605_12.

181.         Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2008). Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins: Structure, Function, and Bioinformatics, 71(1), 189–194.

182.         Kumar, M., Thakur, V., & Raghava, G. P. S. (2008). COPid: composition based protein identification. In Silico Biology, 8(2), 121–128.

183.         Kush, A., & Raghava, G. P. S. (2008). AC2DGel: analysis and comparison of 2D gels. Journal of Proteomics & Bioinformatics, 1(1), 43–46.

184.         Lata, S., & Raghava, G. P. S. (2008). CytoPred: a server for prediction and classification of cytokines. Protein Engineering, Design & Selection, 21(4), 279–282.

185.         Lata, S., & Raghava, G. P. S. (2008). PRRDB: a comprehensive database of pattern-recognition receptors and their ligands. BMC Genomics, 9(1), 180.

186.         Raghava Han JH, H. D. J. (2008). ECGpred: Correlation and prediction of gene expression from nucleotide sequence. The Open Bioinformatics Journal, 2, 64–67.

187.         Sethi, D., Garg, A., & Raghava, G. P. S. (2008). DPROT: prediction of disordered proteins using evolutionary information. Amino Acids, 35(3), 599.

188.         Verma, R., Tiwari, A., Kaur, S., Varshney, G. C., & Raghava, G. P. S. (2008). Identification of Proteins Secreted by Malaria Parasite into Erythrocyte using SVM and PSSM profiles. BMC Bioinformatics, 9(1), 201.

189.         Vivona, S., Gardy, J. L., Ramachandran, S., Brinkman, F. S. L., Raghava, G. P. S., Flower, D. R., & Filippini, F. (2008). Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends in Biotechnology, 26(4), 190–200.

190.         Bhasin, M., Lata, S., & Raghava, G. P. S. (2007). TAPPred prediction of TAP-binding peptides in antigens. In Immunoinformatics (pp. 381–386). Humana Press.

191.         Bhasin, M., Lata, S., & Raghava, G. P. S. (2007). Searching and mapping of T-cell epitopes, MHC binders, and TAP binders. In Immunoinformatics (pp. 95–112). Humana Press.

192.         Bhasin, M., & Raghava, G. P. S. (2007). A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. Journal of Biosciences, 32(1), 31–42.

193.         Greenbaum, J. A., Andersen, P. H., Blythe, M., Bui, H.-H., Cachau, R. E., Crowe, J., Davies, M., Kolaskar, A. S., Lund, O., Morrison, S., & others. (2007). Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. Journal of Molecular Recognition, 20(2), 75–82.

194.         Kaur, H., Garg, A., & Raghava, G. P. S. (2007). PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein and Peptide Letters, 14(7), 626–631.

195.         Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2007). Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics, 8(1), 463.

196.         Lata, S., Bhasin, M., & Raghava, G. P. S. (2007). Application of machine learning techniques in predicting MHC binders. In Immunoinformatics (pp. 201–215). Humana Press.

197.         Lata, S., Sharma, B. K., & Raghava, G. P. S. (2007). Analysis and prediction of antibacterial peptides. BMC Bioinformatics, 8(1), 263.

198.         Mishra, N. K., Kumar, M., & Raghava, G. P. S. (2007). Support vector machine based prediction of glutathione S-transferase proteins. Protein and Peptide Letters, 14(6), 575–580.

199.         Muthukrishnan, S., Garg, A., & Raghava, G. P. S. (2007). Oxypred: prediction and classification of oxygen-binding proteins. Genomics, Proteomics & Bioinformatics, 5(3–4), 250–252.

200.         Pashov, A., Monzavi-Karbassi, B., Raghava, G., & Kieber-Emmons, T. (2007). Peptide mimotopes as prototypic templates of broad-spectrum surrogates of carbohydrate antigens for cancer vaccination. Critical ReviewsTM in Immunology, 27(3).

201.         Rashid, M., Saha, S., & 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(1), 337.

202.         Saha, S., & Raghava, G. P. S. (2007). Prediction methods for B-cell epitopes. In Immunoinformatics (pp. 387–394). Humana Press.

203.         Saha, S., & Raghava, G. P. S. (2007). Predicting virulence factors of immunological interest. In Immunoinformatics (pp. 407–415). Humana Press.

204.         Saha, S., & Raghava, G. P. S. (2007). Prediction of neurotoxins based on their function and source. In Silico Biology, 7(4–5), 369–387.

205.         Saha, S., & Raghava, G. P. S. (2007). BTXpred: prediction of bacterial toxins. In Silico Biology, 7(4–5), 405–412.

206.         Saha, S., & Raghava, G. P. S. (2007). Searching and mapping of B-cell epitopes in Bcipep database. In Methods Mol Biol. (Vol. 409, pp. 113–124). Humana Press.

207.         Singh, H., & Raghava, G. P. S. (2007). Prediction and mapping of promiscuous MHC class II binders in an antigen sequence. Protocol Exchange, 10.

208.         Srivastava, S., Singh, M. K., Raghava, G. P. S., & Varshney, G. C. (2007). Searching haptens, carrier proteins, and anti-hapten antibodies. In Immunoinformatics (pp. 125–139). Humana Press.

209.         Vidyasagar, M., Balakrishnan, N., & others. (2007). BioSuite: A comprehensive bioinformatics software package (A unique industry-academia collaboration). Current Science, 92(1), 29–38.

210.         Bhasin, M., & Raghava, G. P. S. (2006). Computational Methods in Genome Research. In Applied Mycology and Biotechnology (Vol. 6, pp. 179–207). Elsevier.

211.         Kaundal, R., Kapoor, A. S., & Raghava, G. P. S. (2006). Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics, 7(1), 485.

212.         Kaur, H., & Raghava, G. P. S. (2006). Prediction of C$\alpha{\$}-H·O and C$\alpha{\$}-H·$\pi{\$}Interactions in Proteins Using Recurrent Neural Network. In Silico Biology, 6(1–2), 111–125.

213.         Kim, J. K., Raghava, G. P. S., Bang, S.-Y., & Choi, S. (2006). Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine. Pattern Recognition Letters, 27(9), 996–1001.

214.         Kumar, M., Verma, R., & Raghava, G. P. S. (2006). Prediction of mitochondrial proteins using support vector machine and hidden Markov model. Journal of Biological Chemistry, 281(9), 5357–5363.

215.         Raghava, G. P. S. (2006). MANGO: prediction of Genome Ontology (GO) class of a protein from its amino acid and dipeptide composition using nearest neighbor approach. CASP7: Proceedings of the 7th Meeting on the Critical Assessment of Techniques for Protein Structure Prediction, 26–30.

216.         Raghava, G. P. S., & Barton, G. J. (2006). Quantification of the variation in percentage identity for protein sequence alignments. BMC Bioinformatics, 7(1), 415.

217.         Saha, S., & Raghava, G. P. S. (2006). VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, Proteomics & Bioinformatics, 4(1), 42–47.

218.         Saha, S., & Raghava, G. P. S. (2006). AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Research, 34(suppl_2), W202--W209.

219.         Saha, S., & Raghava, G. P. S. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function, and Bioinformatics, 65(1), 40–48.

220.         Saha, S., Zack, J., Singh, B., & Raghava, G. P. S. (2006). VGIchan: prediction and classification of voltage-gated ion channels. Genomics, Proteomics & Bioinformatics, 4(4), 253–258.

221.         Singh, M. K., Srivastava, S., Raghava, G. P. S., & Varshney, G. C. (2006). HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies. Bioinformatics, 22(2), 253–255.

222.         Bhasin, M., Garg, A., & Raghava, G. P. S. (2005). PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics, 21(10), 2522–2524.

223.         Bhasin, M., & Raghava, G. P. S. (2005). Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Research, 33(suppl_2), W202--W207.

224.         Bhasin, M., & Raghava, G. P. S. (2005). GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors. Nucleic Acids Research, 33(suppl_2), W143--W147.

225.         Garg, A., Bhasin, M., & Raghava, G. P. S. (2005). Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. Journal of Biological Chemistry, 280(15), 14427–14432.

226.         Garg, A., Kaur, H., & 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.

227.         Genet, H. (2005). The Indian genome variation database (IGVdb): a project overview. Hum Genet, 1–11.

228.         Issac, B., & Raghava, G. P. S. (2005). FASTA Servers for Sequence Similarity Search. In The Proteomics Protocols Handbook (pp. 503–525). Humana Press.

229.         Kumar, M., Bhasin, M., Natt, N. K., & Raghava, G. P. S. (2005). BhairPred: prediction of $\beta{\$}-hairpins in a protein from multiple alignment information using ANN and SVM techniques. Nucleic Acids Research, 33(suppl_2), W154----W159.

230.         Raghava, G. P. S., & Han, J. H. (2005). Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein. BMC Bioinformatics, 6(1), 59.

231.         Saha, S., Bhasin, M., & Raghava, G. P. S. (2005). Bcipep: a database of B-cell epitopes. BMC Genomics, 6(1), 79.

232.         Bhasin, M., & Raghava, G. P. S. (2004). SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics, 20(3). https://doi.org/10.1093/bioinformatics/btg424

233.         Bhasin, M., & Raghava, G. P. S. (2004). Classification of nuclear receptors based on amino acid composition and dipeptide composition. Journal of Biological Chemistry, 279(22). https://doi.org/10.1074/jbc.M401932200

234.         Bhasin, M., & Raghava, G. P. S. (2004). Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Science, 13(3). https://doi.org/10.1110/ps.03373104

235.         Bhasin, M., & Raghava, G. P. S. (2004). GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Research, 32(suppl_2), W383--W389.

236.         Bhasin, M., & Raghava, G. P. S. (2004). ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Research, 32(suppl_2), W414--W419.

237.         Bhasin, M., & Raghava, G. P. S. (2004). Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine, 22(23–24), 3195–3204.

238.         Issac, B., & Raghava, G. P. S. (2004). EGPred: prediction of eukaryotic genes using ab initio methods after combining with sequence similarity approaches. Genome Research, 14(9), 1756–1766.

239.         Kaur, H., & Raghava, G. P. S. (2004). Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins. FEBS Letters, 564(1–2). https://doi.org/10.1016/S0014-5793(04)00305-9

240.         Kaur, H., & Raghava, G. P. S. (2004). Prediction of α-Turns in Proteins Using PSI-BLAST Profiles and Secondary Structure Information. Proteins: Structure, Function and Genetics, 55(1). https://doi.org/10.1002/prot.10569

241.         Kaur, H., & Raghava, G. P. S. (2004). A neural network method for prediction of $\beta{\$}-turn types in proteins using evolutionary information. Bioinformatics, 20(16), 2751–2758.

242.         Kaur, H., & Raghava, G. P. S. (2004). Prediction of $\beta{\$}-turns in proteins from multiple alignment using neural network. Protein Science, 12(3), 627–634.

243.         Natt, N. K., Kaur, H., & Raghava, G. P. S. (2004). Prediction of transmembrane regions of $\beta{\$}-barrel proteins using ANN-and SVM-based methods. PROTEINS: Structure, Function, and Bioinformatics, 56(1), 11–18.

244.         Saha, S., & Raghava, G. P. S. (2004). Special Session on Immunoinformatics-BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties. Lecture Notes in Computer Science, 3239, 197–204.

245.         Sharma, D., Issac, B., Raghava, G. P. S., & Ramaswamy, R. (2004). Spectral Repeat Finder (SRF): identification of repetitive sequences using Fourier transformation. Bioinformatics, 20(9), 1405–1412.

246.         Bhasin, M., & Raghava, G. P. S. (2003). Prediction of promiscuous and high-affinity mutated MHC binders. Hybridoma and Hybridomics, 22(4). https://doi.org/10.1089/153685903322328956

247.         Bhasin, M., Singh, H., & Raghava, G. P. S. (2003). MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics, 19(5). https://doi.org/10.1093/bioinformatics/btg055

248.         Kaur, H., & Raghava, G. P. (2003). BTEVAL: a server for evaluation of beta-turn prediction methods. J Bioinform Comput Biol, 1(3).

249.         Kaur, H., & Raghava, G. P. S. (2003). Prediction of β-turns in proteins from multiple alignment using neural network. Protein Science, 12(3). https://doi.org/10.1110/ps.0228903

250.         Kaur, H., & Raghava, G. P. S. (2003). A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment. Protein Science, 12(5). https://doi.org/10.1110/ps.0241703

251.         Raghava, G. P. S., Searle, S. M. J., Audley, P. C., Barber, J. D., & Barton, G. J. (2003). OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinformatics, 4. https://doi.org/10.1186/1471-2105-4-47

252.         Sarin, J., Raghava, G. P. S., & Chakraborti, P. K. (2003). Intrinsic contributions of polar amino acid residues toward thermal stability of an ABC-ATPase of mesophilic origin. Protein Science, 12(9). https://doi.org/10.1110/ps.0397603

253.         Singh, H., & Raghava, G. P. S. (2003). ProPred1: Prediction of promiscuous MHC class-I binding sites. Bioinformatics, 19(8). https://doi.org/10.1093/bioinformatics/btg108

254.         Issac, B., Singh, H., Kaur, H., & Raghava, G. P. S. (2002). Locating probable genes using Fourier transform approach. Bioinformatics, 18(1). https://doi.org/10.1093/bioinformatics/18.1.196

255.         Issac, B., & Raghava, G. P. S. (2002). GWFASTA: server for FASTA search in eukaryotic and microbial genomes. Biotechniques, 33(3), 548–556.

256.         Kaur, H., & Raghava, G. P. S. (2002). BetaTPred: Prediction of β-TURNS in a protein using statistical algorithms. Bioinformatics, 18(3). https://doi.org/10.1093/bioinformatics/18.3.498

257.         Kaur, H., & Raghava, G. P. S. (2002). An evaluation of β-turn prediction methods. Bioinformatics, 18(11). https://doi.org/10.1093/bioinformatics/18.11.1508

258.         Raghava, G. P. S. (2002). APSSP2: A combination method for protein secondary structure prediction based on neural network and example based learning. CASP5. A-132.

259.         Shivaji, S., Nautiyal, C. S., Ganguli, B. N., Paul, A. K., Adholeya, A., Raghava, G. P. S., & Chakrabarti, T. (2002). Collection of data on microbial resources of India. In CURRENT SCIENCE (Vol. 83, Issue 1, p. 9). INDIAN ACAD SCIENCES CV RAMAN AVENUE, SADASHIVANAGAR, PB# 8005, BANGALORE~….

260.         Singh, H., & Raghava, G. P. S. (2002). Matrix Optimization Technique for Predicting MHC binding Core. Biotech Software and Internet Report, 3, 146.

261.         Singh, H., & Raghava, G. P. S. (2002). Detection of Orientation of MHC Class II Binding Peptides Using Bioinformatics Tools. Biotech Software & Internet Report: The Computer Software Journal for Scientists, 3(5–6), 146–150.

262.         Raghava, G. P. S. (2001). PDSB: Public domain software in biology. Biotech Software & Internet Report: The Computer Software Journal for Scient, 2(4), 154–156.

263.         Raghava, G. P. S. (2001). PDWSB: public domain web servers in biology. Biotech Software & Internet Report: The Computer Software Journal for Scient, 2(4), 152–153.

264.         Raghava, G. P. S. (2001). A web server for computing the size of DNA/protein fragments using a graphical method. Biotech Software & Internet Report: The Computer Software Journal for Scient, 2(5), 198–200.

265.         Raghava, G. P. S. (2001). A graphical web server for the analysis of protein sequences and alignment. Biotech Software & Internet Report: The Computer Software Journal for Scientists, 2(6), 254–257.

266.         Raghava, G. P. S., & Agrewala, J. N. (2001). A Web-based Method for Computing Endpoint Titer and Concentration of Antibody/Antigen. Biotech Software & Internet Report: The Computer Software Journal for Scient, 2(5), 196–197.

267.         Singh, H., & Raghava, G. P. S. (2001). ProPred: prediction of HLA-DR binding sites. Bioinformatics, 17(12), 1236–1237.

268.         Raghava, G. P. S. (2000). Protein secondary structure prediction using nearest neighbor and neural network approach. CASP, 4, 75–76.

269.         Raghava, G. P. S., Solanki, R. J., Soni, V., & Agrawal, P. (2000). Fingerprinting method for phylogenetic classification and identification of microorganisms based on variation in 16S rRNA gene sequences. Biotechniques, 29(1), 108–115.

270.         Raghava, G. (1999). A computer program for predicting the protein structural classes. J. Biosciences, 24, 176.

271.         NIHALANI, D., RAGHAVA, G. P. S., & SAHNI, A. N. G. (1997). Mapping of the plasminogen binding site of. Protein Science, 6, 1234–1292.

272.         Nihalani, D., Raghava, G. P. S., & Sahni, G. (1997). Mapping of the plasminogen binding site of streptokinase with short synthetic peptides. Protein Science, 6(6), 1284–1292.

273.         Raghava, G. P. (1995). DNAOPT: a computer program to aid optimization of DNA gel electrophoresis and SDS-PAGE. Biotechniques, 18(2), 274–278.

274.         Agrewala, J. N., Raghava, G. P. S., & Mishra, G. C. (1994). MEASUREMENT AND COMPUTATION OF MURINE INTERLEUKIN-4 AND INTERFERON-7 BY EXPLOITING THE UNIQUE ABILITIES OF THESE LYMPHOKINES TO INDUCE THE SECRETION OF IgGl AND IgG2a. Journal of Immunoassay.

275.         Raghava, G. P. S. (1994). Recent excitement about artificial neural networks. Biobytes, 3, 4–5.

276.         Raghava, G. P. S. (1994). Improved estimation of DNA fragment length from gel electrophoresis data using a graphical method. Biotechniques, 17(1), 100–104.

277.         Raghava, G. P. S., Goel, A., Singh, A. M., & Varshney, G. C. (1994). A simple microassay for computing the hemolytic potency of drugs. Biotechniques, 17(6), 1148–1153.

278.         Raghava, G. P. S., & Sahni, G. (1994). GMAP: a multi-purpose computer program to aid synthetic gene design, cassette mutagenesis and the introduction of potential restriction sites into DNA sequences. Biotechniques, 16(6), 1116–1123.

279.         Raghava, G. P. S., Agrewala, J. N. J. N., Ragliava, G. P. S., & Agrewala, J. N. J. N. (1994). Method for determining the affinity of monoclonal antibody using non-competitive ELISA: a computer program. Journal of Immunoassay, 15(2), 115–128. https://doi.org/10.1080/15321819408013942

280.         Agrewala, J. N., Raghava, G. P. S., & Mishra, G. C. (1993). Measurement and computation of murine interleukin-4 and interferon-3 by exploiting the unique abilities of these lymphokines to induce the secretion of igG1 and igG2a. Journal of Immunoassay, 14(1–2). https://doi.org/10.1080/15321819308019842

281.         Raghava, G. P. S., Joshi, A. K., & Agrewala, J. N. (1992). Calculation of antibody and antigen concentrations from ELISA data using a graphical method. Journal of Immunological Methods, 153(1–2).

282.         Tripathy, S. C., BALASUBRAMANTAN, R., RAGHAVA, G. P. S., & CHATTERJEE, J. K. (1988). Microprocessor based active and reactive power measurement. Journal of the Institution of Engineers. India. Electrical Engineering Division, 69(2), 73–77.

 


Annexure III

 

 

 

 

Social Impact of Web Services Developed by Raghava’s Group

Group have developed number of web services (servers and databases), each service is based on novel algorithm or data, published in reputed journals. Most of publications based on these services are highly cited. Scientific community in the field of education, vaccine and drug discovery heavily uses these services. Following is procedure used to calculate social impact on society.

1.     Hits per year for 125 services is computed from Apache log of six months

2.     Total hits per server are computed by multiplying per year hits with time (years) service is online.

3.     Number of scientific pages visited and job submitted were computed by dividing hits by factor of three and six respectively.

4.     Social impact is computed by charging Rs 5 for visiting a scientific page and Rs 500 for executing/submitted a job.

 

Total social impact in term money is around Rs. 792 crore in year 2015

(Detail is given in table below)

Web-Servers

Online (Years)

Hits/year

Total hits

Pages/visited

Jobs executed

Social Impact (Rs in lakhs)

dnabinder

9

1218502

10966518

3655506

1827753

9321

pcmdb

3

2387778

7163334

2387778

1193889

6088

metapred

6

929140

5574840

1858280

929140

4738

mhcbn

13

366192

4760496

1586832

793416

4046

rnapred

7

633188

4432316

1477438

738719

3767

cancerdr

4

916792

3667168

1222389

611194

3117

cppsite

4

762016

3048064

1016021

508010

2590

bcepred

13

214590

2789670

929890

464945

2371

sarpred

11

232384

2556224

852074

426037

2172

proprint

8

292022

2336176

778725

389362

1985

lbtope

3

774590

2323770

774590

387295

1975

propred

14

155590

2178440

726146

363073

1851

pepstr

9

229592

2066328

688776

344388

1756

hmrbase

8

247044

1976352

658784

329392

1679

npact

3

499456

1498368

499456

249728

1273

glycoep

3

484306

1452918

484306

242153

1234

abcpred

10

139888

1398880

466293

233146

1189

ccpdb

4

332040

1328160

442720

221360

1128

nppred

7

189214

1324498

441499

220749

1125

hslpred

12

107388

1288656

429552

214776

1095

apssp

15

77752

1166280

388760

194380

991

cancerppd

2

562058

1124116

374705

187352

955

eslpred

12

93444

1121328

373776

186888

953

antigendb

6

172270

1033620

344540

172270

878

rnacon

4

257562

1030248

343416

171708

875

bcipep

12

83396

1000752

333584

166792

850

btxpred

10

99834

998340

332780

166390

848

dipcell

2

459386

918772

306257

153128

780

hemolytik

3

275756

827268

275756

137878

703

propred1

13

62924

818012

272670

136335

695

haptendb

12

62870

754440

251480

125740

641

algpred

9

72116

649044

216348

108174

551

polyapred

7

92420

646940

215646

107823

549

ctlpred

11

58078

638858

212952

106476

543

Ccdb

5

124528

622640

207546

103773

529

biadb

6

102020

612120

204040

102020

520

betatpred

13

43472

565126

188375

94187

480

tumorhope

4

138970

555880

185293

92646

472

Ftg

14

36358

509012

169670

84835

432

herceptinr

3

169462

508386

169462

84731

432

antibp

9

53776

483984

161328

80664

411

alphapred

12

36738

440856

146952

73476

374

ahtpdb

1

426226

426226

142075

71037

362

betaturns

12

35518

426216

142072

71036

362

nhlapred

11

37572

413292

137764

68882

351

toxipred

5

76006

380030

126676

63338

323

mitpred

8

46524

372192

124064

62032

316

betatpred3

1

366568

366568

122189

61094

311

betatpred2

12

30144

361728

120576

60288

307

tappred

12

30142

361704

120568

60284

307

rslpred

8

44148

353184

117728

58864

300

pslpred

11

31550

347050

115683

57841

294

polysacdb

6

55904

335424

111808

55904

285

pprint

8

41228

329824

109941

54970

280

eslpred2

8

39568

316544

105514

52757

269

rbpred

9

30562

275058

91686

45843

233

parapep

3

89468

268404

89468

44734

228

anticp

3

87962

263886

87962

43981

224

tbbpred

12

21154

253848

84616

42308

215

cellppd

3

82646

247938

82646

41323

210

mmbpred

13

17678

229814

76604

38302

195

ntegfr

2

109954

219908

73302

36651

186

egpred

12

18014

216168

72056

36028

183

antibp2

7

30174

211218

70406

35203

179

gpcrpred

12

17118

205416

68472

34236

174

gammapred

12

15542

186504

62168

31084

158

bteval

12

14552

174624

58208

29104

148

srtpred

8

21758

174064

58021

29010

147

chpredict

14

12348

172872

57624

28812

146

igpred

4

43030

172120

57373

28686

146

vicmpred

7

24384

170688

56896

28448

145

gwblast

11

15240

167640

55880

27940

142

cbtope

6

27918

167508

55836

27918

142

glycopp

4

41256

165024

55008

27504

140

gwfasta

12

13514

162168

54056

27028

137

toxinpred

4

39726

158904

52968

26484

135

hivsir

5

30070

150350

50116

25058

127

Gdoq

8

17854

142832

47610

23805

121

rnapin

3

46612

139836

46612

23306

118

kidoq

7

18914

132398

44132

22066

112

egfrindb

2

65730

131460

43820

21910

111

ifnepitope

3

40010

120030

40010

20005

102

proglycprot

5

23866

119330

39776

19888

101

drugmint

3

36518

109554

36518

18259

93

trnamod

2

52068

104136

34712

17356

88

pcleavage

10

9950

99500

33166

16583

84

ntxpred

9

10900

98100

32700

16350

83

phdcleav

6

15324

91944

30648

15324

78

prrdb

9

10216

91944

30648

15324

78

desirm

5

17962

89810

29936

14968

76

ar_nhpred

12

7452

89424

29808

14904

76

nrpred

13

6368

82784

27594

13797

70

bhairpred

11

7504

82544

27514

13757

70

atpint

7

10556

73892

24630

12315

62

vgichan

9

7076

63684

21228

10614

54

tumorhpd

4

15684

62736

20912

10456

53

egfrpred

2

29308

58616

19538

9769

49

pseapred

8

6662

53296

17765

8882

45

icaars

5

10362

51810

17270

8635

44

dmkpred

5

9452

47260

15753

7876

40

gpcrsclass

10

4618

46180

15393

7696

39

Hlp

2

22370

44740

14913

7456

38

nadbinder

6

7350

44100

14700

7350

37

gstpred

8

5444

43552

14517

7258

37

pfmpred

7

5882

41174

13724

6862

34

premier

6

6628

39768

13256

6628

33

cancer_pred

5

7558

37790

12596

6298

32

gtpbinder

7

5040

35280

11760

5880

29

oxypred

9

3868

34812

11604

5802

29

vitapred

3

11376

34128

11376

5688

29

Mdri

4

8440

33760

11253

5626

28

ahtpin

1

32314

32314

10771

5385

27

oxdbase

7

4538

31766

10588

5294

26

cytopred

8

3810

30480

10160

5080

25

il4pred

3

8942

26826

8942

4471

22

marspred

4

6682

26728

8909

4454

22

dprot

8

3286

26288

8762

4381

22

hivcopred

3

7694

23082

7694

3847

19

chemopred

7

2888

20216

6738

3369

17

vaccineda

1

17388

17388

5796

2898

14

fadpred

6

2682

16092

5364

2682

13

xiapin

1

13030

13030

4343

2171

11

paaint

2

4988

9976

3325

1662

8

antiangiopred

1

8356

8356

2785

1392

7

Total

880

16993588

93216050

31071990

15535982

79233

 


Annexure IV

Copyrights

  1. Raghava, G.P.S. (1996) ASSP: A computer program for comparision of observed and predicted protein secondary structure. L-15582/96.
  2. Raghava, G.P.S. (1996) A computer program for analysing and creating protein secondary structure database. L-15658/96.
  3. Raghava, G.P.S and Agrawal, P. A (1997) Software for phylogcncitic identification of microorganisms. C R. 1/97, date 11.3.97
  4. Raghava, G.P.S (2001) A computer program for assisting the user in using protein modelling software package (PMOD 1.0) . SW-393/2001, date 08/02/2001
  5. Kaur, H. and Raghava, G.P.S. (2002) BETATPRED: Software for predicting b -turns using statistical algorithm. SW-1162/2003
  6. Raghava, G.P.S. (2002) DNASIZE: A software package for computing DNA/Protein fragments. SW-1156/2003.
  7. Issac, B., Singh, H., Kaur, H. and Raghava, G.P.S. (2002) FTG: Software Package for Predicting Gene in DNA using Fast Fourier Transform. SW-1154/2003.
  8. Issac, B. and Raghava, G.P.S. (2002) GWBLAST: Software Package for Genome Wide Similarity Search using BLAST. SW-1149/2003
  9. Issac, B. and Raghava, G.P.S. (2002) GWFASTA: Computer program for genome wide FASTA search. SW-1143/2003.
  10. Raghava, G.P.S. (2002) LibInet: Software Package for Managing Library Resources and Accessing via Internet. SW-1155/2003.
  11. Singh, H. and Raghava, G.P.S. (2002) MHCB ench: Evaluation of MHC Binding Peptide Prediction Algorithms. SW-1160/2003.
  12. Singh, H. and Raghava, G.P.S. (2002) MOT: Matrix Optimization Technique for identifying the MHC binding core. SW-1159/2003
  13. Raghava, G.P.S. (2002) PCLASS: Computer Program for predicting structural class of protein via Internet. SW-1157/2003
  14. Raghava, G.P.S. (2002) PDSB: Software Package for Managing Public Domain Software in Biology. SW-1148/2003.
  15. Raghava, G.P.S. (2002) PDWSB: Software Package for creating and managing Biological Web Servers. SW-1152/2003.
  16. Singh, H. and Raghava, G.P.S. (2002) ProPred: Software package for predicting promiscuous MHC class-II binding peptides. SW-1163/2003
  17. Singh, H. and Raghava, G.P.S. (2002) ProPred-I: Software package for predicting promiscuous MHC Class-I binding peptides. SW-1161/2003
  18. Raghava, G.P.S. (2002) PSAweb: Software package for Analyzing of Protein Sequence and Multiple Alignment. SW-1158/2003.
  19. Bhasin, M. and Raghava, G. P.S. (2003) MHCBN: A software package for managing data of Immunologically related peptides. SW-1151/200
  20. Bhasin, M. and Raghava, G. P.S. (2003) HLA-DR4Pred: An SVM and ANN based software for the prediction of HLA-DRB0401 binding peptides (Submitted).
  21. Saha, H. Bhasin, M. and Raghava, G. P.S. (2003) Bcipep: A software package for management of B cell epitopes. SW-1851/200
  22. Bhasin, M. and Raghava, G. P.S. (2002) A Webserver for Prediction of Promiscuous And High Affinity Mutated MHC Binders (Submitted).
  23. Bhasin, M. and Raghava, G. P.S. (2002) TAPPred: A software package for predicting TAP binding affinity of peptides via internet. SW-1847/2005.
  24. Kaur, H. and Raghava, G.P.S. (2003) AlphaPred: A software for prediction of Alpha-turns in proteins (Submitted)
  25. Kaur, H. and Raghava, G.P.S. (2003) BetatPred2: A software for prediction of beta-turns in proteins from multiple alignment using neural network (Submitted).
  26. Kaur, H. and Raghava, G.P.S. (2003) BetaTurns: A software for prediction of beta-turn types in proteins from evolutionary information using neural network (Submitted).
  27. Kaur, H. and Raghava, G.P.S. (2003) Ar_NHPred: A software for prediction of aromatic-backbone NH interactions in proteins. SW-1850/2005.
  28. Issac, B and G.P.S. Raghava (2003) SVMgene: Predicting protein coding regions in human genomic DNA using SVM. SW-1852/2005.
  29. Kaur, H. and Raghava, G.P.S. (2003) CHPredict: A software for prediction of C-H...O and C-H...( interactions in proteins using recurrent neural network (Submitted).
  30. Bhasin,M. and Raghava, G. P. S. (2005) Pcleavage: A SVM based Method for Prediction of Consitutive and Immuno proteasome Cleavage Sites in Antigenic Sequences. SW-1950/2005.
  31. Bhasin, M. and Raghava, G. P. S. (2004) ESLpred: SVM Based Method for Subcellular Localization of Eukaryotic Proteins using Dipeptide Composition and PSI-BLAST. SW-2104/2005.
  32. 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. SW-2108/2005.
  33. Kush, A. and Raghava, G. P. S. (2008) AC2DGel: Analysis and Comparison of 2D Gels. SW-2123/2005
  34. Bhasin, M. and Raghava, G. P. S.(2005) GPCRsclass : A web tool for classification of amine type of G-protein coupled Receptors. SW-1947/2005.
  35. 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. SW-2287/2005.
  36. Singh, H. and Raghava, G. P. S.(2007) Prediction and mapping of promiscuous MHC class II binders in an antigen sequence. SW-2286/2005
  37. Saha, S. and Raghava, G. P. S. (2006) AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. SW-3013/2006.
  38. Kumar, M., Thakur, V. and Raghava, G. P. S. (2008) COPid: composition based protein identification. SW-3074/2005.
  39. Bhasin,M. and Raghava, G.P.S. (2004)Classification of nuclear receptors based on amino acid composition and dipeptide composition. SW-2124/2005.
  40. Aarti Garg, Manoj Bhasin, and Gajendra P. S. Raghava (2005). SVM-based method for subcellular localization of human proteins using amino acid compositions, their order and similarity search. SW-2269/2005.
  41. Saha, S. and Raghava, G. P. S. (2007) Prediction of neurotoxins based on their function and source.SW-2352/2005.
  42. Saha, S. and Raghava, G. P. S. (2007) Prediction of bacterial proteins. SW-2652/2005.
  43. Rashid M., Saha S. and Raghava, G. P. S. (2007)TBPred:A SVM based subcellular localization prediction method for Mycobacterial proteins. SW-3635/2007
  44. Rakesh Kaundal and Raghava, G. P. S. RSLpred:A svm based method for subcellular localization prediction of rice proteins. SW-3638/2007.
  45. Ahmed Firoz, Kumar M. and Raghava G.P.S. PolyApred: SVM based method for predicting of polyadenylation signals in human DNA sequence. SW-3637/2007.
  46. Ahmed Firoz, Singh J. and Raghava G.P.S. CDpred:Prediction of cleavage site of Dicer using support vector machine. (Submitted)
  47. Lata, S. and Raghava, G. P. S. (2008) PRRDB: A comprehensive database of Pattern-Recognition Receptors and their ligands (Submitted).
  48. Ahmed Firoz, Ansari H.R. and Raghava G.P.S. RISCbinder:Prediction of guide strand of microRNAs from its sequence and secondary structure. (Submitted).
  49. A suite of programs for computer-aided vaccine design. SW-2264/2005.
  50. Ahmed Firoz, Singh J. and Raghava G.P.S. (2009) CDpred:Prediction of cleavage site of Dicer using support vector machine. (SW-4017/2009).
  51. Lata, S. and Raghava, G. P. S. (2009) PRRDB: A comprehensive database of Pattern-Recognition Receptors and their ligands (SW-4018/2009).
  52. Ahmed, F., Ansari, H.R. And Raghava, G.P.S (2009)Prediction of guide strand of microRNAs from its sequence and seconure (RISC binder in Short) (SW-4193/2009).
  53. Panwar, B. And Raghava, G.P.S (2010) Prediction and classification of Aminoacyl tNRA syntheases using signature profile-based descriptors (SW-4633/2010).
  54. Ansari, H.R. And Raghava, G.P.S (2010) Identification of NAD interacting residues in proteins (SW-4637/2010).
  55. Chauhan, J.S. , Mishra, N.K. And Raghava, G.P.S (2010) GTPBinder: A web based tool for prediction of GTP binding residue in protein sequence (SW-4638/2010).
  56. Rashid, M and Raghava, G.P.S (2010) MycoPrint: Prediction of protein-protein interaction in Mycobacterium tuberculosis (SW-4635/2010).
  57. Mishra, N.K and Raghava, G.P.S (2010) FADPred: Prediction of FAD interacting amino acid residue using evolutionary information and SVM (SW-4639/2010)
  58. Chauhan, J.S., Mishra, N.K. And Raghava, G.P.S (2010) ATPrint: Prediction of ATP interacting amino acidacterium tuberculosis (SW-4635/2010).
  59. Kumar, S. And Raghava, G.P.S : GenomeABC: A platform for benchmarking of genome assemblers (CR No. 035CR2011).
  60. Sharma, A. , Singla, D. , Rashid M and Raghava, G.P.S (2011) DESTAMP: Designing of stable antibacterial mutant peptides (CR No. 036CR2011).
  61. Mishra, N.K, Singla, D. , Agarwal, S. And Raghava G.P.S (2011) ToxiPred: A server for prediction of aqueous toxicity of small chemical molecules in T. Pyri(2011)
  62. Ansari, H.R., Agarwal, S. And Raghava, G.P.S (2011) NPTOPE for the prediction of non-peptide epitopes using QSAR based Random Forest model (CR No. 013CR2010).
  63. Agarwal, S, Mishra, N.K and Raghava, G.P.S (2011) PreMieR: Prediction of Mannose Interacting Residues (CR No. 011CR2010).
  64. Rahman, H and Raghava G.P.S (2011) CBTOPE for the identification of conformational B-cell Epitopes in an antigen from its primary sequence. (CR No. 016CR2010 ).
  65. Singh, H. , Agarwal, S. , Ansari, H.R and Raghava, G.P.S (2011) SfePred: Prediction of the year of evolution of swine flu (CR No. 005CR2010).
  66. Panwar, B. And Raghava, G.P.S (2011) Discrimination between cytosolic and mitocondrial tRNA synthetases (CR No. 039CR2010).
  67. Sharma, A, Singla, D., Rashid, M. and Raghava, G.P.S. (2011) “DESTAMP: Designing of stable antibacterial mutant peptides (Applied).
  68. Ansari, H.R., Singla, D., Raghava, G.P.S. (2011) TLR4hi- a software for computing antagonist of human TLR4<9d>.
  69. Panwar,B., Gupta, S., and Raghava, G.P.S. ( Raghava, G.P.S. (2012) “Drugmint: Knowledge based approach for predicting drug-likeness of a molecule (Applied).
  70. Singh, H., Ansari, H.R. and Raghava, G.P.S. (2012) "LBtope: Improved method for linear B-cell epitope prediction using antigen’s primary sequence (Applied).
  71. Dhanda, S.,K., Singla, D., Mondal, A.,K. and Raghava, G.P.S. (2012) “Drugmint: Knowledge based approach for predicting drug-likeness of a molecule (Applied)
  72. Chhauhan, J.,S., Bhat, A.,H., Rao, A. and Raghava, G.P.S. (2012) “GlycoPP: A Webserver for Prediction of N- O-Glycosites in Prokaryotic Protein Sequences (Applied).
  73. Singh, H., Singh, S., and Raghava, G.P.S. (2013) PEP2D: Bayesian based approach for predicting secondary structure of peptides using evolutionary information (Applied).