Curriculam Vitae of Gajendra P.S. Raghava
PDF version of Biodata 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
- 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
- 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
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149.
Tyagi, A., Ahmed, F., Thakur, N., Sharma, A., Raghava, G. P. S., &
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150.
Zhang, G. L., Ansari, H. R., Bradley, P., Cawley, G. C., Hertz, T., Hu,
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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
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152.
Anastas, P., Bejatolah, M.-K., Gajendra, P. S., & others. (2010).
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153.
Ansari, H. R., & Raghava, G. P. S. (2010). Identification of NAD
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154.
Ansari, H. R., Flower, D. R., & Raghava, G. P. S. (2010). AntigenDB:
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155.
Ansari, H. R., & Raghava, G. P. S. (2010). Identification of
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156.
Ansari, H., Flower, D., & Raghava, G. (2010). AntigenDB. Nucleic
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157.
Chauhan, J. S., Mishra, N. K., & Raghava, G. P. S. (2010).
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158.
Garg, A., Tewari, R., & Raghava, G. P. S. (2010). Virtual screening
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159.
Garg, A., Tewari, R., & Raghava, G. P. S. (2010). KiDoQ: using
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160.
Lata, S., Mishra, N. K., & Raghava, G. P. S. (2010). AntiBP2:
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11(Suppl 1), S19.
161.
Mishra, N. K., Agarwal, S., & Raghava, G. P. S. (2010). Prediction
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162.
Mishra, N. K., & Raghava, G. P. S. (2010). Prediction of FAD
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163.
Panwar, B., & Raghava, G. P. S. (2010). Prediction and
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164.
Rashid, M., Ramasamy, S., & Raghava, G. P. S. (2010). A Simple
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165.
Singla, D., Sharma, A., Kaur, J., Panwar, B., & Raghava, G. P. S.
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166.
Verma, R., Varshney, G. C., & Raghava, G. P. S. (2010). Prediction
of mitochondrial proteins of malaria parasite using split amino acid
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167.
Ahmed, F., Ansari, H. R., & Raghava, G. P. S. (2009). Prediction of
guide strand of microRNAs from its sequence and secondary structure. BMC
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168.
Ahmed, F., Kumar, M., & Raghava, G. P. S. (2009). Prediction of
polyadenylation signals in human DNA sequences using nucleotide frequencies. In
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169.
Arora, P. K., Kumar, M., Chauhan, A., Raghava, G. P. S., & Jain, R.
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Research Notes, 2(1), 67.
170.
Chaudhary, N., Mahajan, L., Madan, T., Kumar, A., Raghava, G. P. S.,
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171.
Chauhan, J. S., Mishra, N. K., & Raghava, G. P. S. (2009).
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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),
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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
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175.
Lata, S., & Raghava, G. P. S. (2009). Databases and Web-Based Tools
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176.
Lata, S., & Raghava, G. P. S. (2009). Prediction and classification
of chemokines and their receptors. Protein Engineering Design and Selection,
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177.
Singla, D., Raghava, G. P., Kumar, M., Sharma, A., & Rashid, M.
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178.
Garg, A., & Raghava, G. P. S. (2008). ESLpred2: improved method for
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179.
Garg, A., & Raghava, G. P. S. (2008). A machine learning based
method for the prediction of secretory proteins using amino acid composition,
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180.
Kalita, M. K., Nandal, U. K., Pattnaik, A., Sivalingam, A., Ramasamy,
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181.
Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2008). Prediction of
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182.
Kumar, M., Thakur, V., & Raghava, G. P. S. (2008). COPid:
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183.
Kush, A., & Raghava, G. P. S. (2008). AC2DGel: analysis and
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184.
Lata, S., & Raghava, G. P. S. (2008). CytoPred: a server for
prediction and classification of cytokines. Protein Engineering, Design
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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,
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187.
Sethi, D., Garg, A., & Raghava, G. P. S. (2008). DPROT: prediction
of disordered proteins using evolutionary information. Amino Acids, 35(3),
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188.
Verma, R., Tiwari, A., Kaur, S., Varshney, G. C., & Raghava, G. P.
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189.
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190.
Bhasin, M., Lata, S., & Raghava, G. P. S. (2007). TAPPred prediction
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191.
Bhasin, M., Lata, S., & Raghava, G. P. S. (2007). Searching and
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192.
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193.
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Kaur, H., Garg, A., & Raghava, G. P. S. (2007). PEPstr: a de novo
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195.
Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2007).
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196.
Lata, S., Bhasin, M., & Raghava, G. P. S. (2007). Application of
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197.
Lata, S., Sharma, B. K., & Raghava, G. P. S. (2007). Analysis and
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198.
Mishra, N. K., Kumar, M., & Raghava, G. P. S. (2007). Support vector
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199.
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200.
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201.
Rashid, M., Saha, S., & Raghava, G. P. S. (2007). Support Vector
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202.
Saha, S., & Raghava, G. P. S. (2007). Prediction methods for B-cell
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203.
Saha, S., & Raghava, G. P. S. (2007). Predicting virulence factors
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204.
Saha, S., & Raghava, G. P. S. (2007). Prediction of neurotoxins
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205.
Saha, S., & Raghava, G. P. S. (2007). BTXpred: prediction of
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Kaur, H., & Raghava, G. P. S. (2006). Prediction of C$\alpha{\$}-H·O
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214.
Kumar, M., Verma, R., & Raghava, G. P. S. (2006). Prediction of
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215.
Raghava, G. P. S. (2006). MANGO: prediction of Genome Ontology (GO)
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Saha, S., & Raghava, G. P. S. (2006). VICMpred: an SVM-based method
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218.
Saha, S., & Raghava, G. P. S. (2006). AlgPred: prediction of
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Saha, S., & Raghava, G. P. S. (2006). Prediction of continuous
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220.
Saha, S., Zack, J., Singh, B., & Raghava, G. P. S. (2006). VGIchan:
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221.
Singh, M. K., Srivastava, S., Raghava, G. P. S., & Varshney, G. C.
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Bhasin, M., Garg, A., & Raghava, G. P. S. (2005). PSLpred:
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Bhasin, M., & Raghava, G. P. S. (2005). Pcleavage: an SVM based
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Bhasin, M., & Raghava, G. P. S. (2005). GPCRsclass: a web tool for
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Garg, A., Bhasin, M., & Raghava, G. P. S. (2005). Support vector
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Garg, A., Kaur, H., & Raghava, G. P. S. (2005). Real value
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Kumar, M., Bhasin, M., Natt, N. K., & Raghava, G. P. S. (2005).
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Bhasin, M., & Raghava, G. P. S. (2004). ESLpred: SVM-based method
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Bhasin, M., & Raghava, G. P. S. (2004). Prediction of CTL epitopes
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Bhasin, M., & Raghava, G. P. S. (2003). Prediction of promiscuous
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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
- Raghava, G.P.S. (1996) ASSP: A
computer program for comparision of observed and predicted protein
secondary structure. L-15582/96.
- Raghava, G.P.S. (1996) A
computer program for analysing and creating protein secondary structure
database. L-15658/96.
- Raghava, G.P.S and Agrawal, P.
A (1997) Software for phylogcncitic identification of microorganisms. C R.
1/97, date 11.3.97
- 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
- Kaur, H. and Raghava, G.P.S.
(2002) BETATPRED: Software for predicting b -turns using statistical
algorithm. SW-1162/2003
- Raghava, G.P.S. (2002) DNASIZE:
A software package for computing DNA/Protein fragments. SW-1156/2003.
- 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.
- Issac, B. and Raghava, G.P.S.
(2002) GWBLAST: Software Package for Genome Wide Similarity Search using
BLAST. SW-1149/2003
- Issac, B. and Raghava, G.P.S.
(2002) GWFASTA: Computer program for genome wide FASTA search.
SW-1143/2003.
- Raghava, G.P.S. (2002) LibInet:
Software Package for Managing Library Resources and Accessing via
Internet. SW-1155/2003.
- Singh, H. and Raghava, G.P.S.
(2002) MHCB ench: Evaluation of MHC Binding Peptide Prediction Algorithms.
SW-1160/2003.
- Singh, H. and Raghava, G.P.S.
(2002) MOT: Matrix Optimization Technique for identifying the MHC binding
core. SW-1159/2003
- Raghava, G.P.S. (2002) PCLASS:
Computer Program for predicting structural class of protein via Internet.
SW-1157/2003
- Raghava, G.P.S. (2002) PDSB:
Software Package for Managing Public Domain Software in Biology.
SW-1148/2003.
- Raghava, G.P.S. (2002) PDWSB:
Software Package for creating and managing Biological Web Servers.
SW-1152/2003.
- Singh, H. and Raghava, G.P.S.
(2002) ProPred: Software package for predicting promiscuous MHC class-II
binding peptides. SW-1163/2003
- Singh, H. and Raghava, G.P.S. (2002)
ProPred-I: Software package for predicting promiscuous MHC Class-I binding
peptides. SW-1161/2003
- Raghava, G.P.S. (2002) PSAweb:
Software package for Analyzing of Protein Sequence and Multiple Alignment.
SW-1158/2003.
- Bhasin, M. and Raghava, G. P.S.
(2003) MHCBN: A software package for managing data of Immunologically
related peptides. SW-1151/200
- 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).
- Saha, H. Bhasin, M. and
Raghava, G. P.S. (2003) Bcipep: A software package for management of B
cell epitopes. SW-1851/200
- Bhasin, M. and Raghava, G. P.S.
(2002) A Webserver for Prediction of Promiscuous And High Affinity Mutated
MHC Binders (Submitted).
- Bhasin, M. and Raghava, G. P.S.
(2002) TAPPred: A software package for predicting TAP binding affinity of
peptides via internet. SW-1847/2005.
- Kaur, H. and Raghava, G.P.S.
(2003) AlphaPred: A software for prediction of Alpha-turns in proteins
(Submitted)
- 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).
- 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).
- Kaur, H. and Raghava, G.P.S.
(2003) Ar_NHPred: A software for prediction of aromatic-backbone NH
interactions in proteins. SW-1850/2005.
- Issac, B and G.P.S. Raghava
(2003) SVMgene: Predicting protein coding regions in human genomic DNA
using SVM. SW-1852/2005.
- 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).
- 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.
- 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.
- 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.
- Kush, A. and Raghava, G. P. S.
(2008) AC2DGel: Analysis and Comparison of 2D Gels. SW-2123/2005
- 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.
- 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.
- Singh, H. and Raghava, G. P.
S.(2007) Prediction and mapping of promiscuous MHC class II binders in an
antigen sequence. SW-2286/2005
- Saha, S. and Raghava, G. P. S.
(2006) AlgPred: Prediction of allergenic proteins and mapping of IgE
epitopes. SW-3013/2006.
- Kumar, M., Thakur, V. and
Raghava, G. P. S. (2008) COPid: composition based protein identification.
SW-3074/2005.
- Bhasin,M. and Raghava, G.P.S.
(2004)Classification of nuclear receptors based on amino acid composition
and dipeptide composition. SW-2124/2005.
- 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.
- Saha, S. and Raghava, G. P. S.
(2007) Prediction of neurotoxins based on their function and
source.SW-2352/2005.
- Saha, S. and Raghava, G. P. S.
(2007) Prediction of bacterial proteins. SW-2652/2005.
- Rashid M., Saha S. and Raghava,
G. P. S. (2007)TBPred:A SVM based subcellular localization prediction
method for Mycobacterial proteins. SW-3635/2007
- Rakesh Kaundal and Raghava, G.
P. S. RSLpred:A svm based method for subcellular localization prediction
of rice proteins. SW-3638/2007.
- 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.
- Ahmed Firoz, Singh J. and
Raghava G.P.S. CDpred:Prediction of cleavage site of Dicer using support
vector machine. (Submitted)
- Lata, S. and Raghava, G. P. S.
(2008) PRRDB: A comprehensive database of Pattern-Recognition Receptors
and their ligands (Submitted).
- Ahmed Firoz, Ansari H.R. and Raghava
G.P.S. RISCbinder:Prediction of guide strand of microRNAs from its
sequence and secondary structure. (Submitted).
- A suite of programs for
computer-aided vaccine design. SW-2264/2005.
- Ahmed Firoz, Singh J. and
Raghava G.P.S. (2009) CDpred:Prediction of cleavage site of Dicer using
support vector machine. (SW-4017/2009).
- Lata, S. and Raghava, G. P. S.
(2009) PRRDB: A comprehensive database of Pattern-Recognition Receptors
and their ligands (SW-4018/2009).
- 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).
- Panwar, B. And Raghava, G.P.S
(2010) Prediction and classification of Aminoacyl tNRA syntheases using
signature profile-based descriptors (SW-4633/2010).
- Ansari, H.R. And Raghava, G.P.S
(2010) Identification of NAD interacting residues in proteins
(SW-4637/2010).
- 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).
- Rashid, M and Raghava, G.P.S
(2010) MycoPrint: Prediction of protein-protein interaction in
Mycobacterium tuberculosis (SW-4635/2010).
- 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)
- Chauhan, J.S., Mishra, N.K. And
Raghava, G.P.S (2010) ATPrint: Prediction of ATP interacting amino
acidacterium tuberculosis (SW-4635/2010).
- Kumar, S. And Raghava, G.P.S :
GenomeABC: A platform for benchmarking of genome assemblers (CR No.
035CR2011).
- Sharma, A. , Singla, D. ,
Rashid M and Raghava, G.P.S (2011) DESTAMP: Designing of stable
antibacterial mutant peptides (CR No. 036CR2011).
- 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)
- 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).
- Agarwal, S, Mishra, N.K and
Raghava, G.P.S (2011) PreMieR: Prediction of Mannose Interacting Residues
(CR No. 011CR2010).
- 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 ).
- 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).
- Panwar, B. And Raghava, G.P.S
(2011) Discrimination between cytosolic and mitocondrial tRNA synthetases
(CR No. 039CR2010).
- Sharma, A, Singla, D., Rashid,
M. and Raghava, G.P.S. (2011) “DESTAMP: Designing of stable
antibacterial mutant peptides (Applied).
- Ansari, H.R., Singla, D.,
Raghava, G.P.S. (2011) TLR4hi- a software for computing antagonist of
human TLR4<9d>.
- 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).
- 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).
- 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)
- 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).
- Singh, H., Singh, S., and
Raghava, G.P.S. (2013) PEP2D: Bayesian based approach for predicting
secondary structure of peptides using evolutionary information (Applied).