Full Member
Richa Naveed Ahamed
Membership since:
2025
Field of activity:
Other Memberships/Affiliations
RIKEN Center for Biosystems Dynamics Research (BDR), JAPAN
Degrees:
2015
Doctorate Other
Bioinformatics
Publications resulting from Research
T. Richa, B. Zehra, A. Vijayakumar et al. Artificial intelligence and omics in malignant gliomas.Physiol Genomics. 2024; 56,12: 876-895.
T. Richa, B. Zehra, N. Sharon et al., Single-Cell Reconstruction and Mutation Enrichment Analysis Identifies
Dysregulated Cardiomyocyte and Endothelial Cells in Congenital Heart Disease. Physiol Genomics. 2023; 55(12):634-
646.
T. Richa, R. Abdel Hameid, A. Bankapur, et al. Single Cell Transcriptomics Trajectory and Molecular Convergence of Clinically Relevant Mutations in Brugada Syndrome. American Journal of Physiology-Heart and Circulatory Physiology 2021; 320(5):H1935-H1948.
N. Nasna*, T. Richa*, A. Bankapur, et al. Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar
macrophages in patients with critical COVID-19. Iscience. 2021; 24(9):103030.
*Equal contribution
N. Kosaji, B. Zehra, N. Nassir, T. Richa, R. Orszulak, A. R., Lim et al. Lack of ethnic diversity in single-cell transcriptomics hinders cell type detection and precision medicine inclusivity. Med. 2023, 4(4): 217-219.
N. Nasna*, T. Richa*, A. Bankapur, et al. Analyzing single cell transcriptome data from severe COVID-19 patients. STAR Protocols. 2022; 2(4):103030.
*Equal contribution
A.K. Rooj, E.Cormet-Boyaka, E.B. Clark, Y.J. Qadri, W.Lee, R. Boddu, A. Agarwal, T. Richa, M. Uddin, V.Parpura, E.J.Sorscher, C.M. Fuller and B.K. Berdiev. Association of cystic fibrosis transmembrane conductance regulator with epithelial sodium channel subunits carrying Liddle’s syndrome mutations. American Journal of Physiology-Lung cellular and molecular physiology 2021;321: L308-L32
G. Begum, A. Albanna, A. Bankapur, N. Nassir, T. Richa, B.K. Berdiev, H. Akter, N. Karuvantevida, B. Kellam, D. Alhashmi , W.W. Sung. Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome. International journal of molecular sciences 2021;22(4):2060.
T. Sivaraman and T. Richa. Cryptic intermediates and metastable states of proteins as predicted by OneG computational method. Journal of Biomolecular Structure and Dynamics 2021;18:1-6.
T. Richa, M Gentaro, M Taiji and Y. Kuroda. Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity Scientific Reports 2019; 9: 2530.
T. Richa, S. Ide, R. Suzuki, T. Ebina and Y. Kuroda. Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. Journal of Computer-Aided Molecular Design 2016; 31: 237-244.
D. P. Wankhede, M. Kumari, T. Richa, J. Aravind and S. Rajkumar. Genome wide identification and characterization of Calcium Dependent Protein Kinase gene family in Cajanus cajan. Journal of Environmental Biology 2017; 38: 169-177.
T. Richa and T. Sivaraman Computational analyses of cryptic intermediates in the native unfolding pathways of barnase and thioredoxin Biologia 2015; 70: 420-427.
T. Richa and T. Sivaraman. OneG-Vali: A Webserver for Detecting, Estimating and Validating Cryptic Intermediates of Proteins under Native Conditions. RSC Advances 2014; 4(69): 36325-36335.
D. Sivakumar, T. Richa, S. S. Rajesh, B. Gorai and T. Sivaraman. In silico methods for designing antagonists to anti-apoptotic members of bcl-2 family proteins. Mini-Reviews in Medicinal Chemistry 2012;12(11):1144-1153.
T. Richa and T. Sivaraman. OneG: A computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions. PLoS ONE 2012;7(3) e32465.
K. P. Rao*, T. Richa*, K. Kumar, B. Raghuram and A. K. Sinha. In silico analysis reveals 75 members of mitogen-activated protein kinase kinase kinase gene family in rice. DNA Research 2010;17(3):139-153.
*Equal contribution
Peter Brown, RELISH Consortium, Yaoqi Zhou. Large Expert-Curated Database for Benchmarking Document Similarity Detection in Biomedical Literature Search. Database 2019, 1-66
Y Banerjee, T. Richa, M Gholami, A Alsheikh-Ali, R Bayoumi, P Lansberg. Augmenting flexnerism via twitterism: need for integrating social media application in blueprinting pedagogical strategies for undergraduate medical education. JMIR medical education 5 (1), e12403
T. Richa, R Bayoumi, Lansberg P, Banerjee Y. Blending Gagne's Instructional Model with Peyton's Approach to Design an Introductory Bioinformatics Lesson-plan for Medical Students. JMIR Medical Education 2018. 4(2):e11122.
P. Arasu, T.Richa and T. Sivaraman. Review on Computational Methods for Predicting Residue-Specific Stabilities of Proteins. Journal of Pharmaceutical Sciences and Research 2015; 7(3): 159-162.
P. Das, T. Richa and T. Sivaraman. A computational strategy for predicting residue-specific stabilities of cardiotoxin III, an all β-sheet protein. Research Journal of Pharmaceutical, Biological and Chemical Sciences. 2014; 5(3): 1824-1831.
T. Richa and T. Sivaraman. Cooperative unfolding units and metastable states of cytochrome c551 from Pseudomonas aeruginosa under native conditions. Journal of Pharmaceutical Sciences and Research. 2014; 6(3):144-147.
T. Richa and T. Sivaraman. Structural stability and folding pathways of proteins under native conditions as monitored by hydrogen/deuterium (H/D) exchange methods. International Journal of Research in Pharmaceutical Sciences. 2013; 4(4): 550-562.
T. Richa and T. Sivaraman. CIntX: A software tool for calculating the intrinsic exchange rates of labile protons in proteins. Journal of Pharmaceutical Sciences and Research. 2012;4(6):1852-1858.
T. Richa and T. Sivaraman. META: A computational tool for predicting metastable states in the folding pathways of proteins. Journal of Pharmaceutical Sciences and Research. 2011;3(9):1486-1490.
T. Richa and T. Sivaraman. A novel algorithm for calculating intrinsic exchange rate constants of backbone amide protons in proteins. Proceedings of 2nd International Conference on Bioinformatics and System Biology (Editor-in-chief – M. Sabesan, Annamalai University; ISBN: 9788184352849), 2011, 89-94.
G. Khalique and T. Richa. A Survey of the Structural Parameters Used for Computational Prediction of Protein Folding Process. In: Shanker A. (eds) Bioinformatics: Sequences, Structures, Phylogeny. Springer, Singapore. 2018. Page 255-270. (BOOK CHAPTER)
T. Richa, B. Zehra, N. Sharon et al., Single-Cell Reconstruction and Mutation Enrichment Analysis Identifies
Dysregulated Cardiomyocyte and Endothelial Cells in Congenital Heart Disease. Physiol Genomics. 2023; 55(12):634-
646.
T. Richa, R. Abdel Hameid, A. Bankapur, et al. Single Cell Transcriptomics Trajectory and Molecular Convergence of Clinically Relevant Mutations in Brugada Syndrome. American Journal of Physiology-Heart and Circulatory Physiology 2021; 320(5):H1935-H1948.
N. Nasna*, T. Richa*, A. Bankapur, et al. Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar
macrophages in patients with critical COVID-19. Iscience. 2021; 24(9):103030.
*Equal contribution
N. Kosaji, B. Zehra, N. Nassir, T. Richa, R. Orszulak, A. R., Lim et al. Lack of ethnic diversity in single-cell transcriptomics hinders cell type detection and precision medicine inclusivity. Med. 2023, 4(4): 217-219.
N. Nasna*, T. Richa*, A. Bankapur, et al. Analyzing single cell transcriptome data from severe COVID-19 patients. STAR Protocols. 2022; 2(4):103030.
*Equal contribution
A.K. Rooj, E.Cormet-Boyaka, E.B. Clark, Y.J. Qadri, W.Lee, R. Boddu, A. Agarwal, T. Richa, M. Uddin, V.Parpura, E.J.Sorscher, C.M. Fuller and B.K. Berdiev. Association of cystic fibrosis transmembrane conductance regulator with epithelial sodium channel subunits carrying Liddle’s syndrome mutations. American Journal of Physiology-Lung cellular and molecular physiology 2021;321: L308-L32
G. Begum, A. Albanna, A. Bankapur, N. Nassir, T. Richa, B.K. Berdiev, H. Akter, N. Karuvantevida, B. Kellam, D. Alhashmi , W.W. Sung. Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome. International journal of molecular sciences 2021;22(4):2060.
T. Sivaraman and T. Richa. Cryptic intermediates and metastable states of proteins as predicted by OneG computational method. Journal of Biomolecular Structure and Dynamics 2021;18:1-6.
T. Richa, M Gentaro, M Taiji and Y. Kuroda. Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity Scientific Reports 2019; 9: 2530.
T. Richa, S. Ide, R. Suzuki, T. Ebina and Y. Kuroda. Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. Journal of Computer-Aided Molecular Design 2016; 31: 237-244.
D. P. Wankhede, M. Kumari, T. Richa, J. Aravind and S. Rajkumar. Genome wide identification and characterization of Calcium Dependent Protein Kinase gene family in Cajanus cajan. Journal of Environmental Biology 2017; 38: 169-177.
T. Richa and T. Sivaraman Computational analyses of cryptic intermediates in the native unfolding pathways of barnase and thioredoxin Biologia 2015; 70: 420-427.
T. Richa and T. Sivaraman. OneG-Vali: A Webserver for Detecting, Estimating and Validating Cryptic Intermediates of Proteins under Native Conditions. RSC Advances 2014; 4(69): 36325-36335.
D. Sivakumar, T. Richa, S. S. Rajesh, B. Gorai and T. Sivaraman. In silico methods for designing antagonists to anti-apoptotic members of bcl-2 family proteins. Mini-Reviews in Medicinal Chemistry 2012;12(11):1144-1153.
T. Richa and T. Sivaraman. OneG: A computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions. PLoS ONE 2012;7(3) e32465.
K. P. Rao*, T. Richa*, K. Kumar, B. Raghuram and A. K. Sinha. In silico analysis reveals 75 members of mitogen-activated protein kinase kinase kinase gene family in rice. DNA Research 2010;17(3):139-153.
*Equal contribution
Peter Brown, RELISH Consortium, Yaoqi Zhou. Large Expert-Curated Database for Benchmarking Document Similarity Detection in Biomedical Literature Search. Database 2019, 1-66
Y Banerjee, T. Richa, M Gholami, A Alsheikh-Ali, R Bayoumi, P Lansberg. Augmenting flexnerism via twitterism: need for integrating social media application in blueprinting pedagogical strategies for undergraduate medical education. JMIR medical education 5 (1), e12403
T. Richa, R Bayoumi, Lansberg P, Banerjee Y. Blending Gagne's Instructional Model with Peyton's Approach to Design an Introductory Bioinformatics Lesson-plan for Medical Students. JMIR Medical Education 2018. 4(2):e11122.
P. Arasu, T.Richa and T. Sivaraman. Review on Computational Methods for Predicting Residue-Specific Stabilities of Proteins. Journal of Pharmaceutical Sciences and Research 2015; 7(3): 159-162.
P. Das, T. Richa and T. Sivaraman. A computational strategy for predicting residue-specific stabilities of cardiotoxin III, an all β-sheet protein. Research Journal of Pharmaceutical, Biological and Chemical Sciences. 2014; 5(3): 1824-1831.
T. Richa and T. Sivaraman. Cooperative unfolding units and metastable states of cytochrome c551 from Pseudomonas aeruginosa under native conditions. Journal of Pharmaceutical Sciences and Research. 2014; 6(3):144-147.
T. Richa and T. Sivaraman. Structural stability and folding pathways of proteins under native conditions as monitored by hydrogen/deuterium (H/D) exchange methods. International Journal of Research in Pharmaceutical Sciences. 2013; 4(4): 550-562.
T. Richa and T. Sivaraman. CIntX: A software tool for calculating the intrinsic exchange rates of labile protons in proteins. Journal of Pharmaceutical Sciences and Research. 2012;4(6):1852-1858.
T. Richa and T. Sivaraman. META: A computational tool for predicting metastable states in the folding pathways of proteins. Journal of Pharmaceutical Sciences and Research. 2011;3(9):1486-1490.
T. Richa and T. Sivaraman. A novel algorithm for calculating intrinsic exchange rate constants of backbone amide protons in proteins. Proceedings of 2nd International Conference on Bioinformatics and System Biology (Editor-in-chief – M. Sabesan, Annamalai University; ISBN: 9788184352849), 2011, 89-94.
G. Khalique and T. Richa. A Survey of the Structural Parameters Used for Computational Prediction of Protein Folding Process. In: Shanker A. (eds) Bioinformatics: Sequences, Structures, Phylogeny. Springer, Singapore. 2018. Page 255-270. (BOOK CHAPTER)