| RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes A Srivastava, H Sarkar, N Gupta, R Patro Bioinformatics 32 (12), i192-i200, 2016 | 116 | 2016 |
| Alignment and mapping methodology influence transcript abundance estimation A Srivastava, L Malik, H Sarkar, M Zakeri, F Almodaresi, C Soneson, ... Genome biology 21 (1), 1-29, 2020 | 101 | 2020 |
| A space and time-efficient index for the compacted colored de Bruijn graph F Almodaresi, H Sarkar, A Srivastava, R Patro Bioinformatics 34 (13), i169-i177, 2018 | 62 | 2018 |
| Voronoi game on graphs S Bandyapadhyay, A Banik, S Das, H Sarkar Theoretical Computer Science 562, 270-282, 2015 | 41 | 2015 |
| Towards selective-alignment: Bridging the accuracy gap between alignment-based and alignment-free transcript quantification H Sarkar, M Zakeri, L Malik, R Patro Proceedings of the 2018 ACM International Conference on Bioinformatics …, 2018 | 21 | 2018 |
| Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data D He, M Zakeri, H Sarkar, C Soneson, A Srivastava, R Patro Nature Methods 19 (3), 316-322, 2022 | 14 | 2022 |
| Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level H Sarkar, A Srivastava, R Patro Bioinformatics 35 (14), i136-i144, 2019 | 14 | 2019 |
| Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes T Gao, R Soldatov, H Sarkar, A Kurkiewicz, E Biederstedt, PR Loh, ... Nature Biotechnology 41 (3), 417-426, 2023 | 10 | 2023 |
| Accurate, fast and lightweight clustering of de novo transcriptomes using fragment equivalence classes A Srivastava, H Sarkar, L Malik, R Patro arXiv preprint arXiv:1604.03250, 2016 | 10 | 2016 |
| Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data H Sarkar, A Srivastava, HC Bravo, MI Love, R Patro Bioinformatics 36 (Supplement_1), i102-i110, 2020 | 9 | 2020 |
| Social media attributions in the context of water crisis R Sarkar, H Sarkar, S Mahinder, AR KhudaBukhsh arXiv preprint arXiv:2001.01697, 2020 | 8 | 2020 |
| Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics H Sarkar, M Zakeri, L Malik, R Patro, ... | 8 | 2018 |
| Best practices for single-cell analysis across modalities L Heumos, AC Schaar, C Lance, A Litinetskaya, F Drost, L Zappia, ... Nature Reviews Genetics, 1-23, 2023 | 5 | 2023 |
| A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification A Srivastava, L Malik, H Sarkar, R Patro Bioinformatics 36 (Supplement_1), i292-i299, 2020 | 5 | 2020 |
| Quark enables semi-reference-based compression of RNA-seq data H Sarkar, R Patro Bioinformatics 33 (21), 3380-3386, 2017 | 5 | 2017 |
| A like-for-like comparison of lightweight-mapping pipelines for single-cell RNA-seq data pre-processing M Zakeri, A Srivastava, H Sarkar, R Patro bioRxiv, 2021.02. 10.430656, 2021 | 4 | 2021 |
| A space and time-efficient index for the compacted colored de Bruijn graph. bioRxiv F Almodaresi, H Sarkar, R Patro | 4 | 2017 |
| Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses T Hirz, S Mei, H Sarkar, Y Kfoury, S Wu, BM Verhoeven, AO Subtelny, ... Nature Communications 14 (1), 663, 2023 | 3 | 2023 |
| Compression of quantification uncertainty for scRNA-seq counts S Van Buren, H Sarkar, A Srivastava, NU Rashid, R Patro, MI Love Bioinformatics 37 (12), 1699-1707, 2021 | 3 | 2021 |
| Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets W Mu, H Sarkar, A Srivastava, K Choi, R Patro, MI Love Bioinformatics 38 (10), 2773-2780, 2022 | 2 | 2022 |
Rob PatroAssociate Professor, Department of Computer Science, University of MarylandVerified email at cs.umd.edu
