| Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 MI Love, W Huber, S Anders Genome Biology 15 (12), 2014 | 22749 | 2014 |
| Salmon provides fast and bias-aware quantification of transcript expression R Patro, G Duggal, MI Love, RA Irizarry, C Kingsford Nature methods 14 (4), 417-419, 2017 | 2329 | 2017 |
| Orchestrating high-throughput genomic analysis with Bioconductor W Huber, VJ Carey, R Gentleman, S Anders, M Carlson, BS Carvalho, ... Nature methods 12 (2), 115, 2015 | 1940 | 2015 |
| Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences C Soneson, MI Love, MD Robinson F1000Research 4, 2015 | 1126 | 2015 |
| MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens W Li, H Xu, T Xiao, L Cong, MI Love, F Zhang, RA Irizarry, JS Liu, ... Genome Biology 15 (12), 554, 2014 | 655 | 2014 |
| Airway epithelial miRNA expression is altered in asthma OD Solberg, EJ Ostrin, MI Love, JC Peng, NR Bhakta, L Hou, C Nguyen, ... American Journal of Respiratory and Critical Care Medicine 186 (10), 965-974, 2012 | 199 | 2012 |
| Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences A Zhu, JG Ibrahim, MI Love Bioinformatics 35 (12), 2084-2092, 2019 | 197 | 2019 |
| X-exome sequencing of 405 unresolved families identifies seven novel intellectual disability genes H Hu, SA Haas, J Chelly, H Van Esch, M Raynaud, APM De Brouwer, ... Molecular psychiatry 21 (1), 133-148, 2016 | 189 | 2016 |
| RNA-Seq workflow: gene-level exploratory analysis and differential expression MI Love, S Anders, V Kim, W Huber F1000Research 4, 2015 | 188 | 2015 |
| A benchmark for RNA-seq quantification pipelines M Teng, MI Love, CA Davis, S Djebali, A Dobin, BR Graveley, S Li, ... Genome biology 17 (1), 1-12, 2016 | 135 | 2016 |
| Static and dynamic DNA loops form AP-1-bound activation hubs during macrophage development DH Phanstiel, K Van Bortle, D Spacek, GT Hess, MS Shamim, I Machol, ... Molecular cell 67 (6), 1037-1048. e6, 2017 | 125 | 2017 |
| Deletions of chromosomal regulatory boundaries are associated with congenital disease J Ibn-Salem, S Köhler, MI Love, HR Chung, N Huang, ME Hurles, ... Genome biology 15 (9), 1-16, 2014 | 118 | 2014 |
| ChIP-exo signal associated with DNA-binding motifs provides insight into the genomic binding of the glucocorticoid receptor and cooperating transcription factors SR Starick, J Ibn-Salem, M Jurk, C Hernandez, MI Love, HR Chung, ... Genome research 25 (6), 825-835, 2015 | 95 | 2015 |
| Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation MI Love, JB Hogenesch, RA Irizarry Nature Biotechnology, 2016 | 86 | 2016 |
| Modeling read counts for CNV detection in exome sequencing data MI Love, A Myšičková, R Sun, V Kalscheuer, M Vingron, SA Haas Statistical Applications in Genetics and Molecular Biology 11, 2011 | 69 | 2011 |
| Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications K Van den Berge, F Perraudeau, C Soneson, MI Love, D Risso, JP Vert, ... Genome biology 19 (1), 1-17, 2018 | 65 | 2018 |
| Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification MI Love, C Soneson, R Patro F1000Research 7, 2018 | 49 | 2018 |
| Flexible expressed region analysis for RNA-seq with derfinder L Collado-Torres, A Nellore, AC Frazee, C Wilks, MI Love, B Langmead, ... Nucleic acids research 45 (2), e9-e9, 2017 | 44 | 2017 |
| RNA sequencing data: hitchhiker's guide to expression analysis K Van den Berge, KM Hembach, C Soneson, S Tiberi, L Clement, MI Love, ... Annual Reviews, 2019 | 33 | 2019 |
| SAFE-clustering: single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data Y Yang, R Huh, HW Culpepper, Y Lin, MI Love, Y Li Bioinformatics 35 (8), 1269-1277, 2019 | 31 | 2019 |
Rob PatroAssociate Professor, Department of Computer Science, University of MarylandVerified email at cs.umd.edu
