Love, Michael I

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 95,106

Salmon provides fast and bias-aware quantification of transcript expression

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods

Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

Cited by 12,700

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research

Genomics and Phylogenetic StudiesGene expression and cancer classification

Cited by 3,929

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

Li, W., Xu, H., Xiao, T., Cong, L., Love, M. I., Zhang, F., Irizarry, R. A., Liu, J. S., Brown, M., & Liu, X. S. (2014) · Genome Biology

CRISPR and Genetic EngineeringChromatin Remodeling and Cancer

Cited by 2,684

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

Phanstiel, D. H., Van Bortle, K., Spacek, D., Hess, G. T., Shamim, M. S., Machol, I., Love, M. I., Aiden, E. L., Bassik, M. C., & Snyder, M. P. (2017) · Molecular Cell

Genomics and Chromatin DynamicsChromatin Remodeling and Cancer

Cited by 299

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 228

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology

Genomics and Phylogenetic StudiesGene expression and cancer classification

Cited by 186

Deletions of chromosomal regulatory boundaries are associated with congenital disease

Ibn-Salem, J., Köhler, S., Love, M. I., Chung, H.-R., Huang, N., Hurles, M. E., Haendel, M., Washington, N. L., Smedley, D., Mungall, C. J., et al. (2014) · Genome Biology

Genomic variations and chromosomal abnormalitiesGenomics and Chromatin Dynamics

Cited by 143

An approach for normalization and quality control for NanoString RNA expression data

Bhattacharya, A., Hamilton, A. M., Furberg, H., Pietzak, E., Purdue, M. P., Troester, M. A., Hoadley, K. A., & Love, M. I. (2021) · Briefings in Bioinformatics

Gene expression and cancer classificationMolecular Biology Techniques and Applications

Cited by 122

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

Ryu, J., Barkal, S., Yu, T., Jankowiak, M., Zhou, Y., Francoeur, M., Phan, Q. V., Li, Z., Tognon, M., Brown, L., et al. (2023) · Nature Genetics

CRISPR and Genetic EngineeringGenomics and Rare Diseases

Cited by 32