PMID- 35277707 OWN - NLM STAT- MEDLINE VI - 19 IP - 3 TI - Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data. PG - 316-322 CI - © 2022. The Author(s), under exclusive licence to Springer Nature America, Inc. LA - eng PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't PT - Research Support, U.S. Gov't, Non-P.H.S. PL - United States TA - Nat Methods JT - Nature methods JID - 101215604 IS - 1548-7105 (Electronic) LID - 10.1038/s41592-022-01408-3 [doi] FAU - He, Dongze AU - He D AUID- ORCID: http://orcid.org/0000-0001-8259-7434 AD - Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. FAU - Zakeri, Mohsen AU - Zakeri M AUID- ORCID: http://orcid.org/0000-0002-9856-719X AD - Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. FAU - Sarkar, Hirak AU - Sarkar H AD - Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. FAU - Soneson, Charlotte AU - Soneson C AD - Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. AD - SIB Swiss Institute of Bioinformatics, Basel, Switzerland. FAU - Srivastava, Avi AU - Srivastava A AUID- ORCID: http://orcid.org/0000-0001-9798-2079 AD - New York Genome Center, New York City, NY, USA. FAU - Patro, Rob AU - Patro R AUID- ORCID: http://orcid.org/0000-0001-8463-1675 AD - Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. rob@cs.umd.edu. IS - 1548-7091 (Linking) RN - 0 (RNA, Small Nuclear) SB - IM MH - *Gene Expression Profiling/methods MH - RNA, Small Nuclear MH - RNA-Seq MH - Sequence Analysis, RNA/methods MH - *Single-Cell Analysis/methods MH - Software PMC - PMC8933848 DCOM- 20220427 LR - 20220912 DP - 202203 DEP - 20220311 AB - The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (scRNA-seq and snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate normal gene expression count matrices.