Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin’s approach to UMI deduplication con...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Genome Biology Ročník 20; číslo 1; s. 65
Hlavní autori: Srivastava, Avi, Malik, Laraib, Smith, Tom, Sudbery, Ian, Patro, Rob
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 27.03.2019
Springer Nature B.V
BMC
Predmet:
Algorithms
Animal Genetics and Genomics
Animals
Bioinformatics
Biomedical and Life Sciences
Cellular barcode
Datasets
DNA Barcoding, Taxonomic
Evolutionary Biology
Gene expression
Gene mapping
genes
Genomes
Genomics
Human Genetics
Humans
Life Sciences
Method
Mice
Microbial Genetics and Genomics
nucleotide sequences
pipelines
Plant Genetics and Genomics
Quantification
Ribonucleic acid
RNA
sequence analysis
Sequence Analysis, RNA
Single-Cell Analysis
Single-cell RNA-seq
Software
Transcription
UMI deduplication
Cellular barcode
UMI deduplication
Quantification
Single-cell RNA-seq
ISSN:1474-760X, 1474-7596, 1474-760X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin’s approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Undefined-1
ObjectType-Feature-3
content type line 23
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1670-y