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Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.

ABSTRACT: RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.

SUBMITTER: Davies P

PROVIDER: S-EPMC8574610 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.

Davies Philip P Jones Matt M Liu Juntai J Hebenstreit Daniel D

Briefings in bioinformatics 20211101 6

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing re ...[more]

PMID: 33959753

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