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MBE: model-based enrichment estimation and prediction for differential sequencing data.

ABSTRACT: Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.

SUBMITTER: Busia A

PROVIDER: S-EPMC10544408 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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MBE: model-based enrichment estimation and prediction for differential sequencing data.

Busia Akosua A Listgarten Jennifer J

Genome biology 20231002 1

Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in m ...[more]

PMID: 37784130

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