MBE: model-based enrichment estimation and prediction for differential sequencing data.
In: Genome Biology, Jg. 24 (2023-10-02), Heft 1
Online
academicJournal
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.
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MBE: model-based enrichment estimation and prediction for differential sequencing data.
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Autor/in / Beteiligte Person: | Busia, Akosua ; Listgarten, Jennifer |
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Zeitschrift: | Genome Biology, Jg. 24 (2023-10-02), Heft 1 |
Veröffentlichung: | eScholarship, University of California, 2023 |
Medientyp: | academicJournal |
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