DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

摘要

Schematic overview of a minimal DMS experiment and the DiMSum pipeline. a Schematic of a basic, plasmid-based microbial growth DMS experiment: (1) construction of a plasmid library of mutant variants and independent transformation or integration of plasmid library into host cells, (2) exposure of cell population to selective conditions, and (3) high-throughput sequencing of samples to obtain variant counts before and after selection, which are used to derive fitness estimates for each variant. Indicated are steps at which bottlenecks could arise, potentially restricting variant pool size or complexity (red roman numerals): [i] inefficient library construction (“library bottleneck”), [ii] inefficient plasmid transformations (“replicate bottleneck”), and [iii] inefficient DNA extraction (“DNA extraction bottleneck”). Unforeseen bottlenecks can lead to over-sequencing [iv] of variant pools and thus underestimation of the errors associated with fitness scores or even appearance of sequencing counts for variants not contained in the original variant pool. b DMS experiments typically have a hierarchical abundance structure, where variants with more mutations are orders of magnitude less abundant than the wild-type sequence or single mutants. c DiMSum flow chart. The WRAP module performs low-level processing of raw DNA sequencing reads to produce sample-wise variant counts. The STEAM module transforms the resulting counts to estimates of variant fitness and associated error. See Additional file 1: Fig. S1-6 for example report plots