1. A computer-implemented method for using machine learning to identify anomaly subsets of iteration data, the method comprising: accessing a structure including at least part of a definition for a workflow, the workflow including: a first task of accessing a set of reads based on a material associated with a respective client; a second task of aligning each read of the set of reads to a portion of a reference data set; a third task of generating a client data set for the respective client using the aligned set of reads, the client data set including a set of values associated with each of one or more units, each unit of the one or more units corresponding to a set of defined positions within a data structure; a fourth task of detecting a presence of one or more sparse indicators associated with the respective client by comparing the set of values of the client data set to corresponding values in the reference data set, each sparse indicator of the one or more sparse indicators identifying a distinction between the client data set and the reference data set; and a fifth task of classifying each sparse indicator of the one or more sparse indicators based on a state transition likelihood associated with that sparse indicator; accessing iteration data for the workflow, the iteration data including, for each client of a plurality of clients, a result corresponding to a partial or full performance of the workflow and an iteration identifier; using a machine-learning technique to process the iteration data to identify an anomaly subset of the iteration data; and generating a communication that represents the anomaly subset.