COLT: COnstrained Lineage Tree Generation from sequence data

Abstract

Lineage analysis has been an important method for understanding the mutation patterns and the diversity of genes, such as antibodies. A mutation lineage is typically represented as a tree structure, describing the possible mutation paths. Generating lineage trees from sequence data imposes two unique challenges: (1) Types of constraints might be defined on top of sequence data and tree structures, which have to be appropriately formulated and maintained by the algorithms. (2) Enumerating all possible trees that satisfy constraints is typically computationally intractable. In this paper, we present a COnstrained Lineage Tree generation framework (COLT) that builds lineage trees from sequences, based on local and global constraints specified by domain experts and heuristics derived from the mutation processes. Our formal analysis and experimental results show that this framework can efficiently generate valid lineage trees, while strictly satisfying the constraints specified by domain experts.