Deconvolving sequence variation in mixed DNA populations

We present an original approach to identifying sequence variants in a mixed DNA population from sequence trace data. The heart of the method is based on parsimony: given a wildtype DNA sequence, a set of observed variations at each position collected from sequencing data, and a complete catalog of all possible mutations, determine the smallest set of mutations from the catalog that could fully explain the observed variations. The algorithmic complexity of the problem is analyzed for several classes of mutations, including block substitutions, single-range deletions, and single-range insertions. The reconstruction problem is shown to be NP-complete for single-range insertions and deletions, while for block substitutions, single character insertion, and single character deletion mutations, polynomial time algorithms are provided. Once a minimum set of mutations compatible with the observed sequence is found, the relative frequency of those mutations is recovered by solving a system of l...

• Type: Conference
• Year: 2002
• Where: RECOMB
• Authors: Andy Wildenberg, Steven Skiena, Pavel Sumazin
• Keywords: Block Substitutions, Computational Biology, Polynomial Time Algorithms, RECOMB 2002, Single Nucleotide Polymorphisms