Pangenome-based genome inference using integer programming.

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance.

Integer programming framework for pangenome-based genome inference.

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance.

Haplotype-aware sequence alignment to pangenome graphs.

Modern pangenome graphs are built using haplotype-resolved genome assemblies. When mapping reads to a pangenome graph, prioritizing alignments that are consistent with the known haplotypes improves genotyping accuracy. However, the existing rigorous formulations for colinear chaining and alignment problems do not consider the haplotype paths in a pangenome graph.

Co-linear chaining on pangenome graphs.

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment.