RT Journal
A1 Chandra, Ghanshyam
A1 Hossen, Md Helal
A1 Scholz, Stephan
A1 Dilthey, Alexander T.
A1 Gibney, Daniel
A1 Jain, Chirag
T1 Pangenome-based genome inference using integer programming
JF Genome Research 
JO Genome Research 
YR 2025 
FD December 01 
VO 35 
IS 12 
SP 2661 
OP 2670 
DO 10.1101/gr.280567.125 
UL http://genome.cshlp.org/content/35/12/2661.abstract 
AB 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. Recent works highlight the advantage of pangenome graphs in addressing these challenges. Building on these developments, we propose a rigorous alignment-free genotyping method. Our optimization framework identifies a path through the pangenome graph that maximizes the matches between the path and substrings of sequencing reads (e.g., k-mers) while minimizing recombination events (haplotype switches) along the path. We prove that this problem is NP-hard and develop efficient integer-programming solutions. We benchmark the algorithm using downsampled short-read data sets from homozygous human cell lines with coverage ranging from 0.1× to 10×. Our algorithm accurately estimates complete major histocompatibility complex (MHC) haplotype sequences with small edit distances from the ground-truth sequences, providing a significant advantage over existing methods on low-coverage inputs.