TY  - JOUR
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
Y1  - 2025/12/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 2661 
EP  - 2670 
DO  - 10.1101/gr.280567.125 
VL  - 35 
IS  - 12 
UR  - http://genome.cshlp.org/content/35/12/2661.abstract 
N2  - 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. 
ER  -