Inferring the global structure of chromosomes from structural variations

Next generation sequencing (NGS) technologies have made it possible to exhaustively detect structural variations (SVs) in genomes. Although various methods for detecting SVs have been developed, the global structure of chromosomes, i.e., how segments in a reference genome are extracted and ordered in an unknown target genome, cannot be inferred by detecting only individual SVs.

Here, we formulate the problem of inferring the global structure of chromosomes from SVs as an optimization problem on a bidirected graph. This problem takes into account the aberrant adjacencies of genomic regions, the copy numbers, and the number and length of chromosomes. Although the problem is NP-complete, we propose its polynomial-time solvable variation by restricting instances of the problem using a biologically meaningful condition, which we call the weakly connected constraint. We also explain how to obtain experimental data that satisfies the weakly connected constraint.

Our results establish a theoretical foundation for the development of practical computational tools that could be used to infer the global structure of chromosomes based on SVs. The computational complexity of the inference can be reduced by detecting the segments of the reference genome at the ends of the chromosomes of the target genome and also the segments that are known to exist in the target genome.