Interval scheduling maximizing minimum coverage

TL;DR

This paper introduces a new interval scheduling problem focused on maximizing minimum coverage with at most k machines, relevant for genome sequencing, and provides exact and approximation algorithms with proven efficiency.

Contribution

It formulates a novel scheduling variant with applications in genomics and offers both exact and approximation algorithms with proven time complexities.

Findings

Exact algorithm with time complexity O(n^2 log k / log n) or O(nk log k)

Approximation algorithm with O(n log n) time and ratio k / floor(k/2)

Applicable to genome sequencing and haplotyping problems

Abstract

In the classical interval scheduling type of problems, a set of $n$ jobs, characterized by their start and end time, need to be executed by a set of machines, under various constraints. In this paper we study a new variant in which the jobs need to be assigned to at most $k$ identical machines, such that the minimum number of machines that are busy at the same time is maximized. This is relevant in the context of genome sequencing and haplotyping, specifically when a set of DNA reads aligned to a genome needs to be pruned so that no more than $k$ reads overlap, while maintaining as much read coverage as possible across the entire genome. We show that the problem can be solved in time $\min\left(O(n^2\log k / \log n),O(nk\log k)\right)$ by using max-flows. We also give an $O(n\log n)$-time approximation algorithm with approximation ratio $\rho =\frac{k}{\lfloor k/2 \rfloor}$.