Abstract
As the length of sequencing read increasing, greater bioinformation is demanded from longread aligner. The short-read aligner is often widely used to make alignment very fast and accurate, but the approach is ill-suited to finding longer, gapped alignments with long indels. A wide variety of alignment algorithms and aligners have been subsequently developed over the past few years. In this article, we survey the theoretical foundations that underlie long-read alignments and highlight the options and practical trade-offs that need to be considered. Through the evaluation of the sophisticated experiments both on simulated and real data, we illustrate the performance of these aligners on the accuracy, the time and memory cost, as well as the scalability for the modern multi-core architecture. We also consider the future development of long-read alignment algorithms.
Keywords: Alignment algorithm, long read sequencing, memory usage, performance, runtime, sequence analysis.
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