kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph - Wei, Ze-Gang; Fan, Xing-Guo; Zhang, Hao; Zhang, Xiao-Dan; Liu, Fei; Qian, Yu; Zhang, Shao-Wu (Frontiers in Genetics, 2022)
With the rapid development of single molecular sequencing (SMS) technologies such as PacBio single molecule real time and Oxford Nanopore, the output read length is continuously increasing, which has dramatical potentials on cutting-edge genomic applications. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. However, these long reads contain higher sequencing errors and could more frequently span the breakpoints of structural variants (SVs) than that of shorter reads, leading to many unaligned reads or reads that are partially aligned for most state-of-the-art mappers. As a result, these methods usually focus on producing local mapping results for the query read, other than obtaining the whole end-to-end alignment. We introduce kngMap, a novel k-mer neighborhood graph-based mapper that is specifically designed to align long noisy SMS reads to a reference sequence. By benchmarking exhaustive experiments on both simulated and real-life SMS datasets to assess the performance of kngMap with other popular ten SMS mapping tools (e.g., BLASR, BWA-MEM and minimap2), we demonstrated that kngMap has higher sensitivity that can align more reads and bases to the reference genome, meanwhile, kngMap can produce consecutive alignments for the whole read and span different categories of SVs in the reads. kngMap is implemented in C++ and supports multi-threading, the source code of kngMap can be free downloaded at: https://github.com/zhang134/kngMap for academic usage.