SuffixAligner: A Python-based Aligner for Long Noisy Reads

Third-generation sequencing technologies have revolutionized genomics research by generating long reads that resolve many computational challenges such as long genomics variations and repeats. Mapping a set of sequencing reads against a reference genome is the first step of many genomic data analysis pipelines. Many mapping/alignment tools are introduced and always made different compromises between the alignment accuracy and the resource usage in terms of memory space and processor speed. SuffixAligner is a python-based aligner for long noisy reads generated from third-generation sequencing machines. SuffixAligner follows the seed extending approach and exploits the nature of the biological alphabet that has a fixed size and a predefined lexical ordering to construct a suffix array for indexing a reference genome. A suffix array is used to efficiently search the indexed reference and locate the exactly matched seeds among the reads and the reference. The matched seeds are arranged into windows/clusters and the ones with the maximum number of seeds are reported as candidates for mapping positions. Using real data sets from third-generation sequencing experiments, we evaluated SuffixAligner against lordFAST, BWA, GEM3, and Minimap2, in which the results showed that SuffixAligner mapped more reads compared to the other compared tools. The source code of SuffixAligner is available at: https://github.com/ZeinabRabea/SuffixAligner.

@article{Rabea2023,
  title     = {SuffixAligner: A Python-based Aligner for Long Noisy Reads},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Zeinab Rabea and Sara El-Metwally and Samir Elmougy and M. Z. Rashad},
  doi       = {10.14569/IJACSA.2023.0140419},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140419}
}