FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Accurate alignment of sequencing reads is critical for high-throughput sequencing data analysis.
• Detecting insertions/deletions (indels) and sequencing errors is essential for RNA-Seq and genetic variation studies.

Purpose of the Study:

• To develop a novel, fast, and accurate algorithm for nucleic acid sequence analysis.
• To enable precise mapping of millions of sequencing reads to reference genomes, handling indels and adjustable mismatch settings.

Main Methods:

• Developed FANSe, a seed-based algorithm utilizing whole read information.
• Implemented adjustable mismatch allowance and indel handling capabilities.
• Utilized hotspot scoring and modified Smith-Watermann refinement for accelerated and sensitive mapping.

Main Results:

• FANSe accurately and quantitatively maps millions of reads to diverse genomes (small/large, masked/unmasked).
• The algorithm demonstrates high sensitivity and low ambiguity, even with low-abundance mRNAs.
• Stable processing across various sequencing platforms was achieved.

Conclusions:

• FANSe provides a robust solution for accurate and efficient sequence alignment in genomics and transcriptomics.
• The algorithm enhances quantitative RNA-Seq analysis and genetic variation identification.
• FANSe offers a valuable tool for researchers in high-throughput sequencing data processing.