Correct and bias-free interpretation of the deep sequencing data depends on the complete mapping of all mappable reads to the reference sequence. However, the accuracy and robustness of previous read-mapping algorithms are not satisfactory in many cases, impairing the reproducibility and verifiability. We developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the high accuracy, robustness and verifiability.

The sensitivity and accuracy of FANSe2 are higher than previous algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. Also, the SNV identifications based on FANSe2 results are experimentally validated, while the other algorithms provides false positive and false negative SNV identifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. Its speed can exceed the BWT-based algorithms, matching the speed of the coming generation of sequencers.

In sum, FANSe2 thus provides verifiable and robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications.

FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.