Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or "best" hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present.
AlignerBoost is a generalized software toolkit for boosting the mapping accuracy of model NGS aligners, which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads.
We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds.
AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known
SNPs. AlignerBoost’s algorithm is computationally efficient, and can process one million
alignments within 30 seconds on a typical desktop computer.
AlignerBoost is implemented as a uniform Java application and is freely available at GitHub.
Please cite AlignerBoost on PubMed for using this tool.