AlignerBoost: A Generalized Software Toolkit for Boosting Next-Gen Sequencing Mapping Accuracy Using a Bayesian-Based Mapping Quality Framework

<div><p>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. Here we developed a generalized software toolkit "AlignerBoost", 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 <a href="https://github.com/Grice-Lab/AlignerBoost" target="_blank">https://github.com/Grice-Lab/AlignerBoost</a>.</p></div

The mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the simulated DNA-seq datasets.

<p>The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. “Default” indicates aligners’ default best hits; “AlignerBoost” indicates best hits via AlignerBoost mapping and filtering procedures. A-D: Single-end (SE) mapping; E-H: Paired-end (PE) mapping; A/E: Genome, B/F: RefExome, C/G: Pseudogene, D/H: RMSK.</p

The estimated mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the real capture-seq datasets.

<p>All mappings were performed using STAR. Mapping sensitivity is approximated by the read depth in capture regions. The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. “Default” indicates aligners’ default best hits; “AlignerBoost” indicates best hits via AlignerBoost mapping and filtering procedures. A: Single-end (SE) mapping; B: Paired-end (PE) mapping. Replicate samples have same point types but different line types.</p

The mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the simulated DNA-seq datasets using AlignerBoost and similar tools.

<p>The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. Different tools are labelled with different line points. AlignerBoost is used with Bowtie2 aligner. A-D: Single-end (SE) mapping; E-H: Paired-end (PE) mapping; A/E: Genome, B/F: RefExome, C/G: Pseudogene, D/H: RMSK.</p

Mapping sensitivity and precision of simulated RNA-seq datasets by picking “best” hits using AlignerBoost filtering procedures (AlignerBoost) or the aligner’s default best mode (Default).

<p>A: Single-end (SE) mapping; B: Paired-end (PE) mapping.</p

Skin microbes discussed and their associated skin diseases.

<p>Skin microbes discussed and their associated skin diseases.</p

Gene expression differences between AlignerBoost filtered or “default “best alignments for two replicate Capture-seq datasets from human brain tissues.

<p>Gene expression is represented by RPKM values. Coding gene (red) and pseudogene (blue) annotations are from GENCODE project (v19). Values in parentheses show the mean gene expression changes of the two replicates. A: Single-end (SE) mapping; B: Paired-end (PE) mapping.</p

DFU 16S Sequences

Contains fasta formatted sequences

DFU Group File

Maps sequence identifiers to Subject I

DFU clinical factors

Contains all clinical factors examined and their values for each Subject