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, 18 (11), 1851-8

Mapping Short DNA Sequencing Reads and Calling Variants Using Mapping Quality Scores

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Mapping Short DNA Sequencing Reads and Calling Variants Using Mapping Quality Scores

Heng Li et al. Genome Res.

Abstract

New sequencing technologies promise a new era in the use of DNA sequence. However, some of these technologies produce very short reads, typically of a few tens of base pairs, and to use these reads effectively requires new algorithms and software. In particular, there is a major issue in efficiently aligning short reads to a reference genome and handling ambiguity or lack of accuracy in this alignment. Here we introduce the concept of mapping quality, a measure of the confidence that a read actually comes from the position it is aligned to by the mapping algorithm. We describe the software MAQ that can build assemblies by mapping shotgun short reads to a reference genome, using quality scores to derive genotype calls of the consensus sequence of a diploid genome, e.g., from a human sample. MAQ makes full use of mate-pair information and estimates the error probability of each read alignment. Error probabilities are also derived for the final genotype calls, using a Bayesian statistical model that incorporates the mapping qualities, error probabilities from the raw sequence quality scores, sampling of the two haplotypes, and an empirical model for correlated errors at a site. Both read mapping and genotype calling are evaluated on simulated data and real data. MAQ is accurate, efficient, versatile, and user-friendly. It is freely available at http://maq.sourceforge.net.

Figures

Figure 1.
Figure 1.
Distribution of mapping qualities, consensus qualities, true alignment error rate, and true consensus error rate. The red line shows the fraction of reads whose mapping qualities fall in each interval. (Pink line) The fraction of consensus genotypes whose consensus qualities fall in each interval; (blue line) the true alignment error rate of reads in each interval; (green line) the true consensus error rate of reads in each interval. (A) Reads are aligned without using mate-pair information. Single-end alignments do not contain enough information for MAQ to assign mapping quality larger than 90; therefore, the data in the top bin are missing. (B) Reads are aligned using mate-pair information.
Figure 2.
Figure 2.
Accuracy of variant calling. In the figure, “filtered regions” are regions covered by three or fewer reads or by no reads with mapping quality higher than 60. For substitutions, FP equals the number of positions called as different from homozygous reference that in fact should be identical to the reference according to the simulation, divided by the total number of MAQ substitution calls; FN equals the number of positions that are different from the reference according to the simulation but are missed by MAQ, divided by the total number of mutations added in the simulation. For indels, FP equals the number of indel calls within 5-bp flanking regions of a true indel, divided by the total number of MAQ indel calls; FN equals the number of true indel calls missed by MAQ, divided by the total number of indels in simulation. (A) Variants are called based on single-end alignment. (B) Variants are called based on paired-end alignment. (C) Theoretical accuracy of k-allele method, where we call an allele as long as at least k reads are supporting the allele, assuming all reads are correctly aligned (see also Supplemental material).

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