|Abstract:|| Short Tandem Repeats (STRs) are implicated in dozens of human genetic diseases and contribute significantly to genome variation and instability. Yet profiling STRs from short-read sequencing data is challenging because of their high sequencing error rate. Here we developed STR-FM, Short Tandem Repeat profiling using Flank-based Mapping, a computational pipeline that can detect the full spectrum of STR alleles from short-read data, can adapt to emerging read-mapping algorithms, and can be applied to heterogeneous genetic samples (e.g., tumors, viral populations, and genomes of organelles). We used STR-FM to study STR error rates and patterns in publicly available human, and in-house generated ultra-deep plasmid, sequencing datasets. We discovered that STRs sequenced with a PCR-free protocol have up to 9-fold fewer errors than those sequenced with a PCR-containing protocol. We constructed an error correction model for genotyping STRs that can assign genotypes correctly to 98-100% of STRs and can distinguish heterozygous alleles containing STRs with consecutive repeat numbers. Applying our model and pipeline to Illumina sequencing data with 100-bp reads, we could confidently genotype several disease-related long trinucleotide STRs. Utilizing this pipeline, for the first time we determined the genome-wide STR germ-line mutation rate from a deeply sequenced human pedigree. More mutations originated in the male germ line. Additionally, we built a tool that recommends minimum sequencing depth for accurate STR genotyping, depending on repeat length of interest and sequencing read length. The required read depth increases with STR length and is lower for a PCR-free protocol. This suite of tools addresses the pressing challenges surrounding STR genotyping, and thus is of wide interest to researchers investigating disease-related STRs and STR evolution.
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