Abstract
Across the life sciences, processing next generation sequencing data commonly relies upon a computationally expensive process where reads are mapped onto a reference sequence. Prior to such processing, however, there is a vast amount of information that can be ascertained from the reads, potentially obviating the need for processing, or allowing optimized mapping approaches to be deployed. Here, we present a method termed FlexTyper which facilitates a “reverse mapping” approach in which high throughput sequence queries, in the form of k-mer searches, are run against indexed short-read datasets in order to extract useful information. This reverse mapping approach enables the rapid counting of target sequences of interest. We demonstrate FlexTyper’s utility for recovering depth of coverage, and accurate genotyping of SNP sites across the human genome. We show that genotyping unmapped reads can correctly inform a sample’s population, sex, and relatedness in a family setting. Detection of pathogen sequences within RNA-seq data was sensitive and accurate, performing comparably to existing methods, but with increased flexibility. We present two examples of ways in which this flexibility allows the analysis of genome features not well-represented in a linear reference. First, we analyze contigs from African genome sequencing studies, showing how they distribute across families from three distinct populations. Second, we show how gene-marking k-mers for the killer immune receptor locus allow allele detection in a region that is challenging for standard read mapping pipelines. The future adoption of the reverse mapping approach represented by FlexTyper will be enabled by more efficient methods for FM-index generation and biology-informed collections of reference queries. In the long-term, selection of population-specific references or weighting of edges in pan-population reference genome graphs will be possible using the FlexTyper approach. FlexTyper is available at https://github.com/wassermanlab/OpenFlexTyper.
Highlights
Short-read DNA sequencing enables diverse molecular investigations across life science applications spanning from medicine to agriculture
Generation sequencing data is composed of short sequences of DNA, referred to as “reads”, which are often shorter than 200 base pairs making them many orders of magnitude smaller than the entirety of a human genome
Many experts in the field of genomics have concluded that selecting a single, linear reference genome for mapping reads against is limiting, and several current research endeavors are focused on exploring options for improved analysis methods to unlock the full utility of sequencing data
Summary
Short-read DNA sequencing enables diverse molecular investigations across life science applications spanning from medicine to agriculture. Obtaining useful information from a data set of raw reads (short pieces of DNA read outs from the DNA sequencer) typically involves performing either de novo assembly, or mapping the read sequences against one or more reference genomes. Static linear reference genomes which do not capture these large differences between populations impose challenges for accurate genotyping, with implications in medicine and association studies [1,2]. Global efforts to enrich the linear reference genome have led to the development of graph based representations of pan-genomes, for a comprehensive review of current approaches see [7,8]. As highlighted in an earlier review by [9], a key challenge in the future will be to determine the most appropriate reference genome(s), or path(s) through a graph pan-genome, to maximize genotyping performance. Knowledge regarding the genotypes of single nucleotide polymorphisms (SNPs) or other makers present in a read data set can be used to guide the choice of reference
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