Abstract
Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals or samples in an unbiased manner, preferably de novo. The rapid and unbiased estimation of genetic relatedness has the potential to overcome reference genome bias, to detect mix-ups early, and to verify that biological replicates belong to the same genetic lineage before conclusions are drawn using mislabelled, or misidentified samples.
We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include detecting sample identity and mix-up, non-obvious genomic variation, and population structure.
We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.
Author Summary Current analysis of the genetic similarity of samples is overly dependent on alignment to reference genomes, which are often unavailable and in any case can introduce bias. We address this limitation by implementing an efficient alignment free sequence comparison algorithm (kWIP). The fast, unbiased analysis kWIP performs should be conducted in preliminary stages of any analysis to verify experimental designs and sample metadata, catching catastrophic errors earlier.
kWIP extends alignment-free sequence comparison methods by operating directly on sequencing reads. kWIP uses an entropy-weighted inner product over k-mers as a estimator of genetic relatedness. We validate kWIP using rigorous simulation experiments. We also demonstrate high sensitivity and accuracy even where there is modest divergence between genomes, and/or when sequencing coverage is low. We show high sensitivity in replicate detection, and faithfully reproduce published reports of population structure and stratification of microbiomes. We provide a reproducible workflow for replicating our validation experiments.
kWIP is an efficient, open source software package. Our software is well documented and cross platform, and tutorial-style workflows are provided for new users.
