kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

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. Rapid estimation of genetic...

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Bibliographic Details
Published in:PLoS computational biology 2017-09, Vol.13 (9), p.e1005727
Main Authors: Murray, Kevin D, Webers, Christfried, Ong, Cheng Soon, Borevitz, Justin, Warthmann, Norman
Format: Article
Language:English
Subjects:
Algorithms
Bias
Bioinformatics
Biology
Biology and Life Sciences
Chlamydomonas - genetics
Computer science
Computer simulation
Data structures
Deoxyribonucleic acid
DNA
Gene sequencing
Genetic algorithms
Genetic diversity
Genetic variation
Genetic Variation - genetics
Genetics, Population - methods
Genomes
Genomics
Genomics - methods
Mathematical models
Metadata
Methods
Models, Genetic
Models, Statistical
Population
Population genetics
Population structure
Principal components analysis
Proteins
Research and Analysis Methods
Scholarly publishing
Sequence Analysis, DNA
Similarity
Software
Studies
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Summary: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. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct 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 establishing sample identity and detecting 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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1005727