NDLI: A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data

Content Provider	Springer Nature : BioMed Central
Author	Chan, Ariel W. Williams, Amy L. Jannink, Jean-Luc
Abstract	Background Researchers typically sequence a given individual multiple times, either re-sequencing the same DNA sample (technical replication) or sequencing different DNA samples collected on the same individual (biological replication) or both. Before merging the data from these replicate sequence runs, it is important to verify that no errors, such as DNA contamination or mix-ups, occurred during the data collection pipeline. Methods to detect such errors exist but are often ad hoc, cannot handle missing data and several require phased data. Because they require some combination of genotype calling, imputation, and haplotype phasing, these methods are unsuitable for error detection in low- to moderate-depth sequence data where such tasks are difficult to perform accurately. Additionally, because most existing methods employ a pairwise-comparison approach for error detection rather than joint analysis of the putative replicates, results may be difficult to interpret. Results We introduce a new method for error detection suitable for shallow-, moderate-, and high-depth sequence data. Using Bayes Theorem, we calculate the posterior probability distribution over the set of relations describing the putative replicates and infer which of the samples originated from an identical genotypic source. Conclusions Our method addresses key limitations of existing approaches and produced highly accurate results in simulation experiments. Our method is implemented as an R package called BIGRED (Bayes Inferred Genotype Replicate Error Detector), which is freely available for download: https://github.com/ac2278/BIGRED .
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-018-2512-8.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-018-2512-8
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2018-12-12
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Error detection Biological replication Technical replication Shallow-depth sequence data Mislabeled samples Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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