Reproducibility of SNV-calling in multiple sequencing runs from single tumors

Author:

Derryberry Dakota Z.1,Cowperthwaite Matthew C.23,Wilke Claus O.45

Affiliation:

1. Cell and Molecular Biology, The University of Texas at Austin, Austin, TX, United States

2. NeuroTexas Institute Research Foundation, Austin, TX, United States

3. Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, United States

4. Integrative Biology, The University of Texas at Austin, Austin, TX, United States

5. Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, United States

Abstract

We examined 55 technical sequencing replicates of Glioblastoma multiforme (GBM) tumors from The Cancer Genome Atlas (TCGA) to ascertain the degree of repeatability in calling single-nucleotide variants (SNVs). We used the same mutation-calling pipeline on all pairs of samples, and we measured the extent of the overlap between two replicates; that is, how many specific point mutations were found in both replicates. We further tested whether additional filtering increased or decreased the size of the overlap. We found that about half of the putative mutations identified in one sequencing run of a given sample were also identified in the second, and that this percentage remained steady throughout orders of magnitude of variation in the total number of mutations identified (from 23 to 10,966). We further found that using filtering after SNV-calling removed the overlap completely. We concluded that there is variation in the frequency of mutations in GBMs, and that while some filtering approaches preferentially removed putative mutations found in only one replicate, others removed a large fraction of putative mutations found in both.

Funder

NSF Cooperative Agreement

St. David’s Hospital’s NeuroTexas Institute Research Foundation

The Texas Advanced Computing Center

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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