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A simple strategy for reducing false negatives in calling variants from single-cell sequencing data.

ABSTRACT: Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells.

SUBMITTER: Ji C

PROVIDER: S-EPMC4395317 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A simple strategy for reducing false negatives in calling variants from single-cell sequencing data.

Ji Cong C Miao Zong Z He Xionglei X

PloS one 20150413 4

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in sin ...[more]

PMID: 25876174

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Project description:BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.MethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).ResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.ConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Project description:The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results.

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A simple strategy for reducing false negatives in calling variants from single-cell sequencing data.

Publications

A simple strategy for reducing false negatives in calling variants from single-cell sequencing data.

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