Dataset Information

A comparison of peak callers used for DNase-Seq data.

ABSTRACT: Genome-wide profiling of open chromatin regions using DNase I and high-throughput sequencing (DNase-seq) is an increasingly popular approach for finding and studying regulatory elements. A variety of algorithms have been developed to identify regions of open chromatin from raw sequence-tag data, which has motivated us to assess and compare their performance. In this study, four published, publicly available peak calling algorithms used for DNase-seq data analysis (F-seq, Hotspot, MACS and ZINBA) are assessed at a range of signal thresholds on two published DNase-seq datasets for three cell types. The results were benchmarked against an independent dataset of regulatory regions derived from ENCODE in vivo transcription factor binding data for each particular cell type. The level of overlap between peak regions reported by each algorithm and this ENCODE-derived reference set was used to assess sensitivity and specificity of the algorithms. Our study suggests that F-seq has a slightly higher sensitivity than the next best algorithms. Hotspot and the ChIP-seq oriented method, MACS, both perform competitively when used with their default parameters. However the generic peak finder ZINBA appears to be less sensitive than the other three. We also assess accuracy of each algorithm over a range of signal thresholds. In particular, we show that the accuracy of F-Seq can be considerably improved by using a threshold setting that is different from the default value.

SUBMITTER: Koohy H

PROVIDER: S-EPMC4014496 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A comparison of peak callers used for DNase-Seq data.

Koohy Hashem H Down Thomas A TA Spivakov Mikhail M Hubbard Tim T

PloS one 20140508 5

Genome-wide profiling of open chromatin regions using DNase I and high-throughput sequencing (DNase-seq) is an increasingly popular approach for finding and studying regulatory elements. A variety of algorithms have been developed to identify regions of open chromatin from raw sequence-tag data, which has motivated us to assess and compare their performance. In this study, four published, publicly available peak calling algorithms used for DNase-seq data analysis (F-seq, Hotspot, MACS and ZINBA) ...[more]

PMID: 24810143

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Project description:Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a "gold-standard" method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.

Project description:BackgroundDriven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development.MethodsWe used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs.ResultsOut of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs.ConclusionsOur study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.

Dataset Information

A comparison of peak callers used for DNase-Seq data.

Publications

A comparison of peak callers used for DNase-Seq data.

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