Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data

Abstract

Background: Insertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy.

Methods: In our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programs RESULTS: We found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types.

Conclusions: While more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.

Keywords: Cancer; Deletion; Germline variants; Indel; Insertion; Somatic variants.

Fig. 1

Comparison of different methods regarding false negative indels. A schematic comparison between single-sample based method (a) and pooled-sample based method (b) with a pooled reference benchmark. FN: false negative

Fig. 2

Comparisons of indels from seven general indel calling programs. a Indel size distribution. b Indel type distribution. c Coding indel type distribution. FS frame shift, NFS non-frame shift

Fig. 3

Comparison of different methods for common indels from different programs. A schematic comparison between single-sample based method (a) and pooled-sample based method (b) with a pooled reference benchmark. Green represents true positives. Red represents false positive predictions. Blue blocks are the benchmark indels

Fig. 4

Overlapped indels by GATK_UG, GATK_HC and Dindel. a All indels; b coding indels only

Fig. 5

Somatic indel size distribution. a Program based; and b cancer type based

Fig. 6

Overlapped indel annotations of different cancer types. a Bladder cancer; b breast cancer; and c colon cancer