SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines

Abstract

Background: The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices.

Results: To evaluate RNA-Seq pipelines, we developed a suite of two benchmarking tools. SimCT generates simulated datasets that get as close as possible to specific real biological conditions accompanied by the list of genomic incidents and mutations that have been inserted. BenchCT then compares the output of any bioinformatics pipeline that has been run against a SimCT dataset with the simulated genomic and transcriptional variations it contains to give an accurate performance evaluation in addressing specific biological question. We used these tools to simulate a real-world genomic medicine question s involving the comparison of healthy and cancerous cells. Results revealed that performance in addressing a particular biological context varied significantly depending on the choice of tools and settings used. We also found that by combining the output of certain pipelines, substantial performance improvements could be achieved.

Conclusion: Our research emphasizes the importance of selecting and configuring bioinformatic tools for the specific biological question being investigated to obtain optimal results. Pipeline designers, developers and users should include benchmarking in the context of their biological question as part of their design and quality control process. Our SimBA suite of benchmarking tools provides a reliable basis for comparing the performance of RNA-Seq bioinformatics pipelines in addressing a specific biological question. We would like to see the creation of a reference corpus of data-sets that would allow accurate comparison between benchmarks performed by different groups and the publication of more benchmarks based on this public corpus. SimBA software and data-set are available at http://cractools.gforge.inria.fr/softwares/simba/ .

Keywords: Benchmark; Pipeline optimization; RNA-Seq; Transcriptomics.

Fig. 1

Overview of the SimBA benchmarking procedure. A benchmarking pipeline implemented with SimBA is composed of three components, i/ Simulation of synthetic data using SimCT, ii/ Processing of the synthetic data using a pipeline manager (i.e Snakemake [20], iii/ Qualitative evaluation of the results using BenchCT

Fig. 2

SimCT method. SimCT uses a reference FASTA and GTF annotations as input. A first process is intended to introduced biological variations in this reference to create a mutated reference. This new reference is then transfered to FluxSimulator, in order to generate an RNA-Seq experiment. Finaly FluxSimulator output are post-processed to transfer the coordinates from the mutated genome to the original reference

Fig. 3

BenchCT evaluation procedures. Each event is evaluated with benchCT with a specific procedure that allow approximate matching. For alignement, only overlap between the prediction and the truth is evaluated. For Splice junctions and Fusions we expect an overlap between the prediction and a candidate in the truth database with a limited agreement distance according to the threshold. For mutation (SNV and Indel), similar procedure is used, as well as the verification of the mutation. For SNVs we evaluate the mutated sequence and for insertions and deletions, the length of the mutation

Fig. 4

Precision and recall of SNV calling. a SNV precision/recall in GRCh38-150bp-normal data-set. b SNV detection in GRCh38-150bp-somatic data-set

Fig. 5

Precision and recall of indel calling. a Insertion precision/recall in GRCh38-150bp-somatic. b Intersections of true positives insertions found by calling pipelines in the GRCh38-150bp-somatic data-set

Fig. 6

Precision and recall of gene fusion detection. Evaluation of gene fusions detection pipelines on the GRCh38-101bp-160-somatic dataset. Fusions were splited in two category with an individual evaluation. a Colinear fusion where the fusion involves to genomic locations that are located on the same strand of the same chromosome with a distance superior to 300kb. b non-colinear fusions wich does not satisfy the colinear criteria