Guidelines to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods.

In this article, researchers from the Georgia Institute of Technology and Emory University focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, they developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. The researchers then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). They found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, the researchers provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

The resources provided by this study (i.e., the 278 RNA-seq pipelines, the benchmark metrics, and the SEQC-benchmark datasets) can serve as guidelines for biological and clinical researchers as well as for bioinformaticians and biotechnologists

Figure 6

(a) Depending on the gene expression application, the three metrics (i.e., accuracy, precision, and reliability) may be used to choose a pipeline. We have associated each metric with an RNA-seq application and listed the top-performing pipelines for each metric. The red-highlighted component in each listed RNA-seq pipeline indicates components that occur frequently among the top-performing pipelines for each metric. (b) Biological or clinical researchers who want to analyze Illumina RNA-seq data (or data from similar platforms with short, fixed-length reads) can choose an existing RNA-seq pipeline using the provided table of 278 pipelines ranked by accuracy, precision, or reliability. Bioinformaticians that are developing a new RNA-seq pipeline for Illumina data (or data from similar platforms) can use the SEQC-benchmark datasets and benchmark metrics to evaluate the new pipeline and assess its performance relative to the 278 pipelines. Bioinformaticians or biotechnologists that are developing new RNA-seq protocols can first sequence the same RNA mixture samples (i.e., samples A, B, C, and D), and then evaluate associated data analysis pipelines using the qPCR benchmark dataset and the benchmark metrics.

Tong L, Wu P, Phan JH, Hassazadeh HR, SEQC Consortium, Tong W, Wang MD. (2020) Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction. Sci Rep 10, 17925. [article]

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