1. RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST)
- Author
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Laura B. Ferguson, Yi-Pei Chen, George Zheng, Nihal A Salem, Mohammed Eslami, and R. Dayne Mayfield
- Subjects
Statistics and Probability ,Original Paper ,Candidate gene ,AcademicSubjects/SCI01060 ,Gene Expression ,RNA ,Disease ,Brain tissue ,Computational biology ,Biology ,Biochemistry ,Computer Science Applications ,Transcriptome ,Computational Mathematics ,Computational Theory and Mathematics ,Gene expression ,Differential expression ,Molecular Biology ,Gene - Abstract
Motivation Transcriptomics is a common approach to identify changes in gene expression induced by a disease state. Standard transcriptomic analyses consider differentially expressed genes (DEGs) as indicative of disease states so only a few genes would be treated as signals when the effect size is small, such as in brain tissue. For tissue with small effect sizes, if the DEGs do not belong to a pathway known to be involved in the disease, there would be little left in the transcriptome for researchers to follow up with. Results We developed RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST), a new approach to identify hidden signals in transcriptomic data by linking differential expression and co-expression networks using machine learning. We applied our approach to RNA-seq data of post-mortem brains that compared the Alcohol Use Disorder (AUD) group with the control group. Many of the candidate genes are not differentially expressed so would likely be ignored by standard transcriptomic analysis pipelines. Through multiple validation strategies, we concluded that these RNASSIST-identified genes likely play a significant role in AUD. Availability and implementation The RNASSIST algorithm is available at https://github.com/netrias/rnassist and both the software and the data used in RNASSIST are available at https://figshare.com/articles/software/RNAssist_Software_and_Data/16617250. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2021
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