Abstract
Purpose
RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically accessible tissues (CATs) from blood and skin. RNA tissue specificity makes it difficult to identify aberrations in relevant but nonaccessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs.
Methods
We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from Genotype-Tissue Expression Project (GTEx) and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues.
Results
In non-CATs, 40.2% of genes have splicing that is inadequately represented by at least one CAT; 6.3% of genes have splicing inadequately represented by all CATs. A majority (52.1%) of inadequately represented genes are lowly expressed in CATs (transcripts per million (TPM) < 1), but 5.8% are inadequately represented despite being well expressed (TPM > 10).
Conclusion
Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.
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References
- 1.
Clark MM, Stark Z, Farnaes L, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med. 2018;3:16.
- 2.
Yang Y, Muzny DM, Xia F, et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA. 2014;312:1870–1879.
- 3.
Farwell KD, Shahmirzadi L, El-Khechen D, et al. Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions. Genet Med. 2015;17:578–586.
- 4.
Retterer K, Juusola J, Cho MT, et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med. 2016;18:696–704.
- 5.
Alfares A, Aloraini T, Subaie LA, et al. Whole-genome sequencing offers additional but limited clinical utility compared with reanalysis of whole-exome sequencing. Genet Med. 2018;20:1328.
- 6.
Taylor JC, Martin HC, Lise S, et al. Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nat Genet. 2015;47:717–726.
- 7.
Frésard L, Montgomery SB. Diagnosing rare diseases after the exome. Mol Case Stud. 2018;4:a003392.
- 8.
Gloss BS, Dinger ME. Realizing the significance of noncoding functionality in clinical genomics. Exp Mol Med. 2018;50:97.
- 9.
Scotti MM, Swanson MS. RNA mis-splicing in disease. Nat Rev Genet. 2016;17:19–32.
- 10.
Wang G-S, Cooper TA. Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet. 2007;8:749–761.
- 11.
Fenwick AL, Goos JA, Rankin J, et al. Apparently synonymous substitutions in FGFR2 affect splicing and result in mild Crouzon syndrome. BMC Med Genet. 2014;15:95.
- 12.
Raj T, Li YI, Wong G, et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet. 2018;50:1584–1592.
- 13.
Cummings BB, Marshall JL, Tukiainen T, et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med. 2017;9:eaal5209.
- 14.
Kremer LS, Bader DM, Mertes C, et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun. 2017;8:15824.
- 15.
Hamanaka K, Miyatake S, Koshimizu E, et al. RNA sequencing solved the most common but unrecognized NEB pathogenic variant in Japanese nemaline myopathy. Genet Med. 2019;21:1629.
- 16.
Frésard L, Smail C, Ferraro NM, et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med. 2019;25:911–919.
- 17.
Gonorazky HD, Naumenko S, Ramani AK, et al. Expanding the boundaries of RNA sequencing as a diagnostic tool for rare mendelian disease. Am J Hum Genet. 2019;104:466–483.
- 18.
Gonorazky H, Liang M, Cummings B, et al. RNAseq analysis for the diagnosis of muscular dystrophy. Ann Clin Transl Neurol. 2016;3:55–60.
- 19.
Vaquero-Garcia J, Barrera A, Gazzara MR, et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife. 2016;5:e11752.
- 20.
Norton SS, Vaquero-Garcia J, Lahens NF, Grant GR, Barash Y. Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates. Bioinformatics. 2018;34:1488–1497.
- 21.
The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–585.
- 22.
Lindsay SJ, Xu Y, Lisgo SN, et al. HDBR expression: a unique resource for global and individual gene expression studies during early human brain development. Front Neuroanat. 2016;10:86
- 23.
Pervolaraki E, Dachtler J, Anderson RA, Holden AV. The developmental transcriptome of the human heart. Sci Rep. 2018;8:15362.
- 24.
NCBI. SRA-Tools. http://ncbi.github.io/sra-tools/. Accessed 12 Dec 2018.
- 25.
Babraham Bioinformatics. Trim Galore! http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. Accessed 12 Dec 2018.
- 26.
Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
- 27.
Cunningham F, Achuthan P, Akanni W, et al. Ensembl 2019. Nucleic Acids Res. 2019;47(Database issue):D745–D751.
- 28.
Love MI, Hogenesch JB, Irizarry RA. Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat Biotechnol. 2016;34:1287–1291.
- 29.
Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980–D985.
- 30.
Stenson PD, Ball EV, Mort M, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003;21:577–581.
- 31.
Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics. 2012;28:2520–2522.
- 32.
Aicher JK, Jewell P, Vaquero-Garcia J, Barash Y, Bhoj EJ. Biociphers/Aicher2019-CAT-Splicing-Analysis: analysis for “Mapping RNA splicing variations in clinically-accessible and non-accessible tissues to facilitate Mendelian disease diagnosis using RNA-Seq.” 2020. https://doi.org/10.5281/zenodo.3611492.
- 33.
Barash Y, Calarco JA, Gao W, et al. Deciphering the splicing code. Nature. 2010;465:53–59. https://doi.org/10.1038/nature09000.
- 34.
Barash Y, Blencowe BJ, Frey BJ. Model-based detection of alternative splicing signals. Bioinformatics. 2010;26:i325–i333.
- 35.
Xiong HY, Alipanahi B, Lee LJ, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347:1254806.
- 36.
Jha A, Gazzara MR, Barash Y. Integrative deep models for alternative splicing. Bioinformatics. 2017;33:i274–i282.
- 37.
Zhang Z, Pan Z, Ying Y, et al. Deep-learning augmented RNA-seq analysis of transcript splicing. Nat Methods. 2019;16:307–310.
- 38.
Jaganathan K, Panagiotopoulou SK, McRae JF, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–548.
- 39.
Cheng J, Nguyen TYD, Cygan KJ, et al. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. Genome Biol. 2019;20:48.
Acknowledgements
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM128096. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. J.K.A. acknowledges salary support by NIH/Eunice Kennedy Shriver National Institute of Child Health (NICHD) fellowship F30HD098803.
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Aicher, J.K., Jewell, P., Vaquero-Garcia, J. et al. Mapping RNA splicing variations in clinically accessible and nonaccessible tissues to facilitate Mendelian disease diagnosis using RNA-seq. Genet Med 22, 1181–1190 (2020). https://doi.org/10.1038/s41436-020-0780-y
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Keywords
- clinical genetics
- medical genetics
- alternative splicing
- diagnostic markers
- RNA-seq
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