Browse > Article
http://dx.doi.org/10.5808/GI.2019.17.3.e31

In silico approach to calculate the transcript capacity  

Lee, Young-Sup (Department of Animal Biotechnology, Chonbuk National University)
Won, Kyung-Hye (Department of Animal Biotechnology, Chonbuk National University)
Oh, Jae-Don (Department of Animal Biotechnology, Chonbuk National University)
Shin, Donghyun (Department of Animal Biotechnology, Chonbuk National University)
Publication Information
Genomics & Informatics / v.17, no.3, 2019 , pp. 31.1-31.7 More about this Journal
Abstract
We sought the novel concept, transcript capacity (TC) and analyzed TC. Our approach to estimate TC was through an in silico method. TC refers to the capacity that a transcript exerts in a cell as enzyme or protein function after translation. We used the genome-wide association study (GWAS) beta effect and transcription level in RNA-sequencing to estimate TC. The trait was body fat percent and the transcript reads were obtained from the human protein atlas. The assumption was that the GWAS beta effect is the gene's effect and TC was related to the corresponding gene effect and transcript reads. Further, we surveyed gene ontology (GO) in the highest TC and the lowest TC genes. The most frequent GOs with the highest TC were neuronal-related and cell projection organization related. The most frequent GOs with the lowest TC were wound-healing related and embryo development related. We expect that our analysis contributes to estimating TC in the diverse species and playing a benevolent role to the new bioinformatic analysis.
Keywords
fat; genome-wide association study; in silico method; transcript capacity; RNA-seq;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25-29.   DOI
2 Flavell SW, Greenberg ME. Signaling mechanisms linking neuronal activity to gene expression and plasticity of the nervous system. Annu Rev Neurosci 2008;31:563-590.   DOI
3 Parks BW, Nam E, Org E, Kostem E, Norheim F, Hui ST, et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab 2013;17:141-152.   DOI
4 Vermeirssen V, van der Bent A, Van Camp J, van Amerongen A, Verstraete W. A quantitative in silico analysis calculates the angiotensin I converting enzyme (ACE) inhibitory activity in pea and whey protein digests. Biochimie 2004;86:231-239.   DOI
5 Chen J, Lipska BK, Halim N, Ma QD, Matsumoto M, Melhem S, et al. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am J Hum Genet 2004;75:807-821.   DOI
6 Bailey MJ, Biely P, Poutanen K. Interlaboratory testing of methods for assay of xylanase activity. J Biotechnol 1992;23:257-270.   DOI
7 Kraus WE, Muoio DM, Stevens R, Craig D, Bain JR, Grass E, et al. Metabolomic quantitative trait loci (mQTL) mapping implicates the ubiquitin proteasome system in cardiovascular disease pathogenesis. PLoS Genet 2015;11:e1005553.   DOI
8 Warren CR, O'Sullivan JF, Friesen M, Becker CE, Zhang X, Liu P, et al. Induced pluripotent stem cell differentiation enables functional validation of GWAS variants in metabolic disease. Cell Stem Cell 2017;20:547-557.   DOI
9 Marigorta UM, Denson LA, Hyams JS, Mondal K, Prince J, Walters TD, et al. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn's disease. Nat Genet 2017;49:1517-1521.   DOI
10 Lu Y, Day FR, Gustafsson S, Buchkovich ML, Na J, Bataille V, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun 2016;7:10495.   DOI
11 Lauc G, Essafi A, Huffman JE, Hayward C, Knezevic A, Kattla JJ, et al. Genomics meets glycomics-the first GWAS study of human N-Glycome identifies HNF1alpha as a master regulator of plasma protein fucosylation. PLoS Genet 2010;6:e1001256.   DOI
12 Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016;34:525-527.   DOI
13 Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J, et al. Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies. Diabetes Care 2010;33:2684-2691.   DOI
14 Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76-82.   DOI
15 Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 2015;33:290-295.   DOI
16 Lim S, Jang HC, Lee HK, Kimm KC, Park C, Cho NH. A rural-urban comparison of the characteristics of the metabolic syndrome by gender in Korea: the Korean Health and Genome Study (KHGS). J Endocrinol Invest 2006;29:313-319.   DOI
17 Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534.   DOI
18 Berglund L, Bjorling E, Oksvold P, Fagerberg L, Asplund A, Szigyarto CA, et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol Cell Proteomics 2008;7:2019-2027.   DOI
19 Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010;28:1248-1250.   DOI
20 Kapoor M, Wang JC, Wetherill L, Le N, Bertelsen S, Hinrichs AL, et al. A meta-analysis of two genome-wide association studies to identify novel loci for maximum number of alcoholic drinks. Hum Genet 2013;132:1141-1151.   DOI