Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Quant. Biol.    2014, Vol. 2 Issue (2) : 71-79    https://doi.org/10.1007/s40484-014-0031-9
RESEARCH ARTICLE
Exon expression QTL (eeQTL) analysis highlights distant genomic variations associated with splicing regulation
Leying Guan1,4, Qian Yang1, Mengting Gu2, Liang Chen3, Xuegong Zhang1,2()
1. MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China
2. School of Life Sciences, Tsinghua University, Beijing 100084, China
3. Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
4. Department of Physics, Tsinghua University, Beijing 100084, China
 Download: PDF(159 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Alternative splicing is a ubiquitous mechanism of post-transcriptional regulation of gene expression and produces multiple isoforms from the same genes. Expression quantitative trait loci (eQTL) has been a major method for finding associations between gene expression and genomic variations. Differences in alternative splicing isoforms are resulted from differences in the expression of exons. We propose to use exon expression QTL (eeQTL) to study the genomic variations that are associated with splicing regulation. A stringent criterion was adopted to study gene-level eQTLs and exon-level eeQTLs for both cis- and trans- factors. From experiments on an RNA-sequencing (RNA-Seq) data set of HapMap samples, we observed that compared with eQTLs, more eeQTL trans-factors can be found than cis-factors, and many of the eeQTLs cannot be found at the gene level. This work highlights that the regulation of exons adds another layer of regulation on gene expression, and that eeQTL analysis is a new approach for investigating genome-wide genomic variations that are involved in the regulation of alternative splicing.
Keywords eeQTL      eQTL      alternative splicing      trans-factor      association      regulation     
Corresponding Author(s): Xuegong Zhang   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Online First Date: 02 November 2014    Issue Date: 04 December 2014
 Cite this article:   
Leying Guan,Qian Yang,Mengting Gu, et al. Exon expression QTL (eeQTL) analysis highlights distant genomic variations associated with splicing regulation[J]. Quant. Biol., 2014, 2(2): 71-79.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-014-0031-9
https://academic.hep.com.cn/qb/EN/Y2014/V2/I2/71
All Local loci Distant loci External loci
Number of associations Gene level (eQTLs) 411 315 29 67
Exon level (eeQTLs) 1302 1057 103 142
Number of genes Gene level (eQTLs) 77 50 10 31
Exon level (eeQTLs) 138 36 14 96
Number of SNPs Gene level (eQTLs) 411 315 29 67
Exon level (eeQTLs) 408 244 33 141
Tab.1  Summary on numbers of significant eQTL and eeQTL associations, genes and SNPs
All Local loci Distant loci External loci
Number of associations 3.17 3.36 3.55 2.12
Number of genes 1.79 0.72 1.40 3.10
Number of SNPs 0.99 0.77 1.14 2.10
Tab.2  Ratios of numbers of eeQTLs over eQTLs in each category
Categories Shared* Exon-only Gene-only
Local 22 14 28
Distant 4 10 6
External 5 91 26
All 32 106 45
Tab.3  Numbers of genes as shared, gene-only and exon-only
Categories Shared Exon-only Gene-only
Local 147 97 168
Distant 12 21 17
External 1 140 66
All 161 247 250
Tab.4  Numbers of loci detected as shared, gene-only and exon-only
Fig.1  The association of gene ENSG00000077984 on chr20 with SNP rs1036333 on chr2.
Fig.2  The association of gene ENSG00000160392 with SNP rs11882778, about 1,100 kbp from each other on chr19. The horizontal axis is the genotype and the vertical axis is the normalized expression value. (a) Box-plots of gene expression of different genotypes. (b) Box-plots of expression of the 8th exon of different genotypes. The numbers of samples of each genotype are: CC: 32, CT: 18, TT: 2, NN: 2. Significant QTL was detected for the exon expression but not for the gene expression.
1 Y. Gilad,, S. A. Rifkin, and J. K. Pritchard, (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet., 24, 408–415
https://doi.org/10.1016/j.tig.2008.06.001 pmid: 18597885
2 M. Morley,, C. M. Molony,, T. M. Weber,, J. L. Devlin,, K. G. Ewens,, R. S. Spielman, and V. G. Cheung, (2004) Genetic analysis of genome-wide variation in human gene expression. Nature, 430, 743–747
https://doi.org/10.1038/nature02797 pmid: 15269782
3 J. K. Pickrell,, J. C. Marioni,, A. A. Pai,, J. F. Degner,, B. E. Engelhardt,, E. Nkadori,, J. B. Veyrieras,, M. Stephens,, Y. Gilad, and J. K. Pritchard, (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 464, 768–772
https://doi.org/10.1038/nature08872 pmid: 20220758
4 J. Majewski, and T. Pastinen, (2011) The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet., 27, 72–79
https://doi.org/10.1016/j.tig.2010.10.006 pmid: 21122937
5 E. E. Schadt,, S. A. Monks,, T. A. Drake,, A. J. Lusis,, N. Che,, V. Colinayo,, T. G. Ruff,, S. B. Milligan,, J. R. Lamb,, G. Cavet,, et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature, 422, 297–302
https://doi.org/10.1038/nature01434 pmid: 12646919
6 M. V. Rockman, and L. Kruglyak, (2006) Genetics of global gene expression. Nat. Rev. Genet., 7, 862–872
https://doi.org/10.1038/nrg1964 pmid: 17047685
7 K. Xia,, A. A. Shabalin,, S. Huang,, V. Madar,, Y. H. Zhou,, W. Wang,, F. Zou,, W. Sun,, P. F. Sullivan, and F. A. Wright, (2012) seeQTL: a searchable database for human eQTLs. Bioinformatics, 28, 451–452
https://doi.org/10.1093/bioinformatics/btr678 pmid: 22171328
8 T. P. Yang,, C. Beazley,, S. B. Montgomery,, A. S. Dimas,, M. Gutierrez-Arcelus,, B. E. Stranger,, P. Deloukas, and E. T. Dermitzakis, (2010) Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics, 26, 2474–2476
https://doi.org/10.1093/bioinformatics/btq452 pmid: 20702402
9 S. B. Montgomery,, M. Sammeth,, M. Gutierrez-Arcelus,, R. P. Lach,, C. Ingle,, J. Nisbett,, R. Guigo, and E. T. Dermitzakis, (2010) Transcriptome genetics using second generation sequencing in a Caucasian population. Nature, 464, 773–777
https://doi.org/10.1038/nature08903 pmid: 20220756
10 W. Cookson,, L. Liang,, G. Abecasis,, M. Moffatt, and M. Lathrop, (2009) Mapping complex disease traits with global gene expression. Nat. Rev. Genet., 10, 184–194
https://doi.org/10.1038/nrg2537 pmid: 19223927
11 E. E. Schadt,, C. Molony,, E. Chudin,, K. Hao,, X. Yang,, P. Y. Lum,, A. Kasarskis,, B. Zhang,, S. Wang,, C. Suver,, et al. (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol., 6, e107
https://doi.org/10.1371/journal.pbio.0060107 pmid: 18462017
12 A. J. Myers,, J. R. Gibbs,, J. A. Webster,, K. Rohrer,, A. Zhao,, L. Marlowe,, M. Kaleem, , D. Leung,, L. Bryden,, P. Nath,, et al. (2007) A survey of genetic human cortical gene expression. Nat. Genet., 39, 1494–1499
https://doi.org/10.1038/ng.2007.16 pmid: 17982457
13 B. E. Stranger,, A. C. Nica,, M. S. Forrest,, A. Dimas,, C. P. Bird,, C. Beazley,, C. E. Ingle,, M. Dunning,, P. Flicek,, D. Koller,, et al. (2007) Population genomics of human gene expression. Nat. Genet., 39, 1217–1224
https://doi.org/10.1038/ng2142 pmid: 17873874
14 J. B. Veyrieras,, S. Kudaravalli,, S. Y. Kim,, E. T. Dermitzakis,, Y. Gilad,, M. Stephens, and J. K. Pritchard, (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet., 4, e1000214
https://doi.org/10.1371/journal.pgen.1000214 pmid: 18846210
15 T. Zeller,, P. Wild,, S. Szymczak,, M. Rotival,, A. Schillert,, R. Castagne,, S. Maouche,, M. Germain,, K. Lackner,, H. Rossmann, , et al. (2010) Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS ONE, 5, e10693
https://doi.org/10.1371/journal.pone.0010693 pmid: 20502693
16 S. Stamm,, S. Ben-Ari,, I. Rafalska,, Y. Tang,, Z. Zhang,, D. Toiber,, T. A. Thanaraj, and H. Soreq, (2005) Function of alternative splicing. Gene, 344, 1–20
https://doi.org/10.1016/j.gene.2004.10.022 pmid: 15656968
17 B. R. Graveley, (2001) Alternative splicing: increasing diversity in the proteomic world. Trends Genet., 17, 100–107
https://doi.org/10.1016/S0168-9525(00)02176-4 pmid: 11173120
18 B. Modrek, and C. Lee, (2002) A genomic view of alternative splicing. Nat. Genet., 30, 13–19
https://doi.org/10.1038/ng0102-13 pmid: 11753382
19 D. Brett,, H. Pospisil,, J. Valcárcel,, J. Reich, and P. Bork, (2002) Alternative splicing and genome complexity. Nat. Genet., 30, 29–30
https://doi.org/10.1038/ng803 pmid: 11743582
20 P. J. Gardina,, T. A. Clark,, B. Shimada,, M. K. Staples,, Q. Yang,, J. Veitch,, A. Schweitzer,, T. Awad,, C. Sugnet,, S. Dee,, et al. (2006) Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics, 7, 325
https://doi.org/10.1186/1471-2164-7-325 pmid: 17192196
21 J. P. Venables, (2004) Aberrant and alternative splicing in cancer. Cancer Res., 64, 7647–7654
https://doi.org/10.1158/0008-5472.CAN-04-1910 pmid: 15520162
22 M. A. Garcia-Blanco,, A. P. Baraniak, and E. L. Lasda, (2004) Alternative splicing in disease and therapy. Nat. Biotechnol., 22, 535–546
https://doi.org/10.1038/nbt964 pmid: 15122293
23 L. Wang,, L. Duke,, P. S. Zhang,, R. B. Arlinghaus,, W. F. Symmans,, A. Sahin,, R. Mendez, and J. L. Dai, (2003) Alternative splicing disrupts a nuclear localization signal in spleen tyrosine kinase that is required for invasion suppression in breast cancer. Cancer Res., 63, 4724–4730
pmid: 12907655
24 P. A. Goodman,, C. M. Wood,, A. Vassilev,, C. Mao, and F. M. Uckun, (2001) Spleen tyrosine kinase (Syk) deficiency in childhood pro-B cell acute lymphoblastic leukemia. Oncogene, 20, 3969–3978
https://doi.org/10.1038/sj.onc.1204515 pmid: 11494125
25 H. Nakashima,, S. Natsugoe,, S. Ishigami,, H. Okumura,, M. Matsumoto,, S. Hokita, and T. Aikou, (2006) Clinical significance of nuclear expression of spleen tyrosine kinase (Syk) in gastric cancer. Cancer Lett., 236, 89–94
https://doi.org/10.1016/j.canlet.2005.05.022 pmid: 15993535
26 P. Prinos,, D. Garneau,, J. F. Lucier,, D. Gendron,, S. Couture,, M. Boivin,, J. P. Brosseau,, E. Lapointe,, P. Thibault,, M. Durand,, et al. (2011) Alternative splicing of SYK regulates mitosis and cell survival. Nat. Struct. Mol. Biol., 18, 673–679
https://doi.org/10.1038/nsmb.2040 pmid: 21552259
27 H. Feng,, Z. Qin, and X. Zhang, (2013) Opportunities and methods for studying alternative splicing in cancer with RNA-Seq. Cancer Lett., 340, 179–191
https://doi.org/10.1016/j.canlet.2012.11.010 pmid: 23196057
28 Q. Pan,, O. Shai,, L. J. Lee,, B. J. Frey, and B. J. Blencowe, (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet., 40, 1413–1415
https://doi.org/10.1038/ng.259 pmid: 18978789
29 E. T. Wang,, R. Sandberg,, S. Luo,, I. Khrebtukova,, L. Zhang,, C. Mayr,, S. F. Kingsmore, , G. P. Schroth, and C. B. Burge, (2008) Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476
https://doi.org/10.1038/nature07509 pmid: 18978772
30 S. Marco-Sola,, M. Sammeth,, R. Guigó, and P. Ribeca, (2012) The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods, 9, 1185–1188
https://doi.org/10.1038/nmeth.2221 pmid: 23103880
31 L. Y. Chen,, K. C. Wei,, A. C. Huang,, K. Wang,, C. Y. Huang,, D. Yi,, C. Y. Tang,, D. J. Galas, and L. E. Hood, (2012) RNASEQR—a streamlined and accurate RNA-seq sequence analysis program. Nucleic Acids Res., 40, e42
https://doi.org/10.1093/nar/gkr1248 pmid: 22199257
32 C. Trapnell,, L. Pachter, and S. L. Salzberg, (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111
https://doi.org/10.1093/bioinformatics/btp120 pmid: 19289445
33 J. Wu,, O. Anczuków,, A. R. Krainer,, M. Q. Zhang,and C. Zhang, (2013) OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res., 41, 5149–5163
https://doi.org/10.1093/nar/gkt216 pmid: 23571760
34 A. Dobin,, C. A. Davis,, F. Schlesinger,, J. Drenkow,, C. Zaleski,, S. Jha,, P. Batut,, M. Chaisson, and T. R. Gingeras, (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29, 15–21
https://doi.org/10.1093/bioinformatics/bts635 pmid: 23104886
35 L. Wang,, X. Wang,, X. Wang,, Y. Liang, and X. Zhang, (2011) Observations on novel splice junctions from RNA sequencing data. Biochem. Biophys. Res. Commun., 409, 299–303
https://doi.org/10.1016/j.bbrc.2011.05.005 pmid: 21575597
36 C. Trapnell,, B. A. Williams,, G. Pertea,, A. Mortazavi,, G. Kwan,, M. J. van Baren,, S. L. Salzberg,, B. J. Wold, and L. Pachter, (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511–515
https://doi.org/10.1038/nbt.1621 pmid: 20436464
37 A. Roberts,, H. Pimentel,, C. Trapnell, and L. Pachter, (2011) Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics, 27, 2325–2329
https://doi.org/10.1093/bioinformatics/btr355 pmid: 21697122
38 W. Li,, J. Feng, and T. Jiang, (2011) IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly. J. Comput. Biol., 18, 1693–1707
https://doi.org/10.1089/cmb.2011.0171 pmid: 21951053
39 C. Trapnell,, D. G. Hendrickson,, M. Sauvageau,, L. Goff,, J. L. Rinn, and L. Pachter, (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol., 31, 46–53
https://doi.org/10.1038/nbt.2450 pmid: 23222703
40 X. Ma, and X. Zhang, (2013) NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data. BMC Bioinformatics, 14, 220
https://doi.org/10.1186/1471-2105-14-220 pmid: 23837734
41 H. Jiang, and W. H. Wong, (2009) Statistical inferences for isoform expression in RNA-Seq. Bioinformatics, 25, 1026–1032
https://doi.org/10.1093/bioinformatics/btp113 pmid: 19244387
42 Z. Wu,, X. Wang,, X. Zhang, (2011) Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq. Bioinformatics, 27, 502–508
43 H. Richard,, M. H. Schulz,, M. Sultan,, A. Nürnberger,, S. Schrinner,, D. Balzereit,, E. Dagand,, A. Rasche,, H. Lehrach,, M. Vingron,, et al. (2010) Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Nucleic Acids Res., 38, e112
https://doi.org/10.1093/nar/gkq041 pmid: 20150413
44 J. M. Johnson,, J. Castle,, P. Garrett-Engele,, Z. Kan,, P. M. Loerch,, C. D. Armour,, R. Santos,, E. E. Schadt,, R. Stoughton, and D. D. Shoemaker, (2003) Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science, 302, 2141–2144
https://doi.org/10.1126/science.1090100 pmid: 14684825
45 J. Hull,, S. Campino,, K. Rowlands,, M. S. Chan,, R. R. Copley,, M. S. Taylor,, K. Rockett,, G. Elvidge,, B. Keating,, J. Knight,, et al. (2007) Identification of common genetic variation that modulates alternative splicing. PLoS Genet., 3, e99
https://doi.org/10.1371/journal.pgen.0030099 pmid: 17571926
46 T. Kwan,, D. Benovoy,, C. Dias,, S. Gurd,, C. Provencher,, P. Beaulieu,, T. J. Hudson,, R. Sladek, and J. Majewski, (2008) Genome-wide analysis of transcript isoform variation in humans. Nat. Genet., 40, 225–231
https://doi.org/10.1038/ng.2007.57 pmid: 18193047
47 E. L. Heinzen,, D. Ge,, K. D. Cronin,, J. M. Maia,, K. V. Shianna,, W. N. Gabriel,, K. A. Welsh-Bohmer,, C. M. Hulette,, T. N. Denny, and D. B. Goldstein, (2008) Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol., 6, e1
https://doi.org/10.1371/journal.pbio.1000001 pmid: 19222302
48 J. Coulombe-Huntington,, K. C. Lam,, C. Dias, and J. Majewski, (2009) Fine-scale variation and genetic determinants of alternative splicing across individuals. PLoS Genet., 5, e1000766
https://doi.org/10.1371/journal.pgen.1000766 pmid: 20011102
49 Y. Lee,, E. R. Gamazon,, E. Rebman,, Y. Lee,, S. Lee,, M. E. Dolan,, N. J. Cox, and Y. A. Lussier, (2012) Variants affecting exon skipping contribute to complex traits. PLoS Genet., 8, e1002998
https://doi.org/10.1371/journal.pgen.1002998 pmid: 23133393
50 A. Ramasamy,, D. Trabzuni,, J. R. Gibbs,, A. Dillman,, D. G. Hernandez,, S. Arepalli,, R. Walker,, C. Smith,, G. P. Ilori,, A. A. Shabalin,, et al., (2013) Resolving the polymorphism-in-probe problem is critical for correct interpretation of expression QTL studies. Nucleic Acids Res., 41, e88
https://doi.org/10.1093/nar/gkt069 pmid: 23435227
51 K. Mozhui,, X. Wang,, J. Chen,, M. K. Mulligan,, Z. Li,, J. Ingles,, X. Chen,, L. Lu, and R. W. Williams, (2011) Genetic regulation of Nrnx1 expression: an integrative cross-species analysis of schizophrenia candidate genes. Transl. Psychiatr., 1, e25
https://doi.org/10.1038/tp.2011.39
52 E. Lalonde,, K. C. Ha,, Z. Wang,, A. Bemmo,, C. L. Kleinman,, T. Kwan,, T. Pastinen, and J. Majewski, (2011) RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression. Genome Res., 21, 545–554
https://doi.org/10.1101/gr.111211.110 pmid: 21173033
53 W. Sun, and Y. Hu, (2013) eQTL mapping using RNA-seq data. Stat. Biosci., 5, 198–219
https://doi.org/10.1007/s12561-012-9068-3 pmid: 23667399
54 T. Lappalainen,, M. Sammeth,, M. R. Friedländer,, P. A. ’t Hoen,, J. Monlong,, M. A. Rivas,, M. Gonzàlez-Porta,, N. Kurbatova,, T. Griebel,, P. G. Ferreira,, et al., (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501, 506–511
https://doi.org/10.1038/nature12531 pmid: 24037378
55 W. Wang,, Z. Qin,, Z. Feng,, X. Wang, and X. Zhang, (2013) Identifying differentially spliced genes from two groups of RNA-seq samples. Gene, 518, 164–170
https://doi.org/10.1016/j.gene.2012.11.045 pmid: 23228854
56 The International HapMap Consortium. (2003) The international HapMap project. Nature, 426, 789–796
pmid: 14685227
57 Y. Guan, and M. Stephens, (2008) Practical issues in imputation-based association mapping. PLoS Genet., 4, e1000279
https://doi.org/10.1371/journal.pgen.1000279 pmid: 19057666
58 P. Scheet, and M. Stephens, (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet., 78, 629–644
https://doi.org/10.1086/502802 pmid: 16532393
59 S. Yoon,, Z. Xuan,, V. Makarov,, K. Ye, and J. Sebat, (2009) Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res., 19, 1586–1592
https://doi.org/10.1101/gr.092981.109 pmid: 19657104
60 V. Boeva,, A. Zinovyev,, K. Bleakley,, J.-P. Vert,, I. Janoueix-Lerosey,, O. Delattre, and E. Barillot, (2011) Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics, 27, 268–269
https://doi.org/10.1093/bioinformatics/btq635 pmid: 21081509
61 K. Zhao,, Z. X. Lu,, J. W. Park,, Q. Zhou, and Y. Xing, (2013) GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data. Genome Biol., 14, R74
https://doi.org/10.1186/gb-2013-14-7-r74 pmid: 23876401
62 J. Monlong,, M. Calvo,, P. G. Ferreira, and R. Guigó, (2014) Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat. Commun., 5, 4698
https://doi.org/10.1038/ncomms5698 pmid: 25140736
[1] Supplementary Material 1 Download
[2] Supplementary Material 2 Download
[1] Lei Li, Yumei Li, Xudong Zou, Fuduan Peng, Ya Cui, Eric J. Wagner, Wei Li. Population-scale genetic control of alternative polyadenylation and its association with human diseases[J]. Quant. Biol., 2022, 10(1): 44-54.
[2] Jing Tang, Yongheng Wang, Jianbo Fu, Xianglu Wu, Zhijie Han, Chuan Wang, Maiyuan Guo, Yingxiong Wang, Yubin Ding, Bo Yang, Feng Zhu. Functional characterization of disease/comorbidity-associated lncRNA[J]. Quant. Biol., 2021, 9(4): 411-425.
[3] Huanhuan Zhu, Xiang Zhou. Transcriptome-wide association studies: a view from Mendelian randomization[J]. Quant. Biol., 2021, 9(2): 107-121.
[4] Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu. The statistical practice of the GTEx Project: from single to multiple tissues[J]. Quant. Biol., 2021, 9(2): 151-167.
[5] Yuhan Xie, Nayang Shan, Hongyu Zhao, Lin Hou. Transcriptome wide association studies: general framework and methods[J]. Quant. Biol., 2021, 9(2): 141-150.
[6] Hao Tian, Ying Yang, Sirui Liu, Hui Quan, Yi Qin Gao. Toward an understanding of the relation between gene regulation and 3D genome organization[J]. Quant. Biol., 2020, 8(4): 295-311.
[7] Jianan Lin, Zhengqing Ouyang. Large-scale analysis of the position-dependent binding and regulation of human RNA binding proteins[J]. Quant. Biol., 2020, 8(2): 119-129.
[8] Qingfeng Chen, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, Yi-Ping Pheobe Chen. Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity[J]. Quant. Biol., 2019, 7(3): 202-209.
[9] Lei Wei, Ye Yuan, Tao Hu, Shuailin Li, Tianrun Cheng, Jinzhi Lei, Zhen Xie, Michael Q. Zhang, Xiaowo Wang. Regulation by competition: a hidden layer of gene regulatory network[J]. Quant. Biol., 2019, 7(2): 110-121.
[10] Lijie Hao, Zhuoqin Yang, Marc Turcotte. Time-scale separation and stochasticity conspire to impact phenotypic dynamics in the canonical and inverted Bacillus subtilis core genetic regulation circuits[J]. Quant. Biol., 2019, 7(1): 54-68.
[11] Molly M. Hannigan, Leah L. Zagore, Donny D. Licatalosi. Mapping transcriptome-wide protein-RNA interactions to elucidate RNA regulatory programs[J]. Quant. Biol., 2018, 6(3): 228-238.
[12] Sha Cao, Yi Zhou, Yue Wu, Tianci Song, Burair Alsaihati, Ying Xu. Transcription regulation by DNA methylation under stressful conditions in human cancer[J]. Quant. Biol., 2017, 5(4): 328-337.
[13] Yijun Guo, Bing Wei, Shiyan Xiao, Dongbao Yao, Hui Li, Huaguo Xu, Tingjie Song, Xiang Li, Haojun Liang. Recent advances in molecular machines based on toehold-mediated strand displacement reaction[J]. Quant. Biol., 2017, 5(1): 25-41.
[14] Marc Turcotte. Delineating the respective impacts of stochastic curl- and grad-forces in a family of idealized core genetic commitment circuits[J]. Quant. Biol., 2016, 4(2): 69-83.
[15] Ye Yuan, Xinying Ren, Zhen Xie, Xiaowo Wang. A quantitative understanding of microRNA- mediated competing endogenous RNA regulation[J]. Quant. Biol., 2016, 4(1): 47-57.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed