In this thesis, I have developed a procedure to discover novel lncRNAs using RNA-seq technology, and used a large number of RNA-seq datasets as well as lncRNA databases and ChIP-seq datasets to improve the annotation of lncRNAs in fruit fly. From these efforts, I have provided an enlarged set of D. melanogaster lncRNAs, including known lncRNAs and novel lncRNAs from the two tissue-specific RNA-seq datasets generated in this thesis. The novel lncRNAs I identified suggests that many fruit fly lncRNAs remain to be identified. In order to discover lncRNAs that do not contain poly(A) tails, I have developed a computational approach to identify novel lncRNAs by integrating sequencing read datasets from two different library construction protocols, the poly(A)-enriched and ribo-zero protocols. This approach can be applied to future studies for the same purpose. Moreover, I have also improved the annotation of the curated lncRNAs regarding transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures by utilizing the strand-specific RNA-seq and the ChIP-seq datasets from the modENCODE database and data from the present study. Through RT-qPCR experiments, we demonstrate that RNA-seq is a reliable platform to discover lncRNAs.
In summary, the present study provided a solid foundation for studying the functions of
lncRNAs in Drosophila.
With the improved annotation of transcriptional direction, researchers can investigate the co-expression relationships between lncRNAs and coding genes in order to further understand the functional roles of the set of curated lncRNAs. In conclusion, the present study has integrated many RNA-seq and ChIP-seq datasets to increase the compilation breadth and annotation detail of lncRNAs. The set of curated lncRNAs along with improved annotation serves as an important resource in lncRNA studies.
REFERENCE:
1. Batista, P.J. and H.Y. Chang, Long noncoding RNAs: cellular address codes in
development and disease. Cell, 2013. 152(6): p. 1298-307.
2. Wapinski, O. and H.Y. Chang, Long noncoding RNAs and human disease.
Trends Cell Biol, 2011. 21(6): p. 354-61.
3. Deng, X. and V.H. Meller, roX RNAs are required for increased expression of
X-linked genes in Drosophila melanogaster males. Genetics, 2006. 174(4): p.
1859-66.
4. Zhao, Y., et al., NONCODE 2016: an informative and valuable data source of
long non-coding RNAs. Nucleic Acids Res, 2016. 44(D1): p. D203-8.
5. Young, R.S., et al., Identification and properties of 1,119 candidate lincRNA loci
in the Drosophila melanogaster genome. Genome Biol Evol, 2012. 4(4): p.
427-42.
6. Graveley, B.R., et al., The developmental transcriptome of Drosophila
melanogaster. Nature, 2011. 471(7339): p. 473-9.
7. Gullerova, M. and N.J. Proudfoot, Convergent transcription induces
transcriptional gene silencing in fission yeast and mammalian cells. Nat Struct
Mol Biol, 2012. 19(11): p. 1193-201.8. Hobson, D.J., et al., RNA polymerase II collision interrupts convergent
transcription. Mol Cell, 2012. 48(3): p. 365-74.
9. Sigova, A.A., et al., Divergent transcription of long noncoding RNA/mRNA gene
pairs in embryonic stem cells. Proc Natl Acad Sci U S A, 2013. 110(8): p.
2876-81.
10. Gonzalez, E. and S. Joly, Impact of RNA-seq attributes on false positive rates in
differential expression analysis of de novo assembled transcriptomes. BMC Res
Notes, 2013. 6: p. 503.11. Bullard, J.H., et al., Evaluation of statistical methods for normalization and
differential expression in mRNA-Seq experiments. BMC Bioinformatics, 2010.
11: p. 94.
12. Gierlinski, M., et al., Statistical models for RNA-seq data derived from a
two-condition 48-replicate experiment. Bioinformatics, 2015. 31(22): p.
3625-30.
13. Yang, J.H., et al., ChIPBase: a database for decoding the transcriptional
regulation of long non-coding RNA and microRNA genes from ChIP-Seq data.
Nucleic Acids Res, 2013. 41(Database issue): p. D177-87.
14. Schlitt, T. and A. Brazma, Current approaches to gene regulatory network
modelling. BMC Bioinformatics, 2007. 8.
15. Adryan, B. and S.A. Teichmann, The developmental expression dynamics of
Drosophila melanogaster transcription factors. Genome Biol, 2010. 11(4): p.
R40.
16. Levine, M. and R. Tjian, Transcription regulation and animal diversity. Nature, 2003. 424(6945): p. 147-151.
17. Tsai, H.K., et al., MYBS: a comprehensive web server for mining transcription
factor binding sites in yeast. Nucleic Acids Research, 2007. 35: p. W221-W226.
18. Badis, G., et al., A Library of Yeast Transcription Factor Motifs Reveals a
Widespread Function for Rsc3 in Targeting Nucleosome Exclusion at Promoters.
Molecular Cell, 2008. 32(6): p. 878-887.
19. Zhu, C., et al., High-resolution DNA-binding specificity analysis of yeast
transcription factors. Genome Research, 2009. 19(4): p. 556-566.
20. Mathelier, A., et al., JASPAR 2016: a major expansion and update of the
open-access database of transcription factor binding profiles. Nucleic Acids Res,
2016. 44(D1): p. D110-5.21. Wingender, E., The TRANSFAC project as an example of framework technology
that supports the analysis of genomic regulation. Brief Bioinform, 2008. 9(4): p.
326-32.
22. Enuameh, M.S., et al., Global analysis of Drosophila Cys(2)-His(2) zinc finger
proteins reveals a multitude of novel recognition motifs and binding
determinants. Genome Res, 2013. 23(6): p. 928-40.
23. Eisen, M.B., et al., Cluster analysis and display of genome-wide expression
patterns. Proc Natl Acad Sci U S A, 1998. 95(25): p. 14863-8.
24. Gasch, A.P., et al., Genomic expression programs in the response of yeast cells
to environmental changes. Mol Biol Cell, 2000. 11(12): p. 4241-57.
25. Graveley, B.R., et al., The developmental transcriptome of Drosophila
melanogaster. Nature, 2011. 471(7339): p. 473-479.
26. Chintapalli, V.R., J. Wang, and J.A.T. Dow, Using FlyAtlas to identify better
Drosophila melanogaster models of human disease. Nature Genetics, 2007.
39(6): p. 715-720.
27. Hooper, S.D., et al., Identification of tightly regulated groups of genes during
Drosophila melanogaster embryogenesis. Mol Syst Biol, 2007. 3.
28. Pisarev, A., et al., FlyEx, the quantitative atlas on segmentation gene expression
at cellular resolution. Nucleic Acids Research, 2009. 37: p. D560-D566.
29. Harbison, C.T., et al., Transcriptional regulatory code of a eukaryotic genome.
30. Roy, S., et al., Identification of Functional Elements and Regulatory Circuits by
Drosophila modENCODE. Science, 2010. 330(6012): p. 1787-1797.
31. MacArthur, S., et al., Developmental roles of 21 Drosophila transcription
factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions. Genome Biology, 2009. 10(7).
32. Massie, C.E. and I.G. Mills, ChIPping away at gene regulation. Embo Reports, 2008. 9(4): p. 337-343.
33. Hoffman, B.G. and S.J.M. Jones, Genome-wide identification of DNA-protein
interactions using chromatin immunoprecipitation coupled with flow cell sequencing. Journal of Endocrinology, 2009. 201(1): p. 1-13.
34. Moran, I., et al., Human β cell transcriptome analysis uncovers lncRNAs that are
tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. Cell Metab, 2012. 16(4): p. 435-48.
35. Ilott, N.E. and C.P. Ponting, Predicting long non-coding RNAs using RNA
sequencing. Methods, 2013. 63(1): p. 50-9.
36. Chen, M.J., et al., Integrating RNA-seq and ChIP-seq data to characterize long
non-coding RNAs in Drosophila melanogaster. BMC Genomics, 2016. 17(1): p.
220.
37. Schuettengruber, B., et al., Functional anatomy of polycomb and trithorax
chromatin landscapes in Drosophila embryos. PLoS Biol, 2009. 7(1): p. e13.
38. Barski, A., et al., High-resolution profiling of histone methylations in the human
genome. Cell, 2007. 129(4): p. 823-37.
39. Guenther, M.G., et al., A chromatin landmark and transcription initiation at
most promoters in human cells. Cell, 2007. 130(1): p. 77-88.
40. Navarro, P., et al., Molecular coupling of Xist regulation and pluripotency.
Science, 2008. 321(5896): p. 1693-5.
41. Donohoe, M.E., et al., The pluripotency factor Oct4 interacts with Ctcf and also
controls X-chromosome pairing and counting. Nature, 2009. 460(7251): p.
128-32.
42. Nesterova, T.B., et al., Pluripotency factor binding and Tsix expression act
synergistically to repress Xist in undifferentiated embryonic stem cells.
Epigenetics Chromatin, 2011. 4(1): p. 17.
43. Okazaki, Y., et al., Analysis of the mouse transcriptome based on functional
annotation of 60,770 full-length cDNAs. Nature, 2002. 420(6915): p. 563-573.
44. Cawley, S., et al., Unbiased mapping of transcription factor binding sites along
human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell, 2004. 116(4): p. 499-509.
45. Ravasi, T., et al., Experimental validation of the regulated expression of large
numbers of non-coding RNAs from the mouse genome. Genome Research, 2006.
16(1): p. 11-19.
46. Mercer, T.R., M.E. Dinger, and J.S. Mattick, Long non-coding RNAs: insights
into functions. Nat Rev Genet, 2009. 10(3): p. 155-9.
47. Ponting, C.P., P.L. Oliver, and W. Reik, Evolution and Functions of Long
Noncoding RNAs. Cell, 2009. 136(4): p. 629-641.
48. Wang, K.C. and H.Y. Chang, Molecular Mechanisms of Long Noncoding RNAs.
Molecular Cell, 2011. 43(6): p. 904-914.
49. Quinn, J.J. and H.Y. Chang, Unique features of long non-coding RNA biogenesis
and function. Nature Reviews Genetics, 2016. 17(1): p. 47-62.
50. Fatica, A. and I. Bozzoni, Long non-coding RNAs: new players in cell
differentiation and development. Nature Reviews Genetics, 2014. 15(1): p. 7-21.
51. Lee, C. and N. Kikyo, Strategies to identify long noncoding RNAs involved in
gene regulation. Cell and Bioscience, 2012. 2.
52. Cabili, M.N., et al., Integrative annotation of human large intergenic noncoding
RNAs reveals global properties and specific subclasses. Genes & Development,
2011. 25(18): p. 1915-1927.53. Wang, Y., et al., De novo prediction of RNA-protein interactions from sequence
information. Molecular Biosystems, 2013. 9(1): p. 133-142.
54. Nacher, J.C. and N. Araki, Structural characterization and modeling of
ncRNA-protein interactions. Biosystems, 2010. 101(1): p. 10-19.
55. Guo, X.L., et al., Long non-coding RNAs function annotation: a global
prediction method based on bi-colored networks. Nucleic Acids Research, 2013.
41(2).
56. Brown, J.B., et al., Diversity and dynamics of the Drosophila transcriptome.
Nature, 2014. 512(7515): p. 393-9.
57. Schuettengruber, B., et al., Functional anatomy of polycomb and trithorax
chromatin landscapes in Drosophila embryos. PLoS Biol, 2009. 7(1): p.
e1000013.
58. Wu, S.C., E.M. Kallin, and Y. Zhang, Role of H3K27 methylation in the
regulation of lncRNA expression. Cell Res, 2010. 20(10): p. 1109-16.
59. Sun, Q.W., et al., R-Loop Stabilization Represses Antisense Transcription at the
Arabidopsis FLC Locus. Science, 2013. 340(6132): p. 619-621.
60. Yang, F., et al., Repression of the Long Noncoding RNA-LET by Histone
Deacetylase 3 Contributes to Hypoxia-Mediated Metastasis. Molecular Cell,
2013. 49(6): p. 1083-1096.61. Jiang, Q.H., et al., TF2LncRNA: Identifying Common Transcription Factors for
2014.
62. dos Santos, G., et al., FlyBase: introduction of the Drosophila melanogaster
Release 6 reference genome assembly and large-scale migration of genome annotations. Nucleic Acids Res, 2015. 43(Database issue): p. D690-7.
63. Karolchik, D., et al., The UCSC Genome Browser database: 2014 update.
Nucleic Acids Res, 2014. 42(Database issue): p. D764-70.
64. Matthews, B.B., et al., Gene Model Annotations for Drosophila melanogaster:
Impact of High-Throughput Data. G3 (Bethesda), 2015. 5(8): p. 1721-36.
65. Xie, C., et al., NONCODEv4: exploring the world of long non-coding RNA
genes. Nucleic Acids Res, 2014. 42(Database issue): p. D98-103.
66. Camacho, C., et al., BLAST+: architecture and applications. BMC Bioinf, 2009.
10: p. 421.
67. Trapnell, C., et al., Differential gene and transcript expression analysis of
RNA-seq experiments with TopHat and Cufflinks. Nat Protoc, 2012. 7(3): p.
562-78.
68. Kong, L., et al., CPC: assess the protein-coding potential of transcripts using
sequence features and support vector machine. Nucleic Acids Res, 2007.
35(Web Server issue): p. W345-9.
69. Yang, L., et al., Genomewide characterization of non-polyadenylated RNAs.
Genome Biol, 2011. 12(2): p. R16.
70. Djebali, S., et al., Landscape of transcription in human cells. Nature, 2012.
489(7414): p. 101-8.
71. Livyatan, I., et al., Non-polyadenylated transcription in embryonic stem cells
reveals novel non-coding RNA related to pluripotency and differentiation.
Nucleic Acids Res, 2013. 41(12): p. 6300-15.
72. Novikova, I.V., S.P. Hennelly, and K.Y. Sanbonmatsu, Sizing up long non-coding
RNAs: do lncRNAs have secondary and tertiary structure? Bioarchitecture, 2012.
2(6): p. 189-99.
73. Derrien, T., et al., The GENCODE v7 catalog of human long noncoding RNAs:
analysis of their gene structure, evolution, and expression. Genome Res, 2012.
22(9): p. 1775-89.
74. Wang, F., et al., Characteristics of long non-coding RNAs in the Brown Norway
rat and alterations in the Dahl salt-sensitive rat. Sci Rep, 2014. 4: p. 7146.
75. Flynn, R.A. and H.Y. Chang, Long noncoding RNAs in cell-fate programming
and reprogramming. Cell Stem Cell, 2014. 14(6): p. 752-61.
76. Washington, N.L., et al., The modENCODE Data Coordination Center: lessons
in harvesting comprehensive experimental details. Database (Oxford), 2011.
2011: p. bar023.
77. Langmead, B., et al., Ultrafast and memory-efficient alignment of short DNA
sequences to the human genome. Genome Biol, 2009. 10(3): p. R25.
78. Roberts, A. and L. Pachter, Streaming fragment assignment for real-time
analysis of sequencing experiments. Nat Methods, 2013. 10(1): p. 71-3.
79. Karolchik, D., et al., The UCSC Genome Browser database: 2014 update.
Nucleic Acids Research, 2014. 42(D1): p. D764-D770.
80. St Pierre, S.E., et al., FlyBase 102-advanced approaches to interrogating
FlyBase. Nucleic Acids Research, 2014. 42(D1): p. D780-D788.
81. Hansen, K.D., S.E. Brenner, and S. Dudoit, Biases in Illumina transcriptome
sequencing caused by random hexamer priming. Nucleic Acids Res, 2010.
38(12): p. e131.
82. Mikkelsen, T.S., et al., Genome-wide maps of chromatin state in pluripotent and
lineage-committed cells. Nature, 2007. 448(7153): p. 553-60.
83. Wang, L., et al., CPAT: Coding-Potential Assessment Tool using an
alignment-free logistic regression model. Nucleic Acids Res, 2013. 41(6): p. e74.
84. Ye, J., et al., Primer-BLAST: a tool to design target-specific primers for
polymerase chain reaction. BMC Bioinf, 2012. 13: p. 134.
85. Butler, J.E. and J.T. Kadonaga, The RNA polymerase II core promoter: a key
component in the regulation of gene expression. Genes Dev, 2002. 16(20): p.
2583-92.
86. Pedersen, A.G., et al., The biology of eukaryotic promoter prediction--a review.
Comput Chem, 1999. 23(3-4): p. 191-207.
87. Lee, D.H., et al., Functional characterization of core promoter elements: the
downstream core element is recognized by TAF1. Molecular and Cellular
Biology, 2005. 25(21): p. 9674-9686.88. Bailey, T.L. and C. Elkan, Fitting a mixture model by expectation maximization
to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol, 1994. 2: p.
28-36.
89. Chen, F., X. Gao, and A. Shilatifard, Stably paused genes revealed through
inhibition of transcription initiation by the TFIIH inhibitor triptolide. Genes &
Development, 2015. 29(1): p. 39-47.
90. Gallo, S.M., et al., REDfly v3.0: toward a comprehensive database of
transcriptional regulatory elements in Drosophila. Nucleic Acids Res, 2011.
39(Database issue): p. D118-23.
91. Chen, C.Y., et al., Discovering gapped binding sites of yeast transcription
factors. Proc Natl Acad Sci U S A, 2008. 105(7): p. 2527-32.
92. Kaplan, N., et al., The DNA-encoded nucleosome organization of a eukaryotic
APPENDIX
Non-coding RNAs in Drosophila melanogaster, BMC Genomics 17(1):220.(#authors with equal contribution)
2. Hsu JC*, Lin YY, Chang CC, Hua KH, Chen MJM, Huang LH, Chen CY*. (2016) Discovery of Organophosphate Resistance-Related Genes in Well-known Resistance Mechanisms of the Diamondback Moth (Plutella xylostella) by RNA-Seq, Journal
of Economic Entomology pii: tow070.
3. Lin KI*, Hung KH, Su ST, Chen CY, Hsu PH, Wu PC, Chen HY, Lin FR, Tsai MD, Huang SY, Wu WJ, Chen MJM. (2016) Aiolos collaborates with Blimp-1 to regulate the survival of multiple myeloma cells, Cell Death and Differentiation, doi:10.1038/cdd.2015.167.
4. Kuo TCY, Hu CC, Chien TY, Chen MJM, Feng HT, Chen LFO, Chen CY, Hsu JC.
(2015) Discovery of genes related to formothion resistance in oriental fruit fly (Bactrocera dorsalis) by a constrained functional genomics analysis, Insect
molecular biology 24(3): 338-347.
5. Chen WY, Shih HT, Liu KY, Shih ZS, Chen LK, Tsai TH, Chen MJ, Liu H, Tan BCM, Chen CY, Lee HH, Loppin B, Aït‐Ahmed O, Wu JT*. (2015) Intellectual disability‐associated dBRWD3 regulates gene expression through inhibition of HIRA/YEM ‐ mediated chromatin deposition of histone H3. 3, EMBO reports e201439092.
6. Rajendran SK, Lin IW, Chen MJM, Chen CY, Yeh KW*. (2014) Differential activation of sporamin expression in response to abiotic mechanical wounding and biotic herbivore attack in the sweet potato, BMC plant biology 14:112.
7. Meyer P*, Cokelaer T, Chandran D, Kim KH, Loh PR, Tucker G, Lipson M, Berger B, Kreutz C, Raue A, Steiert B, Timmer J, Bilal E, DREAM 6&7 Parameter
Estimation consortium, Sauro HM, Stolovitzky G and Saez-Rodriguez J*. (2014)
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach, BMC SystemsBiology 8:13 (Chen MJM is one of the authors listed in DREAM 6&7 Parameter
Estimation consortium).
8. Meyer P*, Siwo G, Zeevi D, Sharon E, Norel R, DREAM6 Promoter Prediction
Consortium, Segal E and Stolovitzky G. (2013) Inferring gene expression from
ribosomal promoter sequences, a crowdsourcing approach, Genome research 23:1928-1937 (Chen MJM is listed in DREAM6 Promoter Prediction Consortium).
9. Hsu JC, Chien TY#, Hu CC#, Chen MJM#, Wu WJ, Feng HT, Haymer DS and Chen CY*. (2012) Discovery of Genes Related to Insecticide Resistance in Bactrocera
dorsalis by Functional Genomic Analysis of a De Novo Assembled Transcriptome, PLoS ONE 7(8): e40950. (
#authors with equal contribution)10. Chen MJM, Chou LC, Hsieh TT, Lee DD, Liu KW, Yu CY, Oyang YJ, Tsai HK* and Chen CY*. (2012) De novo motif discovery facilitates identification of interactions between transcription factors in
Saccharomyces cerevisiae.
Bioinformatics 1;28 (5): 701-708.
11. Liu HC, Shih LY, Chen MJM, Wnag CC, Yeh TC, Lin TH, Chen CY, Lin CJ and Liang DC*. (2011) Expression of HOXB genes is significantly different in acute myeloid leukemia with a partial tandem duplication of MLL vs. a MLL translocation:
a cross-laboratory study. Cancer Genetics 204 (5): 252-259.
12. Liu LY, Chen CY, Chen MJM#, Tsai MS#, Lee CHS#, Phang TL, Chang LY, Kuo WH, Hwa HL, Lien HC, Jung SM, Lin YS, Chang KJ and Hsieh FJ*. (2009) Statistical identification of gene association by CID in application of constructing ER regulatory network. BMC Bioinformatics 10: 85.
13. Chen CY, Tsai HK, Hsu CM, Chen MJM, Hung HG, Huang GTW, Li WH*. (2008) Discovering gapped binding sites of yeast transcription factors. Proceedings of the
National Academy of Sciences of the United States of America 105: 2527-2532.
Oral presentation in conference
1. Chen MJM, Chou LC, Hsieh TT, Lee DD, Liu KW, Yu CY, Oyang YJ, Tsai HK* and Chen CY*. De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae. October 19-21, 2012;
International Symposium on Evolutionary Genomics and Bioinformatics (ISEGB),
Kaohsiung, TaiwanConference poster
1. Chen MJM, Lin YY, Li WH, and Chen CY. Common cis-elements suggests co-regulation of coding and long non-coding genes in Drosophila melanogaster.
Poster; September 16-20, 2016; 17th
edition of International Conference on Systems
Biology (ICSB), Barcelona, Spain.
2. Chen MJM, Su YR, Chang P, Hong TR, Cherng BW, Tung YA and Chen CY.
Potential of lncRNA to regulate gene expression through promoter binding in
Drosophila Melanogaster. Poster; September 3-7, 2016; 15
thEuropean Conference on Computational Biology (ECCB), The Hague, Netherlands
3. Poelchau M, Childers C, Moore G, Tsavatapalli V, Pieper U, Chen MJM and Lin YY. The i5k Workspace@NAL - Updates and new developments of an arthropod genome portal. Poster; January 9-13, 2016; Plant and Animal Genome Conference
XXIV (PAG), San Diego, USA
4. Chen MJM, Lin YY, Li WH and Chen CY. Transcriptional regulation of long non-coding gene expression in Drosophila melanogaster: a genome-wide study using RNA-seq. Poster B39 in the Category of Gene Expression; September 7-10, 2014; 13th European Conference on Computational Biology (ECCB), Strasbourg, France
5.
Chang C, Chen MJM, Kuo T, Huang JL, Haymer DS, Hsu JC and Chen CY.
Improving completeness of de novo transcriptome assembly and gene annotation by multi-species transcriptome sequencing in fruit fly genus Bactrocera. Poster A74 in the Category of Sequencing and Sequence Analysis for Genomics;
September 7-10, 2014; 13
thEuropean Conference on Computational Biology, Strasbourg, France
6.
Lin YY, Chen MJM and Chen CY. A study of inter- and intra-protein corelated mutations on highly similar protein sequences. Poster E72 in the Category of Structural Bioinformatics; September 7-10, 2014; 13
thEuropean Conference on Computational Biology, Strasbourg, France alternatively spliced mRNA transcripts discovered by de novo assembler; October 14-19, 2011; 4
thRECOMB Conference on Regulatory Genomics, Systems Biology, and DREAM Challenges, Barcelona, Spain
9.
Tung YA, Chen YS, Chen MJM, Chen CY. Predicting promoter activities by non-linear combination of sequence motifs; October 14-19, 2011; 4
thRECOMB Conference on Regulatory Genomics, Systems Biology, and DREAM Challenges, Barcelona, Spain
10.
Chen MJ, Chen CY. Improving network completeness of yeast transcriptional
interaction network by predicted TF-TF interactions; July 17 to 19, 2011; 19
thAnnual
International Conference on Intelligent Systems for Molecular Biology (ISMB) and
10th European Conference on Computational Biology (ECCB), Vienna, Austria
11.
Wang YT, Chen MJ, Wu HY, Tsai CF, Hong TM, Yen CJ, Chen YJ. Personalized Tissue Phosphoproteomics Screening of Human Hepatocellular Carcinoma for Drug Target Discovery in Cancer Therapy. Arpil 27 to 28, 2011. 2011 Translational Medicine Conference and Taiwan Proteomics Society Annual Symposium (TPS),Taiwan
12.
Wu PC, Chen MJM, Chen CY. Exploiting Cross-Species Conservation to Improve Prediction Accuracy of Discovering Transcription Factor Binding Sites. Poster 44; Aug.
16 to 18, 2010; 9
thAnnual International Conference on Computational Systems Bioinformatics (CSB), Stanford, California
13.
Hsieh TT, Chen MJM, Chen CY. Investigating Consistency between Curated Binding Profiles and PWMs Derived from Protein-DNA Structure Models. Poster 45; Aug. 16 to 18, 2010; 9
thAnnual International Conference on Computational Systems Bioinformatics (CSB), Stanford, California
14.
Chen MJM, Chen CY. Identification of Transcription Factor Interacting Pairs by
Mining ChIP-chip Data. O) Regulation; 2008 July 19 to 23; 16th Annual International
Conference on Intelligent Systems for Molecular Biology (ISMB), Toronto
Appendix Figures
Appendix Figure 1. Parameter tuning for different pattern supports (ratio of pattern-hit promoters/all promoters in the positive set) and different weights used for pattern ranking. Precision is calculated by the ratio of (True
Positives/Predicted instances), and presented as percentage.
Appendix Tables
Appendix Table 1. Primer list of the selected lncRNAs for RT-qPCR experiments
ID 5' primer 3' primer Experiment results
TCONS_00031380 AGTCCTTCGAAACAAACTGTCT TTGGTAAACAATGCGGCAATAC Figure 13 (a) TCONS_00028095 ATACATTGTGCCAAAATAGCCG AATTCACAGCCCTTCTTAGCAT Figure 13 (a) TCONS_00044977 TCGATGATTCTACGGTCAAGTT TTTTTGTTTGCCGAACATCTCG Figure 13 (a) TCONS_00037494 AGCCTATGGACAAGGACATCTA TATGATGTGTAATTGGTCGGCA Figure 13 (a) TCONS_00048859 CCACTTAAAGGAGGCGATCTTC AAGATGCTGAGGATATGGATGC Figure 13 (a) TCONS_00051944 ATCCGGATATTCGACCTTGTTG ATTTTAGTTGCGCTTGCTGTTC Figure 13 (a) TCONS_00020613 GAAAAGGCAGCAAGTGTTACAA ACCAAACTGCTGGTATCGTTAT Figure 13 (a) TCONS_00033121 GCTTCGATCATTTCGCGTATC CCACTAGCGATGATGGTGAAAG Figure 13 (a) TCONS_00017414 TCGCTGACGACAAAATCCTTAT TACGTTTACTTTTCGTGAGGCT Figure 13 (a) TCONS_00050427 ATCCAGATGCCAGAATTCACC ATGTGGATGTGACCTGAATCAC Figure 13 (a) TCONS_00032409 GTGTCGTGCTACATGTGTTTAC GAGAAGAAAACAAGGTGCTGTG Figure 13 (a) TCONS_00036092 ATTTCCATTGTTGTTGCCATGC CGGCGGTCCAATACAAACAATA Figure 13 (a) TCONS_00044754 GGAACTAGGGGCATTTAGTTGT CAACATATGCGGAGGGATTTTG Figure 13 (a) TCONS_00003446 TCTTGGGCTGAGAATAATGCAA ATATTCCAACAGCCCACTAACG Figure 13 (a) TCONS_00043412 CATGGCTACTCACTCAGGTAGA CTAATGGCTTCTTGATGCGTTC Figure 13 (a) TCONS_00036539 ACCAACTCGGCAACAACTATAA CTTACAGTTGCACGACAACAAC Figure 13 TCONS_00044991 AATCGTTACACTAAACACCCGA ACTCGCTACACATCCCTAAGTA Figure 13 TCONS_00044992 TGACGACACATAGCTGAAAAGT CAGAAGCTCAAGCAAATTCCTC Figure 13 TCONS_00034204 CAGCTTGAATTGGGTCAAGTTT CACACCAGCTGACAGTTATTTC Figure 13 TCONS_00011851 GAACGGAACCGCAAAACTAAG CTGCCCTTTGATGCTAAATGTC Figure 13 FBgn0266811 TCATAATGGAACTATGCAGGCG ATTTCAATACGTTTAGGCACGC Figure 13 (b) FBgn0267298 AAACACTTGAAATGGACTTGGC TGTTCGGGTATCCTCGCTAAAT Figure 13 (b) Untranscribed_region1 ACTCTCGTAGAAACAATCTCGT GCAAAAGTTAAAAGGACACAGC Figure 14 Untranscribed_region2 CGCATTTATTATGCCATCCTCA GTATTGATGCCGGTGTACTTTT Figure 14 Untranscribed_region3 ATCACACGATAACAACAAAGGG CTCCTCCGATGATTTTAGTCCT Figure 14 G1_FBgn0083068 ATCGGACGGAAATGCAGAAG CACTGGGAGGGCTAATGAAC Figure 14
G1_FBgn0265590 CAAGAAGTGGAAGGGAGATGG GACAGGCGCAACAACTAAAC Figure 13 (b) and Figure 14 G1_TCONS_00045108 CTAACCAGACGCTCTCAGTC CCCCTCCCTTCAAACAAGATAC Figure 13 (a) and Figure 14 G1_FBgn0001234 CACTGGTGTATCGACTTCTCTG GTATGTCTGCCCTTTACGGAAC Figure 14
G1_FBgn0051144 CTAAGAGGCCGATCAGAAGG CTTCCTACTCCATTTGTCGC Figure 13 (b) and Figure 14 G1_FBgn0262109 TCGTAAAGGGAATCCAACGC GATGCAATCGTCAGCGAAGTC Figure 14
G1_FBgn0265071 CTTCTTCTTGCTACCCGCTTTG TCTGCTCATAATTGCGCTCG Figure 14 G1_FBgn0265295 GTAGTAGACGTGAGCCAAGTTC GTTGGAGGTGCCCACAATTATC Figure 14
G1_ROX1 ACATCAGGCCATAGCCAAGAAG AACACGATCTACTTCTGGTCGG Figure 13 and Figure 14 G1_ROX2 GGTCACACTAAGCTAGGGCTAC CGGAAATCGTTACTCTTGCTTG Figure 14
G2_FBgn0263981 CAGCTCCAGCATTTCCTTAACC CGTACAGCTTATCCATATCGGC Figure 14 G2_FBgn0264869 CTCGACTCAACACAATTCCGAC CAACACGAGGTATGTTTCTCCC Figure 14 G2_FBgn0262993 GGACAACCATAGAATGAGGGAG CGAATGCGAGAAAGAGAGGTAG Figure 14 G2_FBgn0265340 CCCAACCATTGATGAAGCTGTG GTATAGTCTAACGGCGGAGATG Figure 14 G2_FBgn0260720 CCATCACCATCTTCAATAGCCC TGCTACATAAGCCAGTCAGTG Figure 14 G2_TCONS_00012337 ATTTCAAGTTGCCCCCAGTC CTCGATTTCAGGCCAAGAGAG Figure 14 G2_lincRNA.292 CCTTCTGATAACCCTTGTGGC GCTGATAGATACGGAAGTGGTC Figure 14 G2_FBgn0264446 TACCTTCGCATCACTGCTTC GGATTTGGGTTTTGGGCTTG Figure 14 G2_FBgn0264481 CGTCATTCTCTTCCTCCGATG GTCGTGTCTGTGTGTGCTTA Figure 14 G2_FBgn0264504 CAAAGACTGTTCCTGCTCCTG CCATGTTCCCAGCTTACGATTG Figure 14 G2_FBgn0266044 GGAGTGAGTTAAGGGACAACAG CGCTGCTGAGATTGGAGTTAG Figure 14
G3_FBgn0264993 CTTCGATGAGCACCAGGATAC CATGGGATTCAAGTACGACAGC Figure 13 (b) and Figure 14 G3_FBgn0265458 CCCCAATGTCTTCGACTTACTC CAGGAGGATCTGTTTCTGGAC Figure 14
G3_TCONS_00045565 AGTCTAACCTGCCCACTGAA CCAACCATTCATTCCAGCCTTC Figure 14 G3_FBgn0262106 GTCATTCATACTGGGTCTTGCC TCCATTTCGGGTTTGGTGAC Figure 14
G3_FBgn0262107 ATGACCAAGAGGATGAGTCGC GCTACTGCTGTCTATAAGGTGG Figure 13 (b) and Figure 14 G3_FBgn0264980 CTAATTTCACTCTACCCGCCG CTCAACTCAACCGACCCTTAC Figure 13 (b) and Figure 14 G3_FBgn0062928 GAACCGAAAGCACCAGATCC GGAGGAGAGTAAGCCACGTTAG Figure 14
G3_lincRNA.354 GTGGCTATAATGATCCCGGTAG GTGATGATCTCCCATTCTCTGC Figure 14
G3_FBgn0263331 CGCTTGTGGGTGAAGCATTG TGCCGCCAGAATGAGATTCC Figure 13 (b) and Figure 14 G3_FBgn0263626 CTCTACCCCATCCATTTTCAGG CTGTGTGCTCTGTTATGTGTCC Figure 14
G4_FBgn0265530 CGAATCAACCAGACCCATAAGC TGGCGATATTTGACAGACGG Figure 14 G4_TCONS_00054835 CCCATTATCCTCTGCAAGTGTG GAGAGTCGGAAATCGAGAATCG Figure 14 G4_lincRNA.160 GTATGAAAAAGTGGAGCGACGG CCCACCATCCCCTAAACAAAG Figure 14 G4_FBgn0263380 CAATCATGGAGATGGAGGACC CGGAGTCTTCAGTTCGAGTTC Figure 14 G4_FBgn0264840 AAGACAGGTTAAGGCTAGTCGG CTCATGCCGAAACACATTCG Figure 14 G4_FBgn0265302 GCCTTCTCCAGTTTGGTATGAC ACAATTAGCCCGACCATCTC Figure 14
G4_TCONS_00020772 GAGTGGATAGCGGAGATTGC GCCTTCTTGACTTCCTTCTCC Figure 13 (a) and Figure 14 G4_FBgn0263497 ATCGAATCGGTGGTAAGTGAGG GGAAAGTGAGCGGGTTAAAGTG Figure 14
G4_FBgn0262963 GTTCTGGGGTCAGTTGGACT AACCAAAGAGGGAAATGCGG Figure 14 G4_FBgn0265085 CATCTGAACCCCAACCACTTC GAGCACAAGCACCAACAATG Figure 14
Appendix Table 2. Raw Ct values of RT-qPCR experiments for un-transcribed regions and the selected lncRNAs.
RT+ RT-
Replicates P1 P2 P3 P4 N1 N2 N3 N4
Figure 13(a) RT-qPCR experiments for a selected set of lncRNAs in brains
RpL32 21.89 21.92 22.04 21.97 35.73 35.23 35.29 35.55
Figure 13(b) RT-qPCR experiments for a selected set of lncRNAs in brains: 2-fold amount of template brain cDNA
RpL32 21.52 21.45 21.57 21.68 35.49 35.02 36.25 35.31
FBgn0267298 32.68 32.53 32.62 NA 33.63 33.67 33.51 NA
Figure 14. RT-qPCR experiments of a selected set of lncRNAs in male adults
Untranscribed_region1 33.19 33.17 33.43 33.38 33.40 33.40 33.33 33.36
G3_FBgn0262106 26.11 26.08 25.98 26.10 35.99 35.42 36.61 37.08 G3_FBgn0262107 27.02 26.89 26.67 26.76 35.96 35.01 35.10 35.01 G3_FBgn0264980 27.40 27.47 27.48 27.45 36.72 35.26 35.48 35.57 G3_FBgn0062928 25.30 25.24 25.18 25.11 34.47 34.03 34.03 34.25 G3_lincRNA.354 26.06 26.06 25.89 25.89 32.63 32.63 33.71 32.48 G3_FBgn0263331 26.48 26.45 26.46 26.42 33.08 33.20 32.47 32.17
G3_FBgn0263626 24.52 24.46 24.37 NA 27.95 27.83 27.97 28.16
G4_FBgn0265530 31.40 31.54 31.22 31.31 35.44 35.79 35.38 34.41 G4_TCONS_00054835 33.21 33.11 33.29 31.13 33.53 34.73 34.39 33.04 G4_lincRNA.160 27.14 27.10 27.02 28.53 36.44 35.51 35.98 35.52 G4_FBgn0263380 33.05 32.41 32.27 32.47 34.90 35.78 35.34 35.25
G4_FBgn0264840 28.17 28.33 28.24 28.29 35.99 36.14 NA 36.79
G4_FBgn0265302 34.08 34.29 34.19 NA 40.50 40.42 40.24 40.37
G4_TCONS_00020772 26.24 26.21 26.00 26.11 28.26 28.29 28.28 28.18
G4_FBgn0263497 31.50 31.62 31.35 NA 33.71 34.30 33.46 33.99
G4_FBgn0262963 NA 31.82 31.68 31.62 35.14 34.11 34.12 35.19
G4_FBgn0265085 29.31 29.38 29.26 29.38 35.02 35.57 34.57 36.25