Epitranscriptome and RNA Methylation:
Epitranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts.
|Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome.
Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets collected from 10 different experimental conditions (cell type/tissue or treatment) is performed.L Liu, SW Zhang, YC Zhang, H Liu, L Zhang, R Chen, Y Huang, J Meng Molecular BioSystems 11 (1), 262-274
The MethylTranscriptome DataBase is the first comprehensive resource for N6-methyladenosine (m(6)A) in mammalian transcriptome.Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic acids research, 43, D197-203.
A package developed for the differential analysis for MeRIP-seq data of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome.Xiaodong Cui, Lin Zhang, Jia Meng, Manjeet Rao, Yidong Chen,Yufei Huang IEEE/ACM Transactions on Computational Biology and Bioinformatics 03/2015; PP(99):1-1. DOI:10.1109/TCBB.2015.2403355
exomePeak is a R-based software package that performs methylation site detection and differential methylation detection from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data.Meng, J., Lu, Z., Liu, H., Zhang, L., Zhang, S., Chen, Y., Rao, M.K. and Huang, Y. (2014) A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods, 69, 274-281.
microRNAs are recently discovered non-coding RNAs that act in the regulation of gene expression
TRAceRNA is a computational tool and an online application to assist the exploration of ceRNAs.Flores, M., Chen, Y. and Huang, Y. (2014) TraceRNA: a web application for competing endogenous RNA exploration. Circulation. Cardiovascular genetics, 7, 548-557.
|Cellular Transformation and Tumorigenesis mediated by viral miRNAs
Here, we show that regulation of cell cycle progression and apoptosis by KSHV-encoded microRNAs (miRs) is required for KSHV-induced cellular transformation and tumorigenesis.Moody R, Zhu Y, Huang Y, et al. KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways. Grundhoff A, ed. PLoS Pathogens. 2013;9(12):e1003857. doi:10.1371/journal.ppat.1003857.
|Prediction of microRNAs Associated with Human Diseases
More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseasesXuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., & Huang, Y*. (2013). Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors. PLoS one, 8(8), e70204. (IF:3.73)
|Uncover context-specific gene regulation by transcription factors and microRNAs
A novel Bayesian sparse non-negative factor regression (BSNFR) model is proposed for modeling the joint regulations of mRNAs by TFs and miRNAs and integration of multiple data types including gene expressions, microRNA expressions, TF targeted genes, and microRNA targets.J. Meng, Y. Chen, Y. Huang, Uncover context-specific gene regulation by transcription factors and microRNAs using Bayesian sparse nonnegative factor regression analysis, Journal of Biological Systems, Vol. 20, No. 4 (2012) 377402
A Bayesian Decision Fusion Approach for microRNA Target PredictionYue, D., Guo, M.Z., Chen, Y.D. and Huang, Y.F. (2012) A Bayesian decision fusion approach for microRNA target prediction. BMC Genomics, 13.
Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAsXuan P1, Guo M, Huang Y, Li W, Huang Y. MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One. 2011;6(11):e27422. doi: 10.1371/journal.pone.0027422. Epub 2011 Nov 16.
Improving Performance of Mammalian MicroRNA Target PredictionLiu, H., Yue, D., Chen, Y.D., Gao, S.J. and Huang, Y.F. (2010) Improving performance of mammalian microRNA target prediction. BMC bioinformatics.
Transcription and drug Networks; high throughput data analysis
BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray databaseChifeng Ma, Hung-I Harry Chen, Mario Flores, Yufei Huang, Yidong Chen BMC Syst Biol. 2013; 7(Suppl 5): S5. Published online 2013 December 9. doi: 10.1186/1752-0509-7-S5-S5
Application for mRNA Data Exploration in Breast Cancer
Brain Computer Interface
|Predicting Serial Visual Presentation Events from EEG Signals Using Deep learning
First comprehensive investigation of deep learning (DL) methods for target prediction in time-locked rapid serial visual presentation (RSVP) experiments.Z. Mao, V. Lawhern, L. M. Merino, K. Ball, L. Deng, B. J. Lance, K. Robbins, Y. Huang, Predicting Serial Visual Presentation Events from EEG Signals Using Deep learning, IEEE Transactions on Neural Systems & Rehabilitation Engineering, submitted.
|Classification of imperfectly time-locked image RSVP events with EEG device
Classification based on EEG data in an RSVP experiment is considered. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification.J. Meng, L. M. Merino, K. Robbins, Y Huang, Classification of imperfectly time-locked image RSVP events with EEG device, Neuroinformatics, Sept. 2013 DOI 10.1007/s12021-013-9203-4, PMID:24037139 (IF: 3.13)
|Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data.J. Meng, L. M. Merino, N. B. Shamlo, S. Makeig, K. Robbins, Y Huang Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features,” PLoS ONE, 7.9 (Sept. 2012): e44464. PMID: 23028544, PMCID: PMC3445552 (IF: 4.09)