2018
Jae-Woong; Zhang Chang, Wei; Yeh; Yong, Jeongsik#
An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response Journal Article
In: Nucleic Acids Research, vol. 46, no. 12, pp. P5996–6008, 2018.
Abstract | BibTeX | Tags: Transcriptome
@article{chang2018,
title = {An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response},
author = {Chang, Jae-Woong; Zhang, Wei; Yeh, Hsin Sung; Park, Meeyeon; Yao, Chengguo; Shi, Yongsheng; Kuang, Rui# and Yong, Jeongsik#},
year = {2018},
date = {2018-07-06},
journal = {Nucleic Acids Research},
volume = {46},
number = {12},
pages = {P5996–6008},
abstract = {3'-untranslated regions (UTRs) can vary through the use of alternative polyadenylation sites during pre-mRNA processing. Multiple publically available pipelines combining high profiling technologies and bioinformatics tools have been developed to catalog changes in 3'-UTR lengths. In our recent RNA-seq experiments using cells with hyper-activated mammalian target of rapamycin (mTOR), we found that cellular mTOR activation leads to transcriptome-wide alternative polyadenylation (APA), resulting in the activation of multiple cellular pathways. Here, we developed a novel bioinformatics algorithm, IntMAP, which integrates RNA-Seq and PolyA Site (PAS)-Seq data for a comprehensive characterization of APA events. By applying IntMAP to the datasets from cells with hyper-activated mTOR, we identified novel APA events that could otherwise not be identified by either profiling method alone. Several transcription factors including Cebpg (CCAAT/enhancer binding protein gamma) were among the newly discovered APA transcripts, indicating that diverse transcriptional networks may be regulated by mTOR-coordinated APA. The prevention of APA in Cebpg using the CRISPR/cas9-mediated genome editing tool showed that mTOR-driven 3'-UTR shortening in Cebpg is critical in protecting cells from endoplasmic reticulum (ER) stress. Taken together, we present IntMAP as a new bioinformatics algorithm for APA analysis by which we expand our understanding of the physiological role of mTOR-coordinated APA events to ER stress response. IntMAP toolbox is available at http://compbio.cs.umn.edu/IntMAP/.},
keywords = {Transcriptome},
pubstate = {published},
tppubtype = {article}
}
2016
Liang, Lining; Sun, Hao; Zhang, Wei; Zhang, Mengdan; Yang, Xiao; Kuang, Rui; Zheng, Hui
Meta-Analysis of EMT Datasets Reveals Different Types of EMT. Journal Article
In: PloS one, vol. 11, no. 6, pp. e0156839–e0156839, 2016.
Abstract | Links | BibTeX | Tags: Gene Expression, Transcriptome
@article{liang2015meta,
title = {Meta-Analysis of EMT Datasets Reveals Different Types of EMT.},
author = {Lining Liang and Hao Sun and Wei Zhang and Mengdan Zhang and Xiao Yang and Rui Kuang and Hui Zheng},
url = {http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0156839},
doi = {10.1371/journal.pone.0156839},
year = {2016},
date = {2016-06-03},
journal = {PloS one},
volume = {11},
number = {6},
pages = {e0156839--e0156839},
abstract = {As a critical process during embryonic development, cancer progression and cell fate conversions, epithelial-mesenchymal transition (EMT) has been extensively studied over the last several decades. To further understand the nature of EMT, we performed meta-analysis of multiple microarray datasets to identify the related generic signature. In this study, 24 human and 17 mouse microarray datasets were integrated to identify conserved gene expression changes in different types of EMT. Our integrative analysis revealed that there is low agreement among the list of the identified signature genes and three other lists in previous studies. Since removing the datasets with weakly-induced EMT from the analysis did not significantly improve the overlapping in the signature-gene lists, we hypothesized the existence of different types of EMT. This hypothesis was further supported by the grouping of 74 human EMT-induction samples into five distinct clusters, and the identification of distinct pathways in these different clusters of EMT samples. The five clusters of EMT-induction samples also improves the understanding of the characteristics of different EMT types. Therefore, we concluded the existence of different types of EMT was the possible reason for its complex role in multiple biological processes.},
keywords = {Gene Expression, Transcriptome},
pubstate = {published},
tppubtype = {article}
}
2013
Zhang, Wei; Ota, Takayo; Shridhar, Viji; Chien, Jeremy; Wu, Baolin; Kuang, Rui
Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment Journal Article
In: PLoS Comput Biol, vol. 9, no. 3, pp. e1002975, 2013.
Abstract | Links | BibTeX | Tags: Cancer Genomics, Network-based Learning, Survival Analysis, Transcriptome
@article{zhang2013network,
title = {Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment},
author = {Wei Zhang and Takayo Ota and Viji Shridhar and Jeremy Chien and Baolin Wu and Rui Kuang},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002975},
doi = {10.1371/journal.pcbi.1002975},
year = {2013},
date = {2013-03-21},
journal = {PLoS Comput Biol},
volume = {9},
number = {3},
pages = {e1002975},
publisher = {Public Library of Science},
abstract = {Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L1-norm or L2-norm. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://localhost/~raphaelpetegrosso/wpcb/Net-Cox/.},
keywords = {Cancer Genomics, Network-based Learning, Survival Analysis, Transcriptome},
pubstate = {published},
tppubtype = {article}
}
2010
Fang, Gang; Kuang, Rui; Pandey, Gaurav; Steinbach, Michael; Myers, Chad L; Kumar, Vipin
Subspace differential coexpression analysis: problem definition and a general approach. Proceedings Article
In: Pacific symposium on biocomputing, pp. 145–56, 2010.
Abstract | Links | BibTeX | Tags: Gene Expression, Transcriptome
@inproceedings{fang2010subspace,
title = {Subspace differential coexpression analysis: problem definition and a general approach.},
author = {Gang Fang and Rui Kuang and Gaurav Pandey and Michael Steinbach and Chad L Myers and Vipin Kumar},
url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/9789814295291_0017.pdf},
doi = {10.1142/9789814295291_0017},
year = {2010},
date = {2010-01-04},
booktitle = {Pacific symposium on biocomputing},
volume = {15},
pages = {145--56},
abstract = {In this paper, we study methods to identify differential coexpression patterns in case-control gene expression data. A differential coexpression pattern consists of a set of genes that have substantially different levels of coherence of their expression profiles across the two sample-classes, i.e., highly coherent in one class, but not in the other. Biologically, a differential coexpression patterns may indicate the disruption of a regulatory mechanism possibly caused by disregulation of pathways or mutations of transcription factors. A common feature of all the existing approaches for differential coexpression analysis is that the coexpression of a set of genes is measured on all the samples in each of the two classes, i.e., over the full-space of samples. Hence, these approaches may miss patterns that only cover a subset of samples in each class, i.e., subspace patterns, due to the heterogeneity of the subject population and disease causes. In this paper, we extend differential coexpression analysis by defining a subspace differential coexpression pattern, i.e., a set of genes that are coexpressed in a relatively large percent of samples in one class, but in a much smaller percent of samples in the other class. We propose a general approach based upon association analysis framework that allows exhaustive yet efficient discovery of subspace differential coexpression patterns. This approach can be used to adapt a family of biclustering algorithms to obtain their corresponding differential versions that can directly discover differential coexpression patterns. Using a recently developed biclustering algorithm as illustration, we perform experiments on cancer datasets which demonstrates the existence of subspace differential coexpression patterns. Permutation tests demonstrate the statistical significance for a large number of discovered subspace patterns, many of which can not be discovered if they are measured over all the samples in each of the classes. Interestingly, in our experiments, some discovered subspace patterns have significant overlap with known cancer pathways, and some are enriched with the target gene sets of cancer-related microRNA and transcription factors. The source codes and datasets used in this paper are available at http://vk.cs.umn.edu/SDC/.
Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814295291_0017},
keywords = {Gene Expression, Transcriptome},
pubstate = {published},
tppubtype = {inproceedings}
}
Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814295291_0017