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1.  Education, collaboration, and innovation: intelligent biology and medicine in the era of big data 
BMC Genomics  2015;16(Suppl 7):S1.
Here we present a summary of the 2014 International Conference on Intelligent Biology and Medicine (ICIBM 2014) and the editorial report of the supplement to BMC Genomics and BMC Systems Biology that includes 20 research articles selected from ICIBM 2014. The conference was held on December 4-6, 2014 at San Antonio, Texas, USA, and included six scientific sessions, four tutorials, four keynote presentations, nine highlight talks, and a poster session that covered cutting-edge research in bioinformatics, systems biology, and computational medicine.
doi:10.1186/1471-2164-16-S7-S1
PMCID: PMC4474420  PMID: 26099197
2.  Co-modulation analysis of gene regulation in breast cancer reveals complex interplay between ESR1 and ERBB2 genes 
BMC Genomics  2015;16(Suppl 7):S19.
Background
Gene regulation is dynamic across cellular conditions and disease subtypes. From the aspect of regulation under modulation, regulation strength between a pair of genes can be modulated by (dependent on) expression abundance of another gene (modulator gene). Previous studies have demonstrated the involvement of genes modulated by single modulator genes in cancers, including breast cancer. However, analysis of multi-modulator co-modulation that can further delineate the landscape of complex gene regulation is, to our knowledge, unexplored previously. In the present study we aim to explore the joint effects of multiple modulator genes in modulating global gene regulation and dissect the biological functions in breast cancer.
Results
To carry out the analysis, we proposed the Covariability-based Multiple Regression (CoMRe) method. The method is mainly built on a multiple regression model that takes expression levels of multiple modulators as inputs and regulation strength between genes as output. Pairs of genes were divided into groups based on their co-modulation patterns. Analyzing gene expression profiles from 286 breast cancer patients, CoMRe investigated ten candidate modulator genes that interacted and jointly determined global gene regulation. Among the candidate modulators, ESR1, ERBB2, and ADAM12 were found modulating the most numbers of gene pairs. The largest group of gene pairs was composed of ones that were modulated by merely ESR1. Functional annotation revealed that the group was significantly related to tumorigenesis and estrogen signaling in breast cancer. ESR1−ERBB2 co-modulation was the largest group modulated by more than one modulators. Similarly, the group was functionally associated with hormone stimulus, suggesting that functions of the two modulators are performed, at least partially, through modulation. The findings were validated in majorities of patients (> 99%) of two independent breast cancer datasets.
Conclusions
We have showed CoMRe is a robust method to discover critical modulators in gene regulatory networks, and it is capable of achieving reproducible and biologically meaningful results. Our data reveal that gene regulatory networks modulated by single modulator or co-modulated by multiple modulators play important roles in breast cancer. Findings of this report illuminate complex and dynamic gene regulation under modulation and its involvement in breast cancer.
doi:10.1186/1471-2164-16-S7-S19
PMCID: PMC4474423  PMID: 26100352
3.  Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads 
BMC Genomics  2015;16(Suppl 7):S14.
Background
RNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.
Results
We implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.
Conclusions
In this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.
doi:10.1186/1471-2164-16-S7-S14
PMCID: PMC4474535  PMID: 26099631
RNA-Seq; Differential expression analysis; XBSeq; DESeq; Non-exonic mapped reads; Negative binomial distribution; Poisson distribution
4.  HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data 
BMC Genomics  2015;16(Suppl 4):S2.
Background
Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome.
Results
We developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs.
Conclusions
In this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.
doi:10.1186/1471-2164-16-S4-S2
PMCID: PMC4416174  PMID: 25917296
5.  Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers 
BMC Genomics  2015;16(Suppl 4):S1.
Background
In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs of genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) can establish coexpression through competition for a limited pool of the miRNA program. While the dynamics of ceRNA regulation among cellular conditions have been verified based on in silico and in vitro experiments, comprehensive investigation into the strength of ceRNA regulation in human datasets remains largely unexplored. Furthermore, pan-cancer analysis of ceRNA regulation, to our knowledge, has not been systematically investigated.
Results
In the present study we explored optimal conditions for ceRNA regulation, investigated functions governed by ceRNA regulation, and evaluated pan-cancer effects. We started by investigating how essential factors, such as the size of miRNA programs, the number of miRNA program binding sites, and expression levels of miRNA programs and ceRNAs affect the ceRNA regulation capacity in tumors derived from glioblastoma multiforme patients captured by The Cancer Genome Atlas (TCGA). We demonstrated that increased numbers of common targeting miRNAs as well as the abundance of binding sites enhance ceRNA regulation and strengthen coexpression of ceRNA pairs. Also, our investigation revealed that the strength of ceRNA regulation is dependent on expression levels of both miRNA programs and ceRNAs. Through functional annotation analysis, our results indicated that ceRNA regulation is highly associated with essential cellular functions and diseases including cancer. Furthermore, the highly intertwined ceRNA regulatory relationship enables constitutive and effective intra-function regulation of genes in diverse types of cancer.
Conclusions
Using gene and microRNA expression datasets from TCGA, we successfully quantified the optimal conditions for ceRNA regulation, which hinge on four essential parameters of ceRNAs. Our analysis suggests optimized ceRNA regulation is related to disease pathways and essential cellular functions. Furthermore, although the strength of ceRNA regulation is dynamic among cancers, its governing functions are stably maintained. The findings of this report contribute to better understanding of ceRNA dynamics and its crucial roles in cancers.
doi:10.1186/1471-2164-16-S4-S1
PMCID: PMC4416191  PMID: 25917195
6.  Pubertal Bisphenol A Exposure Alters Murine Mammary Stem Cell (MaSC) Function Leading to Early Neoplasia in Regenerated Glands 
Perinatal exposure to bisphenol A (BPA) has been shown to cause aberrant mammary gland morphogenesis and mammary neoplastic transformation. Yet, the underlying mechanism is poorly understood. We tested the hypothesis that mammary glands exposed to BPA during a susceptible window may lead to its susceptibility to tumorigenesis through a stem-cell mediated mechanism. We exposed 21-day-old Balb/c mice to BPA by gavage (25 μg/kg/day) during puberty for 3 weeks, and a subset of animals were further challenged with one oral dose (30 mg/kg) of 7,12-dimethylbenz[a]anthracene (DMBA) at 2 months of age. Primary mammary cells were isolated at 6 weeks, and 2 and 4 months of age for MaSC quantification and function analysis. Pubertal exposure to the low-dose BPA increased lateral branches and hyperplasia in adult mammary glands and caused an acute increase of MaSC in 6-week-old glands and a delayed increase of luminal progenitors in 4-month-old adult gland. Most importantly, pubertal BPA exposure altered the function of MaSC from different age groups, causing early neoplastic lesions in their regenerated glands similar to those induced by DMBA exposure, which indicates that MaSCs are susceptible to BPA-induced transformation. Deep sequencing analysis on MaSC-enriched mammospheres identified a set of aberrantly expressed genes associated with early neoplastic lesions in human breast cancer patients. Thus, our study for the first time shows that pubertal BPA exposure altered MaSC gene expression and function such that they induced early neoplastic transformation.
doi:10.1158/1940-6207.CAPR-13-0260
PMCID: PMC3976434  PMID: 24520039
Bisphenol A; Breast Cancer; Mammary stem cell; Early neoplastic lesion; Risk assessment; RNAseq; Mammosphere
7.  Combined treatment of rapamycin and dietary restriction have a larger effect on the transcriptome and metabolome of liver 
Aging cell  2013;13(2):311-319.
Summary
Rapamycin (Rapa) and dietary restriction (DR) have consistently been shown to increase lifespan. To investigate whether Rapa and DR affect similar pathways in mice, we compared the effects of feeding mice ad libitum (AL), Rapa, DR, or a combination of Rapa and DR (Rapa+DR) on the transcriptome and metabolome of the liver. The principal component analysis shows that Rapa and DR are distinct groups. Over 2500 genes are significantly changed with either Rapa or DR compared to mice fed AL; more than 80% are unique to DR or Rapa. A similar observation was made when genes were grouped into pathways; two-thirds of the pathways are uniquely changed by DR or Rapa. The metabolome shows an even greater difference between Rapa and DR; no metabolites in Rapa-treated mice were changed significantly from AL mice, while 173 metabolites were changed in the DR mice. Interestingly, the number of genes significantly changed by Rapa+DR compared to AL is twice as large as the number of genes significantly altered by either DR or Rapa alone. In summary, the global effects of DR or Rapa on the liver are quite different and a combination of Rapa and DR results in alterations in a large number of genes and metabolites that are not significantly changed by either manipulation alone, suggesting that a combination of DR and Rapa would be more effective in extending longevity than either treatment alone.
doi:10.1111/acel.12175
PMCID: PMC3989927  PMID: 24304444
Rapamycin; dietary restriction; metabolome; transcriptome
8.  A novel significance score for gene selection and ranking 
Bioinformatics  2012;30(6):801-807.
Motivation: When identifying differentially expressed (DE) genes from high-throughput gene expression measurements, we would like to take both statistical significance (such as P-value) and biological relevance (such as fold change) into consideration. In gene set enrichment analysis (GSEA), a score that can combine fold change and P-value together is needed for better gene ranking.
Results: We defined a gene significance score π-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, π-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied π-value to GSEA and found it comparable to P-value and t-statistic based methods, with added protection against false discovery in certain situations. Finally, in a gene functional study of breast cancer profiles, we showed that using π-value helps elucidating otherwise overlooked important biological functions.
Availability: http://gccri.uthscsa.edu/Pi_Value_Supplementary.asp
Contact: xy@ieee.org, cheny8@uthscsa.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr671
PMCID: PMC3957066  PMID: 22321699
9.  AR-V7 and Resistance to Enzalutamide and Abiraterone in Prostate Cancer 
The New England journal of medicine  2014;371(11):1028-1038.
Background
The androgen-receptor isoform encoded by splice variant 7 lacks the ligand-binding domain, which is the target of enzalutamide and abiraterone, but remains constitutively active as a transcription factor. We hypothesized that detection of androgen-receptor splice variant 7 messenger RNA (AR-V7) in circulating tumor cells from men with advanced prostate cancer would be associated with resistance to enzalutamide and abiraterone.
Methods
We used a quantitative reverse-transcriptase–polymerase-chain-reaction assay to evaluate AR-V7 in circulating tumor cells from prospectively enrolled patients with metastatic castration-resistant prostate cancer who were initiating treatment with either enzalutamide or abiraterone. We examined associations between AR-V7 status (positive vs. negative) and prostate-specific antigen (PSA) response rates (the primary end point), freedom from PSA progression (PSA progression–free survival), clinical or radiographic progression–free survival, and overall survival.
Results
A total of 31 enzalutamide-treated patients and 31 abiraterone-treated patients were enrolled, of whom 39% and 19%, respectively, had detectable AR-V7 in circulating tumor cells. Among men receiving enzalutamide, AR-V7–positive patients had lower PSA response rates than AR-V7–negative patients (0% vs. 53%, P = 0.004) and shorter PSA progression–free survival (median, 1.4 months vs. 6.0 months; P<0.001), clinical or radiographic progression–free survival (median, 2.1 months vs. 6.1 months; P<0.001), and overall survival (median, 5.5 months vs. not reached; P = 0.002). Similarly, among men receiving abiraterone, AR-V7–positive patients had lower PSA response rates than AR-V7–negative patients (0% vs. 68%, P = 0.004) and shorter PSA progression–free survival (median, 1.3 months vs. not reached; P<0.001), clinical or radiographic progression–free survival (median, 2.3 months vs. not reached; P<0.001), and overall survival (median, 10.6 months vs. not reached, P = 0.006). The association between AR-V7 detection and therapeutic resistance was maintained after adjustment for expression of full-length androgen receptor messenger RNA.
Conclusions
Detection of AR-V7 in circulating tumor cells from patients with castration-resistant prostate cancer may be associated with resistance to enzalutamide and abiraterone. These findings require large-scale prospective validation. (Funded by the Prostate Cancer Foundation and others.)
doi:10.1056/NEJMoa1315815
PMCID: PMC4201502  PMID: 25184630
10.  PELP1 oncogenic functions involve alternative splicing via PRMT6 
Molecular oncology  2013;8(2):389-400.
Proline-, glutamic acid-, and leucine-rich protein 1 (PELP1) is a proto-oncogene that functions as coactivator of the estrogen receptor and is an independent prognostic predictor of shorter survival of breast cancer patients. The dysregulation of PELP1 in breast cancer has been implicated in oncogenesis, metastasis, and therapy resistance. Although several aspects of PELP1 have been studied, a complete list of PELP1 target genes remains unknown, and the molecular mechanisms of PELP1 mediated oncogenesis remain elusive. In this study, we have performed a whole genome analysis to profile the PELP1 transcriptome by RNA-sequencing and identified 318 genes as PELP1 regulated genes. Pathway analysis revealed that PELP1 modulates several pathways including the molecular mechanisms of cancer, estrogen signaling, and breast cancer progression. Interestingly, RNA-seq analysis also revealed that PELP1 regulates the expression of several genes involved in alternative splicing. Accordingly, the PELP1 regulated genome includes several uniquely spliced isoforms. Mechanistic studies show that PELP1 binds RNA with a preference to poly-C, co-localizes with the splicing factor SC35 at nuclear speckles, and participates in alternative splicing. Further, PELP1 interacts with the arginine methyltransferase PRMT6 and modifies PRMT6 functions. Inhibition of PRMT6 reduced PELP1-mediated estrogen receptor activation, cellular proliferation, and colony formation. PELP1 and PRMT6 are co-recruited to estrogen receptor target genes, PELP1 knockdown affects the enrichment of histone H3R2 di-methylation, and PELP1 and PRMT6 coordinate to regulate the alternative splicing of genes involved in cancer. Collectively, our data suggest that PELP1 oncogenic functions involve alternative splicing leading to the activation of unique pathways that support tumor progression and that the PELP1-PRMT6 axis may be a potential target for breast cancer therapy.
doi:10.1016/j.molonc.2013.12.012
PMCID: PMC3943689  PMID: 24447537
Alternative splicing; breast cancer; epigenetics; PELP1; PRMT61
11.  miR-93-directed down-regulation of DAB2 defines a novel oncogenic pathway in lung cancer 
Oncogene  2013;33(34):4307-4315.
The disabled homolog 2 (DAB2) gene was recently identified as a tumor suppressor gene with its expression down-regulated in multiple cancer types. The role of DAB2 in lung tumorigenesis, however, is not fully characterized, and the mechanisms of DAB2 dysregulation in lung cancer are not defined. Here we show that low DAB2 levels in lung tumor specimens are significantly correlated with poor patient survival, and that DAB2 over-expression significantly inhibits cell growth in cultured lung cancer cells, indicating its potent tumor suppressor function. We next identify that microRNA miR-93 functions as a potent repressor of DAB2 expression by directly targeting the 3′UTR of the DAB2 mRNA. Using in vitro and in vivo approaches, we demonstrate that miR-93 over-expression plays an important role in promoting lung cancer cell growth, and that its oncogenic function is primarily mediated by down-regulating DAB2 expression. Our clinical investigations further indicate that high tumor levels of miR-93 are correlated with poor survival of lung cancer patients. The correlations of both low DAB2 and high miR-93 expression with poor patient survival strongly support the critical role of the miR-93/DAB2 pathway in determining lung cancer progression.
doi:10.1038/onc.2013.381
PMCID: PMC4281941  PMID: 24037530
DAB2; miRNA; miR-93; lung cancer
12.  Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm 
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
doi:10.1155/2015/685404
PMCID: PMC4321098  PMID: 25691895
13.  Microarray profile of human kidney from diabetes, renal cell carcinoma and renal cell carcinoma with diabetes 
Genes & Cancer  2015;6(1-2):62-70.
Recent study from our laboratory showed that patients with diabetes are at a higher risk of developing kidney cancer. In the current study, we have screened whole human DNA genome from healthy control, patients with diabetes or renal cell carcinoma (RCC) or RCC+diabetes. We found that 883 genes gain/163 genes loss of copy number in RCC+diabetes group, 669 genes gain/307 genes loss in RCC group and 458 genes gain/38 genes loss of copy number in diabetes group, after removing gain/loss genes obtained from healthy control group. Data analyzed for functional annotation enrichment pathways showed that control group had the highest number (280) of enriched pathways, 191 in diabetes+RCC group, 148 in RCC group, and 81 in diabetes group. The overlap GO pathways between RCC+diabetes and RCC groups showed that nine were enriched, between RCC+diabetes and diabetes groups was four and between diabetes and RCC groups was eight GO pathways. Overall, we observed majority of DNA alterations in patients from RCC+diabetes group. Interestingly, insulin receptor (INSR) is highly expressed and had gains in copy number in RCC+diabetes and diabetes groups. The changes in INSR copy number may use as a biomarker for predicting RCC development in diabetic patients.
PMCID: PMC4362485  PMID: 25821562
microarray; renal; diabetes; RCC
14.  Negative Elongation Factor Controls Energy Homeostasis in Cardiomyocytes 
Cell reports  2014;7(1):79-85.
SUMMARY
Negative elongation factor (NELF) is known to enforce promoter-proximal pausing of RNA polymerase II (Pol II), a pervasive phenomenon observed across multicellular genomes. However, the physiological impact of NELF on tissue homeostasis remains unclear. Here, we show that whole-body conditional deletion of the B subunit of NELF (NELF-B) in adult mice results in cardiomyopathy and impaired response to cardiac stress. Tissue-specific knockout of NELF-B confirms its cell-autonomous function in cardiomyocytes. NELF directly supports transcription of those genes encoding rate-limiting enzymes in fatty acid oxidation (FAO) and the tricarboxylic acid (TCA) cycle. NELF also shares extensively transcriptional target genes with peroxisome proliferator-activated receptor α (PPARα), a master regulator of energy metabolism in the myocardium. Mechanistically, NELF helps stabilize the transcription initiation complex at the metabolism-related genes. Our findings strongly indicate that NELF is part of the PPARα-mediated transcription regulatory network that maintains metabolic homeostasis in cardiomyocytes.
doi:10.1016/j.celrep.2014.02.028
PMCID: PMC4277258  PMID: 24656816
15.  BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data 
BMC Bioinformatics  2014;15(Suppl 12):S6.
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.
doi:10.1186/1471-2105-15-S12-S6
PMCID: PMC4243086  PMID: 25474268
DNA methylation; differential methylation; MBDCap-seq; Hidden Markov Model (HMM)
16.  MeT-DB: a database of transcriptome methylation in mammalian cells 
Nucleic Acids Research  2014;43(Database issue):D197-D203.
Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. The MethylTranscriptome DataBase (MeT-DB, http://compgenomics.utsa.edu/methylation/) is the first comprehensive resource for N6-methyladenosine (m6A) in mammalian transcriptome. It includes a database that records publicaly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of microRNA, splicing factor and RNA binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. Analysis of differential m6A methylation and the related differential gene expression under two conditions is also available in the browser. A global perspective of the genome-wide distribution of m6A methylation in all the data is provided in circular ideograms, which also act as a navigation portal. The query results and the entire data set can be exported to assist publication and additional analysis.
doi:10.1093/nar/gku1024
PMCID: PMC4383883  PMID: 25378335
17.  In silico functional analyses and discovery of survival-associated microRNA signatures in pediatric osteosarcoma 
Oncoscience  2014;1(9):599-608.
Purpose
Osteosarcoma is the most common bone tumor in children, adolescents, and young adults. In contrast to other childhood malignancies, no biomarkers have been consistently identified as predictors of outcome. This study was conducted to assess the microRNAs(miRs) expression signatures in pre-treatment osteosarcoma specimens and correlate with outcome to identify biomarkers for disease relapse.
Results
A 42-miRs signature whose expression levels were associated with overall and relapse-free survival waas identified. There were 8 common miRs between the two sets of survival-associated miRs. Bioinformatic analyses of these survival-associated miRs suggested that they might regulate genes involved in ubiquitin proteasome system, TGFb, IGF, PTEN/AKT/mTOR, MAPK, PDGFR/RAF/MEK/ERK, and ErbB/HER pathways.
Methods
The cohort consisted of 27 patients of 70% Mexican-American ethnicity. High-throughput RT-qPCR approach was used to generate quantitative expression of 754 miRs in the human genome. We examined tumor recurrence status, survival time and their association with miR expression levels by Cox proportional hazard regression analysis. TargetScan was used to predict miR/genes interactions, and functional analyses using KEGG, BioCarta, Gene Ontology were applied to these potential targets to predict deregulated pathways.
Conclusions
Our findings suggested that these miRs might be potentially useful as prognostic biomarkers and therapeutic targets in pediatric osteosarcoma.
PMCID: PMC4278335  PMID: 25594070
osteosarcoma; microRNA expression; prognosis; pathways; pediatric cancers
18.  Amplification of Distant Estrogen Response Elements Deregulates Target Genes Associated with Tamoxifen Resistance in Breast Cancer 
Cancer cell  2013;24(2):197-212.
SUMMARY
A causal role of gene amplification in tumorigenesis is well-known, while amplification of DNA regulatory elements as an oncogenic driver remains unclear. In this study, we integrated next-generation sequencing approaches to map distant estrogen response elements (DEREs) that remotely control transcription of target genes through chromatin proximity. Two densely mapped DERE regions located on chromosomes 17q23 and 20q13 were frequently amplified in ERα-positive luminal breast cancer. These aberrantly amplified DEREs deregulated target gene expression potentially linked to cancer development and tamoxifen resistance. Progressive accumulation of DERE copies was observed in normal breast progenitor cells chronically exposed to estrogenic chemicals. These findings may extend to other DNA regulatory elements, the amplification of which can profoundly alter target transcriptome during tumorigenesis.
doi:10.1016/j.ccr.2013.07.007
PMCID: PMC3890247  PMID: 23948299
19.  Exome-based analysis for RNA epigenome sequencing data 
Bioinformatics  2013;29(12):1565-1567.
Motivation: Fragmented RNA immunoprecipitation combined with RNA sequencing enabled the unbiased study of RNA epigenome at a near single-base resolution; however, unique features of this new type of data call for novel computational techniques.
Result: Through examining the connections of RNA epigenome sequencing data with two well-studied data types, ChIP-Seq and RNA-Seq, we unveiled the salient characteristics of this new data type. The computational strategies were discussed accordingly, and a novel data processing pipeline was proposed that combines several existing tools with a newly developed exome-based approach ‘exomePeak’ for detecting, representing and visualizing the post-transcriptional RNA modification sites on the transcriptome.
Availability: The MATLAB package ‘exomePeak’ and additional details are available at http://compgenomics.utsa.edu/exomePeak/.
Contact: yufei.huang@utsa.edu or jmeng@mit.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt171
PMCID: PMC3673212  PMID: 23589649
20.  A high-content morphological screen identifies novel microRNAs that regulate neuroblastoma cell differentiation 
Oncotarget  2014;5(9):2499-2512.
Neuroblastoma, the most common extracranial solid tumor of childhood, arises from neural crest cell precursors that fail to differentiate. Inducing cell differentiation is an important therapeutic strategy for neuroblastoma. We developed a direct functional high-content screen to identify differentiation-inducing microRNAs, in order to develop microRNA-based differentiation therapy for neuroblastoma. We discovered novel microRNAs, and more strikingly, three microRNA seed families that induce neuroblastoma cell differentiation. In addition, we showed that microRNA seed families were overrepresented in the identified group of fourteen differentiation-inducing microRNAs, suggesting that microRNA seed families are functionally more important in neuroblastoma differentiation than microRNAs with unique sequences. We further investigated the differentiation-inducing function of the microRNA-506-3p/microRNA-124-3p seed family, which was the most potent inducer of differentiation. We showed that the differentiation-inducing function of microRNA-506-3p/microRNA-124-3p is mediated, at least partially, by down-regulating expression of their targets CDK4 and STAT3. We further showed that expression of miR-506-3p, but not miR-124-3p, is dramatically upregulated in differentiated neuroblastoma cells, suggesting the important role of endogenous miR-506-3p in differentiation and tumorigenesis. Overall, our functional screen on microRNAs provided the first comprehensive analysis on the involvements of microRNA species in neuroblastoma cell differentiation and identified novel differentiation-inducing microRNAs. Further investigations are certainly warranted to fully characterize the function of the identified microRNAs in order to eventually benefit neuroblastoma therapy.
PMCID: PMC4058022  PMID: 24811707
neuroblastoma; microRNA; high-content screen; differentiation; differentiation therapy
21.  Combined treatment of rapamycin and dietary restriction has a larger effect on the transcriptome and metabolome of liver 
Aging Cell  2013;13(2):311-319.
Rapamycin (Rapa) and dietary restriction (DR) have consistently been shown to increase lifespan. To investigate whether Rapa and DR affect similar pathways in mice, we compared the effects of feeding mice ad libitum (AL), Rapa, DR, or a combination of Rapa and DR (Rapa + DR) on the transcriptome and metabolome of the liver. The principal component analysis shows that Rapa and DR are distinct groups. Over 2500 genes are significantly changed with either Rapa or DR when compared with mice fed AL; more than 80% are unique to DR or Rapa. A similar observation was made when genes were grouped into pathways; two-thirds of the pathways were uniquely changed by DR or Rapa. The metabolome shows an even greater difference between Rapa and DR; no metabolites in Rapa-treated mice were changed significantly from AL mice, whereas 173 metabolites were changed in the DR mice. Interestingly, the number of genes significantly changed by Rapa + DR when compared with AL is twice as large as the number of genes significantly altered by either DR or Rapa alone. In summary, the global effects of DR or Rapa on the liver are quite different and a combination of Rapa and DR results in alterations in a large number of genes and metabolites that are not significantly changed by either manipulation alone, suggesting that a combination of DR and Rapa would be more effective in extending longevity than either treatment alone.
doi:10.1111/acel.12175
PMCID: PMC3989927  PMID: 24304444
dietary restriction; metabolome; rapamycin; transcriptome
22.  A Two-Stage Exon Recognition Model Based on Synergetic Neural Network 
Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.
doi:10.1155/2014/503132
PMCID: PMC3984832  PMID: 24790638
23.  Jmjd3 Negatively Regulates Reprogramming Through Histone Demethylase Activity- Dependent and -Independent Pathways 
Cell  2013;152(5):1037-1050.
Although somatic cell reprogramming to generate inducible pluripotent stem cells (iPSCs) is associated with profound epigenetic changes, the roles and mechanisms of epigenetic factors in this process remain poorly understood. Here we identify Jmjd3 as a potent negative regulator of reprogramming. Jmjd3-deficient MEFs produced significantly more iPSC colonies than did wild-type cells, while ectopic expression of Jmjd3 markedly inhibited reprogramming. We show that the inhibitory effects of Jmjd3 are produced through both histone demethylase-dependent and -independent pathways. The latter pathway is entirely novel and involves Jmjd3 targeting of PHF20 for ubiquitination and degradation via recruitment of an E3 ligase, Trim26. Importantly, PHF20-deficient MEFs could not be converted to fully reprogrammed iPSCs, even with knockdown of Jmjd3, Ink4a or p21, indicating that this protein exerts predominant effects on reprogramming. Our findings demonstrate a previously unrecognized role of Jmjd3 in cellular reprogramming and provide molecular insight into the mechanisms by which the Jmjd3-PHF20 axis controls this process.
doi:10.1016/j.cell.2013.02.006
PMCID: PMC3742052  PMID: 23452852
24.  Mice Fed Rapamycin Have an Increase in Lifespan Associated with Major Changes in the Liver Transcriptome 
PLoS ONE  2014;9(1):e83988.
Rapamycin was found to increase (11% to 16%) the lifespan of male and female C57BL/6J mice most likely by reducing the increase in the hazard for mortality (i.e., the rate of aging) term in the Gompertz mortality analysis. To identify the pathways that could be responsible for rapamycin's longevity effect, we analyzed the transcriptome of liver from 25-month-old male and female mice fed rapamycin starting at 4 months of age. Few changes (<300 transcripts) were observed in transcriptome of rapamycin-fed males; however, a large number of transcripts (>4,500) changed significantly in females. Using multidimensional scaling and heatmap analyses, the male mice fed rapamycin were found to segregate into two groups: one group that is almost identical to control males (Rapa-1) and a second group (Rapa-2) that shows a change in gene expression (>4,000 transcripts) with more than 60% of the genes shared with female mice fed Rapa. Using ingenuity pathway analysis, 13 pathways were significantly altered in both Rapa-2 males and rapamycin-fed females with mitochondrial function as the most significantly changed pathway. Our findings show that rapamycin has a major effect on the transcriptome and point to several pathways that would likely impact the longevity.
doi:10.1371/journal.pone.0083988
PMCID: PMC3883653  PMID: 24409289
25.  BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database 
BMC Systems Biology  2013;7(Suppl 5):S5.
Background
Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.
Method
Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.
Result
BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.
Conclusions
The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.
Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/
doi:10.1186/1752-0509-7-S5-S5
PMCID: PMC4029357  PMID: 24564956
Connectivity Map; Mode of Action (MoA); Breast Cancer Mode of Action Network (BRCA-MoNet)

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