Po-Yen Wu

Detecting and correcting systematic variation in large-scale RNA sequencing data

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Omic and Electronic Health Record Big Data Analytics for Precision Medicine

<italic>Objective:</italic> Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of –omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. <italic>Methods:</italic> In this paper, we present –omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. <italic>Results:</italic> To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR. <italic>Conclusion: </italic> Big data analytics is able to address –omic and EHR data challenges for paradigm shift toward precision medicine. <italic>Significance:</italic> Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact.OBJECTIVE Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. METHODS In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. RESULTS To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. CONCLUSION Big data analytics is able to address -omic and EHR data challenges for paradigm shift towards precision medicine. SIGNIFICANCE Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

Benchmarking RNA-Seq quantification tools

RNA-Seq, a deep sequencing technique, promises to be a potential successor to microarraysfor studying the transcriptome. One of many aspects of transcriptomics that are of interest to researchers is gene expression estimation. With rapid development in RNA-Seq, there are numerous tools available to estimate gene expression, each producing different results. However, we do not know which of these tools produces the most accurate gene expression estimates. In this study we have addressed this issue using Cufflinks, IsoEM, HTSeq, and RSEM to quantify RNA-Seq expression profiles. Comparing results of these quantification tools, we observe that RNA-Seq relative expression estimates correlate with RT-qPCR measurements in the range of 0.85 to 0.89, with HTSeq exhibiting the highest correlation. But, in terms of root-mean-square deviation of RNA-Seq relative expression estimates from RT-qPCR measurements, we find HTSeq to produce the greatest deviation. Therefore, we conclude that, though Cufflinks, RSEM, and IsoEM might not correlate as well as HTSeq with RT-qPCR measurements, they may produce expression values with higher accuracy.

A Review of Emerging Technologies for the Management of Diabetes Mellitus

Objective: High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods: A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results: Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion: Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance: The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

Assessing the impact of human genome annotation choice on RNA-seq expression estimates

BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Cardiovascular transcriptomics and epigenomics using next-generation sequencing: challenges, progress, and opportunities.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Prediction and prevention of CVD, such as coronary artery disease and atherosclerosis, traditionally depend on identification of risk factors.1,2 These factors are effective in the general assessment of CVD risk but are not consistent indicators for all individuals.3 Therefore, CVD research has been recently expanded to include the identification of omic biomarkers (eg, genomic, transcriptomic, and epigenomic) that may (1) improve our understanding of the molecular mechanisms of CVD, (2) facilitate the development of personalized CVD care, and (3) reduce CVD mortality rates by accurately identifying high-risk individuals.4 Next-generation sequencing (NGS) is a promising technology to identify omic biomarkers. Because of its high-throughput capability in discovering novel genomic features with base-pair resolution, NGS is projected to play an increasingly important role in clinical diagnostics and personalized medicine for CVD.5,6 NGS and associated bioinformatics methods have been applied to cardiovascular genomics, transcriptomics, and epigenomics. Figure 1 illustrates 4 NGS applications such as (1) identification of differentially expressed genes (DEGs) using RNA sequencing (RNA-seq), (2) identification of protein-binding regions in the genome using chromatin immunoprecipitation sequencing (ChIP-seq), (3) identification of genetic variants in exon regions using exome sequencing, and (4) identification of genomic methylation patterns using methyl-CpG-binding domain sequencing. These applications identify and quantify omic biomarkers that may be clinically viable for early disease diagnosis and effective disease treatment and management. In this article, we focus on 2 major applications of NGS technology: (1) RNA-seq, which has enabled researchers to characterize CVD by studying transcriptome-wide expression profiles,7 alternative splicing patterns,8 and miRNA regulatory networks9 and (2) ChIP-seq, which has enabled researchers to examine the epigenetic mechanisms of CVD by profiling the genome-wide pattern of protein-binding regions (eg, transcription factors and enhancers) …

The impact of RNA-seq aligners on gene expression estimation

While numerous RNA-seq data analysis pipelines are available, research has shown that the choice of pipeline influences the results of differentially expressed gene detection and gene expression estimation. Gene expression estimation is a key step in RNA-seq data analysis, since the accuracy of gene expression estimates profoundly affects the subsequent analysis. Generally, gene expression estimation involves sequence alignment and quantification, and accurate gene expression estimation requires accurate alignment. However, the impact of aligners on gene expression estimation remains unclear. We address this need by constructing nine pipelines consisting of nine spliced aligners and one quantifier. We then use simulated data to investigate the impact of aligners on gene expression estimation. To evaluate alignment, we introduce three alignment performance metrics, (1) the percentage of reads aligned, (2) the percentage of reads aligned with zero mismatch (ZeroMismatchPercentage), and (3) the percentage of reads aligned with at most one mismatch (ZeroOneMismatchPercentage). We then evaluate the impact of alignment performance on gene expression estimation using three metrics, (1) gene detection accuracy, (2) the number of genes falsely quantified (FalseExpNum), and (3) the number of genes with falsely estimated fold changes (FalseFcNum). We found that among various pipelines, FalseExpNum and FalseFcNum are correlated. Moreover, FalseExpNum is linearly correlated with the percentage of reads aligned and ZeroMismatchPercentage, and FalseFcNum is linearly correlated with ZeroMismatchPercentage. Because of this correlation, the percentage of reads aligned and ZeroMismatchPercentage may be used to assess the performance of gene expression estimation for all RNA-seq datasets.

PHARM - Association Rule Mining for Predictive Health

Predictive health is a new and innovative healthcare model that focuses on maintaining health rather than treating diseases. Such a model may benefit from computer-based decision support systems, which provide more quantitative health assessment, enabling more objective advice and action plans from predictive health providers. However, data mining for predictive health is more challenging compared to that for diseases. This is a reason why there are relatively fewer predictive health decision support systems embedded with data mining. The purpose of this study is to research and develop an interactive decision support system, called PHARM, in conjunction with Emory Center for Health Discovery and Well Being (CHDWB®). PHARM adopts association rule mining to generate quantitative and objective rules for health assessment and prediction. A case study results in 12 rules that predict mental illness based on five psychological factors. This study shows the value and usability of the decision support system to prevent the development of potential illness and to prioritize advice and action plans for reducing disease risks.

Systematic Assessment of RNA-Seq Quantification Tools Using Simulated Sequence Data

RNA-sequencing (RNA-seq) technology has emerged as the preferred method for quantification of gene and isoform expression. Numerous RNA-seq quantification tools have been proposed and developed, bringing us closer to developing expression-based diagnostic tests based on this technology. However, because of the rapidly evolving technologies and algorithms, it is essential to establish a systematic method for evaluating the quality of RNA-seq quantification. We investigate how different RNA-seq experimental designs (i.e., variations in sequencing depth and read length) affect various quantification algorithms (i.e., HTSeq, Cufflinks, and MISO). Using simulated data, we evaluate the quantification tools based on four metrics, namely: (1) total number of usable fragments for quantification, (2) detection of genes and isoforms, (3) correlation, and (4) accuracy of expression quantification with respect to the ground truth. Results show that Cufflinks is able to use the largest number of fragments for quantification, leading to better detection of genes and isoforms. However, HTSeq produces more accurate expression estimates. Moreover, each quantification algorithm is affected differently by varying sequencing depth and read length, suggesting that the selection of quantification algorithms should be application-dependent.