RNA editing is a post-transcriptional event that recodes hereditary information. Here we describe a comprehensive profile of the RNA editome of a male Han Chinese individual based on analysis of ∼ 767 million sequencing reads from poly(A)⁺, poly(A)⁻ and small RNA samples. We developed a computational pipeline that carefully controls for false positives while calling RNA editing events from genome and whole-transcriptome data of the same individual. We identified 22,688 RNA editing events in noncoding genes and introns, untranslated regions and coding sequences of protein-coding genes. Most changes ( ∼ 93%) converted A to I(G), consistent with known editing mechanisms based on adenosine deaminase acting on RNA (ADAR). We also found evidence of other types of nucleotide changes; however, these were validated at lower rates. We found 44 editing sites in microRNAs (miRNAs), suggesting a potential link between RNA editing and miRNA-mediated regulation. Our approach facilitates large-scale studies to profile and compare editomes across a wide range of samples.

Background: Gene 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. Results: We 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. Conclusions: We 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.

Background: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? Results: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Conclusions: Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Chromosomal microarray analysis is the gold standard for copy-number variant (CNV) detection in prenatal and postnatal diagnosis. We aimed to determine whether next-generation sequencing (NGS) technology could be an alternative method for CNV detection in routine clinical application. Genome-wide CNV analysis (>50 kb) was performed on a multicenter group of 570 patients using a low-coverage whole-genome sequencing pipeline. These samples were referred for chromosomal analysis; CNVs (i.e., pathogenic CNVs, pCNVs) were classified according to the American College of Medical Genetics and Genomics guidelines. Overall, a total of 198 abortuses, 37 stillbirths, 149 prenatal, and 186 postnatal samples were tested. Our approach yielded results in 549 samples (96.3%). In addition to 119 subjects with aneuploidies, 103 pCNVs (74 losses and 29 gains) were identified in 82 samples, giving diagnostic yields of 53.2% (95% confidence interval: 45.8, 60.5), 14.7% (5.0, 31.1), 28.5% (21.1, 36.6), and 30.1% (23.6, 37.3) in each group, respectively. Mosaicism was observed at a level as low as 25%. Patients with chromosomal diseases or microdeletion/microduplication syndromes were diagnosed using a high-resolution genome-wide method. Our study revealed the potential of NGS to facilitate genetic diagnoses that were not evident in the prenatal and postnatal groups. Genet Med 18 9, 940–948.

Balanced chromosomal rearrangement (or balanced chromosome abnormality, BCA) is a common chromosomal structural variation. Next‐generation sequencing has been reported to detect BCA‐associated breakpoints with the aid of karyotyping. However, the complications associated with this approach and the requirement for cytogenetics information has limited its application. Here, we provide a whole‐genome low‐coverage sequencing approach to detect BCA events independent of knowing the affected regions and with low false positives. First, six samples containing BCAs were used to establish a detection protocol and assess the efficacy of different library construction approaches. By clustering anomalous read pairs and filtering out the false‐positive results with a control cohort and the concomitant mapping information, we could directly detect BCA events for each sample. Through optimizing the read depth, BCAs in all samples could be blindly detected with only 120 million read pairs per sample for data from a small‐insert library and 30 million per sample for data from nonsize‐selected mate‐pair library. This approach was further validated using another 13 samples that contained BCAs. Our approach advances the application of high‐throughput whole‐genome low‐coverage analysis for robust BCA detection—especially for clinical samples—without the need for karyotyping.