A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

The central goal of human genetics is to understand the inherited basis of human variation in phenotypes, elucidating human physiology, evolution and disease. Rare mutations have been found underlying two thousand mendelian diseases; more recently, it has become possible to assess systematically the contribution of common SNPs to complex disease. The known role of copy-number alterations in sporadic genomic disorders, combined with emerging information about inherited copy-number variation, indicate the importance of systematically assessing copy-number variants (CNVs), including common copy-number polymorphisms (CNPs), in disease. Here we discuss evidence that CNVs affect phenotypes, directions for basic knowledge to support clinical study of CNVs, the challenge of genotyping CNPs in clinical cohorts, the use of SNPs as markers for CNPs and statistical challenges in testing CNVs for association with disease. Critical needs are high-resolution maps of common CNPs and techniques that accurately determine the allelic state of affected individuals.

Copy number variation in association studies of human disease

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

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RNA Sequencing and Analysis

Advances in genome sequencing have progressed at a rapid pace, with increased throughput accompanied by plunging costs. But these advances go far beyond faster and cheaper. High-throughput sequencing technologies are now routinely being applied to a wide range of important topics in biology and medicine, often allowing researchers to address important biological questions that were not possible before. In this review, we discuss these innovative new approaches—including ever finer analyses of transcriptome dynamics, genome structure and genomic variation—and provide an overview of the new insights into complex biological systems catalyzed by these technologies. We also assess the impact of genotyping, genome sequencing and personal omics profiling on medical applications, including diagnosis and disease monitoring. Finally, we review recent developments in single-cell sequencing, and conclude with a discussion of possible future advances and obstacles for sequencing in biology and health.

https://www.embopress.org/doi/pdf/10.1038/msb.2012.61

High‐throughput sequencing for biology and medicine

Triple-negative breast cancer (TNBC) is characterized by the absence of expression of estrogen receptor, progesterone receptor, and HER-2. Thirty percent of patients recur after first-line treatment, and metastatic TNBC (mTNBC) has a poor prognosis with median survival of one year. Here, we present initial analyses of whole genome and transcriptome sequencing data from 14 prospective mTNBC. We have cataloged the collection of somatic genomic alterations in these advanced tumors, particularly those that may inform targeted therapies. Genes mutated in multiple tumors included TP53, LRP1B, HERC1, CDH5, RB1, and NF1. Notable genes involved in focal structural events were CTNNA1, PTEN, FBXW7, BRCA2, WT1, FGFR1, KRAS, HRAS, ARAF, BRAF, and PGCP. Homozygous deletion of CTNNA1 was detected in 2 of 6 African Americans. RNA sequencing revealed consistent overexpression of the FOXM1 gene when tumor gene expression was compared with nonmalignant breast samples. Using an outlier analysis of gene expression comparing one cancer with all the others, we detected expression patterns unique to each patient9s tumor. Integrative DNA/RNA analysis provided evidence for deregulation of mutated genes, including the monoallelic expression of TP53 mutations. Finally, molecular alterations in several cancers supported targeted therapeutic intervention on clinical trials with known inhibitors, particularly for alterations in the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways. In conclusion, whole genome and transcriptome profiling of mTNBC have provided insights into somatic events occurring in this difficult to treat cancer. These genomic data have guided patients to investigational treatment trials and provide hypotheses for future trials in this irremediable cancer. Mol Cancer Ther; 12(1); 104–16. ©2012 AACR.

https://mct.aacrjournals.org/content/molcanther/12/1/104.full.pdf

Genome and Transcriptome Sequencing in Prospective Metastatic Triple-Negative Breast Cancer Uncovers Therapeutic Vulnerabilities

Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor.

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Adam J. Broomer

Papers

Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations.