Brian A. Williams

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 105 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.

Landscape of transcription in human cells

Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell’s regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene.

Dynamic DNA methylation across diverse human cell lines and tissues

As studies of DNA methylation increase in scope, it has become evident that methylation has a complex relationship with gene expression, plays an important role in defining cell types, and is disrupted in many diseases. We describe large-scale single-base resolution DNA methylation profiling on a diverse collection of 82 human cell lines and tissues using reduced representation bisulfite sequencing (RRBS). Analysis integrating RNA-seq and ChIP-seq data illuminates the functional role of this dynamic mark. Loci that are hypermethylated across cancer types are enriched for sites bound by NANOG in embryonic stem cells, which supports and expands the model of a stem/progenitor cell signature in cancer. CpGs that are hypomethylated across cancer types are concentrated in megabase-scale domains that occur near the telomeres and centromeres of chromosomes, are depleted of genes, and are enriched for cancer-specific EZH2 binding and H3K27me3 (repressive chromatin). In noncancer samples, there are cell-type specific methylation signatures preserved in primary cell lines and tissues as well as methylation differences induced by cell culture. The relationship between methylation and expression is context-dependent, and we find that CpG-rich enhancers bound by EP300 in the bodies of expressed genes are unmethylated despite the dense gene-body methylation surrounding them. Non-CpG cytosine methylation occurs in human somatic tissue, is particularly prevalent in brain tissue, and is reproducible across many individuals. This study provides an atlas of DNA methylation across diverse and well-characterized samples and enables new discoveries about DNA methylation and its role in gene regulation and disease.

An encyclopedia of mouse DNA elements (Mouse ENCODE)

To complement the human Encyclopedia of DNA Elements (ENCODE) project and to enable a broad range of mouse genomics efforts, the Mouse ENCODE Consortium is applying the same experimental pipelines developed for human ENCODE to annotate the mouse genome.

From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Single-cell RNA-seq mammalian transcriptome studies are at an early stage in uncovering cell-to-cell variation in gene expression, transcript processing and editing, and regulatory module activity. Despite great progress recently, substantial challenges remain, including discriminating biological variation from technical noise. Here we apply the SMART-seq single-cell RNA-seq protocol to study the reference lymphoblastoid cell line GM12878. By using spike-in quantification standards, we estimate the absolute number of RNA molecules per cell for each gene and find significant variation in total mRNA content: between 50,000 and 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/split design and find that there are significant differences in expression between individual cells, over and above technical variation. Specific gene coexpression modules were preferentially expressed in subsets of individual cells, including one enriched for mRNA processing and splicing factors. We assess cell-to-cell variation in alternative splicing and allelic bias and report evidence of significant differences in splice site usage that exceed splice variation in the pool/split comparison. Finally, we show that transcriptomes from small pools of 30-100 cells approach the information content and reproducibility of contemporary RNA-seq from large amounts of input material. Together, our results define an experimental and computational path forward for analyzing gene expression in rare cell types and cell states.

Characterization of the human ESC transcriptome by hybrid sequencing

Significance Isoform identification and discovery are an important goal for transcriptome analysis because the majority of human genes express multiple isoforms with context- and tissue-specific functions. Better annotation of isoforms will also benefit downstream analysis such as expression quantification. Current RNA-Seq methods based on short-read sequencing are not reliable for isoform discovery. In this study we developed a new method based on the combined analysis of short reads and long reads generated, respectively, by second- and third-generation sequencing and applied this method to obtain a comprehensive characterization of the transcriptome of the human embryonic stem cell. The results showed that large gain in sensitivity and specificity can be achieved with this strategy. Although transcriptional and posttranscriptional events are detected in RNA-Seq data from second-generation sequencing, full-length mRNA isoforms are not captured. On the other hand, third-generation sequencing, which yields much longer reads, has current limitations of lower raw accuracy and throughput. Here, we combine second-generation sequencing and third-generation sequencing with a custom-designed method for isoform identification and quantification to generate a high-confidence isoform dataset for human embryonic stem cells (hESCs). We report 8,084 RefSeq-annotated isoforms detected as full-length and an additional 5,459 isoforms predicted through statistical inference. Over one-third of these are novel isoforms, including 273 RNAs from gene loci that have not previously been identified. Further characterization of the novel loci indicates that a subset is expressed in pluripotent cells but not in diverse fetal and adult tissues; moreover, their reduced expression perturbs the network of pluripotency-associated genes. Results suggest that gene identification, even in well-characterized human cell lines and tissues, is likely far from complete.

The genome and developmental transcriptome of the strongylid nematode Haemonchus contortus

BackgroundThe barbers pole worm, Haemonchus contortus, is one of the most economically important parasites of small ruminants worldwide. Although this parasite can be controlled using anthelmintic drugs, resistance against most drugs in common use has become a widespread problem. We provide a draft of the genome and the transcriptomes of all key developmental stages of H. contortus to support biological and biotechnological research areas of this and related parasites.ResultsThe draft genome of H. contortus is 320 Mb in size and encodes 23,610 protein-coding genes. On a fundamental level, we elucidate transcriptional alterations taking place throughout the life cycle, characterize the parasites gene silencing machinery, and explore molecules involved in development, reproduction, host-parasite interactions, immunity, and disease. The secretome of H. contortus is particularly rich in peptidases linked to blood-feeding activity and interactions with host tissues, and a diverse array of molecules is involved in complex immune responses. On an applied level, we predict drug targets and identify vaccine molecules.ConclusionsThe draft genome and developmental transcriptome of H. contortus provide a major resource to the scientific community for a wide range of genomic, genetic, proteomic, metabolomic, evolutionary, biological, ecological, and epidemiological investigations, and a solid foundation for biotechnological outcomes, including new anthelmintics, vaccines and diagnostic tests. This first draft genome of any strongylid nematode paves the way for a rapid acceleration in our understanding of a wide range of socioeconomically important parasites of one of the largest nematode orders.

Effects of sequence variation on differential allelic transcription factor occupancy and gene expression

A complex interplay between transcription factors (TFs) and the genome regulates transcription. However, connecting variation in genome sequence with variation in TF binding and gene expression is challenging due to environmental differences between individuals and cell types. To address this problem, we measured genome-wide differential allelic occupancy of 24 TFs and EP300 in a human lymphoblastoid cell line GM12878. Overall, 5% of human TF binding sites have an allelic imbalance in occupancy. At many sites, TFs clustered in TF-binding hubs on the same homolog in especially open chromatin. While genetic variation in core TF binding motifs generally resulted in large allelic differences in TF occupancy, most allelic differences in occupancy were subtle and associated with disruption of weak or noncanonical motifs. We also measured genome-wide differential allelic expression of genes with and without heterozygous exonic variants in the same cells. We found that genes with differential allelic expression were overall less expressed both in GM12878 cells and in unrelated human cell lines. Comparing TF occupancy with expression, we found strong association between allelic occupancy and expression within 100 bp of transcription start sites (TSSs), and weak association up to 100 kb from TSSs. Sites of differential allelic occupancy were significantly enriched for variants associated with disease, particularly autoimmune disease, suggesting that allelic differences in TF occupancy give functional insights into intergenic variants associated with disease. Our results have the potential to increase the power and interpretability of association studies by targeting functional intergenic variants in addition to protein coding sequences.

Significance and statistical errors in the analysis of DNA microarray data

DNA microarrays are important devices for high throughput measurements of gene expression, but no rational foundation has been established for understanding the sources of within-chip statistical error. We designed a specialized chip and protocol to investigate the distribution and magnitude of within-chip errors and discovered that, as expected from theoretical expectations, measurement errors follow a Lorentzian-like distribution, which explains the widely observed but unexplained ill-reproducibility in microarray data. Using this specially designed chip, we examined a data set of repeated measurements to extract estimates of the distribution and magnitude of statistical errors in DNA microarray measurements. Using the common “ratio of medians” method, we find that the measurements follow a Lorentzian-like distribution, which is problematic for subsequent analysis. We show that a method of analysis dubbed ”median of ratios“ yields a more Gaussian-like distribution of errors. Finally, we show that the bootstrap algorithm can be used to extract the best estimates of the error in the measurement. Quantifying the statistical error in such measurements has important applications for estimating significance levels, clustering algorithms, and process optimization.