Zhi-xiang Lu

rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

Significance Alternative splicing (AS) is an important mechanism of eukaryotic gene regulation. Deep RNA sequencing (RNA-Seq) has become a powerful approach for quantitative profiling of AS. With the increasing capacity of high-throughput sequencers, it has become common for RNA-Seq studies of AS to examine multiple biological replicates. We developed rMATS, a new statistical method for robust and flexible detection of differential AS from replicate RNA-Seq data. Besides the analysis of unpaired replicates, rMATS includes a model specifically designed for paired replicates, such as case–control matched pairs in clinical RNA-Seq datasets. We expect rMATS will be useful for genome-wide studies of AS in diverse research projects. Our data also provide new insights about the experimental design for RNA-Seq studies of AS. Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.

MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data

Ultra-deep RNA sequencing has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We develop MATS (multivariate analysis of transcript splicing), a Bayesian statistical framework for flexible hypothesis testing of differential alternative splicing patterns on RNA-Seq data. MATS uses a multivariate uniform prior to model the between-sample correlation in exon splicing patterns, and a Markov chain Monte Carlo (MCMC) method coupled with a simulation-based adaptive sampling procedure to calculate the P-value and false discovery rate (FDR) of differential alternative splicing. Importantly, the MATS approach is applicable to almost any type of null hypotheses of interest, providing the flexibility to identify differential alternative splicing events that match a given user-defined pattern. We evaluated the performance of MATS using simulated and real RNA-Seq data sets. In the RNA-Seq analysis of alternative splicing events regulated by the epithelial-specific splicing factor ESRP1, we obtained a high RT–PCR validation rate of 86% for differential exon skipping events with a MATS FDR of <10%. Additionally, over the full list of RT–PCR tested exons, the MATS FDR estimates matched well with the experimental validation rate. Our results demonstrate that MATS is an effective and flexible approach for detecting differential alternative splicing from RNA-Seq data.

Transcriptome landscape of the human placenta

BackgroundThe placenta is a key component in understanding the physiological processes involved in pregnancy. Characterizing genes critical for placental function can serve as a basis for identifying mechanisms underlying both normal and pathologic pregnancies. Detailing the placental tissue transcriptome could provide a valuable resource for genomic studies related to placental disease.ResultsWe have conducted a deep RNA sequencing (RNA-Seq) study on three tissue components (amnion, chorion, and decidua) of 5 human placentas from normal term pregnancies. We compared the placental RNA-Seq data to that of 16 other human tissues and observed a wide spectrum of transcriptome differences both between placenta and other human tissues and between distinct compartments of the placenta. Exon-level analysis of the RNA-Seq data revealed a large number of exons with differential splicing activities between placenta and other tissues, and 79% (27 out of 34) of the events selected for RT-PCR test were validated. The master splicing regulator ESRP1 is expressed at a proportionately higher level in amnion compared to all other analyzed human tissues, and there is a significant enrichment of ESRP1-regulated exons with tissue-specific splicing activities in amnion. This suggests an important role of alternative splicing in regulating gene function and activity in specific placental compartments. Importantly, genes with differential expression or splicing in the placenta are significantly enriched for genes implicated in placental abnormalities and preterm birth. In addition, we identified 604-1007 novel transcripts and 494-585 novel exons expressed in each of the three placental compartments.ConclusionsOur data demonstrate unique aspects of gene expression and splicing in placental tissues that provide a basis for disease investigation related to disruption of these mechanisms. These data are publicly available providing the community with a rich resource for placental physiology and disease-related studies.

GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data.

To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.

Genetic variation of pre-mRNA alternative splicing in human populations.

The precise splicing outcome of a transcribed gene is controlled by complex interactions between cis regulatory splicing signals and trans‐acting regulators. In higher eukaryotes, alternative splicing is a prevalent mechanism for generating transcriptome and proteome diversity. Alternative splicing can modulate gene function, affect organismal phenotype and cause disease. Common genetic variation that affects splicing regulation can lead to differences in alternative splicing between human individuals and consequently impact expression level or protein function. In several well‐documented examples, such natural variation of alternative splicing has indeed been shown to influence disease susceptibility and drug response. With new microarray and sequencing‐based genomic technologies that can analyze eukaryotic transcriptomes at the exon or nucleotide level, it has become possible to globally compare the alternative splicing profiles across human individuals in any tissue or cell type of interest. Recent large‐scale transcriptome studies using high‐density splicing‐sensitive microarray and deep RNA sequencing (RNA‐Seq) have revealed widespread genetic variation of alternative splicing in humans. In the future, an extensive catalog of alternative splicing variation in human populations will help elucidate the molecular underpinnings of complex traits and human diseases, and shed light on the mechanisms of splicing regulation in human cells. WIREs RNA 2012, 3:581–592. doi: 10.1002/wrna.120

Transcriptome-wide landscape of pre-mRNA alternative splicing associated with metastatic colonization.

Metastatic colonization is an ominous feature of cancer progression. Recent studies have established the importance of pre-mRNA alternative splicing (AS) in cancer biology. However, little is known about the transcriptome-wide landscape of AS associated with metastatic colonization. Both in vitro and in vivo models of metastatic colonization were utilized to study AS regulation associated with cancer metastasis. Transcriptome profiling of prostate cancer cells and derivatives crossing in vitro or in vivo barriers of metastasis revealed splicing factors with significant gene expression changes associated with metastatic colonization. These include splicing factors known to be differentially regulated in epithelial–mesenchymal transition (ESRP1, ESRP2, and RBFOX2), a cellular process critical for cancer metastasis, as well as novel findings (NOVA1 and MBNL3). Finally, RNA-seq indicated a large network of AS events regulated by multiple splicing factors with altered gene expression or protein activity. These AS events are enriched for pathways important for cell motility and signaling, and affect key regulators of the invasive phenotype such as CD44 and GRHL1. Implications: Transcriptome-wide remodeling of AS is an integral regulatory process underlying metastatic colonization, and AS events affect the metastatic behavior of cancer cells. Mol Cancer Res; 13(2); 305–18. ©2014 AACR.

The contribution of Alu exons to the human proteome

BackgroundAlu elements are major contributors to lineage-specific new exons in primate and human genomes. Recent studies indicate that some Alu exons have high transcript inclusion levels or tissue-specific splicing profiles, and may play important regulatory roles in modulating mRNA degradation or translational efficiency. However, the contribution of Alu exons to the human proteome remains unclear and controversial. The prevailing view is that exons derived from young repetitive elements, such as Alu elements, are restricted to regulatory functions and have not had adequate evolutionary time to be incorporated into stable, functional proteins.ResultsWe adopt a proteotranscriptomics approach to systematically assess the contribution of Alu exons to the human proteome. Using RNA sequencing, ribosome profiling, and proteomics data from human tissues and cell lines, we provide evidence for the translational activities of Alu exons and the presence of Alu exon derived peptides in human proteins. These Alu exon peptides represent species-specific protein differences between primates and other mammals, and in certain instances between humans and closely related primates. In the case of the RNA editing enzyme ADARB1, which contains an Alu exon peptide in its catalytic domain, RNA sequencing analyses of A-to-I editing demonstrate that both the Alu exon skipping and inclusion isoforms encode active enzymes. The Alu exon derived peptide may fine tune the overall editing activity and, in limited cases, the site selectivity of ADARB1 protein products.ConclusionsOur data indicate that Alu elements have contributed to the acquisition of novel protein sequences during primate and human evolution.

Discover hidden splicing variations by mapping personal transcriptomes to personal genomes

RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify ‘hidden’ splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.

Concerted effects of heterogeneous nuclear ribonucleoprotein C1/C2 to control vitamin D-directed gene transcription and RNA splicing in human bone cells

Traditionally recognized as an RNA splicing regulator, heterogeneous nuclear ribonucleoprotein C1/C2 (hnRNPC1/C2) can also bind to double-stranded DNA and function in trans as a vitamin D response element (VDRE)-binding protein. As such, hnRNPC1/C2 may couple transcription induced by the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)2D) with subsequent RNA splicing. In MG63 osteoblastic cells, increased expression of the 1,25(OH)2D target gene CYP24A1 involved immunoprecipitation of hnRNPC1/C2 with CYP24A1 chromatin and RNA. Knockdown of hnRNPC1/C2 suppressed expression of CYP24A1, but also increased expression of an exon 10-skipped CYP24A1 splice variant; in a minigene model the latter was attenuated by a functional VDRE in the CYP24A1 promoter. In genome-wide analyses, knockdown of hnRNPC1/C2 resulted in 3500 differentially expressed genes and 2232 differentially spliced genes, with significant commonality between groups. 1,25(OH)2D induced 324 differentially expressed genes, with 187 also observed following hnRNPC1/C2 knockdown, and a further 168 unique to hnRNPC1/C2 knockdown. However, 1,25(OH)2D induced only 10 differentially spliced genes, with no overlap with differentially expressed genes. These data indicate that hnRNPC1/C2 binds to both DNA and RNA and influences both gene expression and RNA splicing, but these actions do not appear to be linked through 1,25(OH)2D-mediated induction of transcription.