recount3: summaries and queries for large-scale RNA-seq expression and splicing

Wilks, Christopher; Chen, Feng Yong; Charles, Rone; Solomon, Brad; Ling, Jonathan P.; Imada, Eddie Luidy; Zhang, David; Joseph, Lance; Leek, Jeffrey T.; Jaffe, Andrew E.; Nellore, Abhinav; Collado‐Torres, Leonardo; Hansen, Kasper D.; Langmead, Ben

doi:10.1101/2021.05.21.445138

Cited by 44 publications

(66 citation statements)

References 60 publications

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“…Alzheimer's disease, for example, was signi cantly associated with LV21 (FDR < 1e-18) and with LV5 (FDR < 0.01) (Supplementary Tables 31 and 33). LV21 was strongly expressed in a variety of soft tissue sarcomas, monocytes/macrophages (including microglia from cortex samples), and aortic valves (Supplementary Figure 30); as discussed previously, macrophages play a key role in the reverse cholesterol transport and thus atherogenesis [70]. LV5 was expressed in breast cancer and brain glioma samples, microglia (cortex), liver, and kidney, among other cell types (Supplementary Figure 31).…”

Section: Projections Reveal Trait Clusters With Shared Transcriptomic Propertiesmentioning

confidence: 61%

“…Also, the underlying factorization method rests on linear combinations of variables, which could miss important and more complex co-expression patterns. In addition, recount2, the training dataset used, has since been surpassed in size and scale by other resources [15,83]. The second approach we used in this study is TWAS, where we are only considering the hypothesis that GWAS loci affect traits via changes in gene expression, and other effects such as coding variants disrupting protein-protein interactions are not captured.…”

Section: Discussionmentioning

confidence: 99%

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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Pividori

et al. 2021

Preprint

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Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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Section: Projections Reveal Trait Clusters With Shared Transcriptomic Propertiesmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Pividori

et al. 2021

Preprint

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“…However, rapid adoption of full-length RNA sequencing (RNA-Seq) over the past decade has led to the public archival of datasets obtained from various cell types across multiple species. Furthermore, recent computational methods have been developed to comprehensively analyze patterns of alternative splicing across hundreds of thousands of publicly archived RNA-Seq datasets [27][28][29] . We have used these databases to identify many cell type-specific alternative exons that are suitable for use in AAV vectors.…”

Section: Introductionmentioning

confidence: 99%

Cell-specific regulation of gene expression using splicing-dependent frameshifting

Ling

Bygrave

Santiago

et al. 2022

Preprint

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Precise and reliable gene delivery remains technically challenging. Here we report a splicing-based approach for controlling gene expression whereby separate translational reading frames are coupled to the inclusion or exclusion of cell-specific alternative exons. Candidate exons are identified by analyzing thousands of publicly available RNA-Seq datasets, and filtered by cell specificity, conservation, and local intron length. This method, which we denote splicing-linked expression design (SLED), can be combined in a Boolean manner with existing techniques such as minipromoters and viral capsids. SLED vectors can leverage the strong expression of constitutive promoters, without sacrificing precision, by decoupling the tradeoff between promoter strength and selectivity. We generated SLED vectors to selectively target all neurons, photoreceptors, or excitatory neurons, and demonstrated that specificity was retained in vivo when delivered using AAV vectors. We further demonstrated the utility of SLED by creating what would otherwise be unobtainable research tools, specifically a GluA2 flip/flop reporter and a dual excitatory/inhibitory neuronal calcium indicator. Finally, we show that SLED vectors can rescue photoreceptor degeneration in Prph2rds/rds mice, and selectively induce apoptosis in SF3B1 mutant cancer cells with an oncolytic vector. The flexibility of SLED technology enables new avenues for basic and translational research.

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“…Efforts have been made to simplify the access to public RNA-seq data by creating unified resources and databases. For example, the latest iteration of the recount database (recount3) uniformly processed >750 000 RNA-seq samples in humans and mice, enabling secondary analyses of RNA-seq datasets across different studies ( 5 ). RNA-seq data in the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) have also been uniformly processed to provide normalized gene expression data ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

MetazExp: a database for gene expression and alternative splicing profiles and their analyses based on 53 615 public RNA-seq samples in 72 metazoan species

Liu

Yin

Lang

et al. 2021

Nucleic Acids Research

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RNA-seq has been widely used in experimental studies and produced a massive amount of data deposited in public databases. New biological insights can be obtained by retrospective analyses of previously published data. However, the barrier to efficiently utilize these data remains high, especially for those who lack bioinformatics skills and computational resources. We present MetazExp (https://bioinfo.njau.edu.cn/metazExp), a database for gene expression and alternative splicing profiles based on 53 615 uniformly processed publicly available RNA-seq samples from 72 metazoan species. The gene expression and alternative splicing profiles can be conveniently queried by gene IDs, symbols, functional terms and sequence similarity. Users can flexibly customize experimental groups to perform differential and specific expression and alternative splicing analyses. A suite of data visualization tools and comprehensive links with external databases allow users to efficiently explore the results and gain insights. In conclusion, MetazExp is a valuable resource for the research community to efficiently utilize the vast public RNA-seq datasets.

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recount3: summaries and queries for large-scale RNA-seq expression and splicing

Cited by 44 publications

References 60 publications

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Cell-specific regulation of gene expression using splicing-dependent frameshifting

MetazExp: a database for gene expression and alternative splicing profiles and their analyses based on 53 615 public RNA-seq samples in 72 metazoan species

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