recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

Collado‐Torres, Leonardo; Nellore, Abhinav; Jaffe, Andrew E.

doi:10.12688/f1000research.12223.1

Cited by 51 publications

(45 citation statements)

References 28 publications

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“…Interactive display). Once a study of interest has been identified, the user can download the gene expression data from recount2 (Collado-Torres et al, 2017c) and append the recount-brain metadata as shown in Figure 2 ; this process is equivalent to appending custom metadata from Figure 2 of the recount workflow (Collado-Torres et al, 2017a) . Once the expression data from recount2 and the sample metadata from recount-brain have been combined, the user can proceed to perform analyses such as identification of differentially expressed genes and enriched gene ontologies, examples of them are illustrated in Figure 2 with data from SRA study SRP027383 (Bao et al, 2014) .…”

Section: Resultsmentioning

confidence: 99%

“…Because the SRA is made up of individual submissions, data are not provided in a consistent format and annotations (such as methodology and technical sequencing details) are often unclear or missing (Bernstein et al, 2017) . We previously developed recount2 (Collado-Torres et al, 2017c, 2017a , a public resource with over 70,000 uniformly processed human RNA-seq samples, enabling comparisons across studies for human expression data in the public repository. However, inconsistent phenotype annotation still remains a barrier to taking advantage of public uniformly processed reads.…”

Section: Introductionmentioning

confidence: 99%

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recount-brain: a curated repository of human brain RNA-seq datasets metadata

Razmara

Ellis

Sokolowski

et al. 2019

Preprint

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The usability of publicly-available gene expression data is often limited by the availability of high-quality, standardized biological phenotype and experimental condition information ("metadata"). We released the recount2 project, which involved re-processing ~70,000 samples in the Sequencing Read Archive (SRA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) projects. While samples from the latter two projects are well-characterized with extensive metadata, the ~50,000 RNA-seq samples from SRA in recount2 are inconsistently annotated with metadata. Tissue type, sex, and library type can be 1 estimated from the RNA sequencing (RNA-seq) data itself. However, more detailed and harder to predict metadata, like age and diagnosis, must ideally be provided by labs that deposit the data.To facilitate more analyses within human brain tissue data, we have complemented phenotype predictions by manually constructing a uniformly-curated database of public RNA-seq samples present in SRA and recount2 . We describe the reproducible curation process for constructing recount-brain that involves systematic review of the primary manuscript, which can serve as a guide to annotate other studies and tissues. We further expanded recount-brain by merging it with GTEx and TCGA brain samples as well as linking to controlled vocabulary terms for tissue, Brodmann area and disease. Furthermore, we illustrate how to integrate the sample metadata in recount-brain with the gene expression data in recount2 to perform differential expression analysis. We then provide three analysis examples involving modeling postmortem interval, glioblastoma, and meta-analyses across GTEx and TCGA. Overall, recount-brain facilitates expression analyses and improves their reproducibility as individual researchers do not have to manually curate the sample metadata. recount-brain is available via the add_metadata() function from the recount Bioconductor package at bioconductor.org/packages/recount .

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

recount-brain: a curated repository of human brain RNA-seq datasets metadata

Razmara

Ellis

Sokolowski

et al. 2019

Preprint

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“…Previously called exon-exon junction data including phenotype table, bed and coverage files for both TCGA and GTEx v6 were downloaded from the recount2 service at https://jhubiostatistics.shinyapps.io/recount/ (Collado-Torres et al, 2017). The metaSRA (Bernstein et al, 2017) web query form at http://metasra.biostat.wisc.edu/ was queried for experiment accession numbers for 1) non-cancer cell and tissue type samples (see Table S1 for cancer-matched samples and Table S3 for non-cancer samples, and "Selection of SRA tissue and cell types" for a description of how these samples were chosen) and 2) TCGAmatched cancer types (see Table S1).…”

Section: Methods Details Data Downloadmentioning

confidence: 99%

Putatively cancer-specific alternative splicing is shared across patients and present in developmental and other non-cancer cells

David

Maden

Weeder

et al. 2019

Preprint

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“…One of the best examples of this approach is DEE2 -Digital Expression Explorer 2 (DEE2) [3] -a repository of uniformly processed RNA-seq data obtained from NCBI Short Read Archive. There are also other examples: ARCHS4 the massive collection of uniformly processed murine and human public transcriptomic datasets [4], recount2 [5] etc. However, the most important task in transcriptomic data harmonization is the correction of batch effects and in general it remains unresolved.…”

Section: Introductionmentioning

confidence: 99%

Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis

Russkikh¹,

Антонец

Shtokalo

et al. 2019

Preprint

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The transcriptomic data is being frequently used in the research of biomarker genes of different diseases and biological states. Generally, researchers have data from hundreds, rarely thousands of specimens at hand. In most cases, the proposed candidate biomarker genes and corresponding decision rules fail in prospective research studies, especially for diseases with complex polygenic background. The naive addition of training data usually also does not improve performance due to batch effects, resulting from various discrepancies between different datasets. To get a better understanding of factors underlying the observed gene expression data variation, we applied a style transfer technique. The most of style transfer studies are focused on image data, and, to our knowledge, this is the first attempt to adapt this procedure to gene expression domain. As a style component, there might be used either technical factors of data variance, such as sequencing platform, RNA extraction protocols, or any biological details about the samples which we would like to control (gender, biological state, treatment etc.). The proposed solution is based on Variational Autoencoder artificial neural network. To disentangle the style components, we trained the encoder with discriminator in an adversarial manner. This approach can be useful for both data harmonization and data augmentation -for obtaining semisynthetic samples when the real data is scarce. We demonstrated the applicability of our framework using single cell RNA-Seq data from Mouse Cell Atlas, where we were able to transfer the mammary gland biological state (virgin, pregnancy and involution state)

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recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

Cited by 51 publications

References 28 publications

recount-brain: a curated repository of human brain RNA-seq datasets metadata

recount-brain: a curated repository of human brain RNA-seq datasets metadata

Putatively cancer-specific alternative splicing is shared across patients and present in developmental and other non-cancer cells

Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis

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