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

Abstract

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 .

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

“…First, it will be important to continue to build better models for predicting missing metadata and correcting mistakes in metadata (Ellis et al, 2018). Second, it will be important to enable users with more detailed knowledge of the datasets to create their own collections of related datasets, possibly with their own hand-curated metadata (Razmara et al, 2019), and allow the sharing of such hand-curated collections with the wider community. Third, since metadata can sometimes be an unreliable way to find relevant datasets, it will be important to design methods that search for related datasets based on their contents rather than their metadata, e.g.…”

FINER: enhancing the prediction of tissue-specific functions of isoforms by refining isoform interaction networks

“…Isoform expression profiles : To collect expression profiles of isoforms, we first retrieved RNA-seq experiments for different types of normal human tissues from the NCBI Sequence Read Archive (SRA) database ( 40 ), where corresponding accession numbers were obtained from the Human Protein Atlas (HPA) database ( 41 ) and the recount-brain project ( 42 ) (see Supplementary Table S1 for a list of RNA-seq experiments). Next, we applied the tool Kallisto ( 43 ) to obtain quantified isoform expression profiles in each experiment (measured in Transcripts Per Million or TPM).…”

Metadata retrieval from sequence databases with ffq

“…The extraordinary large volume of user-generated sequencing data available in public databases is increasingly being utilized in research projects alongside novel experiments (Simon et al ., 2018; Razmara et al ., 2019; Lung et al ., 2020; Rajesh et al ., 2021; Hippen and Greene, 2021; Wartmann et al ., 2021; Kasmanas et al ., 2021; Huang et al ., 2021; Klie et al ., 2021; Booeshaghi et al ., 2022). Collation of metadata is crucial for such reuse of publicly available data since it can provide information about the samples assayed and can facilitate the acquisition of raw data.…”