Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets.
A team led by researchers at the University of North Carolina-Chapel Hill have developed a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility.
Flowchart of Salmon quantification followed by tximeta
The quantification and import pipeline results in a SummarizedExperiment object with reference transcript provenance metadata added by tximeta. The Summarized Experiment object contains estimated counts and other relevant metadata, and can be used with downstream statistical packages.
Availability -The tximeta package is available at https://bioconductor.org/packages/tximeta.