Tximeta: Reference sequence checksums for provenance identification in RNA-seq

• Published in: PLoS computational biology Vol. 16; no. 2; p. e1007664
• Main Authors: Love, Michael I., Soneson, Charlotte, Hickey, Peter F., Johnson, Lisa K., Pierce, N. Tessa, Shepherd, Lori, Morgan, Martin, Patro, Rob
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.02.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Annotations; Automation; Bioinformatics; Biology and Life Sciences; Computational Biology - methods; Computer and Information Sciences; Computer applications; Cost analysis; Data analysis; Drosophila melanogaster; Engineering and Technology; Gene expression; Gene Expression Profiling; Genes; Genomes; Genomics; Humans; Identification; Metadata; Mice; Models, Statistical; Nucleotide sequence; Pattern Recognition, Automated; Programming Languages; Reproducibility; Reproducibility of Results; Research and Analysis Methods; Ribonucleic acid; RNA; RNA-Seq; Software; Transcription; Transcriptome; Transcriptomics; North Carolina; New York; United States > US; Victoria Australia; California; Australia; Switzerland
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007664

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. We provide 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. The tximeta package is available at https://bioconductor.org/packages/tximeta.