Current RNA-seq methodology reporting limits reproducibility

Simoneau, Joël; Dumontier, Simon; Gosselin, Ryan; Scott, Michelle S.

doi:10.1093/bib/bbz124

Cited by 74 publications

(57 citation statements)

References 25 publications

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“…Lack of reproducibility for large dataset analysis is a chronic issue (Łabaj & Kreil, 2016;Simoneau et al, 2019). While some programs are commonly used for differential expression analysis (edgeR, DESeq2, limma), we felt justified to include a T test method, as it increased transparency and showed that a bulk of the DEGs identified by the two commonly used programs were supported by simple T tests.…”

Section: Rna-seq Time-series Experimentsmentioning

confidence: 99%

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Brusslan

2020

Plant Direct

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In plants, the vegetative to reproductive phase transition (termed bolting in Arabidopsis) generally precedes age‐dependent leaf senescence (LS). Many studies describe a temporal link between bolting time and LS, as plants that bolt early, senesce early, and plants that bolt late, senesce late. The molecular mechanisms underlying this relationship are unknown and are potentially agriculturally important, as they may allow for the development of crops that can overcome early LS caused by stress‐related early‐phase transition. We hypothesized that leaf gene expression changes occurring in synchrony with bolting were regulating LS. ARABIDOPSIS TRITHORAX (ATX) enzymes are general methyltransferases that regulate the adult vegetative to reproductive phase transition. We generated an atx1, atx3, and atx4 (atx1,3,4) triple T‐DNA insertion mutant that displays both early bolting and early LS. This mutant was used in an RNA‐seq time‐series experiment to identify gene expression changes in rosette leaves that are likely associated with bolting. By comparing the early bolting mutant to vegetative WT plants of the same age, we were able to generate a list of differentially expressed genes (DEGs) that change expression with bolting as the plants age. We trimmed the list by intersection with publicly available WT datasets, which removed genes from our DEG list that were atx1,3,4 specific. The resulting 398 bolting‐associated genes (BAGs) are differentially expressed in a mature rosette leaf at bolting. The BAG list contains many well‐characterized LS regulators (ORE1, WRKY45, NAP, WRKY28), and GO analysis revealed enrichment for LS and LS‐related processes. These bolting‐associated LS regulators may contribute to the temporal coupling of bolting time to LS.

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Section: Rna-seq Time-series Experimentsmentioning

confidence: 99%

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Brusslan

2020

Plant Direct

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“…While the format of this study does not let us provide direct recommendations regarding what methodological choices to prefer, we can highlight choices that we would not recommend. While Tophat2 is still one of the primary aligner software in use in the literature 17 , we cannot recommend its usage due to its bias towards genes with processed pseudogenes. Furthermore, HISAT2 is a reimplementation of TopHat2, correcting some issues that have become apparent through the years.…”

Section: Discussionmentioning

confidence: 99%

“…We performed RNA-seq using only methods that rely on genome-based alignment, and software that are restricted to a single methodological step. We kept default parameters for most of the options, as to mimic what is being done in the literature 17 while Ensembl 98 uses GRCh38.p13 genome. Because the primary assembly of both these reference genome versions is the same, and because we restricted our studied genes to the primary assembly, only GRCh38.p13 was used.…”

Section: Rna-seq Methodsologymentioning

confidence: 99%

Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures

Simoneau

Gosselin

Scott

2020

Preprint

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RNA-seq is a modular experimental and computational approach that aims in identifying and quantifying RNA molecules. The modularity of the RNA-seq technology enables adaptation of the protocol to develop new ways to explore RNA biology, but this modularity also brings forth the importance of methodological thoroughness. Liberty of approach comes with the responsibility of choices, and such choices must be informed. Here, we present an approach that identifies gene group specific quantification biases in currently used RNA-seq software and references by processing sequenced datasets using a wide variety of RNA-seq computational pipelined, and by decomposing these expression datasets using an independent component analysis matrix factorisation method. By exploring the RNA-seq pipeline using a systemic approach, we highlight the yet inadequately characterized central importance of genome annotations in quantification results. We also show that the different choices in RNA-seq methodology are not independent, through interactions between genome annotations and quantification software. Genes were mainly found to be affected by differences in their sequence, by overlapping genes and genes with similar sequence. Our approach offers an explanation for the observed biases by identifying the common features used differently by the software and references, therefore providing leads for the betterment of RNA-seq methodology.

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“…Likewise, Simoneau and Scott [8] described information on genome assembly and annotation as "essential" for describing the computational analysis of RNA-seq data, and contended that, "no study using RNA-seq should be published without these methodological details." Simoneau and co-authors have recently performed a detailed analysis of hundreds of published RNA-seq studies, finding that the majority did not include annotation source and release information, thus hindering reproducible analysis [9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Current RNA-seq methodology reporting limits reproducibility

Cited by 74 publications

References 25 publications

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures

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

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