Raising orphans from a metadata morass: A researcher's guide to re-use of public ’omics data

Abstract

More than 15 petabases of raw RNAseq data is now accessible through public repositories. Acquisition of other 'omics data types is expanding, though most lack a centralized archival repository. Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments. We illustrate how predictive research can be accelerated by meta-analysis with a study of orphan (species-specific) genes. Computational predictions are critical to infer orphan function because their coding sequences provide very few clues. The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata. This metadata morass significantly diminishes the insight that can be extracted from these data. We provide tips for data submitters and users, including specific recommendations to improve metadata quality by more use of controlled vocabulary and by metadata reviews. Finally, we advocate for a unified, straightforward metadata submission and retrieval system.

Foster thy young: Enhanced prediction of orphan genes in assembled genomes

“…Using RNA-Seq data with high representation of orphan gene expression dramatically improves the efficacy of gene prediction by MAKER, by the Direct Inference pipeline, and by the BIND and MIND combination pipelines. Thus, the accuracy of an initial genome annotation will be maximized by selecting RNA-Seq data from diverse samples, including samples that typically express high levels of young genes, e.g., reproductive tissues and stressed tissues 10,14,54,[72][73][74][75][76][77][78]83 . Additionally, reannotation 70,84,85 outcomes can be optimized by utilizing evidence from diverse samples in the expanded body of public RNA-Seq datasets.…”

“…The "orphan-rich" dataset is designed to maximize orphan gene representation. We reasoned that selecting samples which contain a breadth of orphan gene transcripts could be important because many, though by no means all 10 , orphans are highly expressed under only a very limited set of conditions 10,14,54,[72][73][74][75][76][77][78] . The "orphan-rich" RNA-Seq samples were chosen from over a thousand samples in SRA; over 60% of all Araport11-annotated orphans are transcribed in each sample.…”

“…Eukaryotic and prokaryotic genomes contain genes ("orphan genes") whose proteins are recognizable only in a single species; some of these have emerged de novo from the genome, while others have diverged so quickly as to be unrecognizable by homology [1][2][3][4][5][6][7][8][9] . Of the billions of extant orphan genes in eukaryotic species 10 , the precise function of only a few is understood 2, 10-12, 12-19, 19-25 . However, orphans appear to play a crucial role in adaptation to new biological niches.…”

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

“…For example, a researcher might wish to add fields for references, dates, notes, or keywords. A caveat is that leveraging archived metadata is only as good as the metadata provided; metadata about the samples may be incomplete or misleading, and the quality varies from study to study (7,36). Despite this, metadata is key to understanding the significance of the experiments (7,36).…”

“…Integrative analysis of data from the multiple studies representing diverse biological conditions is key to fully exploit these vast data resources for scientific discovery (5,6). Such analysis allows efficient reuse and recycling of these available data and its metadata (1,5,7,8). Higher statistical power * To whom correspondence should be addressed.…”