The ‘big data’ revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define ‘successful reuse’ as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.

Background: Plants undergo programmed chromatin changes in response to environment, influencing heritable phenotypic plasticity. The RNA-directed DNA methylation (RdDM) pathway is an essential component of this reprogramming process. The relationship of epigenomic changes to gene networks on a genome-wide basis has been elusive, particularly for intragenic DNA methylation repatterning. Results: Epigenomic reprogramming is tractable to detailed study and cross-species modeling in the MSH1 system, where perturbation of the plant-specific gene MSH1 triggers at least four distinct nongenetic states to impact plant stress response and growth vigor. Within this system, we have defined RdDM target loci toward decoding phenotype-relevant methylome data. We analyze intragenic methylome repatterning associated with phenotype transitions, identifying state-specific cytosine methylation changes in pivotal growth-versus-stress, chromatin remodeling, and RNA spliceosome gene networks that encompass 871 genes. Over 77% of these genes, and 81% of their central network hubs, are functionally confirmed as RdDM targets based on analysis of mutant datasets and sRNA cluster associations. These dcl2/dcl3/dcl4-sensitive gene methylation sites, many present as singular cytosines, reside within identifiable sequence motifs. These data reflect intragenic methylation repatterning that is targeted and amenable to prediction. Conclusions: A prevailing assumption that biologically relevant DNA methylation variation occurs predominantly in density-defined differentially methylated regions overlooks behavioral features of intragenic, single-site cytosine methylation variation. RdDM-dependent methylation changes within identifiable sequence motifs reveal gene hubs within networks discriminating stress response and growth vigor epigenetic phenotypes. This study uncovers components of a methylome “code” for de novo intragenic methylation repatterning during plant phenotype transitions.

The scientific community has long benefited from the opportunities provided by data reuse. Recognizing the need to identify the challenges and bottlenecks to reuse in the agricultural research community and propose solutions for them, the data reuse working group was started within the AgBioData consortium framework. Here, we identify the limitations of data standards, metadata deficiencies, data interoperability, data ownership, data availability, user skill level, resource availability, and equity issues, with a specific focus on agricultural genomics research. We propose possible solutions stakeholders could implement to mitigate and overcome these challenges and provide an optimistic perspective on the future of genomics and transcriptomics data reuse.

Cytosine methylation is an epigenetic mark that participates in regulation of gene expression and chromatin stability in plants. Advancements in whole genome sequencing technologies have enabled investigation of methylome dynamics under different conditions. However, the computational methods for analyzing bisulfite sequence data have not been unified. Contention remains in the correlation of differentially methylated positions with the investigated treatment and exclusion of noise, inherent to these stochastic datasets. The prevalent approaches apply Fisher’s exact test, logistic, or beta regression, followed by an arbitrary cut-off for differences in methylation levels. A different strategy, the MethylIT pipeline, utilizes signal detection to determine cut-off based on a fitted generalized gamma probability distribution of methylation divergence. Re-analysis of publicly available BS-seq data from two epigenetic studies in Arabidopsis and applying MethylIT revealed additional, previously unreported results. Methylome repatterning in response to phosphate starvation was confirmed to be tissue-specific and included phosphate assimilation genes in addition to sulfate metabolism genes not implicated in the original study. During seed germination plants undergo major methylome reprogramming and use of MethylIT allowed us to identify stage-specific gene networks. We surmise from these comparative studies that robust methylome experiments must account for data stochasticity to achieve meaningful functional analyses.

The scientific community has long benefited from the opportunities provided by data reuse. Recognizing the need to identify the challenges and bottlenecks to reuse in the agricultural research community and propose solutions for them, the data reuse working group was started within the AgBioData consortium (https://www.agbiodata.org/) framework. Here, we identify the limitations of data standards, metadata deficiencies, data interoperability, data ownership, data availability, user skill level, resource availability, and equity issues, with a specific focus on agricultural genomics research. We propose possible solutions stakeholders could implement to mitigate and overcome these challenges and provide an optimistic perspective on the future of genomics and transcriptomics data reuse.

The role of intragenic cytosine methylation in shaping phenotypes has been contentious. Recent studies show association between stress and alternative splicing of transcripts, but without functional genome-wide or single-position analysis. We utilized the msh1 experimental system in Arabidopsis as a model of reproducible epigenetic states with stress-responsive phenotypes, including commitment to heritable memory for at least seven generations. We mapped the methylome to single-cytosine resolution with signal-detection, verified by machine learning. Differentially methylated genes were overlapped with msh1 -derived transcript isoforms to show that different patterns of exonic methylation led to different levels of isoform expression. Alternatively spliced and differentially methylated genes were enriched in key regulators of growth and development and spliceosome components. Genes targeted for differential methylation also contained a known CTT motif. These results demonstrate a direct relationship in plants between environmentally responsive differential methylation and alternative splicing behavior leading to phenotype changes.