Abstract
BackgroundLong non-coding RNAs (lncRNAs) exhibit remarkable cell-type specificity and disease association. LncRNA’s functional versatility includes epigenetic modification, nuclear domain organization, transcriptional control, regulation of RNA splicing and translation, and modulation of protein activity. However, most lncRNAs remain uncharacterized due to a shortage of predictive tools available to guide functional experiments.ResultsTo address this gap for lymphoma-associated lncRNAs identified in our studies, we developed a new computational method, Predicting LncRNA Activity through Integrative Data-driven ‘Omics and Heuristics (PLAIDOH), which has several unique features not found in other methods. PLAIDOH integrates transcriptome, subcellular localization, enhancer landscape, genome architecture, chromatin interaction, and RNA-binding (eCLIP) data and generates statistically defined output scores. PLAIDOH’s approach identifies and ranks functional connections between individual lncRNA, coding gene, and protein pairs using enhancer, transcript cis-regulatory, and RNA-binding protein interactome scores that predict the relative likelihood of these different lncRNA functions. When applied to ‘omics datasets that we collected from lymphoma patients, or to publicly available cancer (TCGA) or ENCODE datasets, PLAIDOH identified and prioritized well-known lncRNA-target gene regulatory pairs (e.g., HOTAIR and HOX genes, PVT1 and MYC), validated hits in multiple lncRNA-targeted CRISPR screens, and lncRNA-protein binding partners (e.g., NEAT1 and NONO). Importantly, PLAIDOH also identified novel putative functional interactions, including one lymphoma-associated lncRNA based on analysis of data from our human lymphoma study. We validated PLAIDOH’s predictions for this lncRNA using knock-down and knock-out experiments in lymphoma cell models.ConclusionsOur study demonstrates that we have developed a new method for the prediction and ranking of functional connections between individual lncRNA, coding gene, and protein pairs, which were validated by genetic experiments and comparison to published CRISPR screens. PLAIDOH expedites validation and follow-on mechanistic studies of lncRNAs in any biological system. It is available at https://github.com/sarahpyfrom/PLAIDOH.
Highlights
Long non-coding RNAs exhibit remarkable cell-type specificity and disease association
In pursuit of novel gene regulatory mechanisms that are altered in human Non-Hodgkin Lymphoma (NHL) [21], we identified hundreds of annotated and novel Long Non-coding RNA (lncRNA) that are significantly altered in primary NHL compared to normal control B cell samples sorted from tonsils and peripheral blood
Similar to previous reports [1, 38], we found that putative novel single exon lncRNA transcripts are abundant, representing 74% (191,762 of 259,429 unique transcripts detected) (Additional file 1: Figure S1A)
Summary
Long non-coding RNAs (lncRNAs) exhibit remarkable cell-type specificity and disease association. Long non-coding RNAs exhibit remarkable cell type specificity and disease association, yet the vast majority remain uncharacterized. Transcribed pervasively across the genome, they are defined by only two characteristics: length greater than 200 bp and lacking protein coding potential [1]. LncRNAs exhibit much less conservation between species compared to coding genes and structurefunction relationships have yet to be defined [3]. A major obstacle to functional discovery is the paucity of established rules and algorithms for functional prediction, and the lack of sequence conservation or homology across species further limits the potential for in silico screening. Pyfrom et al BMC Genomics (2019) 20:137 or prioritization for experimental studies [4] For these reasons, the study of lncRNAs remains challenging
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