An update on recent methods applied for deciphering the diversity of the noncoding RNA genome structure and function

Network-Based Methods and Other Approaches for Predicting lncRNA Functions and Disease Associations

The discovery that a considerable portion of eukaryotic genomes is transcribed and gives rise to long noncoding RNAs (lncRNAs) provides an important new perspective on the transcriptome and raises questions about the centrality of these lncRNAs in gene-regulatory processes and diseases. The rapidly increasing number of mechanistically investigated lncRNAs has provided evidence for distinct functional classes, such as enhancer-like lncRNAs, which modulate gene expression via chromatin looping, and noncoding competing endogenous RNAs (ceRNAs), which act as microRNA decoys. Despite great progress in the last years, the majority of lncRNAs are functionally uncharacterized and their implication for disease biogenesis and progression is unknown. Here, we summarize recent developments in lncRNA function prediction in general and lncRNA-disease associations in particular, with emphasis on in silico methods based on network analysis and on ceRNA function prediction. We believe that such computational techniques provide a valuable aid to prioritize functional lncRNAs or disease-relevant lncRNAs for targeted, experimental follow-up studies.

Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

Background

Although several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them have unknown function. Recent evidence has shown the importance of both lncRNAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of ’guilt-by-association’, no strategy exists so far which identifies mRNA-lncRNA functional modules based on the 3D chromatin interaction graph.

Results

To better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides in the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs.

Conclusions

Interestingly, we find that lncRNA network hubs tend to be significantly enriched in evolutionary conserved lncRNAs and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we mine putative regulatory functions for uncharacterized lncRNAs.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2900-8) contains supplementary material, which is available to authorized users.

A critical regulator of Bcl2 revealed by systematic transcript discovery of lncRNAs associated with T-cell differentiation

Normal T-cell differentiation requires a complex regulatory network which supports a series of maturation steps, including lineage commitment, T-cell receptor (TCR) gene rearrangement, and thymic positive and negative selection. However, the underlying molecular mechanisms are difficult to assess due to limited T-cell models. Here we explore the use of the pro-T-cell line P5424 to study early T-cell differentiation. Stimulation of P5424 cells by the calcium ionophore ionomycin together with PMA resulted in gene regulation of T-cell differentiation and activation markers, partially mimicking the CD4^-CD8^- double negative (DN) to double positive (DP) transition and some aspects of subsequent T-cell maturation and activation. Global analysis of gene expression, along with kinetic experiments, revealed a significant association between the dynamic expression of coding genes and neighbor lncRNAs including many newly-discovered transcripts, thus suggesting potential co-regulation. CRISPR/Cas9-mediated genetic deletion of Robnr, an inducible lncRNA located downstream of the anti-apoptotic gene Bcl2, demonstrated a critical role of the Robnr locus in the induction of Bcl2. Thus, the pro-T-cell line P5424 is a powerful model system to characterize regulatory networks involved in early T-cell differentiation and maturation.

TARDIS, a targeted RNA directional sequencing method for rare RNA discovery

High-throughput transcriptional analysis has unveiled a myriad of novel RNAs. However, technical constraints in RNA sequencing library preparation and platform performance hamper the identification of rare transcripts contained within the RNA repertoire. Herein we present targeted-RNA directional sequencing (TARDIS), a hybridization-based method that allows subsets of RNAs contained within the transcriptome to be interrogated independently of transcript length, function, the presence or absence of poly-A tracts, or the mechanism of biogenesis. TARDIS is a modular protocol that is subdivided into four main phases, including the generation of random DNA traps covering the region of interest, purification of input RNA material, DNA trap-based RNA capture, and finally RNA-sequencing library construction. Importantly, coupling RNA capture to strand-specific RNA sequencing enables robust identification and reconstruction of novel transcripts, the definition of sense and antisense RNA pairs and, by the concomitant analysis of long and natural small RNA pools, it allows the user to infer potential precursor-product relations. TARDIS takes ∼10 d to implement.

IVT-seq reveals extreme bias in RNA sequencing

BACKGROUND

RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.

RESULTS

We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.

CONCLUSIONS

These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.