Computational prediction of polycomb-associated long non-coding RNAs. PLoS One. 2012;7:e44878. [PMID: 23028655 PMCID: PMC3441527 DOI: 10.1371/journal.pone.0044878]

The pleiotropic roles of circular and long noncoding RNAs in cutaneous melanoma

Cutaneous melanoma (CM) is a very aggressive disease, often characterized by unresponsiveness to conventional therapies and high mortality rates worldwide. The identification of the activating BRAFV600 mutations in approximately 50% of CM patients has recently fueled the development of novel small-molecule inhibitors that specifically target BRAFV600 -mutant CM. In addition, a major progress in CM treatment has been made by monoclonal antibodies that regulate the immune checkpoint inhibitors. However, although target-based therapies and immunotherapeutic strategies have yielded promising results, CM treatment remains a major challenge. In the last decade, accumulating evidence points to the aberrant expression of different types of noncoding RNAs (ncRNAs) in CM. While studies on microRNAs have grown exponentially leading to significant insights on CM biology, the role of circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) in this tumor is less understood, and much remains to be discovered. Here, we summarize and critically review the available evidence on the molecular functions of circRNAs and lncRNAs in BRAFV600 -mutant CM and CM immunogenicity, providing recent updates on their functional role in targeted therapy and immunotherapy resistance. In addition, we also include an evaluation of several algorithms and databases for prediction and validation of circRNA and lncRNA functional interactions.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.

A Review on Recent Computational Methods for Predicting Noncoding RNAs

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and k-mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction.

The PRC2-binding long non-coding RNAs in human and mouse genomes are associated with predictive sequence features

Recently, long non-coding RNAs (lncRNAs) have emerged as an important class of molecules involved in many cellular processes. One of their primary functions is to shape epigenetic landscape through interactions with chromatin modifying proteins. However, mechanisms contributing to the specificity of such interactions remain poorly understood. Here we took the human and mouse lncRNAs that were experimentally determined to have physical interactions with Polycomb repressive complex 2 (PRC2), and systematically investigated the sequence features of these lncRNAs by developing a new computational pipeline for sequences composition analysis, in which each sequence is considered as a series of transitions between adjacent nucleotides. Through that, PRC2-binding lncRNAs were found to be associated with a set of distinctive and evolutionarily conserved sequence features, which can be utilized to distinguish them from the others with considerable accuracy. We further identified fragments of PRC2-binding lncRNAs that are enriched with these sequence features, and found they show strong PRC2-binding signals and are more highly conserved across species than the other parts, implying their functional importance.

Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs

Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies.

Non-coding yet non-trivial: a review on the computational genomics of lincRNAs

Long intergenic non-coding RNAs (lincRNAs) represent one of the most mysterious RNA species encoded by the human genome. Thanks to next generation sequencing (NGS) technology and its applications, we have recently witnessed a surge in non-coding RNA research, including lincRNA research. Here, we summarize the recent advancement in genomics studies of lincRNAs. We review the emerging characteristics of lincRNAs, the experimental and computational approaches to identify lincRNAs, their known mechanisms of regulation, the computational methods and resources for lincRNA functional predictions, and discuss the challenges to understanding lincRNA comprehensively.

The non-coding transcriptome as a dynamic regulator of cancer metastasis

Since the discovery of microRNAs, non-coding RNAs (NC-RNAs) have increasingly attracted the attention of cancer investigators. Two classes of NC-RNAs are emerging as putative metastasis-related genes: long non-coding RNAs (lncRNAs) and small nucleolar RNAs (snoRNAs). LncRNAs orchestrate metastatic progression through several mechanisms, including the interaction with epigenetic effectors, splicing control and generation of microRNA-like molecules. In contrast, snoRNAs have been long considered “housekeeping” genes with no relevant function in cancer. However, recent evidence challenges this assumption, indicating that some snoRNAs are deregulated in cancer cells and may play a specific role in metastasis. Interestingly, snoRNAs and lncRNAs share several mechanisms of action, and might synergize with protein-coding genes to generate a specific cellular phenotype. This evidence suggests that the current paradigm of metastatic progression is incomplete. We propose that NC-RNAs are organized in complex interactive networks which orchestrate cellular phenotypic plasticity. Since plasticity is critical for cancer cell metastasis, we suggest that a molecular interactome composed by both NC-RNAs and proteins orchestrates cancer metastasis. Interestingly, expression of lncRNAs and snoRNAs can be detected in biological fluids, making them potentially useful biomarkers. NC-RNA expression profiles in human neoplasms have been associated with patients’ prognosis. SnoRNA and lncRNA silencing in pre-clinical models leads to cancer cell death and/or metastasis prevention, suggesting they can be investigated as novel therapeutic targets. Based on the literature to date, we critically discuss how the NC-RNA interactome can be explored and manipulated to generate more effective diagnostic, prognostic, and therapeutic strategies for metastatic neoplasms.

Transposable Element Insertions in Long Intergenic Non-Coding RNA Genes

Transposable elements (TEs) are abundant in mammalian genomes and appear to have contributed to the evolution of their hosts by providing novel regulatory or coding sequences. We analyzed different regions of long intergenic non-coding RNA (lincRNA) genes in human and mouse genomes to systematically assess the potential contribution of TEs to the evolution of the structure and regulation of expression of lincRNA genes. Introns of lincRNA genes contain the highest percentage of TE-derived sequences (TES), followed by exons and then promoter regions although the density of TEs is not significantly different between exons and promoters. Higher frequencies of ancient TEs in promoters and exons compared to introns implies that many lincRNA genes emerged before the split of primates and rodents. The content of TES in lincRNA genes is substantially higher than that in protein-coding genes, especially in exons and promoter regions. A significant positive correlation was detected between the content of TEs and evolutionary rate of lincRNAs indicating that inserted TEs are preferentially fixed in fast-evolving lincRNA genes. These results are consistent with the repeat insertion domains of LncRNAs hypothesis under which TEs have substantially contributed to the origin, evolution, and, in particular, fast functional diversification, of lincRNA genes.

Databases for lncRNAs: a comparative evaluation of emerging tools

The vast majority of the human transcriptome does not code for proteins. Advances in transcriptome arrays and deep sequencing are giving rise to a fast accumulation of large data sets, particularly of long noncoding RNAs (lncRNAs). Although it is clear that individual lncRNAs may play important and diverse biological roles, there is a large gap between the number of existing lncRNAs and their known relation to molecular/cellular function. This and related information have recently been gathered in several databases dedicated to lncRNA research. Here, we review the content of general and more specialized databases on lncRNAs. We evaluate these resources in terms of the quality of annotations, the reporting of validated or predicted molecular associations, and their integration with other resources and computational analysis tools. We illustrate our findings using known and novel cancer-related lncRNAs. Finally, we discuss limitations and highlight potential future directions for these databases to help delineating functions associated with lncRNAs.

Role of lncRNAs in health and disease-size and shape matter

Most of the mammalian genome including a large fraction of the non-protein coding transcripts has been shown to be transcribed. Studies related to these non-coding RNA molecules have predominantly focused on smaller molecules like microRNAs. In contrast, long non-coding RNAs (lncRNAs) have long been considered to be transcriptional noise. Accumulating evidence suggests that lncRNAs are involved in key cellular and developmental processes. Several critical questions regarding functions and properties of lncRNAs and their circular forms remain to be answered. Increasing evidence from high-throughput sequencing screens also suggests the involvement of lncRNAs in diseases such as cancer, although the underlying mechanisms still need to be elucidated. Here, we discuss the current state of research in the field of lncRNAs, questions that need to be addressed in light of recent genome-wide studies documenting the landscape of lncRNAs, their functional roles and involvement in diseases. We posit that with the availability of high-throughput data sets it is not only possible to improve methods for predicting lncRNAs but will also facilitate our ability to elucidate their functions and phenotypes by using integrative approaches.

Role of non-coding RNAs in pancreatic cancer: The bane of the microworld

Our understanding of the mechanisms underlying the development of pancreatic cancer has been greatly advanced. However, the molecular events involved in the initiation and development of pancreatic cancer remain inscrutable. None of the present medical technologies have been proven to be effective in significantly improving early detection or reducing the mortality/morbidity of this disease. Thus, a better understanding of the molecular basis of pancreatic cancer is required for the identification of more effective diagnostic markers and therapeutic targets. Non-coding RNAs (ncRNAs), generally including microRNAs and long non-coding RNAs, have recently been found to be deregulated in many human cancers, which provides new opportunities for identifying both functional drivers and specific biomarkers of pancreatic cancer. In this article, we review the existing literature in the field documenting the significance of aberrantly expressed and functional ncRNAs in human pancreatic cancer, and discuss how oncogenic ncRNAs may be involved in the genetic and epigenetic networks regulating functional pathways that are deregulated in this malignancy, particularly of the ncRNAs’ role in drug resistance and epithelial-mesenchymal transition biological phenotype, with the aim of analyzing the feasibility of clinical application of ncRNAs in the diagnosis and treatment of pancreatic cancer.

The RIDL hypothesis: transposable elements as functional domains of long noncoding RNAs

Our genome contains tens of thousands of long noncoding RNAs (lncRNAs), many of which are likely to have genetic regulatory functions. It has been proposed that lncRNA are organized into combinations of discrete functional domains, but the nature of these and their identification remain elusive. One class of sequence elements that is enriched in lncRNA is represented by transposable elements (TEs), repetitive mobile genetic sequences that have contributed widely to genome evolution through a process termed exaptation. Here, we link these two concepts by proposing that exonic TEs act as RNA domains that are essential for lncRNA function. We term such elements Repeat Insertion Domains of LncRNAs (RIDLs). A growing number of RIDLs have been experimentally defined, where TE-derived fragments of lncRNA act as RNA-, DNA-, and protein-binding domains. We propose that these reflect a more general phenomenon of exaptation during lncRNA evolution, where inserted TE sequences are repurposed as recognition sites for both protein and nucleic acids. We discuss a series of genomic screens that may be used in the future to systematically discover RIDLs. The RIDL hypothesis has the potential to explain how functional evolution can keep pace with the rapid gene evolution observed in lncRNA. More practically, TE maps may in the future be used to predict lncRNA function.

Biological and bioinformatical approaches to study crosstalk of long-non-coding RNAs and chromatin-modifying proteins

Long-non-coding RNA (lncRNA) regulates gene expression through transcriptional and epigenetic regulation as well as alternative splicing in the nucleus. In addition, regulation is achieved at the levels of mRNA translation, storage and degradation in the cytoplasm. During recent years, several studies have described the interaction of lncRNAs with enzymes that confer so-called epigenetic modifications, such as DNA methylation, histone modifications and chromatin structure or remodelling. LncRNA interaction with chromatin-modifying enzymes (CME) is an emerging field that confers another layer of complexity in transcriptional regulation. Given that CME-lncRNA interactions have been identified in many biological processes, ranging from development to disease, comprehensive understanding of underlying mechanisms is important to inspire basic and translational research in the future. In this review, we highlight recent findings to extend our understanding about the functional interdependencies between lncRNAs and CMEs that activate or repress gene expression. We focus on recent highlights of molecular and functional roles for CME-lncRNAs and provide an interdisciplinary overview of recent technical and methodological developments that have improved biological and bioinformatical approaches for detection and functional studies of CME-lncRNA interaction.

Computational approaches in detecting non- coding RNA

The important role of non coding RNAs (ncRNAs) in the cell has made their identification a critical issue in the biological research. However, traditional approaches such as PT-PCR and Northern Blot are costly. With recent progress in bioinformatics and computational prediction technology, the discovery of ncRNAs has become realistically possible. This paper aims to introduce major computational approaches in the identification of ncRNAs, including homologous search, de novo prediction and mining in deep sequencing data. Furthermore, related software tools have been compared and reviewed along with a discussion on future improvements.

Predicting long non-coding RNAs using RNA sequencing

The advent of next-generation sequencing, and in particular RNA-sequencing (RNA-seq), technologies has expanded our knowledge of the transcriptional capacity of human and other animal, genomes. In particular, recent RNA-seq studies have revealed that transcription is widespread across the mammalian genome, resulting in a large increase in the number of putative transcripts from both within, and intervening between, known protein-coding genes. Long transcripts that appear to lack protein-coding potential (long non-coding RNAs, lncRNAs) have been the focus of much recent research, in part owing to observations of their cell-type and developmental time-point restricted expression patterns. A variety of sequencing protocols are currently available for identifying lncRNAs including RNA polymerase II occupancy, chromatin state maps and - the focus of this review - deep RNA sequencing. In addition, there are numerous analytical methods available for mapping reads and assembling transcript models that predict the presence and structure of lncRNAs from RNA-seq data. Here we review current methods for identifying lncRNAs using large-scale sequencing data from RNA-seq experiments and highlight analytical considerations that are required when undertaking such projects.

Meeting the methodological challenges in molecular mapping of the embryonic epigenome

The past decade of life sciences research has been driven by progress in genomics. Many voices are already proclaiming the post-genomics era, in which phenomena other than sequence polymorphism influence gene expression and also explain complex phenotypes. One of these burgeoning fields is the study of the epigenome. Although the mechanisms by which chromatin structure and reorganization as well as cytosine methylation influence gene expression are not fully understood, they are being invoked to explain the now-accepted long-term impact of the environment on gene expression, which appears to be a factor in the development of numerous diseases. Such studies are particularly relevant in early embryonic development, during which waves of epigenetic reprogramming are known to have profound impacts. Since gametes and zygotes are in the process of resetting the genome in order to create embryonic stem cells that will each differentiate to create one of many specific tissue types, this phase of life is now viewed as a window of susceptibility to epigenetic reprogramming errors. Epigenetics could explain the influence of factors such as the nutritional/metabolic status of the mother or the artificial environment of assisted reproductive technologies. However, the peculiar nature of early embryos in addition to their scarcity poses numerous technological challenges that are slowly being overcome. The principal subject of this article is to review the suitability of various current and emerging technological platforms to study oocytes and early embryonic epigenome with more emphasis on studying DNA methylation. Furthermore, the constraint of samples size, inherent to the study of preimplantation embryo development, was put in perspective with the various molecular platforms described.

ANRIL: molecular mechanisms and implications in human health

ANRIL is a recently discovered long non-coding RNA encoded in the chromosome 9p21 region. This locus is a hotspot for disease-associated polymorphisms, and it has been consistently associated with cardiovascular disease, and more recently with several cancers, diabetes, glaucoma, endometriosis among other conditions. ANRIL has been shown to regulate its neighbor tumor suppressors CDKN2A/B by epigenetic mechanisms and thereby regulate cell proliferation and senescence. However, the clear role of ANRIL in the pathogenesis of these conditions is yet to be understood. Here, we review the recent findings on ANRIL molecular characterization and function, with a particular focus on its implications in human disease.