Structured RNAs in the ENCODE selected regions of the human genome

Comparative RNA Genomics

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

Tailored machine learning models for functional RNA detection in genome-wide screens

The in silico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

Thirteen dubious ways to detect conserved structural RNAs

Covariation induced by compensatory base substitutions in RNA alignments is a great way to deduce conserved RNA structure, in principle. In practice, success depends on many factors, importantly the quality and depth of the alignment and the choice of covariation statistic. Measuring covariation between pairs of aligned positions is easy. However, using covariation to infer evolutionarily conserved RNA structure is complicated by other extraneous sources of covariation such as that resulting from homologous sequences having evolved from a common ancestor. In order to provide evidence of evolutionarily conserved RNA structure, a method to distinguish covariation due to sources other than RNA structure is necessary. Moreover, there are several sorts of artifactually generated covariation signals that can further confound the analysis. Additionally, some covariation signal is difficult to detect due to incomplete comparative data. Here, we investigate and critically discuss the practice of inferring conserved RNA structure by comparative sequence analysis. We provide new methods on how to approach and decide which of the numerous long non-coding RNAs (lncRNAs) have biologically relevant structures.

Androgen Receptor Blockade Using Enzalutamide Suppresses Long Non-Coding RNA ARLNC1 in Prostate Cancer Cells

Prostate cancer (PCa) is a common malignant disease with high mortality rates that develops and progresses in an androgen-dependent way. In recent years, RNA sequencing enabled identification of many PCa-related long noncoding RNAs including androgen receptor-regulated long non-coding RNA 1 (ARLNC1) and prostate cancer-associated transcript 1 (PCAT1). In the present study, our goal was to illuminate expression changes of ARLNC1 and PCAT1 in the context of androgen stimulation or androgen receptor (AR) blockade with respect to AR expression status. In this experimental study, LNCaP cells and higher AR-expressing LNCaP-AR++ cells were used as cell models. Cells were treated with dihydrotestosterone (DHT) as an androgen stimulator and/or enzalutamide as an AR inhibitor. Cell viability was assessed using annexin V and propidium iodide (PI) staining in flow cytometry. Androgen stimulation prompted baseline ARLNC1 levels by 53.5-fold in the LNCaP cells (P=0.01) and by 25-fold in the LNCAP-AR+ cells (P=0.18). AR inhibition by enzalutamide reduced baseline ARLNC1 in LNCaP-AR++ cells by 2-fold (P=0.01), but to a lesser extent in LNCaP cells. Co-treatment of cells with DHT and enzalutamide led to a remarkable decrease in the DHT effect on ARLNC1 expression. No specific effect of androgen stimulation or AR blockade on PCAT1 expression was detected. Our results revealed that the extent of induction of ARLNC1 by androgen is modulated by receptor expression status. In addition, we determined that AR blockade, via enzalutamide, effectively suppresses ARLNC1 both at baseline and after induction by DHT.

A peptide encoded by pri-miRNA-31 represses autoimmunity by promoting Treg differentiation

Recent evidence has revealed that small polypeptides (containing fewer than 100 amino acids) can be translated from noncoding RNAs (ncRNAs), which are usually defined as RNA molecules that do not encode proteins. However, studies on functional products translated from primary transcripts of microRNA (pri-miRNA) are quite limited. Here, we describe a peptide termed miPEP31 that is encoded by pri-miRNA-31. miPEP31 is highly expressed in Foxp3⁺ regulatory T cells (T_regs ) and significantly promotes the differentiation of T_regs without affecting their inhibitory ability. Our results show that miPEP31 is a cell-penetrating peptide both in vitro and in vivo. miPEP31 downregulates miR-31 expression, enhances peripheral T_reg induction, and dramatically suppresses experimental autoimmune encephalomyelitis. Mechanistically, we show that miPEP31 acts as a transcriptional repressor inhibiting the expression of miRNA-31, a negative regulator of T_regs . Our results reveal an indispensable role of miPEP31 in maintaining immune homeostasis by promoting T_reg differentiation and also present a potential therapeutic peptide for modulating miRNA expression and treating autoimmune diseases.

Long Noncoding RNAs and Mitochondrial Homeostasis in the Development of Diabetic Retinopathy

Retinopathy is one of the most devastating complications of diabetes, which a patient fears the most. Hyperglycemic environment results in many structural, functional, molecular and biochemical abnormalities in the retina, and overproduction of mitochondrial superoxide, induced by hyperglycemic milieu, is considered to play a central role in the development of diabetic retinopathy. Expression of many genes associated with maintaining mitochondrial homeostasis is also altered. Recent research has shown that several long noncoding RNAs, RNAs with more than 200 nucleotides but without any reading frames, are aberrantly expressed in diabetes, and altered expression of these long noncoding RNAs is now being implicated in the development of diabetes and its complications including retinopathy. This review focuses the role of long noncoding RNAs in the development of diabetic retinopathy, with a special emphasis on the maintenance of mitochondrial homeostasis.

Exploring Evidence of Non-coding RNA Translation With Trips-Viz and GWIPS-Viz Browsers

Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.

Evolutionary conservation of RNA sequence and structure

An RNA structure prediction from a single‐sequence RNA folding program is not evidence for an RNA whose structure is important for function. Random sequences have plausible and complex predicted structures not easily distinguishable from those of structural RNAs. How to tell when an RNA has a conserved structure is a question that requires looking at the evolutionary signature left by the conserved RNA. This question is important not just for long noncoding RNAs which usually lack an identified function, but also for RNA binding protein motifs which can be single stranded RNAs or structures. Here we review recent advances using sequence and structural analysis to determine when RNA structure is conserved or not. Although covariation measures assess structural RNA conservation, one must distinguish covariation due to RNA structure from covariation due to independent phylogenetic substitutions. We review a statistical test to measure false positives expected under the null hypothesis of phylogenetic covariation alone (specificity). We also review a complementary test that measures power, that is, expected covariation derived from sequence variation alone (sensitivity). Power in the absence of covariation signals the absence of a conserved RNA structure. We analyze artifacts that falsely identify conserved RNA structure such as the misuse of programs that do not assess significance, the use of inappropriate statistics confounded by signals other than covariation, or misalignments that induce spurious covariation. Among artifacts that obscure the signal of a conserved RNA structure, we discuss the inclusion of pseudogenes in alignments which increase power but destroy covariation.

This article is categorized under:

RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry

RNA Evolution and Genomics > Computational Analyses of RNA

RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution

The implication of long non-coding RNAs in the diagnosis, pathogenesis and drug resistance of pancreatic ductal adenocarcinoma and their possible therapeutic potential

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies with the lowest median and overall survival rate among all human malignancies. The major problems with the PDAC are the late diagnosis, metastasis, and acquired resistance to chemotherapeutic agents in the clinic. Over the last decade, the long non-coding RNAs (lncRNAs) have been discovered and occupies a significantly large proportion of the human genome. Recent studies have proved that lncRNAs can play a crucial role in the majority of key cellular processes involved in the maintenance of cellular homeostasis by regulating various molecular mechanisms. The deregulation of lncRNAs has been associated with various chronic diseases including human malignancies. Several lncRNAs have tumor-specific expression making them an ideal and excellent target for designing the novel therapeutic strategies against human malignancies. We have discussed how lncRNA expression can be used for the diagnosis and prognosis of PDAC. The current review discusses the potential role and molecular mechanism of lncRNA in regulating the prominent hallmarks of cancer including abnormal growth, survival, metastasis, and drug-resistance in PDAC. Importantly, we also highlight the possible application of various therapeutic strategies including small interfering RNA, CRISPR-Cas9, antisense oligonucleotides, locked nucleic acid Gapmers, small molecules, aptamers, lncRNA promoter to target the lncRNA as a novel and viable options for treatment of PDAC.

Mapping the RNA structural landscape of viral genomes

Functional RNA structures are prevalent in viral genomes, and have been shown to play roles in almost every aspect of their biology. However, the majority of viral RNA remains structurally uncharacterized. This is likely to remain true as the cost of sequencing decreases much faster than the cost of structural characterizations. Because of this, there is a need for rapid, inexpensive methods to highlight regions of viral RNA which are ideal candidates for structure-function analyses. The ScanFold method was developed as a single sequence alternative to traditional RNA structural motif discovery pipelines, which rely heavily on well curated sequence alignments to identify conserved RNA structures. ScanFold focuses on identifying (based on their more stable than expected folding energies) the most likely functional structures encoded within a single large RNA sequence, while allowing predicted motifs to be tested for evidence of structural conservation later. Decoupling these processes can be a benefit to researchers studying viruses lacking the ideal phylogenetic depth to yield evidence of structural conservation. Here, we demonstrate how the most significant ScanFold predicted structures correspond to higher base pairing probabilities, SHAPE reactivities, and predict known functional structures within the ZIKV and HIV-1 genomes with accuracy. Best practices and examples are also shown to aid users in utilizing ScanFold for their own systems of interest. ScanFold is available as a Webserver (https://mosslabtools.bb.iastate.edu/scanfold) or can be downloaded (https://github.com/moss-lab/ScanFold) and run locally.

Evolutionary Patterns of Non-Coding RNA in Cardiovascular Biology

Cardiovascular diseases (CVDs) affect the heart and the vascular system with a high prevalence and place a huge burden on society as well as the healthcare system. These complex diseases are often the result of multiple genetic and environmental risk factors and pose a great challenge to understanding their etiology and consequences. With the advent of next generation sequencing, many non-coding RNA transcripts, especially long non-coding RNAs (lncRNAs), have been linked to the pathogenesis of CVD. Despite increasing evidence, the proper functional characterization of most of these molecules is still lacking. The exploration of conservation of sequences across related species has been used to functionally annotate protein coding genes. In contrast, the rapid evolutionary turnover and weak sequence conservation of lncRNAs make it difficult to characterize functional homologs for these sequences. Recent studies have tried to explore other dimensions of interspecies conservation to elucidate the functional role of these novel transcripts. In this review, we summarize various methodologies adopted to explore the evolutionary conservation of cardiovascular non-coding RNAs at sequence, secondary structure, syntenic, and expression level.

Identification and characterization of novel conserved RNA structures in Drosophila

BACKGROUND

Comparative genomics approaches have facilitated the discovery of many novel non-coding and structured RNAs (ncRNAs). The increasing availability of related genomes now makes it possible to systematically search for compensatory base changes - and thus for conserved secondary structures - even in genomic regions that are poorly alignable in the primary sequence. The wealth of available transcriptome data can add valuable insight into expression and possible function for new ncRNA candidates. Earlier work identifying ncRNAs in Drosophila melanogaster made use of sequence-based alignments and employed a sliding window approach, inevitably biasing identification toward RNAs encoded in the more conserved parts of the genome.

RESULTS

To search for conserved RNA structures (CRSs) that may not be highly conserved in sequence and to assess the expression of CRSs, we conducted a genome-wide structural alignment screen of 27 insect genomes including D. melanogaster and integrated this with an extensive set of tiling array data. The structural alignment screen revealed ∼30,000 novel candidate CRSs at an estimated false discovery rate of less than 10%. With more than one quarter of all individual CRS motifs showing sequence identities below 60%, the predicted CRSs largely complement the findings of sliding window approaches applied previously. While a sixth of the CRSs were ubiquitously expressed, we found that most were expressed in specific developmental stages or cell lines. Notably, most statistically significant enrichment of CRSs were observed in pupae, mainly in exons of untranslated regions, promotors, enhancers, and long ncRNAs. Interestingly, cell lines were found to express a different set of CRSs than were found in vivo. Only a small fraction of intergenic CRSs were co-expressed with the adjacent protein coding genes, which suggests that most intergenic CRSs are independent genetic units.

CONCLUSIONS

This study provides a more comprehensive view of the ncRNA transcriptome in fly as well as evidence for differential expression of CRSs during development and in cell lines.

RNA Structure Elements Conserved between Mouse and 59 Other Vertebrates

In this work, we present a computational screen conducted for functional RNA structures, resulting in over 100,000 conserved RNA structure elements found in alignments of mouse (mm10) against 59 other vertebrates. We explicitly included masked repeat regions to explore the potential of transposable elements and low-complexity regions to give rise to regulatory RNA elements. In our analysis pipeline, we implemented a four-step procedure: (i) we screened genome-wide alignments for potential structure elements using RNAz-2, (ii) realigned and refined candidate loci with LocARNA-P, (iii) scored candidates again with RNAz-2 in structure alignment mode, and (iv) searched for additional homologous loci in mouse genome that were not covered by genome alignments. The 3'-untranslated regions (3'-UTRs) of protein-coding genes and small noncoding RNAs are enriched for structures, while coding sequences are depleted. Repeat-associated loci make up about 95% of the homologous loci identified and are, as expected, predominantly found in intronic and intergenic regions. Nevertheless, we report the structure elements enriched in specific genome elements, such as 3'-UTRs and long noncoding RNAs (lncRNAs). We provide full access to our results via a custom UCSC genome browser trackhub freely available on our website (http://rna.tbi.univie.ac.at/trackhubs/#RNAz).

Comparative RNA Genomics

Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

RNAStructuromeDB: A genome-wide database for RNA structural inference

RNA plays important roles in almost every aspect of biology, and every aspect of RNA biology is influenced by its folding. This is a particularly important consideration in the era of high-throughput sequencing, when the discovery of novel transcripts far outpaces our knowledge of their functions. To gain a comprehensive picture of biology requires a structural framework for making functional inferences on RNA. To this end we have developed the RNA Structurome Database ( https://structurome.bb.iastate.edu ), a comprehensive repository of RNA secondary structural information that spans the entire human genome. Here, we compile folding information for every base pair of the genome that may be transcribed: coding, noncoding, and intergenic regions, as well as repetitive elements, telomeres, etc. This was done by fragmenting the GRCh38 reference genome into 154,414,320 overlapping sequence fragments and, for each fragment, calculating a set of metrics based on the sequence's folding properties. These data will facilitate a wide array of investigations: e.g. discovery of structured regulatory elements in differential gene expression data or noncoding RNA discovery, as well as allow genome-scale analyses of RNA folding.

Functional annotation of structural ncRNAs within enhancer RNAs in the human genome: implications for human disease

Enhancer RNAs (eRNAs) are a novel class of non-coding RNA (ncRNA) molecules transcribed from the DNA sequences of enhancer regions. Despite extensive efforts devoted to revealing the potential functions and underlying mechanisms of eRNAs, it remains an open question whether eRNAs are mere transcriptional noise or relevant biologically functional species. Here, we identified a catalogue of eRNAs in a broad range of human cell/tissue types and extended our understanding of eRNAs by demonstrating their multi-omic signatures. Gene Ontology (GO) analysis revealed that eRNAs play key roles in human cell identity. Furthermore, we detected numerous known and novel functional RNA structures within eRNA regions. To better characterize the cis-regulatory effects of non-coding variation in these structural ncRNAs, we performed a comprehensive analysis of the genetic variants of structural ncRNAs in eRNA regions that are associated with inflammatory autoimmune diseases. Disease-associated variants of the structural ncRNAs were disproportionately enriched in immune-specific cell types. We also identified riboSNitches in lymphoid eRNAs and investigated the potential pathogenic mechanisms by which eRNAs might function in autoimmune diseases. Collectively, our findings offer valuable insights into the function of eRNAs and suggest that eRNAs might be effective diagnostic and therapeutic targets for human diseases.

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.

SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification

Background

snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and γ; and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms.

Results

Results of the training and test phases of boxes H/ACA and C/D snoRNAs, in both versions of snoReport, are discussed. Validation on real data was performed to evaluate the predictions of snoReport 2.0. Our program was applied to a set of previously annotated sequences, some of them experimentally confirmed, of humans, nematodes, drosophilids, platypus, chickens and leishmania. We significantly improved the predictions for vertebrates, since the training phase used information of these organisms, but H/ACA box snoRNAs identification was improved for the other ones.

Conclusion

We presented snoReport 2.0, to predict H/ACA box and C/D box snoRNAs, an efficient method to find true positives and avoid false positives in vertebrate organisms. H/ACA box snoRNA classifier showed an F-score of 93 % (an improvement of 10 % regarding the previous version), while C/D box snoRNA classifier, an F-Score of 94 % (improvement of 14 %). Besides, both classifiers exhibited performance measures above 90 %. These results show that snoReport 2.0 avoid false positives and false negatives, allowing to predict snoRNAs with high quality. In the validation phase, snoReport 2.0 predicted 67.43 % of vertebrate organisms for both classes. For Nematodes and Drosophilids, 69 % and 76.67 %, for H/ACA box snoRNAs were predicted, respectively, showing that snoReport 2.0 is good to identify snoRNAs in vertebrates and also H/ACA box snoRNAs in invertebrates organisms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1345-6) contains supplementary material, which is available to authorized users.

Epigenetic silencing of miR-218 by the lncRNA CCAT1, acting via BMI1, promotes an altered cell cycle transition in the malignant transformation of HBE cells induced by cigarette smoke extract

Cigarette smoking is the strongest risk factor for the development of lung cancer, the leading cause of cancer-related deaths. However, the molecular mechanisms leading to lung cancer are largely unknown. A long-noncoding RNA (lncRNA), CCAT1, regarded as cancer-associated, has been investigated extensively. Moreover, the molecular mechanisms of lncRNAs in regulation of microRNAs (miRNAs) induced by cigarette smoke remain unclear. In the present investigation, cigarette smoke extract (CSE) caused an altered cell cycle and increased CCAT1 levels and decreased miR-218 levels in human bronchial epithelial (HBE) cells. Depletion of CCAT1 attenuated the CSE-induced decreases of miR-218 levels, suggesting that miR-218 is negatively regulated by CCAT1 in HBE cells exposed to CSE. The CSE-induced increases of BMI1 levels and blocked by CCAT1 siRNA were attenuated by an miR-218 inhibitor. Moreover, in CSE-transformed HBE cells, the CSE-induced cell cycle changes and elevated neoplastic capacity were reversed by CCAT1 siRNA or BMI1 siRNA. This epigenetic silencing of miR-218 by CCAT1 induces an altered cell cycle transition through BMI1 and provides a new mechanism for CSE-induced lung carcinogenesis.

Evolutionary clues in lncRNAs

The diversity of long non-coding RNAs (lncRNAs) in the human transcriptome is in stark contrast to the sparse exploration of their functions concomitant with their conservation and evolution. The pervasive transcription of the largely non-coding human genome makes the evolutionary age and conservation patterns of lncRNAs to a topic of interest. Yet it is a fairly unexplored field and not that easy to determine as for protein-coding genes. Although there are a few experimentally studied cases, which are conserved at the sequence level, most lncRNAs exhibit weak or untraceable primary sequence conservation. Recent studies shed light on the interspecies conservation of secondary structures among lncRNA homologs by using diverse computational methods. This highlights the importance of structure on functionality of lncRNAs as opposed to the poor impact of primary sequence changes. Further clues in the evolution of lncRNAs are given by selective constraints on non-coding gene structures (e.g., promoters or splice sites) as well as the conservation of prevalent spatio-temporal expression patterns. However, a rapid evolutionary turnover is observable throughout the heterogeneous group of lncRNAs. This still gives rise to questions about its functional meaning. WIREs RNA 2017, 8:e1376. doi: 10.1002/wrna.1376 For further resources related to this article, please visit the WIREs website.

ncRNA orthologies in the vertebrate lineage

Annotation of orthologous and paralogous genes is necessary for many aspects of evolutionary analysis. Methods to infer these homology relationships have traditionally focused on protein-coding genes and evolutionary models used by these methods normally assume the positions in the protein evolve independently. However, as our appreciation for the roles of non-coding RNA genes has increased, consistently annotated sets of orthologous and paralogous ncRNA genes are increasingly needed. At the same time, methods such as PHASE or RAxML have implemented substitution models that consider pairs of sites to enable proper modelling of the loops and other features of RNA secondary structure. Here, we present a comprehensive analysis pipeline for the automatic detection of orthologues and paralogues for ncRNA genes. We focus on gene families represented in Rfam and for which a specific covariance model is provided. For each family ncRNA genes found in all Ensembl species are aligned using Infernal, and several trees are built using different substitution models. In parallel, a genomic alignment that includes the ncRNA genes and their flanking sequence regions is built with PRANK. This alignment is used to create two additional phylogenetic trees using the neighbour-joining (NJ) and maximum-likelihood (ML) methods. The trees arising from both the ncRNA and genomic alignments are merged using TreeBeST, which reconciles them with the species tree in order to identify speciation and duplication events. The final tree is used to infer the orthologues and paralogues following Fitch's definition. We also determine gene gain and loss events for each family using CAFE. All data are accessible through the Ensembl Comparative Genomics ('Compara') API, on our FTP site and are fully integrated in the Ensembl genome browser, where they can be accessed in a user-friendly manner. Database URL: http://www.ensembl.org.

PRINS, a primate-specific long non-coding RNA, plays a role in the keratinocyte stress response and psoriasis pathogenesis

In the last few years with the recent emergence of high-throughput technologies, thousands of long non-coding RNAs (lncRNAs) have been identified in the human genome. However, assigning functional annotation and determining cellular contexts for these RNAs are still in its infancy. As information gained about lncRNA structure, interacting partners, and roles in human diseases may be helpful in the characterization of novel lncRNAs, we review our knowledge on a selected group of lncRNAs that were identified serendipitously years ago by large-scale gene expression methods used to study human diseases. In particular, we focus on the Psoriasis-susceptibility-Related RNA Gene Induced by Stress (PRINS) lncRNA, first identified by our research group as a transcript highest expressed in psoriatic non-lesional epidermis. Results gathered for PRINS in the last 10 years indicate that it is conserved in primates and plays a role in keratinocyte stress response. Elevated levels of PRINS expression in psoriatic non-lesional keratinocytes alter the stress response of non-lesional epidermis and contribute to disease pathogenesis. Finally, we propose a categorization for the PRINS lncRNA based on a recently elaborated system for lncRNA classification.

Modelling toehold-mediated RNA strand displacement

We study the thermodynamics and kinetics of an RNA toehold-mediated strand displacement reaction with a recently developed coarse-grained model of RNA. Strand displacement, during which a single strand displaces a different strand previously bound to a complementary substrate strand, is an essential mechanism in active nucleic acid nanotechnology and has also been hypothesized to occur in vivo. We study the rate of displacement reactions as a function of the length of the toehold and temperature and make two experimentally testable predictions: that the displacement is faster if the toehold is placed at the 5' end of the substrate; and that the displacement slows down with increasing temperature for longer toeholds.

Conserved piRNA Expression from a Distinct Set of piRNA Cluster Loci in Eutherian Mammals

The Piwi pathway is deeply conserved amongst animals because one of its essential functions is to repress transposons. However, many Piwi-interacting RNAs (piRNAs) do not base-pair to transposons and remain mysterious in their targeting function. The sheer number of piRNA cluster (piC) loci in animal genomes and infrequent piRNA sequence conservation also present challenges in determining which piC loci are most important for development. To address this question, we determined the piRNA expression patterns of piC loci across a wide phylogenetic spectrum of animals, and reveal that most genic and intergenic piC loci evolve rapidly in their capacity to generate piRNAs, regardless of known transposon silencing function. Surprisingly, we also uncovered a distinct set of piC loci with piRNA expression conserved deeply in Eutherian mammals. We name these loci Eutherian-Conserved piRNA cluster (ECpiC) loci. Supporting the hypothesis that conservation of piRNA expression across ~100 million years of Eutherian evolution implies function, we determined that one ECpiC locus generates abundant piRNAs antisense to the STOX1 transcript, a gene clinically associated with preeclampsia. Furthermore, we confirmed reduced piRNAs in existing mouse mutations at ECpiC-Asb1 and -Cbl, which also display spermatogenic defects. The Asb1 mutant testes with strongly reduced Asb1 piRNAs also exhibit up-regulated gene expression profiles. These data indicate ECpiC loci may be specially adapted to support Eutherian reproduction.

Animal genomes from flies to humans contain many hundreds of non-coding elements called Piwi-interacting RNAs (piRNAs) cluster loci (piC loci). Some of these elements generate piRNAs that direct the silencing of transposable elements, which are pervasive genetic parasites. However, we lack an understanding of the targeting function for the remaining bulk of piRNAs because their loci are not complementarity to transposable elements. In addition, the field does not know if all piC loci are quickly evolving, or if some piC loci might be deeply conserved in piRNA expression, an indication of its potentially functional importance. Our study confirms the highly rapid evolution in piRNA expression capacity for the majority of piC loci in flies and mammals, with many clade- and species-specific piC loci expression patterns. In spite of this, we also discover a cohort of piC loci that are deeply conserved in piRNA expression from the human to the dog, a significantly broad phylogenetic spectrum of eutherian mammals. However, this conservation in piRNA expression ends at non-eutherian mammals like marsupials and monotremes. Existing mutations in two of these Eutherian-Conserved piC (ECpiC) loci impair mouse reproduction and abrogate piRNA production. Therefore, we suggest these ECpiC loci are conserved for piRNA expression due to their important function in eutherian reproduction and stand out as prime candidates for future genetic studies.

Mechanisms of Evolutionary Innovation Point to Genetic Control Logic as the Key Difference Between Prokaryotes and Eukaryotes

The evolution of life from the simplest, original form to complex, intelligent animal life occurred through a number of key innovations. Here we present a new tool to analyze these key innovations by proposing that the process of evolutionary innovation may follow one of three underlying processes, namely a Random Walk, a Critical Path, or a Many Paths process, and in some instances may also constitute a "Pull-up the Ladder" event. Our analysis is based on the occurrence of function in modern biology, rather than specific structure or mechanism. A function in modern biology may be classified in this way either on the basis of its evolution or the basis of its modern mechanism. Characterizing key innovations in this way helps identify the likelihood that an innovation could arise. In this paper, we describe the classification, and methods to classify functional features of modern organisms into these three classes based on the analysis of how a function is implemented in modern biology. We present the application of our categorization to the evolution of eukaryotic gene control. We use this approach to support the argument that there are few, and possibly no basic chemical differences between the functional constituents of the machinery of gene control between eukaryotes, bacteria and archaea. This suggests that the difference between eukaryotes and prokaryotes that allows the former to develop the complex genetic architecture seen in animals and plants is something other than their chemistry. We tentatively identify the difference as a difference in control logic, that prokaryotic genes are by default 'on' and eukaryotic genes are by default 'off.' The Many Paths evolutionary process suggests that, from a 'default off' starting point, the evolution of the genetic complexity of higher eukaryotes is a high probability event.

Discovery of Novel ncRNA Sequences in Multiple Genome Alignments on the Basis of Conserved and Stable Secondary Structures

Recently, non-coding RNAs (ncRNAs) have been discovered with novel functions, and it has been appreciated that there is pervasive transcription of genomes. Moreover, many novel ncRNAs are not conserved on the primary sequence level. Therefore, de novo computational ncRNA detection that is accurate and efficient is desirable. The purpose of this study is to develop a ncRNA detection method based on conservation of structure in more than two genomes. A new method called Multifind, using Multilign, was developed. Multilign predicts the common secondary structure for multiple input sequences. Multifind then uses measures of structure conservation to estimate the probability that the input sequences are a conserved ncRNA using a classification support vector machine. Multilign is based on Dynalign, which folds and aligns two sequences simultaneously using a scoring scheme that does not include sequence identity; its structure prediction quality is therefore not affected by input sequence diversity. Additionally, ensemble defect was introduced to Multifind as an additional discriminating feature that quantifies the compactness of the folding space for a sequence. Benchmarks showed Multifind performs better than RNAz and LocARNATE+RNAz, a method that uses RNAz on structure alignments generated by LocARNATE, on testing sequences extracted from the Rfam database. For de novo ncRNA discovery in three genomes, Multifind and LocARNATE+RNAz had an advantage over RNAz in low similarity regions of genome alignments. Additionally, Multifind and LocARNATE+RNAz found different subsets of known ncRNA sequences, suggesting the two approaches are complementary.

The Landscape of long noncoding RNA classification

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets.

Decoding mechanisms by which silent codon changes influence protein biogenesis and function

SCOPE

Synonymous codon usage has been a focus of investigation since the discovery of the genetic code and its redundancy. The occurrences of synonymous codons vary between species and within genes of the same genome, known as codon usage bias. Today, bioinformatics and experimental data allow us to compose a global view of the mechanisms by which the redundancy of the genetic code contributes to the complexity of biological systems from affecting survival in prokaryotes, to fine tuning the structure and function of proteins in higher eukaryotes. Studies analyzing the consequences of synonymous codon changes in different organisms have revealed that they impact nucleic acid stability, protein levels, structure and function without altering amino acid sequence. As such, synonymous mutations inevitably contribute to the pathogenesis of complex human diseases. Yet, fundamental questions remain unresolved regarding the impact of silent mutations in human disorders. In the present review we describe developments in this area concentrating on mechanisms by which synonymous mutations may affect protein function and human health.

PURPOSE

This synopsis illustrates the significance of synonymous mutations in disease pathogenesis. We review the different steps of gene expression affected by silent mutations, and assess the benefits and possible harmful effects of codon optimization applied in the development of therapeutic biologics.

PHYSIOLOGICAL AND MEDICAL RELEVANCE

Understanding mechanisms by which synonymous mutations contribute to complex diseases such as cancer, neurodegeneration and genetic disorders, including the limitations of codon-optimized biologics, provides insight concerning interpretation of silent variants and future molecular therapies.

Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases

We have generated a novel, neuro-specific ncRNA microarray, covering 1472 ncRNA species, to investigate their expression in different mouse models for central nervous system diseases. Thereby, we analyzed ncRNA expression in two mouse models with impaired calcium channel activity, implicated in Epilepsy or Parkinson's disease, respectively, as well as in a mouse model mimicking pathophysiological aspects of Alzheimer's disease. We identified well over a hundred differentially expressed ncRNAs, either from known classes of ncRNAs, such as miRNAs or snoRNAs or which represented entirely novel ncRNA species. Several differentially expressed ncRNAs in the calcium channel mouse models were assigned as miRNAs and target genes involved in calcium signaling, thus suggesting feedback regulation of miRNAs by calcium signaling. In the Alzheimer mouse model, we identified two snoRNAs, whose expression was deregulated prior to amyloid plaque formation. Interestingly, the presence of snoRNAs could be detected in cerebral spine fluid samples in humans, thus potentially serving as early diagnostic markers for Alzheimer's disease. In addition to known ncRNAs species, we also identified 63 differentially expressed, entirely novel ncRNA candidates, located in intronic or intergenic regions of the mouse genome, genomic locations, which previously have been shown to harbor the majority of functional ncRNAs.

Long non-coding RNAs differentially expressed between normal versus primary breast tumor tissues disclose converse changes to breast cancer-related protein-coding genes

Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes.

The persistent contributions of RNA to eukaryotic gen(om)e architecture and cellular function

Currently, the best scenario for earliest forms of life is based on RNA molecules as they have the proven ability to catalyze enzymatic reactions and harbor genetic information. Evolutionary principles valid today become apparent in such models already. Furthermore, many features of eukaryotic genome architecture might have their origins in an RNA or RNA/protein (RNP) world, including the onset of a further transition, when DNA replaced RNA as the genetic bookkeeper of the cell. Chromosome maintenance, splicing, and regulatory function via RNA may be deeply rooted in the RNA/RNP worlds. Mostly in eukaryotes, conversion from RNA to DNA is still ongoing, which greatly impacts the plasticity of extant genomes. Raw material for novel genes encoding protein or RNA, or parts of genes including regulatory elements that selection can act on, continues to enter the evolutionary lottery.

Functional interactions among microRNAs and long noncoding RNAs

In mammals, the vast majority of transcripts expressed are noncoding RNAs, ranging from short RNAs (including microRNAs) to long RNAs spanning up to hundreds of kb. While the actions of microRNAs as destabilizers and repressors of the translation of protein-coding transcripts (mRNAs) have been studied in detail, the influence of microRNAs on long noncoding RNA (lncRNA) function is only now coming into view. Conversely, the influence of lncRNAs upon microRNA function is also rapidly emerging. In some cases, lncRNA stability is reduced through the interaction of specific miRNAs. In other cases, lncRNAs can act as microRNA decoys, with the sequestration of microRNAs favoring expression of repressed target mRNAs. Other lncRNAs derepress gene expression by competing with miRNAs for interaction with shared target mRNAs. Finally, some lncRNAs can produce miRNAs, leading to repression of target mRNAs. These microRNA-lncRNA regulatory paradigms modulate gene expression patterns that drive major cellular processes (such as cell differentiation, proliferation, and cell death) which are central to mammalian physiologic and pathologic processes. We review and summarize the types of microRNA-lncRNA crosstalk identified to-date and discuss their influence on gene expression programs.

The impact of RNA structure on coding sequence evolution in both bacteria and eukaryotes

Background

Many studies have found functional RNA secondary structures are selectively conserved among species. But, the effect of RNA structure selection on coding sequence evolution remains unknown. To address this problem, we systematically investigated the relationship between nucleotide conservation level and its structural sensitivity in four model organisms, Escherichia coli, yeast, fly, and mouse.

Results

We define structurally sensitive sites as those with putative local structure-disruptive mutations. Using both the Mantel-Haenszel procedure and association test, we found structurally sensitive nucleotide sites evolved more slowly than non-sensitive sites in all four organisms. Furthermore, we observed that this association is more obvious in highly expressed genes and region near the start codon.

Conclusion

We conclude that structurally sensitive sites in mRNA sequences normally have less nucleotide divergence in all species we analyzed. This study extends our understanding of the impact of RNA structure on coding sequence evolution, and is helpful to the development of a codon model with RNA structure information.

Dihedral-based segment identification and classification of biopolymers II: polynucleotides

In an accompanying paper (Nagy, G.; Oostenbrink, C. Dihedral-based segment identification and classification of biopolymers I: Proteins. J. Chem. Inf. Model. 2013, DOI: 10.1021/ci400541d), we introduce a new algorithm for structure classification of biopolymeric structures based on main-chain dihedral angles. The DISICL algorithm (short for DIhedral-based Segment Identification and CLassification) classifies segments of structures containing two central residues. Here, we introduce the DISICL library for polynucleotides, which is based on the dihedral angles ε, ζ, and χ for the two central residues of a three-nucleotide segment of a single strand. Seventeen distinct structural classes are defined for nucleotide structures, some of which—to our knowledge—were not described previously in other structure classification algorithms. In particular, DISICL also classifies noncanonical single-stranded structural elements. DISICL is applied to databases of DNA and RNA structures containing 80,000 and 180,000 segments, respectively. The classifications according to DISICL are compared to those of another popular classification scheme in terms of the amount of classified nucleotides, average occurrence and length of structural elements, and pairwise matches of the classifications. While the detailed classification of DISICL adds sensitivity to a structure analysis, it can be readily reduced to eight simplified classes providing a more general overview of the secondary structure in polynucleotides.

De novo discovery of structured ncRNA motifs in genomic sequences

De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.

Computational analysis of conserved RNA secondary structure in transcriptomes and genomes

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

Energy-based RNA consensus secondary structure prediction in multiple sequence alignments

Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs.

Identification and characterisation of non-coding small RNAs in the pathogenic filamentous fungus Trichophyton rubrum

BACKGROUND

Accumulating evidence demonstrates that non-coding RNAs (ncRNAs) are indispensable components of many organisms and play important roles in cellular events, regulation, and development.

RESULTS

Here, we analysed the small non-coding RNA (ncRNA) transcriptome of Trichophyton rubrum by constructing and sequencing a cDNA library from conidia and mycelia. We identified 352 ncRNAs and their corresponding genomic loci. These ncRNA candidates included 198 entirely novel ncRNAs and 154 known ncRNAs classified as snRNAs, snoRNAs and other known ncRNAs. Further bioinformatic analysis detected 96 snoRNAs, including 56 snoRNAs that had been annotated in other organisms and 40 novel snoRNAs. All snoRNAs belonged to two major classes--C/D box snoRNAs and H/ACA snoRNAs--and their potential target sites in rRNAs and snRNAs were predicted. To analyse the evolutionary conservation of the ncRNAs in T. rubrum, we aligned all 352 ncRNAs to the genomes of six dermatophytes and to the NCBI non-redundant nucleotide database (NT). The results showed that most of the identified snRNAs were conserved in dermatophytes. Of the 352 ncRNAs, 102 also had genomic loci in other dermatophytes, and 27 were dermatophyte-specific.

CONCLUSIONS

Our systematic analysis may provide important clues to the function and evolution of ncRNAs in T. rubrum. These results also provide important information to complement the current annotation of the T. rubrum genome, which primarily comprises protein-coding genes.

Characterization of the human ESC transcriptome by hybrid sequencing

Although transcriptional and posttranscriptional events are detected in RNA-Seq data from second-generation sequencing, full-length mRNA isoforms are not captured. On the other hand, third-generation sequencing, which yields much longer reads, has current limitations of lower raw accuracy and throughput. Here, we combine second-generation sequencing and third-generation sequencing with a custom-designed method for isoform identification and quantification to generate a high-confidence isoform dataset for human embryonic stem cells (hESCs). We report 8,084 RefSeq-annotated isoforms detected as full-length and an additional 5,459 isoforms predicted through statistical inference. Over one-third of these are novel isoforms, including 273 RNAs from gene loci that have not previously been identified. Further characterization of the novel loci indicates that a subset is expressed in pluripotent cells but not in diverse fetal and adult tissues; moreover, their reduced expression perturbs the network of pluripotency-associated genes. Results suggest that gene identification, even in well-characterized human cell lines and tissues, is likely far from complete.

Solving nucleic acid structures by molecular replacement: examples from group II intron studies

Structured RNA molecules are key players in ensuring cellular viability. It is now emerging that, like proteins, the functions of many nucleic acids are dictated by their tertiary folds. At the same time, the number of known crystal structures of nucleic acids is also increasing rapidly. In this context, molecular replacement will become an increasingly useful technique for phasing nucleic acid crystallographic data in the near future. Here, strategies to select, create and refine molecular-replacement search models for nucleic acids are discussed. Using examples taken primarily from research on group II introns, it is shown that nucleic acids are amenable to different and potentially more flexible and sophisticated molecular-replacement searches than proteins. These observations specifically aim to encourage future crystallographic studies on the newly discovered repertoire of noncoding transcripts.

Comparative genomics as a tool to understand evolution and disease

When the human genome project started, the major challenge was how to sequence a 3 billion letter code in an organized and cost-effective manner. When completed, the project had laid the foundation for a huge variety of biomedical fields through the production of a complete human genome sequence, but also had driven the development of laboratory and analytical methods that could produce large amounts of sequencing data cheaply. These technological developments made possible the sequencing of many more vertebrate genomes, which have been necessary for the interpretation of the human genome. They have also enabled large-scale studies of vertebrate genome evolution, as well as comparative and human medicine. In this review, we give examples of evolutionary analysis using a wide variety of time frames—from the comparison of populations within a species to the comparison of species separated by at least 300 million years. Furthermore, we anticipate discoveries related to evolutionary mechanisms, adaptation, and disease to quickly accelerate in the coming years.

Widespread purifying selection on RNA structure in mammals

Evolutionarily conserved RNA secondary structures are a robust indicator of purifying selection and, consequently, molecular function. Evaluating their genome-wide occurrence through comparative genomics has consistently been plagued by high false-positive rates and divergent predictions. We present a novel benchmarking pipeline aimed at calibrating the precision of genome-wide scans for consensus RNA structure prediction. The benchmarking data obtained from two refined structure prediction algorithms, RNAz and SISSIz, were then analyzed to fine-tune the parameters of an optimized workflow for genomic sliding window screens. When applied to consistency-based multiple genome alignments of 35 mammals, our approach confidently identifies >4 million evolutionarily constrained RNA structures using a conservative sensitivity threshold that entails historically low false discovery rates for such analyses (5-22%). These predictions comprise 13.6% of the human genome, 88% of which fall outside any known sequence-constrained element, suggesting that a large proportion of the mammalian genome is functional. As an example, our findings identify both known and novel conserved RNA structure motifs in the long noncoding RNA MALAT1. This study provides an extensive set of functional transcriptomic annotations that will assist researchers in uncovering the precise mechanisms underlying the developmental ontologies of higher eukaryotes.

Identification and function of long non-coding RNAs

It is now clear that eukaryotic cells produce many thousands of non-coding RNAs. The least well-studied of these are longer than 200 nt and are known as lncRNAs (long non-coding RNAs). These loci are of particular interest as their biological relevance remains uncertain. Sequencing projects have identified thousands of these loci in a variety of species, from flies to humans. Genome-wide scans for functionality, such as evolutionary and expression analyses, suggest that many of these molecules have functional roles to play in the cell. Nevertheless, only a handful of lncRNAs have been experimentally investigated, and most of these appear to possess roles in regulating gene expression at a variety of different levels. Several lncRNAs have also been implicated in cancer. This evidence suggests that lncRNAs represent a new class of non-coding gene whose importance should become clearer upon further experimental investigation.

A review on the electrochemical biosensors for determination of microRNAs

MicroRNAs (miRNAs) are a family of non-protein-coding, endogenous, small RNAs. They are a group of gene regulators that function mainly by binding the 3' untranslated regions of specific target messenger RNA (mRNA) leading to gene inactivation by repression of mRNA transcription or induction of mRNA. Mature miRNAs are short molecules approximately 22 nucleotides in length. They regulate a wide range of biological functions from cell proliferation and death to cancer progression. Cellular miRNA expression levels can be used as biomarkers for the onset of disease states and in gene therapy for genetic disorders. Methods for detection of miRNA mainly include northern blotting, microarray, polymerase chain reaction (PCR). This review focuses on the use of electrochemical biosensors for the detection of microRNA.

What does our genome encode?

In its first production phase, The ENCODE Project Consortium (ENCODE) has generated thousands of genome-scale data sets, resulting in a genomic “parts list” that encompasses transcripts, sites of transcription factor binding, and other functional features that now number in the millions of distinct elements. These data are reshaping many long-held beliefs concerning the information content of the human and other complex genomes, including the very definition of the gene. Here I discuss and place in context many of the leading findings of ENCODE, as well as trends that are shaping the generation and interpretation of ENCODE data. Finally, I consider prospects for the future, including maximizing the accuracy, completeness, and utility of ENCODE data for the community.