Detecting and comparing non-coding RNAs in the high-throughput era.

In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences has become important. Aligning nucleotide sequences is a key requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of amino-acids. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structural level and not at the sequence level. This results in very poor sequence conservation impeding comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results to their full potential. This review focuses on the comparative genomics of non-coding RNAs in the context of new sequencing technologies and especially dealing with two extremely important and timely research aspects: the development of new methods to align RNAs and the analysis of high-throughput data

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations

Functional Characterization of an In-Frame Deletion in the Basic Domain of the Retinal Transcription Factor ATOH7

Basic helix-loop-helix (bHLH) transcription factors are evolutionarily conserved and structurally similar proteins important in development. The temporospatial expression of atonal bHLH transcription factor 7 (ATOH7) directs the differentiation of retinal ganglion cells and mutations in the human gene lead to vitreoretinal and/or optic nerve abnormalities. Characterization of pathogenic ATOH7 mutations is needed to understand the functions of the conserved bHLH motif. The published ATOH7 in-frame deletion p.(Arg41_Arg48del) removes eight highly conserved amino acids in the basic domain. We functionally characterized the mutant protein by expressing V5-tagged ATOH7 constructs in human embryonic kidney 293T (HEK293T) cells for subsequent protein analyses, including Western blot, cycloheximide chase assays, Förster resonance energy transfer fluorescence lifetime imaging, enzyme-linked immunosorbent assays and dual-luciferase assays. Our results indicate that the in-frame deletion in the basic domain causes mislocalization of the protein, which can be rescued by a putative dimerization partner transcription factor 3 isoform E47 (E47), suggesting synergistic nuclear import. Furthermore, we observed (i) increased proteasomal degradation of the mutant protein, (ii) reduced protein heterodimerization, (iii) decreased DNA-binding and transcriptional activation of a reporter gene, as well as (iv) inhibited E47 activity. Altogether our observations suggest that the DNA-binding basic domain of ATOH7 has additional roles in regulating the nuclear import, dimerization, and protein stability

Towards the Understanding of the Human Genome: A Holistic Conceptual Modeling Approach

[EN] Understanding the human genome is a great scientific challenge, whose achievement requires effective data manipulation mechanisms. The non-stop evolution of both new knowledge and more efficient sequencing technologies generates a kind of genome data chaos. This chaos complicates the use of computational resources that obtain data and align them into specific actions. Conceptual model-based techniques should play a fundamental role in turning data into actionable knowledge. However, current solutions do not give a crucial role in the task of modeling that it should have to obtain a precise understanding of this domain. Hundreds of different data sources exist, but they have heterogeneous, imprecise, and inconsistent data. It is remarkably hard to have a unified data perspective that covers the genomic data from genome to transcriptome and proteome, which could facilitate semantic data integration. This paper focuses on how to design a conceptual model of the human genome that could be used as the key artifact to share, integrate, and understand the various types of datasets used in the genomic domain. We provide a full conceptual picture of relevant data in genomics and how semantic data integration is much more effective by conceptually integrating the diverse types of existing data. We show how such a conceptual model has been built, focusing on the conceptual problems that were solved to adequately model concepts whose knowledge is under constant evolution. We show how the use of the initial versions of the conceptual model in practice has allowed us to identify new features to incorporate in the model, achieving a continuous improvement process. The current version is ready to be used as the key artifact in projects where conceptually combining multiple levels of data helps to provide valuable insights that would be hard to obtain without it.This work was supported in part by the Spanish State Research Agency, in part by the Generalitat Valenciana under Grant TIN2016-80811-P and Grant PROMETEO/2018/176, and in part by European Regional Development Fund (ERDF).García-Simón, A.; León-Palacio, A.; Reyes Román, JF.; Casamayor Rodenas, JC.; Pastor López, O. (2020). Towards the Understanding of the Human Genome: A Holistic Conceptual Modeling Approach. IEEE Access. 8:197111-197123. https://doi.org/10.1109/ACCESS.2020.3034793S197111197123

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information

Predicting RNA-Protein Interactions Using Only Sequence Information

<p>Abstract</p> <p>Background</p> <p>RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.</p> <p>Results</p> <p>We propose <b><it>RPISeq</it></b>, a family of classifiers for predicting <b><it>R</it></b>NA-<b><it>p</it></b>rotein <b><it>i</it></b>nteractions using only <b><it>seq</it></b>uence information. Given the sequences of an RNA and a protein as input, <it>RPIseq </it>predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of <it>RPISeq </it>are presented: <it>RPISeq-SVM</it>, which uses a Support Vector Machine (SVM) classifier and <it>RPISeq-RF</it>, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), <it>RPISeq </it>achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of <it>RPISeq </it>was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, <it>RPISeq </it>classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from <it>E. coli, S. cerevisiae, D. melanogaster, M. musculus</it>, and <it>H. sapiens</it>.</p> <p>Conclusions</p> <p>Our experiments with <it>RPISeq </it>demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. <it>RPISeq </it>offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. <it>RPISeq </it>is freely available as a web-based server at <url>http://pridb.gdcb.iastate.edu/RPISeq/.</url></p