Single-cell transcriptional landscape of long non-coding RNAs orchestrating mouse heart development.

Long non-coding RNAs (lncRNAs) comprise the most representative transcriptional units of the mammalian genome. They are associated with organ development linked with the emergence of cardiovascular diseases. We used bioinformatic approaches, machine learning algorithms, systems biology analyses, and statistical techniques to define co-expression modules linked to heart development and cardiovascular diseases. We also uncovered differentially expressed transcripts in subpopulations of cardiomyocytes. Finally, from this work, we were able to identify eight cardiac cell-types; several new coding, lncRNA, and pcRNA markers; two cardiomyocyte subpopulations at four different time points (ventricle E9.5, left ventricle E11.5, right ventricle E14.5 and left atrium P0) that harbored co-expressed gene modules enriched in mitochondrial, heart development and cardiovascular diseases. Our results evidence the role of particular lncRNAs in heart development and highlight the usage of co-expression modular approaches in the cell-type functional definition.

RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction.

Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied seven machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and Transdecoder) and our results outperformed them, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/.

CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles.

OBJECTIVE: Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills. RESULTS: We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute's influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me .