Computational Methods to Investigate the Impact of miRNAs on Pathways

Small Non-Coding RNAs in Soft-Tissue Sarcomas: State of the Art and Future Directions

Soft-tissue sarcomas (STS) are a rare and heterogeneous group of tumors that arise from connective tissue and can occur anywhere in the body. Among the plethora of over 50 different STS types, liposarcoma (LPS) is one of the most common. The subtypes of STS are characterized by distinct differences in tumor biology that drive responses to pharmacologic therapy and disparate oncologic outcomes. Small non-coding RNAs (sncRNA) are a heterogeneous class of regulatory RNAs involved in the regulation of gene expression by targeting mRNAs. Among the several types of sncRNAs, miRNAs and tRNA-derived ncRNAs are the most studied in the context of tumor biology, and we are learning more about the role of these molecules as important regulators of STS tumorigenesis and differentiation. However, challenges remain in translating these findings and no biomarkers or therapeutic approaches targeting sncRNAs have been developed for clinical use. In this review, we summarize the current landscape of sncRNAs in the context of STS with an emphasis on LPS, including the role of sncRNAs in the tumorigenesis and differentiation of these rare malignancies and their potential as novel biomarkers and therapeutic targets. Finally, we provide an appraisal of published studies and outline future directions to study sncRNAs in STS, including tRNA-derived ncRNAs.

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool’s applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach’s reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. In this context, ’in silico’ simulations can be extensively applied in massive scales, testing thousands of hypotheses under various conditions, which is usually experimentally infeasible. At present, many simulation models have become available. However, complex biological networks might pose challenges to their performance.

We propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. We implemented our tool as a freely accessible web application, hoping to allow ’in silico’ simulations to play a more central role in the modeling and understanding of biological phenomena.

RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

Background

RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.

Results

Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.

Conclusions

RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04211-7.