A Fecal MicroRNA Signature by Small RNA Sequencing Accurately Distinguishes Colorectal Cancers: Results From a Multicenter Study.

Background & Aims: Fecal tests currently used for colorectal cancer (CRC) screening show limited accuracy in detecting early tumors or precancerous lesions. In this respect, we comprehensively evaluated stool microRNA (miRNA) profiles as biomarkers for noninvasive CRC diagnosis.

Methods: A total of 1273 small RNA sequencing experiments were performed in multiple biospecimens.

GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases.

Background: Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target.

Computational Analysis of circRNA Expression Data.

Analysis of circular RNA (circRNA) expression from RNA-Seq data can be performed with different algorithms and analysis pipelines, tools allowing the extraction of heterogeneous information on the expression of this novel class of RNAs. Computational pipelines were developed to facilitate the analysis of circRNA expression by leveraging different public tools in easy-to-use pipelines. This chapter describes the complete workflow for a computationally reproducible analysis of circRNA expression starting for a public RNA-Seq experiment.

Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining.

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA).

rCASC: reproducible classification analysis of single-cell sequencing data.

Background: Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g.