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RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

RNA secondary structure plays critical roles in several biological processes. For example, many trans-acting noncoding RNA genes and cis-acting RNA regulatory elements present functional motifs, conserved both in structure and sequence, that can be hardly detected by primary sequence analysis alone. We describe here how conserved secondary structure motifs shared by functionally related RNA sequences can be detected through the software tool RNAProfile. RNAProfile takes as input a set of unaligned RNA sequences expected to share a common motif, and outputs the regions that are most conserved throughout the sequences, according to a similarity measure that takes into account both the sequence of the regions and the secondary structure they can form according to base-pairing and thermodynamic rules. The method is split into two parts. First, it identifies candidate regions within the input sequences, and associates with each region a locally optimal secondary structure. Then, it compares candidate regions to one another, both at sequence and structure level, and builds motifs exploring the search space through a greedy heuristic. We provide a detailed guide to the different parameters that can be employed, and usage examples showing the different software capabilities.

Next-generation sequencing (NGS) technologies have opened new avenues of unprecedented power for research in molecular biology and genetics. In particular, their application to the study of RNA-binding proteins (RBPs), extracted through immunoprecipitation (RIP), permits to sequence and characterize all RNAs that were found to be bound in vivo by a given RBP (RIP-Seq). On the other hand, NGS-based experiments, including RIP-Seq, produce millions of short sequence fragments that have to be processed with suitable bioinformatic tools and methods to recover and/or quantify the original sequence sample. In this chapter we provide a survey of different approaches that can be taken for the analysis of RIP-Seq data and the identification of the RNAs bound by a given RBP.

The ever increasing amount of annotated genomic sequences permits now to shed light on the molecular dynamics at the basis of evolution. Genome evolution involves both changes at the sequence level, but also rearrangements on the gene organization along the genome. Comparative analysis of gene order in multiple genomes may be thus of help in the investigation of general or lineage-specific genome plasticity, as well as in the inference of phylogenetic relationships. In particular, conserved gene contiguity in a chromosome, even if interrupted by intervening genes, may suggest potential functional couplings. This unit explains the usage of GeneSyn, a software tool for the automatic identification of conserved gene order across multiple annotated genomes.