Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

RNA sequencing (RNAseq) has become the method of choice for transcriptome analysis, yet no consensus exists as to the most appropriate pipeline for its analysis, with current benchmarks suffering important limitations. Here, we address these challenges through a rich benchmarking resource harnessing (i) two RNAseq datasets including ERCC ExFold spike-ins; (ii) Nanostring measurements of a panel of 150 genes on the same samples; (iii) a set of internal, genetically-determined controls; (iv) a reanalysis of the SEQC dataset; and (v) a focus on relative quantification (i.e. across-samples). We use this resource to compare different approaches to each step of RNAseq analysis, from alignment to differential expression testing. We show that methods providing the best absolute quantification do not necessarily provide good relative quantification across samples, that count-based methods are superior for gene-level relative quantification, and that the new generation of pseudo-alignment-based software performs as well as established methods, at a fraction of the computing time. We also assess the impact of library type and size on quantification and differential expression analysis. Finally, we have created a R package and a web platform to enable the simple and streamlined application of this resource to the benchmarking of future methods.

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The Polycomb Repressive Complex 2 (PRC2) plays important roles in the epigenetic regulation of cellular development and differentiation through H3K27me3-dependent transcriptional repression. Aberrant PRC2 activity has been associated with cancer and neurodevelopmental disorders, particularly with respect to the malfunction of sits catalytic subunit EZH2. Here, we investigated the role of the EZH2-mediated H3K27me3 apposition in neuronal differentiation. We made use of a transgenic mouse model harboring Ezh2 conditional KO alleles to derive embryonic stem cells and differentiate them into glutamatergic neurons. Time course transcriptomics and epigenomic analyses of H3K27me3 in absence of EZH2 revealed a significant dysregulation of molecular networks affecting the glutamatergic differentiation trajectory that resulted in: (i) the deregulation of transcriptional circuitries related to neuronal differentiation and synaptic plasticity, in particular LTD, as a direct effect of EZH2 loss and (ii) the appearance of a GABAergic gene expression signature during glutamatergic neuron differentiation. These results expand the knowledge about the molecular pathways targeted by Polycomb during glutamatergic neuron differentiation.