Dimitrios M. Vitsios

Chimira: analysis of small RNA sequencing data and microRNA modifications

Summary: Chimira is a web-based system for microRNA (miRNA) analysis from small RNA-Seq data. Sequences are automatically cleaned, trimmed, size selected and mapped directly to miRNA hairpin sequences. This generates count-based miRNA expression data for subsequent statistical analysis. Moreover, it is capable of identifying epi-transcriptomic modifications in the input sequences. Supported modification types include multiple types of 3′-modifications (e.g. uridylation, adenylation), 5′-modifications and also internal modifications or variation (ADAR editing or single nucleotide polymorphisms). Besides cleaning and mapping of input sequences to miRNAs, Chimira provides a simple and intuitive set of tools for the analysis and interpretation of the results (see also Supplementary Material). These allow the visual study of the differential expression between two specific samples or sets of samples, the identification of the most highly expressed miRNAs within sample pairs (or sets of samples) and also the projection of the modification profile for specific miRNAs across all samples. Other tools have already been published in the past for various types of small RNA-Seq analysis, such as UEA workbench, seqBuster, MAGI, OASIS and CAP-miRSeq, CPSS for modifications identification. A comprehensive comparison of Chimira with each of these tools is provided in the Supplementary Material. Chimira outperforms all of these tools in total execution speed and aims to facilitate simple, fast and reliable analysis of small RNA-Seq data allowing also, for the first time, identification of global microRNA modification profiles in a simple intuitive interface. Availability and implementation: Chimira has been developed as a web application and it is accessible here: http://www.ebi.ac.uk/research/enright/software/chimira. Contact: aje@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

mRNA 3′ uridylation and poly(A) tail length sculpt the mammalian maternal transcriptome

A fundamental principle in biology is that the program for early development is established during oogenesis in the form of the maternal transcriptome. How the maternal transcriptome acquires the appropriate content and dosage of transcripts is not fully understood. Here we show that 3′ terminal uridylation of mRNA mediated by TUT4 and TUT7 sculpts the mouse maternal transcriptome by eliminating transcripts during oocyte growth. Uridylation mediated by TUT4 and TUT7 is essential for both oocyte maturation and fertility. In comparison to somatic cells, the oocyte transcriptome has a shorter poly(A) tail and a higher relative proportion of terminal oligo-uridylation. Deletion of TUT4 and TUT7 leads to the accumulation of a cohort of transcripts with a high frequency of very short poly(A) tails, and a loss of 3′ oligo-uridylation. By contrast, deficiency of TUT4 and TUT7 does not alter gene expression in a variety of somatic cells. In summary, we show that poly(A) tail length and 3′ terminal uridylation have essential and specific functions in shaping a functional maternal transcriptome.

Large-scale analysis of microRNA expression, epi-transcriptomic features and biogenesis.

Abstract MicroRNAs are important genetic regulators in both animals and plants. They have a range of functions spanning development, differentiation, growth, metabolism and disease. The advent of next-generation sequencing technologies has made it a relatively straightforward task to detect these molecules and their relative expression via sequencing. There are a large number of published studies with deposited datasets. However, there are currently few resources that capitalize on these data to better understand the features, distribution and biogenesis of miRNAs. Herein, we focus on Human and Mouse for which the majority of data are available. We reanalyse sequencing data from 461 samples into a coordinated catalog of microRNA expression. We use this to perform large-scale analyses of miRNA function and biogenesis. These analyses include global expression comparison, co-expression of miRNA clusters and the prediction of miRNA strand-specificity and underlying constraints. Additionally, we report for the first time a global analysis of miRNA epi-transcriptomic modifications and assess their prevalence across tissues, samples and families. Finally, we report a list of potentially mis-annotated miRNAs in miRBase based on their aggregated modification profiles. The results have been collated into a comprehensive online repository of miRNA expression and features such as modifications and RNA editing events, which is available at: http://wwwdev.ebi.ac.uk/enright-dev/miratlas. We believe these findings will further contribute to our understanding of miRNA function in animals and benefit the miRNA community in general.

Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.

Abstract The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5′ end and a more flexible 3′ end with the possibility of 3′ tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage).

A MILI-independent piRNA biogenesis pathway empowers partial germline reprogramming

In mice, the pathway involving PIWI and PIWI-interacting RNA (PIWI–piRNA) is essential to re-establish transposon silencing during male-germline reprogramming. The cytoplasmic PIWI protein MILI mediates piRNA-guided transposon RNA cleavage as well as piRNA amplification. MIWI2s binding to piRNA and its nuclear localization are proposed to be dependent upon MILI function. Here, we demonstrate the existence of a piRNA biogenesis pathway that sustains partial MIWI2 function and reprogramming activity in the absence of MILI.

BioPAXViz: a cytoscape application for the visual exploration of metabolic pathway evolution

BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. AVAILABILITY AND IMPLEMENTATION BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU-CERTH/BioPAX.Viz The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. CONTACT ouzounis@certh.gr.Summary: BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre‐computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. Availability and Implementation: BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU‐CERTH/BioPAX.Viz. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. Contact: ouzounis@certh.gr.

In situ functional dissection of RNA cis-regulatory elements by multiplex CRISPR-Cas9 genome engineering.

RNA regulatory elements (RREs) are an important yet relatively under-explored facet of gene regulation. Deciphering the prevalence and functional impact of this post-transcriptional control layer requires technologies for disrupting RREs without perturbing cellular homeostasis. Here we describe genome-engineering based evaluation of RNA regulatory element activity (GenERA), a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 platform for in situ high-content functional analysis of RREs. We use GenERA to survey the entire regulatory landscape of a 3′UTR, and apply it in a multiplex fashion to analyse combinatorial interactions between sets of miRNA response elements (MREs), providing strong evidence for cooperative activity. We also employ this technology to probe the functionality of an entire MRE network under cellular homeostasis, and show that high-resolution analysis of the GenERA dataset can be used to extract functional features of MREs. This study provides a genome editing-based multiplex strategy for direct functional interrogation of RNA cis-regulatory elements in a native cellular environment.RNA regulatory elements (RREs) are important post-transcriptional control features but studying them requires disrupting their activity without disturbing cellular homeostasis. Here the authors present GenERA, a CRISPR-Cas9 screening platform of in situ analysis of native RREs.

Multi-genome Core Pathway Identification through Gene Clustering

In the wake of gene-oriented data analysis in large-scale bioinformatics studies, focus in research is currently shifting towards the analysis of the functional association of genes, namely the metabolic pathways in which genes participate. The goal of this paper is to attempt to identify the core genes in a specific pathway, based on a user-defined selection of genomes. To this end, a novel methodology has been developed that uses data from the KEGG database, and through the application of the MCL clustering algorithm, identifies clusters that correspond to different “layers” of genes, either on a phylogenetic or a functional level. The algorithm’s complexity, evaluated experimentally, is presented and the results on a characteristic case study are discussed.

Inference of Pathway Decomposition Across Multiple Species Through Gene Clustering

In the wake of gene-oriented data analysis in large-scale bioinformatics studies, focus in research is currently shifting towards the analysis of the functional association of genes, namely the metabolic pathways in which genes participate. The goal of this paper is to attempt to identify the core genes in a specific pathway, based on a user-defined selection of genomes. To this end, a novel algorithm has been developed that uses data from the KEGG database, and through the application of the MCL clustering algorithm, identifies clusters that correspond to different “layers” of genes, either on a phylogenetic or a functional level. The algorithms complexity, evaluated experimentally, is presented and the results on three characteristic case studies are discussed.