RESUMEN
RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.
Asunto(s)
Genómica/métodos , ARN Pequeño no Traducido/genética , Arabidopsis/genética , Bases de Datos Genéticas , Perfilación de la Expresión Génica/métodos , Regulación de la Expresión Génica , Sitios Genéticos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Interferencia de ARN , ARN de Planta/genética , Programas Informáticos , TranscriptomaRESUMEN
Small RNAs (sRNAs) are 20-25 nt non-coding RNAs that act as guides for the highly sequence-specific regulatory mechanism known as RNA silencing. Due to the recent increase in sequencing depth, a highly complex and diverse population of sRNAs in both plants and animals has been revealed. However, the exponential increase in sequencing data has also made the identification of individual sRNA transcripts corresponding to biological units (sRNA loci) more challenging when based exclusively on the genomic location of the constituent sRNAs, hindering existing approaches to identify sRNA loci. To infer the location of significant biological units, we propose an approach for sRNA loci detection called CoLIde (Co-expression based sRNA Loci Identification) that combines genomic location with the analysis of other information such as variation in expression levels (expression pattern) and size class distribution. For CoLIde, we define a locus as a union of regions sharing the same pattern and located in close proximity on the genome. Biological relevance, detected through the analysis of size class distribution, is also calculated for each locus. CoLIde can be applied on ordered (e.g., time-dependent) or un-ordered (e.g., organ, mutant) series of samples both with or without biological/technical replicates. The method reliably identifies known types of loci and shows improved performance on sequencing data from both plants (e.g., A. thaliana, S. lycopersicum) and animals (e.g., D. melanogaster) when compared with existing locus detection techniques. CoLIde is available for use within the UEA Small RNA Workbench which can be downloaded from: http://srna-workbench.cmp.uea.ac.uk.