1,720,971 research outputs found
Systematic benchmarking of statistical methods to assess differential expression of circular RNAs
Full text linkCircular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments
Identification of differentially expressed small RNAs and prediction of target genes in Italian Large White pigs with divergent backfat deposition.
No full textCircIMPACT: An R package to explore circular RNA impact on gene expression and pathways
Full text linkCircular RNAs (circRNAs) are transcripts generated by back-splicing. CircRNAs might regulate cellular processes by different mechanisms, including interaction with miRNAs and RNA-binding proteins. CircRNAs are pleiotropic molecules whose dysregulation has been linked to human diseases and can drive cancer by impacting gene expression and signaling pathways. The detection of circRNAs aberrantly expressed in disease conditions calls for the investigation of their functions. Here, we propose CircIMPACT, a bioinformatics tool for the integrative analysis of circRNA and gene expression data to facilitate the identification and visualization of the genes whose expression varies according to circRNA expression changes. This tool can highlight regulatory axes potentially governed by circRNAs, which can be prioritized for further experimental study. The usefulness of CircIMPACT is exemplified by a case study analysis of bladder cancer RNA-seq data. The link between circHIPK3 and heparanase (HPSE) expression, due to the circHIPK3-miR558-HPSE regulatory axis previously determined by experimental studies on cell lines, was successfully detected
miRNome of Italian Large White pig subcutaneous fat tissue: new miRNAs, isomiRs and moRNAs
No full textSmall RNAs, such as micro-RNAs (miRNAs), are decisive regulators of gene expression, and they could determine adipose tissue traits. A better knowledge of porcine fat genomics is relevant given that the pig is a biomedical model for metabolic and cardiovascular human pathologies. Adipose tissue is particularly important for the meat industry. We explored the miRNome of two adult Italian Large White pig backfat samples by Illumina RNA-Seq. Using custom bioinformatic methods, the expressed miRNAs were identified and quantified and the nucleotide sequence variability of miRNA isoforms were analysed. We detected 222 known miRNAs, 68 new miRNAs and 17 miRNA-offset RNAs (moRNAs) expressed from known hairpins, and 312 new miRNAs expressed from 253 new hairpins. Porcine transcripts targeted by the most expressed miRNAs were predicted, showing that these miRNAs may have an impact on Wnt, insulin signalling and axon guidance pathways. The expression of five small RNAs, including moRNA ssc-5′-moR-21 and a miRNA from a new hairpin, was validated by a qRT-PCR assay, thus confirming the robustness of our results. The depicted miRNome complexity suggests that quantitative and qualitative features of miRNAs and non-canonical products of their precursors are worthy of further investigation to clarify their roles in the adipose tissue biology
Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2
Full text linkCircular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2), a new automated computational pipeline for circRNA discovery and quantification, which consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptome characterization
Bioinformatic Analysis of Circular RNA Expression
No full textCircular RNAs (circRNAs) are stable RNA molecules generated by backsplicing that play regulatory functions through interaction with other RNA and proteins, as well as by encoding peptides. Dysregulation of circRNA expression can drive cancer development and progression with different mechanisms. CircRNAs are currently regarded as extremely attractive molecules in cancer research for the identification of new and possibly targetable disease regulatory networks and for the development of biomarkers for cancer diagnosis, prognosis definition, and monitoring. Using specific experimental and computational protocols, circRNAs can be identified through RNA-seq by spotting the reads spanning backsplice junctions, which are specific to circular molecules. In this chapter, we report a state-of-the-art computational protocol for a genome-wide analysis of circRNAs from RNA-seq data, which considers circRNA detection, quantification, and differential expression testing. Finally, we indicate how to determine circular transcript sequences and the resources for an in silico functional characterization of circRNAs
Transcriptional profiling of subcutaneous adipose tissue in Italian Large White pigs divergent for backfat thickness
Full text linkFat deposition is a widely studied trait in pigs because of its implications with animal growth efficiency, technological and nutritional characteristics of meat products, but the global framework of the biological and molecular processes regulating fat deposition in pigs is still incomplete. This study describes the backfat tissue transcription profile in Italian Large White pigs and reports genes differentially expressed between fat and lean animals according to RNA-seq data. The backfat transcription profile was characterised by the expression of 23 483 genes, of which 54.1% were represented by known genes. Of 63 418 expressed transcripts, about 80% were non-previously annotated isoforms. By comparing the expression level of fat vs. lean pigs, we detected 86 robust differentially expressed transcripts, 72 more highly expressed (e.g. ACP5, BCL2A1, CCR1, CD163, CD1A, EGR2, ENPP1, GPNMB, INHBB, LYZ, MSR1, OLR1, PIK3AP1, PLIN2, SPP1, SLC11A1, STC1) and 14 lower expressed (e.g. ADSSL1, CDO1, DNAJB1, HSPA1A, HSPA1B, HSPA2, HSPB8, IGFBP5, OLFML3) in fat pigs. The main functional categories enriched in differentially expressed genes were immune system process, response to stimulus, cell activation and skeletal system development, for the overexpressed genes, and unfolded protein binding and stress response, for the underexpressed genes, which included five heat shock proteins. Adipose tissue alterations and impaired stress response are linked to inflammation and, in turn, to adipose tissue secretory activity, similar to what is observed in human obesity. Our results provide the opportunity to identify biomarkers of carcass fat traits to improve the pig production chain and to identify genetic factors that regulate the observed differential expression
Identification of differentially expressed small RNAs and prediction of target genes in Italian Large White pigs with divergent backfat deposition
Full text linkThe identification of the molecular mechanisms regulating pathways associated with the potential for fat deposition in pigs can lead to the detection of key genes and markers for the genetic improvement of fat traits. Interactions of microRNAs (miRNAs) with target RNAs regulate gene expression and modulate pathway activation in cells and tissues. In pigs, miRNA discovery is far from saturation, and the knowledge of miRNA expression in backfat tissue and particularly of the impact of miRNA variations is still fragmentary. Using RNA-seq, we characterized the small RNA (sRNA) expression profiles in Italian Large White pig backfat tissue. Comparing two groups of pigs divergent for backfat deposition, we detected 31 significant differentially expressed (DE) sRNAs: 14 up-regulated (including ssc-miR-132, ssc-miR-146b, ssc-miR-221-5p, ssc-miR-365-5p and the moRNA ssc-moR-21-5p) and 17 down-regulated (including ssc-miR-136, ssc-miR-195, ssc-miR-199a-5p and ssc-miR-335). To understand the biological impact of the observed miRNA expression variations, we used the expression correlation of DE miRNA target transcripts expressed in the same samples to define a regulatory network of 193 interactions between DE miRNAs and 40 DE target transcripts showing opposite expression profiles and being involved in specific pathways. Several miRNAs and mRNAs in the network were found to be expressed from backfat-related pig QTL. These results are informative for the complex mechanisms influencing fat traits, shed light on a new aspect of the genetic regulation of fat deposition in pigs and facilitate the prospective implementation of innovative strategies of pig genetic improvement based on genomic markers
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