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Mohorianu, I.

Publications and source records attributed to Mohorianu, I..

24 records · Page 2Linked to original sources
bioRxiv2019

Effects of technical noise on bulk RNA-seq differential gene expression inference

MotivationInconsistent, analytical noise introduced either by the sequencing technology or by the choice of read-processing tools can bias bulk RNA-seq analyses by shifting the focus to the variation in expression of low-abundance transcripts; as a consequence these highly-variable genes are often included the differential expression (DE) call and impact the interpretation of results. ResultsTo illustrate the effects of "noise", we present simulated datasets following closely the characteristics of a H.sapiens and a M.musculus dataset, respectively, highlighting the extent of technical-noise in both a high inter-individual variability (H. sapiens) and reduced variability (M. Musculus) setup. The sequencing-induced noise is assessed using correlations of distributions of expression across transcripts; analytical noise is evaluated through side-by-side comparisons of several standard choices. The proportion of genes in the noise-range differs for each tool combi-nation. Data-driven, sample-specific noise-thresholds were applied to reduce the impact of low-level variation. Noise-adjustment reduced the number of significantly DE genes and gave rise to convergent calls across tool combinations. AvailabilityThe code for determining the sequence-derived noise is available for download from: https://github.com/yry/noiseAnalysis/tree/master/noiseDetection_mRNA; the code for running the analysis is available for download from: https://github.com/sheerind/noise_detection.

bioinformatics↗
bioRxiv2019

PAREameters: computational inference of plant microRNA-mRNA targeting rules using RNA sequencing data

MicroRNAs (miRNAs) are short, non-coding RNAs that influence the translation-rate of mRNAs by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. This can be captured on a high-throughput scale using degradome sequencing, which supports miRNA target prediction by aligning degradation fragments to reference mRNAs enabling the identification of causal miRNA(s). The current criteria used for target prediction were inferred on experimentally validated A. thaliana interactions, which were adapted to fit that specific subset of miRNA interactions. In addition, the miRNA pathway in other organisms may have acquired specific changes, e.g. lineage-specific miRNAs or new miRNA-mRNA interactions, thus previous criteria may not be optimal. We present a new tool, PAREameters, for inferring targeting criteria from RNA sequencing datasets; the stability of inferred criteria under subsampling and the effect of input-size are discussed. We first evaluate its performance using experimentally validated miRNA-mRNA interactions in multiple A. thaliana datasets, including conserved and species-specific miRNAs. We then perform comprehensive analyses on the differences in flower miRNA-mRNA interactions in several non-model organisms and quantify the observed variations. PAREameters highlights an increase in sensitivity on most tested datasets when data-inferred criteria are used.

bioinformatics↗
bioRxiv2018

Mate choice and gene expression signatures associated with nutritional adaptation in the medfly (Ceratitis capitata)

Evolutionary responses to nutrition are key to understanding host shifts and the potential for reproductive isolation. Previously, experimental evolution was used to describe responses of the medfly (Ceratitis capitata) to divergent larval diets. Within 30 generations there was divergence in larval development time, egg to adult survival and adaptation in adult body size. In this study, the gene expression signatures associated with these changes were examined, using mRNA-seq on males following 60 generations of nutritional selection. Variation in gene expression was also validated using qRT-PCR. The results showed observed an over-representation of differential expression in metabolism, oxidative phosphorylation and proteolysis genes. In addition, at generations 60 and 90, we tested whether these evolved patterns (differences in gene expression) were associated with mate choice. We found evidence for assortative mating by diet at generation 60, but not in mating tests within and between replicate lines at generation 90. Hence, there was no consistent evidence for assortative mating by diet, which emphasises the importance of replicate tests of evolutionary responses over time. Overall, the study provides the first genome-wide survey of the putative mechanisms underpinning evolved responses to nutritional adaptation.

evolutionary biology↗
bioRxiv2017

Sulforaphane modulates microRNA expression in colon cancer cells to implicate the regulation of oncogenes CDC25A, HMGA2 and MYC

Colorectal cancer is an increasingly important cause of morbidity and mortality, whose incidence is associated with dietary and lifestyle factors, particularly inversely so with the consumption of cruciferous vegetables. These vegetables contain glucosinolates, from the breakdown of which are derived isothiocyanates, such as sulforaphane. Sulforaphane is well-characterised for wide-ranging tumour-suppressive and chemoprotective activities in vitro, yet deeper elucidation of its biological interactions would aid in better realising its potential in chemoprevention and/or chemotherapy. There is evidence to suggest that sulforaphane modulates microRNA expression in the colon, thus implying the potential for microRNA modulation to play a role in the anti-cancer effects of sulforaphane. Therefore, the effects of sulforaphane on microRNA expression profiles in the colonic adenocarcinoma Caco-2 and non-cancerous colonic CCD-841 cell lines were investigated by small RNA cloning and deep sequencing, followed by Northern Blot validation experiments. Sulforaphane upregulated let-7f-5p and let-7g-5p expression at 24 h in Caco-2 cells, but not in CCD-841. Such treatment also downregulated miR-29b-3p in Caco-2. Dual luciferase assays with a let-7f-5p mimic and inhibitor confirmed the binding of the miRNA to predicted binding sites in the mRNA transcript 3-UTRs of cell division cycle 25A (CDC25A), high-mobility group AT-hook-2 (HMGA2) and MYC. Therefore, we hypothesize that let-7f-5p translationally represses CDC25A, HMGA2 and MYC, thereby playing a role in the tumour-suppressive effects of sulforaphane. The apparent selectivity of let-7f-5p induction towards tumour cells would be therapeutically desirable if applicable in vivo. MiR-29b-3p is predicted to target a number of tumour-suppressing genes, further investigation of which could be informative regarding the potential of sulforaphane to suppress tumour progression.

cancer biology↗
bioRxiv2017

Side-By-Side Analysis Of Alternative Approaches On Multi-Level RNA-Seq Data

BackgroundRNA sequencing (RNA-seq) is widely used for RNA quantification across environmental, biological and medical sciences; it enables the description of genome-wide patterns of expression and the deduction of regulatory interactions and networks. The aim of computational analyses is to achieve an accurate output, i.e. rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite the variable levels of noise and biases present in sequencing data. The evaluation of sequencing quality and normalization are essential components of this process. ResultsWe investigate the discriminative power of existing approaches for the quality checking of mRNA-seq data and also propose additional, quantitative, quality checks. To accommodate the analysis of a nested, multi-level design using data on D. melanogaster, we incorporated the sample layout into the analysis. We describe a "subsampling without replacement"-based normalization and identification of DE that accounts for the experimental design i.e. the hierarchy and amplitude of effect sizes within samples. We also evaluate the differential expression call in comparison to existing approaches. To assess the broader applicability of these methods, we applied this series of steps to a published set of H. sapiens mRNA-seq samples. ConclusionsThe dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. Overall, the proposed approach offers the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments into the data analysis. 38

bioinformatics↗