A novel approach uncovering the biological signal in Hi-C data affected by technical biases
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PROVIDER: PRJNA822840 | ENA |
REPOSITORIES: ENA
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Project description:The chromatin interaction assays, particularly Hi-C, enabled detailed studies of chromatin architecture in multiple organisms and model systems, resulting in a deeper understanding of gene expression regulation mediated by epigenetics mechanisms. However, the analysis and interpretation of Hi-C data remain challenging due to technical biases, limiting direct comparisons of datasets obtained in different experiments and laboratories. As a result, removing biases from Hi-C-generated chromatin contact matrices is a critical data analysis step. Our novel approach HiConfidence eliminates biases from the Hi-C data by weighing chromatin contacts according to their consistency between replicates so that low-quality replicates do not influence the result. The algorithm is effective for the analysis of global changes in chromatin structures such as compartments and TADs. We apply the HiConfidence approach to several Hi-C datasets with significant technical biases that could not be analyzed effectively using existing methods, and obtain meaningful biological conclusions. In particular, HiConfidence aids in the study of how changes in the histone acetylation pattern affect chromatin organization in Drosophila cells.
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Project description:In the teleost fish Fundulus heteroclitus there is extensive variation in gene expression among individuals within and between populations. Accurate measures of the variation in mRNA expression using microarrays can be confounded by technical variation, which includes variation in RNA isolation procedures, day of hybridization and methods used to amplify and dye label RNA for hybridization. In this manuscript we analyze the relationship between the amount of mRNA and the fluorescent signal from the microarray hybridizations demonstrating that for a wide-range of mRNA concentrations the fluorescent signal is a linear function of the amount of mRNA. Additionally, the separate isolation, labeling or hybridization of RNA does not add significant amounts of variation in microarray measures of gene expression. However, single or double rounds of amplification for labeling do have small but significant affects on 10% of genes, but this source of technical variation is easy to avoid. To examine both technical and stochastic biological variation, mRNA expression was measured from the same five individuals over a six-week time course. There were few, if any, meaningful differences in gene expression among time points. Thus, microarray measures using standard laboratory procedures can be precise and quantitative and are not subject to significant random biological noise.
2008-11-21 | GSE12858 | GEO
Project description:RNA sequencing (RNA-seq) is a widely used method for quantifying RNA levels across the environmental, biological and medical sciences. The accuracy of the output from an RNA-seq experiment is known to vary due to sequencing biases or errors in quantification. These have the potential to lead to false calls of differential expression (DE), and hence, to affect the accuracy of the biological inference. A proposed solution to reduce the number of false positives and increase confidence in the quality of results from such experiments is to increase the number of biological replicates. In addition, more recent suggestions are to create additional technical replicates within biological replicates (i.e. to split samples across sequencing lanes). The optimal strategy for analysing and normalizing such data, and for maximising accuracy as a function of cost, biological and technical replication is important to understand, yet currently unclear. The aim of this study was to test the effect of technical replication and sample splitting on the overall outcome of gene expression profiling for RNA-seq data.
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Project description:DNA metabarcoding from the ethanol used to store macroinvertebrate bulk samples is a convenient methodological option in molecular biodiversity assessment and biomonitoring of aquatic ecosystems, as it preserves specimens and reduces problems associated with sample sorting. However, this method may be affected by errors and biases, which need to be thoroughly quantified before it can be mainstreamed into biomonitoring programmes. Here, we used 80 unsorted macroinvertebrate samples collected in Portugal under a Water Framework Directive monitoring programme, to compare community diversity and taxonomic composition metrics estimated through morphotaxonomy versus metabarcoding from storage ethanol using three markers (COI-M19BR2, 16S-Inse01 and 18S-Euka02) and a multimarker approach. A preliminary in silico analysis showed that the three markers were adequate for the target taxa, with detection failures related primarily to the lack of adequate barcodes in public databases. Metabarcoding of ethanol samples retrieved far less taxa per site (alpha diversity) than morphotaxonomy, albeit with smaller differences for COI-M19BR2 and the multimarker approach, while estimates of taxa turnover (beta diversity) among sites were similar across methods. Using generalized linear mixed models, we found that after controlling for differences in read coverage across samples, the probability of detection of a taxon was positively related to its proportional abundance, and negatively so to the presence of heavily sclerotized exoskeleton (e.g., Coleoptera). Overall, using our experimental protocol with different template dilutions, the COI marker showed the best performance, but we recommend the use of a multimarker approach to detect a wider range of taxa in freshwater macroinvertebrate samples. Further methodological development and optimization efforts are needed to reduce biases associated with body armouring and rarity in some macroinvertebrate taxa.
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Project description:In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We find that immunoStates significantly reduces biological and technical biases. Importantly, we find that different methods have virtually no or minimal effect once the basis matrix is chosen. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.
| S-EPMC6226523 | biostudies-literature
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
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