CinaR: A comprehensive R package for the differential analyses and interpretation of ATAC-seq data
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PROVIDER: PRJNA693296 | ENA |
REPOSITORIES: ENA
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Project description:ATAC-seq is a frequently used assay to study chromatin accessibility levels. Differential chromatin accessibility level analyses between biological groups and functional interpretation of these differential regions are essential in ATAC-seq data analyses. Although distinct methods and analyses pipelines are developed for this purpose, we are missing a stand-along R package that combines state-of-the art differential analyses and functional enrichment analyses. To fill this gap, we developed cinaR, which is a single wrapper function and provides users with various configurable plots. To show-case this pipeline we provide these bulk ATAC-seq samples from bone-marrow of 3 & 18 months old New Zelland Obese (NZO) mice and show the activation of pro-inflammatory pathways.
2021-01-20 | GSE165120 | GEO
Project description:Background and aim: Analysis of data obtained from genome wide gene expression experiments is challenging, due to the huge amount of variables, management of the data and the need for multivariate analysis. We here present the R package: pcaGoPromoter that facilitates the interpretation of genome wide expression data to overcome these problems. In a first step principal component analysis is applied to overview any differences between the observations and possible groupings. The next step is interpretation of the principal components with respect to both biological function and involvement of predicted transcription factor binding sites. The robustness of the results is evaluated using cross validation. Illustrative plots of PCA score plots and Gene Ontology terms are available. To illustrate the functionality of the R package, we designed a serum stimulation experiment, where the main biological outcome is well documented. Results: Samples from the serum stimulation experiment were analyzed using the Affymetrix Human Genome U133 Plus 2.0 chip. The array data were analyzed by the tools of the pcaGoPromoter package, which resulted in a clear separation of the observations into the three experimental groups - controls, serum only and serum with inhibitor. The functional annotation of the axes in the PCA score plot showed the expected serum promoted biological processes such as cell cycle progression and the predicted involvement of the expected transcription factors including E2F. In addition unexpected results, e.g. the cholesterol synthesis in serum depleted cells and NF-?B activation in inhibitor treated cells were uncovered. Conclusion: The pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. It works with any platform using gene symbols or Entrez Ids as probe identifiers. In addition support for several popular Affymetrix GeneChip platforms is provided. The tools give an overview of the data via principal component analysis, functional interpretation by Gene Ontology terms (biological processes), and indication of involvement of possible transcription factors. Thus, pcaGoPromoter structures the high-dimensional data of gene expression experiments and can be applied to generate hypotheses for further exploration. A serum starved human monocyte cell line (ATCC® Number: CRL-9853) was stimulated by serum in the presence or absence of the specific Erk-1/2 inhibitor U0126.
2011-05-01 | E-GEOD-27071 | biostudies-arrayexpress
Project description:Background and aim: Analysis of data obtained from genome wide gene expression experiments is challenging, due to the huge amount of variables, management of the data and the need for multivariate analysis. We here present the R package: pcaGoPromoter that facilitates the interpretation of genome wide expression data to overcome these problems. In a first step principal component analysis is applied to overview any differences between the observations and possible groupings. The next step is interpretation of the principal components with respect to both biological function and involvement of predicted transcription factor binding sites. The robustness of the results is evaluated using cross validation. Illustrative plots of PCA score plots and Gene Ontology terms are available. To illustrate the functionality of the R package, we designed a serum stimulation experiment, where the main biological outcome is well documented. Results: Samples from the serum stimulation experiment were analyzed using the Affymetrix Human Genome U133 Plus 2.0 chip. The array data were analyzed by the tools of the pcaGoPromoter package, which resulted in a clear separation of the observations into the three experimental groups - controls, serum only and serum with inhibitor. The functional annotation of the axes in the PCA score plot showed the expected serum promoted biological processes such as cell cycle progression and the predicted involvement of the expected transcription factors including E2F. In addition unexpected results, e.g. the cholesterol synthesis in serum depleted cells and NF-κB activation in inhibitor treated cells were uncovered. Conclusion: The pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. It works with any platform using gene symbols or Entrez Ids as probe identifiers. In addition support for several popular Affymetrix GeneChip platforms is provided. The tools give an overview of the data via principal component analysis, functional interpretation by Gene Ontology terms (biological processes), and indication of involvement of possible transcription factors. Thus, pcaGoPromoter structures the high-dimensional data of gene expression experiments and can be applied to generate hypotheses for further exploration.
2011-05-01 | GSE27071 | GEO
Project description:Background: ATAC-seq is widely used to measure the chromatin accessibility and identify the open chromatin regions (OCRs). OCRs usually indicate the active regulatory elements in the genome and are directly associated with gene regulatory networks. Identification of differential accessibility regions (DARs) between different biological conditions is critical to measure the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities to differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal is different from RNA-seq data, and higher sensitivity is desired for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still missing. Methods: Here, we used simulated datasets to systematically measure the sensitivity and specificity of 6 different methods. We further discussed the statistical and signal density cutoff in the differential analysis of ATAC-seq by applying to real data. Batch-effect is very common in high-throughput sequencing experiments. Results: We illustrated that batch-effect correction can dramatically improve the sensitivity in differential analysis of ATAC-seq data. Finally, we developed an easily usable package, BeCorrect, to perform batch-effort correction for visualizing corrected ATAC-seq signals on a genome browser. Conclusions: It is important to use PCA to check the samples distribution, and the Remove Unwanted Variation strategy can be used to correct the data to improve the sensitivity when strong batch effects are found in the data. Finally, BeCorrect can be used to correct the batch-effect of ATAC-seq data signal based on DARs analysis, and generate a proper visualization on a genome browser.
2020-06-29 | GSE131144 | GEO
Project description:Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volumes of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.
2021-01-03 | GSE126724 | GEO
Project description:Comprehensive Analyses of 723 Transcriptomes Enhance GWAS Biological Interpretation and Genomic Prediction for Complex Traits in Cattle
| PRJNA526257 | ENA
Project description: Not available
| S-EPMC7178746 | biostudies-literature
Project description:A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data
| PRJNA542717 | ENA
Project description:Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools. Here we have developed massPix - an R package for analysing and interpreting data from MSI of lipids in tissue. MassPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications. MassPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries. Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering. Mouse cerebellum was analysed using matrix assisted laser desorption ionisation (MALDI) MSI. The resulting MSI dataset forms the test data for massPix.
2017-08-31 | MTBLS487 | MetaboLights
Project description:RNA molecules have secondary and tertiary structures in vivo which play a crucial role in various cellular processes such as the regulation of gene expression, RNA processing, and localization. Investigating these structures will enhance our understanding of their functions and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analysing complex in vivo RNA structural data. Here, we present COMRADES Object-Oriented (comrades-OO), a novel software package for the comprehensive analysis of data derived from the COMRADES experiment. comrades-OO offers a comprehensive pipeline from raw sequencing reads to the identification of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. COMRADES-OO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction.
2023-10-31 | GSE246412 | GEO