Differential Gene and Transcript Expression Analysis with TopHat and Cufflinks
Ontology highlight
ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE32038 | GEO | 2011/09/10
SECONDARY ACCESSION(S): PRJNA147681
REPOSITORIES: GEO
Similar Datasets
Project description:This submission includes the sample data for a protocol covering differential expression analysis with TopHat and Cufflinks. The protocol also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-Seq analysis results. While the procedure assumes basic informatics skills, these tools assume little to no background with RNA-Seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The example data was generated in silico to closely resemble a real experiment in Drosophila melanogaster. First, expression values in cultured S2 cells were calculated for FlyBase 5.2 transcripts. These values were used to generate 3 sequencing replicates for condition "C1", with underlying variability in expression across replicates simulated by fitting a negative binomial model through the real S2 read count data. A second simulated condition "C2" was generated by perturbing expression for 300 randomly selected genes. Genes were perturbed by selecting the most highly expressed isoform and increasing its relative expression by three fold. Three replicates of this condtion were sequenced as above. Simulated sequencing was performed by picking a transcript from the FlyBase transcriptome with equal to its abundance, choosing a fragment length from a normal distribution with mean = 180bp and standard deviation = 20bp, and then choosing a start point for the fragment within the transcript uniformly at random. Total sequencing yield for each replicate was chosen to match that of the real S2 data. Each replicate was mapped to the fly genome with TopHat v 1.3.1 seperately. The replicates were assembled seperately with Cufflinks v 1.1.0. The replicate assemblies were merged with Cuffmerge. This merged assembly was then analysed for differentially expressed and regulated genes with Cuffdiff.
2011-09-09 | E-GEOD-32038 | biostudies-arrayexpress
Project description:Pseudouridine (Ψ) is an abundant RNA modification that is present in and impacts the functions of diverse non-coding RNA species, including rRNA, tRNA, snRNA, etc. Pseudouridine also exists in mammalian mRNA and likely exhibits functional roles; however, functional investigations of pseudouridine in mammalian mRNA have been hampered by the lack of a quantitative method that detects Ψ at base precision. We have recently developed Bisulfite-Induced Deletion sequencing (BID-seq), which provides the community with a quantitative method to map RNA Ψ distribution transcriptome-wide at single-base resolution. Here, we describe an optimized BID-seq protocol to generate highly reproducible results, displaying nearly zero background deletions at unmodified uridines after bisulfite treatment and uncovering 8,407 Ψ sites starting from as little as 10 ng mouse embryonic stem cell (mESC) polyA+ RNA, with the steps that are fast in both library preparation and data analysis. This optimized BID-seq workflow takes 5 days to complete and includes four main sections: RNA preparation, library construction, next-generation sequencing (NGS), and data analysis. The library construction can be completed by researchers who have basic knowledge and skills in molecular biology and genetics. In addition to the experimental protocol, we provide BID-pipe (https://github.com/y9c/pseudoU-BIDseq), a user-friendly data analysis pipeline for BID-seq, requiring only basic bioinformatic and computational skills to uncover Ψ signatures from NGS data.
2023-08-07 | GSE238245 | GEO
Project description:We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope® Diagnostic System (MMDx). Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ~93% agreement (balanced accuracy) signing out the reports, and random forest-based automated sign-outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign-outs for T cell-mediated (TCMR) and antibody-mediated rejection (ABMR) respectively). In most cases disagreements, whether between experts or between experts and automated sign-outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign-outs for histology diagnoses of TCMR and ABMR were 73% and 78% respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).
2019-11-07 | GSE124203 | GEO
Project description:Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these tools remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperformed (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allowed for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This deep learning approach constitutes a major step forward in engineering and understanding of RNA synthetic biology.
2020-08-12 | GSE149225 | GEO
Project description:Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
2024-06-20 | GSE248376 | GEO
Project description:The immune system operates at the scale of the whole organism in mammals. We currently lack experimental approaches to systematically track and study organism-wide molecular processes in mice. Here we describe an integrated toolkit for measuring gene expression in whole tissues, 3-prime mRNA extension sequencing, that is applicable to most mouse organs and any mouse model of interest. Further, the methods of RNA-seq described in this protocol are broadly applicable to other sample types beyond whole organs, such as tissue samples or isolated cell populations. We report procedures to collect, store and lyse a dozen organ types using conditions compatible with the extraction of high-quality RNA. In addition, we detail protocols to perform high-throughput and low-cost RNA extraction and sequencing, as well as downstream data analysis. The protocol takes 5 d to process 384 mouse organs from collecting tissues to obtaining raw sequencing data, with additional time required for data analysis and mining. The protocol is accessible to individuals with basic skills in (i) mouse perfusion and dissection for sample collection and (ii) computation using Unix and R for data analysis. Overall, the methods presented here fill a gap in our toolbox for studying organism-wide processes in immunology and physiology.
2019-10-01 | GSE138103 | GEO
Project description:Developmental language disorder (DLD), previously known as specific language impairment, is a neurodevelopmental disorder. It affects approximately 7% of school-age children. The affected children fail to develop normal speech and language skills. This is a major public health concern as it adversely impacts the communication, academic, and social skills of the affected individual. The human brain development is a complex process that involves the accurate orchestration of the expression of multiple genes. Precise temporal and spatial regulation of gene expression is essential for proper brain development. Epigenetic factors such as DNA methylation can modulate gene expression without altering the DNA sequence. They are, therefore, considered as key regulators of the expression of genes involved in neurodevelopment. In this study, we examined any altered DNA methylation between children affected with DLD and healthy control subjects. We looked into genome-wide methylation differences between the DLD and control groups using Infinium HumanMethylation850 (EPICarray). Twelve children with DLD and 12 healthy controls were recruited for the study. Five milliliters of peripheral blood samples were collected from the study subjects in EDTA vials. Genomic DNA (gDNA) was extracted from the blood samples using the standard salting out protocol. Five micrograms of each DNA at 50 ng/µl concentration were used for genome-wide methylation analysis. The gDNA samples were bisulfite-treated with EpiTect Bisulfite Kit. Further to this, the DNA samples were subjected to whole genome amplification and enzymatic fragmentation. Human Infinium Methylation EPIC BeadChip (Illumina), which covers more than 850,000 genome-wide methylation sites, was used for genome-wide methylation analysis. The DNA methylation profiles of each sample were visualized at the single-CpG level and for the genomic regions of interest.
2024-01-10 | E-MTAB-13583 | biostudies-arrayexpress
Project description:Despite the immense importance of enzyme-substrate reactions, there is a lack of general and unbiased tools for identifying the molecular components participating in these reactions on a cellular level. Here we developed a universal method called System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that post-translational modification of the substrate protein changes its thermal stability, and applies the concept of specificity to reveal the potential substrates. For selenoprotein thioredoxin reductase 1, SIESTA confirmed several known protein substrates and suggested novel candidates. For poly-(ADP-ribose) polymerase-10, SIESTA revealed a number of PARP10 putative substrates, which were confirmed by targeted mass spectrometry and functional assays. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, and facilitate drug discovery.
2021-01-11 | PXD010554 | Pride
Project description:Tumors cause the induction or repression of many genes associated with inflammation. To investigate the up and down regulation of genes associated with immune stimulation or immune tolerance RNA was isolated from dendritic cells from normal or tumor bearing prostate for microarray analysis. Using normal or tumorogenic sections of human prostates as determined by Pathology experts (UMD). Keywords: Comparative gene expression, prostate cancer, dendritic cells
2011-01-21 | GSE26747 | GEO