Project description:This dataset contains the RNA-seq data associated with a CRISPR/Cas9 study for which the screen results (sgRNA reads) are included in the GEO record GSE15547. Specifically, this dataset contains the RNA-seq results used to assess absolute gene expression in Nalm-6 cells, results for 3 PPP3CA knockout polulations, 3 PPP3R1 knockout populations, one FK-506 (tacrolimus) treated population and ERCC spike-in experiments on PABIR1 (formerly FAM122A) knockout cells, each with their own wild type and/or DMSO-treated control samples.
Project description:Using measurements of absolute protein and mRNA concentrations in cellular lysate from a Daoy medulloblastoma cell line, we quantitatively evaluate the influence of absolute transcript levels and a large number of sequence-encoded regulatory elements on steady-state human protein expression levels.
Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.
Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.
Project description:Missense variants that change the amino acid sequences of proteins cause one third of human genetic diseases. Tens of millions of missense variants exist in the current human population, with the vast majority having unknown functional consequences. Here we present the first large-scale experimental analysis of human missense variants. Using DNA synthesis and cellular selection experiments we quantify the impact of >500,000 variants on the abundance of >500 human protein domains. This dataset, Domainome 1.0, reveals that >60% of disease-causing variants destabilize proteins. The contribution of stability to protein fitness varies across proteins and diseases, and is particularly important in recessive disorders. Combining experimental stability measurements with large language models we annotate functionally important sites across domains. Fitting energy models to the data demonstrates the conservation of mutation effects in homologous domains and allows stability to be accurately predicted for entire domain families. Domainome 1.0 demonstrates the feasibility of assaying human protein variant effects at scale and provides a large consistent reference dataset for clinical variant interpretation and the training and benchmarking of computational methods.
Project description:RNA-seq experiments measuring global RNA abundance Temperature sensitive (TS) mutants are a tool that have been foundational for the study of many essential life processes. Despite the long-term use of TS mutants, the mechanisms that lead to temperature sensitivity are not fully understood. Furthermore, a high-throughput workflow to characterize biophysical changes occurring in TS mutants is lacking. We developed Temperature sensitive Mutant Proteome Profiling (TeMPP), a novel application of mass spectrometry based thermal proteome profiling (TPP) as a way to measure the effects of missense mutations on protein stability and protein-protein interactions. This study characterized the global changes in mRNA abundance, protein abundance, and protein thermal stability as a result of missense mutants within two subunits of the yeast ubiquitin-proteasome system. Global protein abundance measurements and RNA sequencing data resulted in a large number of possible candidates that could be causing the phenotypic changes observed in the mutant strains. The additional information gained from TeMPP along with complementary proteomic and transcriptomic experiments allows for multiomic intersection analysis that may reveal interesting regulatory categories to pursue in follow-up mechanistic experiments.
Project description:This dataset contains bulk RNA sequencing data from primary tumor biopsy samples of patients with prostate adenocarcinoma. TPM-normalized expression values are provided along with sample metadata. The data were generated to support transcriptomic profiling of human prostate tumors and are related to a companion single-cell RNA-seq dataset from the same cohort.
Project description:This dataset contains untargeted proteomic measurements from H441 epithelial cells cultured under physiologic metabolic conditions. Cells were conditioned in human plasma–like medium (HPLM) and treated with or without an amino acid supplement. Proteomic profiling was performed to quantify global protein abundance changes associated with amino acid supplementation under these metabolic conditions. Pathway-level analyses were conducted to characterize cellular processes altered by supplementation. This dataset provides a resource for examining proteome-level responses to metabolic modulation in epithelial cells.
Project description:Using measurements of absolute protein and mRNA concentrations in cellular lysate from a Daoy medulloblastoma cell line, we quantitatively evaluate the influence of absolute transcript levels and a large number of sequence-encoded regulatory elements on steady-state human protein expression levels. mRNA profiling: We prepared two biological replicates, each one was used in a 4-plex format array.