Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

ABSTRACT: Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

SUBMITTER: Itoh T

PROVIDER: S-EPMC8604932 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Normalization and noise reduction for single cell RNA-seq experiments.

Project description:UnlabelledA major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules.Availability and implementationThe software is implemented by R and the download version is available at http://wanglab.ucsd.edu/star/GRM.Contactwei-wang@ucsd.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4481848 | biostudies-literature

Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites.

Project description:Single-cell RNA-sequencing is revolutionising our understanding of seemingly homogeneous cell populations but has not yet been widely applied to single-celled organisms. Transcriptional variation in unicellular malaria parasites from the Plasmodium genus is associated with critical phenotypes including red blood cell invasion and immune evasion, yet transcriptional variation at an individual parasite level has not been examined in depth. Here, we describe the adaptation of a single-cell RNA-sequencing (scRNA-seq) protocol to deconvolute transcriptional variation for more than 500 individual parasites of both rodent and human malaria comprising asexual and sexual life-cycle stages. We uncover previously hidden discrete transcriptional signatures during the pathogenic part of the life cycle, suggesting that expression over development is not as continuous as commonly thought. In transmission stages, we find novel, sex-specific roles for differential expression of contingency gene families that are usually associated with immune evasion and pathogenesis.

| S-EPMC5871331 | biostudies-literature

PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data.

Project description:The iCLIP and eCLIP techniques facilitate the detection of protein-RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP ( https://github.com/skrakau/PureCLIP ), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.

| S-EPMC5746957 | biostudies-literature

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing.

Project description:With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules.

| S-EPMC7961184 | biostudies-literature

High level and nature of transcriptional noise in yeast cells [RNA-seq]

Project description:“Biological noise” is defined as functionally insignificant events that occur in living cells due to imperfect fidelity of biological processes. Distinguishing between biological function and biological noise is often difficult, and experiments to measure biological noise have not been performed. Here, we measure biological noise in yeast cells by analyzing chromatin structure and transcription of an 18 kb region of DNA whose sequence was randomly generated and hence is functionally irrelevant. Nucleosome occupancy on random-sequence DNA is comparable to that on yeast genomic DNA. However, nucleosome-depleted regions are much less frequent, and there are fewer well-positioned nucleosomes and shorter nucleosome arrays. Steady-state levels of RNAs expressed from random-sequence DNA are comparable to those of yeast mRNAs, although transcription and mRNA decay rates are at higher levels. Transcriptional initiation (5’ ends) from random-sequence DNA occurs at numerous sites at low levels, indicating very low intrinsic specificity of the Pol II machinery. In contrast, poly(A) profiles (relative levels and clustering of 3’ isoforms) of random-sequence RNAs are roughly comparable to those of endogenous yeast RNAs, which are restricted to 3’ untranslated regions. RNAs expressed from random-sequence DNA show higher cell-to-cell variability than RNAs expressed from yeast genomic DNA, suggesting that functional elements limit the variability among individual cells within a population. These observations indicate that transcriptional noise occurs at high levels in yeast, and they provide insight into how chromatin and transcription patterns arise from the evolved yeast genome.

2023-01-05 | GSE216320 | GEO

Single-cell RNA-seq reveals early heterogeneity during aging in yeast.

Project description:The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single-cell resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing (scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell-to-cell transcriptional variability during aging. The single-cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation-reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.

| S-EPMC9649600 | biostudies-literature

Yeast Single-cell RNA-seq, Cell by Cell and Step by Step.

Project description:Single-cell RNA-seq (scRNA-seq) has become an established method for uncovering the intrinsic complexity within populations. Even within seemingly homogenous populations of isogenic yeast cells, there is a high degree of heterogeneity that originates from a compact and pervasively transcribed genome. Research with microorganisms such as yeast represents a major challenge for single-cell transcriptomics, due to their small size, rigid cell wall, and low RNA content per cell. Because of these technical challenges, yeast-specific scRNA-seq methodologies have recently started to appear, each one of them relying on different cell-isolation and library-preparation methods. Consequently, each approach harbors unique strengths and weaknesses that need to be considered. We have recently developed a yeast single-cell RNA-seq protocol (yscRNA-seq), which is inexpensive, high-throughput and easy-to-implement, tailored to the unique needs of yeast. yscRNA-seq provides a unique platform that combines single-cell phenotyping via index sorting with the incorporation of unique molecule identifiers on transcripts that allows to digitally count the number of molecules in a strand- and isoform-specific manner. Here, we provide a detailed, step-by-step description of the experimental and computational steps of yscRNA-seq protocol. This protocol will ease the implementation of yscRNA-seq in other laboratories and provide guidelines for the development of novel technologies.

| S-EPMC7854150 | biostudies-literature

Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression.

Project description:Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

| S-EPMC4627577 | biostudies-literature

IDEAS: individual level differential expression analysis for single-cell RNA-seq data.

Project description:We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms.

| S-EPMC8784862 | biostudies-literature

Capturing change in clonal composition amongst single mouse germinal centers.

Project description:Understanding cellular processes occurring in vivo on time scales of days to weeks requires repeatedly interrogating the same tissue without perturbing homeostasis. We describe a novel setup for longitudinal intravital imaging of murine peripheral lymph nodes (LNs). The formation and evolution of single germinal centers (GCs) was visualized over days to weeks. Naïve B cells encounter antigen and form primary foci, which subsequently seed GCs. These experience widely varying rates of homogenizing selection, even within closely confined spatial proximity. The fluidity of GCs is greater than previously observed with large shifts in clonality over short time scales; and loss of GCs is a rare, observable event. The observation of contemporaneous, congruent shifts in clonal composition between GCs within the same animal suggests inter-GC trafficking of memory B cells. This tool refines approaches to resolving immune dynamics in peripheral LNs with high temporospatial resolution and minimal perturbation of homeostasis.

| S-EPMC6070335 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data