Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene Meter: Accurate abundance calculations of gene expression.

ABSTRACT: We previously reported that thousands of transcripts in the mouse and zebrafish significantly increased in abundance in a time series spanning from life to several days after death. Transcript abundances were determined by: calibrating each microarray probe using a dilution series of pooled RNAs, fitting the probe-responses to adsorption models, and back-calculating abundances using the probe signal intensity of a sample and the best fitting model. The accuracy of the abundance measurements was not assessed in our previous study because individual transcript concentrations in the calibration pool were not known. Accurate transcript abundances are highly desired for modeling the dynamics of biological systems and investigating how systems respond to perturbations. In this study, we show that accurate transcript abundances can be determined by calibrating the probes using a calibration pool of transcripts with known concentrations. Instructions for determining accurate transcript abundances using the Gene Meter approach are provided.

SUBMITTER: Pozhitkov AE

PROVIDER: S-EPMC5595416 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Gene Meter: Accurate abundance calculations of gene expression.

Pozhitkov Alexander E AE Noble Peter A PA

Communicative & integrative biology 20170906 4

We previously reported that thousands of transcripts in the mouse and zebrafish significantly increased in abundance in a time series spanning from life to several days after death. Transcript abundances were determined by: calibrating each microarray probe using a dilution series of pooled RNAs, fitting the probe-responses to adsorption models, and back-calculating abundances using the probe signal intensity of a sample and the best fitting model. The accuracy of the abundance measurements was ...[more]

PMID: 28919937

Similar Datasets

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes.

Project description:Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.

| S-EPMC4643905 | biostudies-literature

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns.

Project description:Immunotherapies have attracted lots of research interests recently. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, e.g., inconsistent cell type-specific gene expression between purified reference samples and tumor samples. We propose a novel statistical method for expression deconvolution called ICeD-T (Immune Cell Deconvolution in Tumor tissues). ICeD-T automatically identifies aberrant genes whose expression are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors.

| S-EPMC7529339 | biostudies-literature

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.

Project description:We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.

| S-EPMC4479612 | biostudies-other

Accurate, robust, and reliable calculations of Poisson-Boltzmann binding energies.

Project description:Poisson-Boltzmann (PB) model is one of the most popular implicit solvent models in biophysical modeling and computation. The ability of providing accurate and reliable PB estimation of electrostatic solvation free energy, ?Gel, and binding free energy, ??Gel, is important to computational biophysics and biochemistry. In this work, we investigate the grid dependence of our PB solver (MIBPB) with solvent excluded surfaces for estimating both electrostatic solvation free energies and electrostatic binding free energies. It is found that the relative absolute error of ?Gel obtained at the grid spacing of 1.0 Å compared to ?Gel at 0.2 Å averaged over 153 molecules is less than 0.2%. Our results indicate that the use of grid spacing 0.6 Å ensures accuracy and reliability in ??Gel calculation. In fact, the grid spacing of 1.1 Å appears to deliver adequate accuracy for high throughput screening. © 2017 Wiley Periodicals, Inc.

| S-EPMC5844473 | biostudies-literature

Accurate Modeling of the Polarizability of Dyes for Electromagnetic Calculations.

Project description:The wavelength-dependent complex linear polarizability of a dye is a crucial input for the modeling of the optical properties of dye-containing systems. We here propose and discuss methods to obtain an accurate polarizability model by combining absorption spectrum measurements, Kramers-Kronig (KK) tranformations, and density functional theory (DFT) calculations. We focus, in particular, on the real part of the polarizability and its link with static polarizability. In addition, we introduce simple KK-consistent analytic functions based on the theory of critical points as a much more accurate approach to model dye polarizabilities compared with existing models based on Lorentz oscillators. Accurate polarizability models based on critical points and DFT calculations of the static polarizability are derived for five commonly used dyes: Rhodamine 6G, Rhodamine 700, Crystal Violet, Nile Blue A, and Methylene Blue. Finally, we demonstrate explicitly, using examples of Mie Theory calculations of nanoparticle-dye interactions, how an inaccurate polarizability model can result in fundamentally different predictions, further emphasizing the importance of accurate models, such as the one proposed here.

| S-EPMC6640948 | biostudies-literature

Exon arrays provide accurate assessments of gene expression.

Project description:We have developed a strategy for estimating gene expression on Affymetrix Exon arrays. The method includes a probe-specific background correction and a probe selection strategy in which a subset of probes with highly correlated intensities across multiple samples are chosen to summarize gene expression. Our results demonstrate that the proposed background model offers improvements over the default Affymetrix background correction and that Exon arrays may provide more accurate measurements of gene expression than traditional 3' arrays.

| S-EPMC1929160 | biostudies-literature

Accurate benchmark calculations on the gas-phase basicities of small molecules.

Project description:Accurate benchmark calculations of gas-phase basicities of small molecules are presented and compared with available experimental results. The optimized geometries and thermochemical analyses were obtained from MP2/aug-cc-pVTZ calculations. Two different ab initio electron-correlated methods MP2 and CCSD(T) were employed for subsequent gas-phase basicity calculations, and the single-point energies were extrapolated to the complete basis set (CBS) limit. The overall accuracy for different ab initio methods is compared, and the accuracy in descending order is CCSD(T)_CBS > CCSD(T)/aug-cc-pVDZ > (MP2/aug-cc-pVQZ approximately MP2_CBS) > HF/aug-cc-pVQZ. The best root-mean-squared-error obtained was 1.0 kcal mol(-1) at the CCSD(T)_CBS//MP2/aug-cc-pVTZ level for a test set of 41 molecules. Clearly, accurate calculations for the electron correlation energy are important for the theoretical prediction of molecular gas-phase basicities. However, conformational effects were also found to be relevant in several instances when more complicated molecules were examined.

| S-EPMC2749970 | biostudies-literature

The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data.

Project description:BackgroundThe cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied.ResultsWe present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available.ConclusionsGEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.

| S-EPMC7931818 | biostudies-literature

Accurate Quantum Mechanical/Molecular Mechanical Calculations of Reduction Potentials in Azurin Variants.

Project description:Understanding the regulation mechanism and molecular determinants of the reduction potential of metalloprotein is a major challenge. An ab initio quantum mechanical/molecular mechanical (QM/MM) method combining the minimum free energy path (MFEP) and fractional number of electron (FNE) approaches has been developed in our group to simulate the redox processes of large systems. The FNE scheme provides an efficient unique description for the redox process, while the MFEP method provides improved conformational sampling on complex environments such as protein in the QM/MM calculations. The reduction potentials of wild-type and seven mutants of azurin, a type 1 copper metalloprotein, were simulated with the QM/MM-MFEP+FNE approach in this paper. A range of 350 mV for the variations of the reduction potentials of these azurin proteins was reproduced faithfully with relative errors around 20 mV. The correlation between structural interactions and reduction potentials observed in simulations provides in-depth insight into the regulation of reduction potentials, which potentially can also be very useful to the engineering of metalloprotein-based electrocatalysts in artificial photosynthesis. The excellent accuracy and efficiency of the QM/MM-MFEP+FNE approach demonstrate the potential for simulations of many electron transfer processes in condensed phases and biochemical systems.

| S-EPMC6195766 | biostudies-literature

Precrec: fast and accurate precision-recall and ROC curve calculations in R.

Project description:The precision-recall plot is more informative than the ROC plot when evaluating classifiers on imbalanced datasets, but fast and accurate curve calculation tools for precision-recall plots are currently not available. We have developed Precrec, an R library that aims to overcome this limitation of the plot. Our tool provides fast and accurate precision-recall calculations together with multiple functionalities that work efficiently under different conditions.Availability and implementationPrecrec is licensed under GPL-3 and freely available from CRAN (https://cran.r-project.org/package=precrec). It is implemented in R with C?++.Contacttakaya.saito@ii.uib.noSupplementary information: Supplementary data are available at Bioinformatics online.

| S-EPMC5408773 | 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