Sentences with phrase «used gene expression data»

A research team, led by Chao Cheng, Ph.D., Assistant Professor in the Department of Genetics at The Geisel School of Medicine at Dartmouth, used gene expression data from breast cancer patients to computationally infer the presence of different types of immune cells.

A new mathematical model uses gene expression data to predict where neurons are located throughout the brain

Not exact matches

«Together, our data strongly suggest that cutaneous gene therapy with inducible expression of GLP1 can be used for the treatment and prevention of diet - induced obesity and pathologies,» the authors wrote.

«We used the Allen Human Brain Atlas data to quantify how consistent the patterns of expression for various genes are across human brains, and to determine the importance of the most consistent and reproducible genes for brain function.»

«For several years the potential for the use of gene expression data in research and clinical applications has been underappreciated due to the inconsistency of the data coming from the various types of equipment.

The data are already being used to improve gene information and expression on VectorBase, a National Institute of Allergy and Infectious Diseases resource center for the scientific community.

Using breast cancer patient data taken from The Cancer Genome Atlas, molecular biologists Curt M. Horvath and Robert W. Tell used powerful computational and bioinformatics techniques to detect patterns of gene expression in two cancer subtypes.

In the Nature Methods paper, Corrada Bravo, UMD computer science doctoral student Florin Chelaru, and undergraduate research assistants from Williams College in Mass. and Washington University in St. Louis used Epiviz to visualize and analyze DNA methylation and gene expression data in colon cancer.

Epiviz implements multiple visualization methods for location - based data (such as genomic regions of interest) and feature - based data (such as gene expression), using interactive data visualization techniques not available in web - based genome browsers.

Using clinical, genetic, and gene expression data as filters to distinguish genes whose copy number alteration causes cancer from those for whom copy number changes are incidental, the team whittled down their list from 14,000 to a more manageable number, each of which they systematically tested using genetic experiments in aniUsing clinical, genetic, and gene expression data as filters to distinguish genes whose copy number alteration causes cancer from those for whom copy number changes are incidental, the team whittled down their list from 14,000 to a more manageable number, each of which they systematically tested using genetic experiments in aniusing genetic experiments in animals.

The researchers developed algorithms to use in a «systems biology modeling cycle,» in which they repeatedly fit a model to gene expression data obtained from laboratory experiments until a good fit was obtained between the predicted and the measured outcomes.

They tested this theory in mice, rats, flies and fish using publicly available gene - expression data.

The organisers used 884 lymphoblastoid cell lines that had SNP and gene - expression data available through the 1000 Genomes Project.

The researchers» next step is to use the genomic data they collected from the families — including full genome sequences and gene expression data — to begin identifying the specific genes that contribute to risk for bipolar disorder.

In a Cell paper published on April 7, Lanner's team analysed gene expression in 88 early human embryos and is using those data to identify genes to disrupt in embryos using CRISPR — Cas9.

HDNetDB allows users to obtain, visualise and prioritise molecular interaction networks using Huntington's disease - related gene expression and other types of data obtained from human samples and other sources.

A: Sheltzer: Professionally, we're working on a paper together using Joan's data - analysis ability to parse through gene - expression data from more than 20,000 cancer patients.

Using quantitative RT - PCR we confirmed the expression profile of five genes (colony - stimulating factor 1 receptor [Csf1r], Cd68, Pex11a, Emr1, and Mcp1) in each of the 24 samples and found excellent agreement between the microarray and RT - PCR expression data (mean Pearson correlation coefficient = 0.91, microarray versus RT - PCR expression; Supplemental Table 3, http://www.jci.org/cgi/content/full/112/12/1796/DC1).

To normalize expression data, we used multiple internal control genes as described by Vandesompele et al. (44).

In the current study, the researchers used infradian cyclic locomotor activity in the mutant mice as a proxy for mood - associated changes, and examined their molecular basis in the brain by conducting prediction analyses of the gene expression data.

«We analyzed dozens of variants of this gene and quantitatively measured expression in about 1,000 embryos, creating a quantitative data set that could be used to train mathematical models, utilizing parameter optimization,» Arnosti said.

For expression analysis in mice, we used microarray data as described above to select two internal control genes, cyclophilin B (Cphn2) and ribosomal protein S3 (Rps3).

The atlas now also includes RNA transcript data for 27 of these organ - specific tissues using next generation sequencing, providing a tissue distribution map of both protein and gene expression.

(Mixture - of - Isoforms) for isoform quantitation using RNA - Seq is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA - Seq data, and identifies differentially regulated isoforms or exons across samples.

Gene Set Enrichment Analysis (GSEA) is a microarray data - mining technique used to determine whether there is coordinated differential expression or «enrichment» in a set of functionally related genes when comparing control and experimental samples [35].

The new dependency data complement the Dependency Map team's ongoing efforts to use functional genomic technologies like CRISPR and RNA interference (RNAi) to locate vulnerabilities that arise within cancer cells as they compensate for the loss of critical genes due to mutations or expression changes.

He group is also interested in high throughput gene expression data analysis, especially using Bayesian network (BN) approaches.

This section describes the wheat genome assemblies available, gene models, using EnsemblPlants to access wheat data, accessing wheat expression data, finding variation data and finding the wheat orthologue of genes from other species.

We have developed new BN algorithms and tools for analysis of gene interaction networks using high throughput gene expression data.

We applied gene set enrichment analysis (GSEA)(23) to expression array data using KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways.

We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project.

The authors highlight the importance of measuring the variability of transcript expression and location in so many cells by using their data to discover genes with related functions in the cell.

From these data, we first measured allele - specific expression, where a heterozygous site in a gene can be used to distinguish gene expression from the two copies of a gene.

We have employed exactly the same methodology, but using the transcript integrity number15 (TIN) data instead of gene expression.

To evaluate whether altered expression of the ABL genes is associated with breast cancer progression and metastasis, we examined the expression of ABL1 and ABL2 in normal and invasive breast tumor specimens using published TCGA (The Cancer Genome Atlas) data sets (14 — 16).

Members of the TCGA Research Network identified and characterized four glioblastoma subtypes using gene expression, somatic mutation, and copy number data.

Dr Gilchrist said: «This study suggests that we may improve significantly on the widely used analysis methods for determining gene expression levels from high throughput sequence data: absolute quantitation offers a much sounder basis for determining changes in gene expression level, a measure widely used to determine the consequence of genetic, chemical or physical disturbances in living systems.»

The aim of his research was to use microarray gene expression data in order to characterize ageing in different human tissues and identify the age at which major changes in genetic expression profiles occur.

Furthermore, all data will be presented in a searchable «Tomato Expression Atlas,» a data - visualizing platform that will link gene expression information with images from a computer tomography (CT) scanner, which uses X-rays to render 3 - D virtual images that include internal sExpression Atlas,» a data - visualizing platform that will link gene expression information with images from a computer tomography (CT) scanner, which uses X-rays to render 3 - D virtual images that include internal sexpression information with images from a computer tomography (CT) scanner, which uses X-rays to render 3 - D virtual images that include internal structures.

We find evidence of expression for all VR, and almost all OR genes that are annotated as functional in the reference genome, and use the data to generate over 1100 new, multi-exonic, significantly extended receptor gene annotations.

Unfortunately these studies used a different strain of mouse and / or the gene - level expression data is not publically available, thus we were unable to compare those abundance estimates with the data reported here.

Nevertheless, we found OR gene expression estimates using this very different technology were consistent with our RNAseq data, lending support to both methods.

Researchers will use GTEx data to follow up on findings from genome - wide association studies and as a resource for the general study of gene expression networks.

The Bioinformatics group uses computational methods to analyse genome sequences, amino acid sequences, and gene expression data, both to identify new genes of interest and to determine their structure, function and role in the cell.

To annotate the genome, the team generated transcriptome sequence data — which can be used to measure gene expression based on RNA levels — in 12 different tissues types.

We combine biochemical, structural, cellular and functional information using purified proteins, mutant and transgenic plants, yeast and chemical genomic screening systems, transient gene expression assays, confocal microscopy and in silico data analysis to compare ROP - centered kinase signaling during cell polarity (in vitro pollen tubes), morphogenesis (whole plant) and pathogenesis (fungi - infected cells).

Title: Digital spatial profiling platform allows for spatially - resolved, high - plex quantification of mRNA distribution and abundance on FFPE and fresh frozen tissue sections Date / Time: Tuesday, April 17 2018, 8am - 12:00 pm CT Author: Daniel Zollinger, NanoString Poster # / Location: 3434 / Section 18, Board 16 Hyperlink: http://www.abstractsonline.com/pp8/#!/4562/presentation/7119 Digital Spatial Profiling can be used to obtain high - plex, spatial mRNA expression data (10's to 100's of genes) and protein expression data on FFPE and fresh frozen tissue sections.

In this activity students analyze data on the expression of the tb1 gene and use it to formulate an explanation as to how a specific difference in the corn version of the gene explains the phenotype of less branching.

Provided data analysis that included statistics package development with staff statisticians, and high throughput assay advent and development (immediate early gene expression); Taq - man primer - probe research and design using primer express.

Moreover, using a Bayesian deconvolution method to estimate methylation levels from the data [53], we found promoter methylation levels to be significantly inversely correlated (p value = 2.3e - 25) with previously published gene expression levels of T cells from human samples (Figure S1).