Sentences with phrase «gene expression data as»

Monitoring these expression ratios in live cells would provide prospective gene expression data as opposed to traditional retrospective characterization.

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 animals.

Not exact matches

They processed data relating to genetics, including DNA sequences, and to gene expression, as well as epigenetic features — chemical reactions that influence genome functioning and hence phenotype by activating and deactivating genes.

Computational genomics includes: bio-sequence analysis, gene expression data analysis, phylogenetic analysis, and more specifically pattern recognition and analysis problems such as gene finding, motif finding, gene function prediction, fusion of sequence and expression information, and evolutionary models.

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.

The researchers named the method after a fish famous for swimming upstream because it employs an algorithm that can estimate the effect of biases and the expression level of genes as experimental data streams by.

MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis) projects gene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and AlzheimeGene Co-expression Network Analysis) projects gene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimegene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer's.

The second tool, SuperExactTest, establishes the very first theoretical framework for assessing the statistical significance of multi-set intersections and enables users to compare very large sets of data, such as gene sets produced from genome - wide association studies (GWAS) and differential expression analysis.

Andrechek's federally funded study looked at mice containing all subtypes and compared the makeup of the rodent tumors and the way the genes acted, known as gene expression, to human tumor data.

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

These data demonstrate that variations in continuous quantitative traits such as body mass, adipocyte size, and BMI are correlated with quantitative variations in the expression of genes.

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.

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).

«This data allows classification of all human protein - coding genes into those coding for house - hold functions (present in all cells) and those that are tissue - specific genes with highly specialized expression in particular organs and tissues, such as kidney, liver, brain, heart, pancreas.

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.

Inspired by the knowledge - based analysis methods developed for gene expression data analysis, we implemented methods for examining functionally - related SNPs as a group.

The raw data as well as the processed data of the microarray were deposited under Gene Expression Omnibus (GEO)(http://www.ncbi.nlm.nih.gov/geo) accession number GSE87159.

We focus on developing computational methods and tools for (a) analyzing large - scale gene expression data related to human cancer in search for gene markers and disease sub-categories, (b) identifying regulatory elements such as miRNA precursors and their targets in whole genomes of plants and mammals, (c) building theoretical models of gene regulatory networks.

On Supplementary Fig. 36c, we show the number of most informative genes (defined as the union of genes with importance > = 0.1 across all the 13 models in the case of the gene expression model, and the union of the genes with importance > = 0.1 across the 3 models in the case of TIN, with «importance» being a measure computed by xgboost) with respect to each tissue and data type.

The idea behind our work in bioinformatics is to build on existing methodologies regarding large - scale data analysis and to develop novel algorithms for processing and merging complex biological data from multiple sources such as gene expression data, sequence information, protein - to - protein interaction data, clinico - pathological data etc..

As calculations are based on DNA sequence data and not physical measurements, it can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environmenAs calculations are based on DNA sequence data and not physical measurements, it can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environmenas environment.

These techniques and functional analysis of the resulting data revealed a number of up - and down - regulated proteins and mRNAs; i.e., up - regulated by a signal (originating internal or external to the cell) that results in increased expression of one or more genes and as a result the protein (s) encoded by those genes, and down - regulated by a process resulting in decreased gene and corresponding protein expression.

This is in accordance with previous reports that decitabine and 5 - azacytidine produce a marked synergistic effect in combination with suberoylanilide hydroxamic acid and romidepsin in T - lymphoma cell lines by modulating cell cycle arrest and apoptosis.26, 27 As a mechanism of action, KMT2D mutations of B - lymphoma cells promote malignant outgrowth by perturbing methylation of H3K4 that affect the JAK - STAT, Toll - like receptor, or B - cell receptor pathway.28, 29 Here our study indicated that dual treatment with chidamide and decitabine enhanced the interaction of KMT2D with the transcription factor PU.1, thereby inactivating the H3K4me - associated signaling pathway MAPK, which is constitutively activated in T - cell lymphoma.13, 30,31 The transcription factor PU.1 is involved in the development of all hematopoietic lineages32 and regulates lymphoid cell growth and transformation.33 Aberrant PU.1 expression promotes acute myeloid leukemia and is related to the pathogenesis of multiple myeloma via the MAPK pathway.34, 35 On the other hand, PU.1 is also shown to interact with chromatin remodeler and DNA methyltransferease to control hematopoiesis and suppress leukemia.36 Our data thus suggested that the combined action of chidamide and decitabine may interfere with the differentiation and / or viability of PTCL - NOS through a PU.1 - dependent gene expression program.

In mouse embryo fibroblasts (MEFs)[2], as well as in the PCa cell lines [unpublished data], DAXX represses expression of these genes via mechanisms that include its binding to DNA methyl transferases (DNMTases) and hypermethylation of the promoter regions of the corresponding genes [2].

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.

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.

Besides information gathered to answer specific experimental questions, as determined by the interests of individual partners [35]--[41], the collective data offered the opportunity to search for coordinated gene expression patterns in a systematic exploration of the mouse ES transcriptome under a battery of different experimental settings, thus minimizing possible site - specific artifacts.

If human cells are sorted together mouse cells, the data will have heterogenous expression patterns as single cell data does, only with human genes rather than mouse genes.

Whole genome expression data of livers of wildtype and PPARα − / − mice fasted for 24 h served as positive control for PPARα - dependent gene regulation.

Makarevitch I, Martinez - Vaz B. (2017) Killing two birds with one stone: Model plant systems as a tool to teach the fundamental concepts of gene expression while analyzing biological data.

Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty - seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in «Expression Waves» and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in «Expression Waves» and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in «Expression Waves» and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic Expression Waves» and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources.

High levels of gene expression were found in both strains for genes involved in growth, energy and respiration (e.g., ribosomal proteins, ATP synthase, pseudoazurin), and the C - 1 metabolic carbon such as methanol dehydrogenase, ribulose monophosphate enzymes (D - arabino -3-hexulose 6 - phosphate formaldehyde - lyase, 6 - phospho -3-hexuloisomerase), formaldehyde activating enzymes (Data S6, Fig.

-LSB-...] Our data provide evidence that the distal part of the gut has the ability to sense nutrients such as butyrate, resulting in the up - regulation of PYY and proglucagon gene expression.»

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.