Not exact matches
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.
«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.
They are also developing sgRNAs that will enable even more fine - tuning of
gene expression levels and on software for
analyzing the
data.
The quintile normalized
data were
analyzed to identify
genes with significantly up - or down - regulated
expression (FDR p - value < 0.05) with an arbitrary cutoff of at least a two-fold change.
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.
Gene expression data of embryos were
analyzed by one - way ANOVA in JMP ® software (SAS Institute, Cary, NC).
A reliability score is set manually for all
genes and indicates the level of reliability of the
analyzed protein
expression pattern based on available protein / RNA /
gene characterization
data from both HPA and the UniProtKB / Swiss - Prot database.
Gene expression data (CNRQ) of oocytes and cumulus cells were
analyzed by one - way ANOVA with Bonferroni post test in Prism version 5.02.
A reliability score is set for all
genes and indicates the level of reliability of the
analyzed protein
expression pattern based on available protein / RNA /
gene characterization
data.
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.
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.