Topic
GeneCalling
About: GeneCalling is a research topic. Over the lifetime, 94 publications have been published within this topic receiving 28302 citations.
Papers published on a yearly basis
Papers
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Abstract: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is de- scribed that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be inter- preted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly charac- terized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
17,586 citations
TL;DR: A high-capacity system was developed to monitor the expression of many genes in parallel by means of simultaneous, two-color fluorescence hybridization, which enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA.
Abstract: A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization.
10,916 citations
TL;DR: The use of translated mRNA in profiling experiments might depict the proteome more closely than does the use of total mRNA, which would combine the technical potential of genomics with the physiological relevance of proteomics.
412 citations
TL;DR: qRT‐PCR evaluation and analysis demonstrated that traditionally used reference genes, such as 18s RNA, β‐actin, and GAPDH, are not reliable reference genes for pharmacogenomics and toxicogenomics studies, and hTBCA and small RNAs are more stable during drug treatment, and they are better reference genes in pharmacogenetics and toxicgenomics studies.
Abstract: Pharmacogenomics, toxicogenomics, and small RNA expression analysis are three of the most active research topics in the biological, biomedical, pharmaceutical, and toxicological fields. All of these studies are based on gene expression analysis, which requires reference genes to reduce the variations derived from different amounts of starting materials and different efficiencies of RNA extraction and cDNA synthesis. Thus, accurate normalization to one or several constitutively expressed reference genes is a prerequisite to valid gene expression studies. Although selection of reliable reference genes has been conducted in previous studies in several animals and plants, no research has been focused on pharmacological targets, and very few studies have had a toxicological context. More interestingly, no studies have been performed to identify reference genes for small RNA analysis although small RNA, particularly microRNA (miRNA)-related research is currently one of the fastest-moving topics. In this study, using MCF-7 breast cancer cells as a model, we employed quantitative real-time PCR (qRT-PCR), one of the most reliable methods for gene expression analysis in many research fields, to evaluate and to determine the most reliable reference genes for pharmacogenomics and toxicogenomics studies as well as for small RNA expression analysis. We tested the transcriptional expression of five protein-coding genes as well as five non-coding genes in MCF-7 cells treated with five different pharmaceuticals or toxicants [paclitaxel (PTX), gossypol (GOS), methyl jasmonate (JAS), L-nicotine (NIC), and melamine (mela)] and analyzed the stability of the selected reference genes by four different methods: geNorm, NormFinder, BestKeeper, and the comparative ΔCt method. According to our analysis, a protein-coding gene, hTBCA and four non-coding genes, hRNU44, hRNU48, hU6, and hRNU47, appear to be the most reliable reference genes for the five chemical treatments. Similar results were also obtained in dose-response and time-course assays with gossypol (GOS) treatment. Our results demonstrated that traditionally used reference genes, such as 18s RNA, β-actin, and GAPDH, are not reliable reference genes for pharmacogenomics and toxicogenomics studies. In contrast, hTBCA and small RNAs are more stable during drug treatment, and they are better reference genes for pharmacogenomics and toxicogenomics studies. To widely use these genes as reference genes, these results should be corroborated by studies with other human cell lines and additional drugs classes and hormonal treatments.
145 citations
TL;DR: This work has inactivated many thousands of worm genes in two large-scale analyses, finding out what genes do and studying the effects, in 'model' organisms.
Abstract: One way of finding out what genes do is to inactivate them, and to study the effects, in 'model' organisms. That has now been done for many thousands of worm genes in two large-scale analyses.
137 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2017 | 3 |
| 2016 | 5 |
| 2015 | 1 |
| 2014 | 1 |
| 2013 | 2 |
| 2012 | 5 |