Dataset Information

Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells.

ABSTRACT:

Background

Quantification of transcripts, proteins, or metabolites is straightforward when the factor used to normalize these values remains constant between samples. However, normalization factors often vary among samples and thus must be developed for each new analytical method.

Results

We demonstrate quantification of transcript and protein levels in Arabidopsis based on genomic DNA copy number. We extracted total nucleic acid from 3-week-old rosette leaves of wild-type Arabidopsis and the pale-green/dwarf mutant, abc4, and quantified the number of transcripts by quantitative reverse-transcription PCR using genomic DNA copy number and ploidy (as determined by cytometry) for normalization. Our data indicated that normalization using genes commonly employed as references resulted in inaccuracies in transcript levels of the genes RBC-L and RBC-S (encoding the large and small subunits, respectively, of ribulose 1,5-bisphosphate carboxylase/oxygenase) in wild type and mutant. Normalization using genomic DNA copy number and ploidy, however, appropriately showed that the RBC-L and RBC-S transcript levels per cell in the mutant were significantly lower than that in wild type. Furthermore, quantification revealed that a cell of a 3-week-old wild-type Arabidopsis rosette leaf had an average of 7.5 × 103 transcripts of RBC-L, 9.9 × 103 transcripts of RBC-S, and 1.4 × 106 18S rRNA. We similarly analyzed the accumulation of RBC-L and LHCP (light-harvesting chlorophyll a/b protein) in wild type and mutant based on ploidy and genomic DNA copy number that was determined by direct quantitative PCR analysis of extracts using a DNA polymerase tolerant to a wide range of common PCR inhibitors. Furthermore, we estimated the number of RBC-L molecules (2.63 × 108) and chlorophyll molecules (1.85 × 109) in each cell in 3-week-old wild-type rosette leaves; these values had relatively low coefficients of variation, underscoring the reliability of our method.

Conclusion

Genomic DNA copy number and ploidy are useful as general normalization factors, providing an easy method for determining the number of transcripts, proteins, and metabolites in a cell.

SUBMITTER: Shimada H

PROVIDER: S-EPMC3023742 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Publications

Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells.

Shimada Hiroshi H Obayashi Takeshi T Takahashi Naoki N Matsui Minami M Sakamoto Atsushi A

Plant methods 20101229

<h4>Background</h4>Quantification of transcripts, proteins, or metabolites is straightforward when the factor used to normalize these values remains constant between samples. However, normalization factors often vary among samples and thus must be developed for each new analytical method.<h4>Results</h4>We demonstrate quantification of transcript and protein levels in Arabidopsis based on genomic DNA copy number. We extracted total nucleic acid from 3-week-old rosette leaves of wild-type Arabido ...[more]

PMID: 21190547

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Project description:Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/.aCNViewer@cephb.fr.

Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Dataset Information

Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells.

Background

Results

Conclusion

Publications

Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells.

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