Dataset Information

Bayesian proteoform modeling improves protein quantification of global proteomic measurements

ABSTRACT: Data used for the implementation of a Bayesian Proteoform Quantification model (BP-Quant) that uses statistically derived peptide signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. BP-Quant is available on GitHub as both MatLab and R packages: https://github.com/PNNL-Comp-Mass-Spec/BP-Quant Plasma samples collected from standard inbred mice were digested with trypsin then analyzed with an LTQ-Orbitrap Velos mass spectrometer. Data was searched with SEQUEST using PNNL's DMS processing pipeline.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Richard Smith Katrina Waters

PROVIDER: MSV000087701 | MassIVE | Thu Jun 24 18:24:00 BST 2021

REPOSITORIES: MassIVE

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Publications

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

Webb-Robertson Bobbie-Jo M BJ Matzke Melissa M MM Datta Susmita S Payne Samuel H SH Kang Jiyun J Bramer Lisa M LM Nicora Carrie D CD Shukla Anil K AK Metz Thomas O TO Rodland Karin D KD Smith Richard D RD Tardiff Mark F MF McDermott Jason E JE Pounds Joel G JG Waters Katrina M KM

Molecular & cellular proteomics : MCP 20141201 12

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as al ...[more]

PMID: 25433089

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Project description:Although the evidence for a genetic predisposition to human essential hypertension is compelling, the genetic control of blood pressure (BP) is poorly understood. The Dahl salt-sensitive (S) rat is a model for studying the genetic component of BP. Using this model we previously reported the identification of 16 different genomic regions that contain one or more BP quantitative trait loci (QTLs). The proximal region of rat chromosome 1 contains multiple BP QTLs. Of these, we have localized the BP QTL1b region to a 13.5cM (20Mb) region. Interestingly, five additional independent studies in rats and four independent studies in humans have reported genetic linkage for BP control by regions homologous to QTL1b. To view the overall renal transcriptional topography of the positional candidate genes for this QTL, we sought a comparative gene expression profiling between a congenic strain containing QTL1b and control S rats by employing: (1) a saturated QTL1b interval specific oligonucleotide array, and (2) a whole genome cDNA microarray representing 20,465 unique genes that are positioned outside the QTL. Results indicated that 19 out of the 231 positional candidate genes for this QTL are differentially expressed between the two strains tested. Surprisingly, over 1,500 genes outside of QTL1b were differentially expressed between the two rat strains. Integrating the results from the two approaches revealed at least one complex network of transcriptional control initiated by the positional candidate Nr2f2. This network appears to account for the majority of gene expression differences occurring outside of the QTL interval. Further substitution mapping is currently underway to test the validity of each of these differentially expressed positional candidate genes. These results demonstrate the importance of using a saturated oligonucleotide array for identifying and prioritizing differentially expressed positional candidate genes of a BP QTL. Keywords: : rat, hypertension, genetics, polygenic trait, microarray, gene expression

Project description:Although the evidence for a genetic predisposition to human essential hypertension is compelling, the genetic control of blood pressure (BP) is poorly understood. The Dahl salt-sensitive (S) rat is a model for studying the genetic component of BP. Using this model we previously reported the identification of 16 different genomic regions that contain one or more BP quantitative trait loci (QTLs). The proximal region of rat chromosome 1 contains multiple BP QTLs. Of these, we have localized the BP QTL1b region to a 13.5cM (20Mb) region. Interestingly, five additional independent studies in rats and four independent studies in humans have reported genetic linkage for BP control by regions homologous to QTL1b. To view the overall renal transcriptional topography of the positional candidate genes for this QTL, we sought a comparative gene expression profiling between a congenic strain containing QTL1b and control S rats by employing: (1) a saturated QTL1b interval specific oligonucleotide array, and (2) a whole genome cDNA microarray representing 20,465 unique genes that are positioned outside the QTL. Results indicated that 19 out of the 231 positional candidate genes for this QTL are differentially expressed between the two strains tested. Surprisingly, over 1,500 genes outside of QTL1b were differentially expressed between the two rat strains. Integrating the results from the two approaches revealed at least one complex network of transcriptional control initiated by the positional candidate Nr2f2. This network appears to account for the majority of gene expression differences occurring outside of the QTL interval. Further substitution mapping is currently underway to test the validity of each of these differentially expressed positional candidate genes. These results demonstrate the importance of using a saturated oligonucleotide array for identifying and prioritizing differentially expressed positional candidate genes of a BP QTL. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either the oligonucleotide microarray or a custom TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL1b genes. A âflip-dyeâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Seven âflip dyeâ normalized files are submitted for the oligonucleotide array and twelve individual hybridizations are submitted for the cDNA array.

Dataset Information

Bayesian proteoform modeling improves protein quantification of global proteomic measurements

Publications

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

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