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In-depth Analysis of Protein Inference Algorithms using a Workflow Framework and Well-Defined Metrics - iPRG 2008 provided DB analysis

ABSTRACT: This dataset is no actual new study but the results of the benchmark of the iPRG2008 benchmark dataset used in the manuscript "In-depth Analysis of Protein Inference Algorithms using a Workflow Framework and Well-Defined Metrics".

INSTRUMENT(S): Q TRAP

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Julian Uszkoreit

LAB HEAD: Katrin Marcus

PROVIDER: PXD003956 | Pride | 2016-08-10

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	MGI_UniProtMouse_20071203_iPRG2008_fixed__tdr.fasta	Fasta
	PT_model.txt	Txt
	iPRG2008_combined_faked_rt.mgf	Mgf
	mascot-iPRG2008_combined_faked_rt.idXML	Other
	merge1-mascot-fdr_filtered-iPRG2008_combined_faked_rt.idXML	Other

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Publications

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.

Audain Enrique E Uszkoreit Julian J Sachsenberg Timo T Pfeuffer Julianus J Liang Xiao X Hermjakob Henning H Sanchez Aniel A Eisenacher Martin M Reinert Knut K Tabb David L DL Kohlbacher Oliver O Perez-Riverol Yasset Y

Journal of proteomics 20160804

In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the prot ...[more]

PMID: 27498275

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In-depth Analysis of Protein Inference Algorithms using a Workflow Framework and Well-Defined Metrics - iPRG 2008 UniProt proteome analysis

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Project description:Histone modifications are a key epigenetic mechanism to activate or repress the expression of genes. Data sets of matched microarray expression data and histone modification data measured by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatic approach to detect genes that are differentially expressed between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene expression data and demonstrate that a proper normalization of the ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene expression and histone modification. The method is applied to different data sets and its superiority to a naive separate analysis of both data types is demonstrated. This GEO series contains the expression data of the Cebpa example data set. This data set was derived from sorted Cebpafl/fl and Cebpafl/fl;Mx1Cre murine hematopoietic LSKCD150- 18 post pIpC injections (conditional deletion of Cebpa). The specimens from three Cebpafl/fl and three Cebpafl/fl;Mx1Cre mice were hybridized separately on six Affymetrix Mouse Gene 1.0 ST arrays. Associated histone modification ChIP-seq data is provided by series GSE43007.

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