Dataset Information

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

ABSTRACT: In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments characterizing a series of mixtures containing known proteins. In general, results from characterization of known proteins show good agreement with our predictions. Finally, we consider how these approaches may be applied to more complicated data sets, as when peptides are separated by charge state prior to false-positive determination.

SUBMITTER: Huttlin EL

PROVIDER: S-EPMC2572755 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

Huttlin Edward L EL Hegeman Adrian D AD Harms Amy C AC Sussman Michael R MR

Journal of proteome research 20070101 1

In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments char ...[more]

PMID: 17203984

Similar Datasets

Project description:BackgroundIn microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.ResultsThis study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.ConclusionsMicroarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.

Project description:BackgroundProtein isoform generation, which may derive from alternative splicing, genetic polymorphism, and posttranslational modification, is an essential source of achieving molecular diversity by eukaryotic cells. Previous studies have shown that protein isoforms play critical roles in disease diagnosis, risk assessment, sub-typing, prognosis, and treatment outcome predictions. Understanding the types, presence, and abundance of different protein isoforms in different cellular and physiological conditions is a major task in functional proteomics, and may pave ways to molecular biomarker discovery of human diseases. In tandem mass spectrometry (MS/MS) based proteomics analysis, peptide peaks with exact matches to protein sequence records in the proteomics database may be identified with mass spectrometry (MS) search software. However, due to limited annotation and poor coverage of protein isoforms in proteomics databases, high throughput protein isoform identifications, particularly those arising from alternative splicing and genetic polymorphism, have not been possible.ResultsTherefore, we present the PEPtidomics Protein Isoform Database (PEPPI, http://bio.informatics.iupui.edu/peppi), a comprehensive database of computationally-synthesized human peptides that can identify protein isoforms derived from either alternatively spliced mRNA transcripts or SNP variations. We collected genome, pre-mRNA alternative splicing and SNP information from Ensembl. We synthesized in silico isoform transcripts that cover all exons and theoretically possible junctions of exons and introns, as well as all their variations derived from known SNPs. With three case studies, we further demonstrated that the database can help researchers discover and characterize new protein isoform biomarkers from experimental proteomics data.ConclusionsWe developed a new tool for the proteomics community to characterize protein isoforms from MS-based proteomics experiments. By cataloguing each peptide configurations in the PEPPI database, users can study genetic variations and alternative splicing events at the proteome level. They can also batch-download peptide sequences in FASTA format to search for MS/MS spectra derived from human samples. The database can help generate novel hypotheses on molecular risk factors and molecular mechanisms of complex diseases, leading to identification of potentially highly specific protein isoform biomarkers.

Dataset Information

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

Publications

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets