Dataset Information

A new approach to evaluating statistical significance of spectral identifications.

ABSTRACT: While nonlinear peptide natural products such as Vancomycin and Daptomycin are among the most effective antibiotics, the computational techniques for sequencing such peptides are still in their infancy. Previous methods for sequencing peptide natural products are based on Nuclear Magnetic Resonance spectroscopy and require large amounts (milligrams) of purified materials. Recently, development of mass spectrometry-based methods has enabled accurate sequencing of nonlinear peptide natural products using picograms of material, but the question of evaluating statistical significance of Peptide Spectrum Matches (PSM) for these peptides remains open. Moreover, it is unclear how to decide whether a given spectrum is produced by a linear, cyclic, or branch-cyclic peptide. Surprisingly, all previous mass spectrometry studies overlooked the fact that a very similar problem has been successfully addressed in particle physics in 1951. In this work, we develop a method for estimating statistical significance of PSMs defined by any peptide (including linear and nonlinear). This method enables us to identify whether a peptide is linear, cyclic, or branch-cyclic, an important step toward identification of peptide natural products.

SUBMITTER: Mohimani H

PROVIDER: S-EPMC5590104 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A new approach to evaluating statistical significance of spectral identifications.

Mohimani Hosein H Kim Sangtae S Pevzner Pavel A PA

Journal of proteome research 20130308 4

While nonlinear peptide natural products such as Vancomycin and Daptomycin are among the most effective antibiotics, the computational techniques for sequencing such peptides are still in their infancy. Previous methods for sequencing peptide natural products are based on Nuclear Magnetic Resonance spectroscopy and require large amounts (milligrams) of purified materials. Recently, development of mass spectrometry-based methods has enabled accurate sequencing of nonlinear peptide natural product ...[more]

PMID: 23343606

Similar Datasets

Project description:The rapid development of mass spectrometry (MS) technologies has solidified shotgun proteomics as the most powerful analytical platform for large-scale proteome interrogation. The ability to map and determine differential expression profiles of the entire proteome is the ultimate goal of shotgun proteomics. Label-free quantitation has proven to be a valid approach for discovery shotgun proteomics, especially when sample is limited. Label-free spectral count quantitation is an approach analogous to RNA sequencing whereby count data is used to determine differential expression. Here we show that statistical approaches developed to evaluate differential expression in RNA sequencing experiments can be applied to detect differential protein expression in label-free discovery proteomics. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. Furthermore, to remove bias associated with a single statistical approach a single ranked list of differentially expressed proteins is assembled by comparing edgeR and DESeq q-values directly with the false discovery rate (FDR) calculated by baySeq. This statistical approach is then extended when applied to spectral count data derived from multiple proteomic pipelines. The individual statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups.Spectral count data from shotgun proteomics experiments is semi-quantitative and semi-random, yet a robust way to estimate protein concentration. Tag-count approaches are routinely used to analyze RNA sequencing data sets. This approach, termed MultiSpec, utilizes multiple tag-count based statistical tests to determine differential protein expression from spectral counts. The statistical results from these tag-count approaches are combined in order to reach a final MultiSpec q-value to re-rank protein candidates. This re-ranking procedure is completed to remove bias associated with a single approach in order to better understand the true proteomic differences driving the biology in question. The MultiSpec approach can be extended to multiple proteomic pipelines. In such an instance, MultiSpec statistical results are integrated by collapsing protein groups across proteomic pipelines to provide a single ranked list of differentially expressed proteins. This integration mechanism is seamlessly integrated with the statistical analysis and provides the means to cross-validate protein inferences from multiple proteomic pipelines.

Dataset Information

A new approach to evaluating statistical significance of spectral identifications.

Publications

A new approach to evaluating statistical significance of spectral identifications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets