Improving Collision Induced Dissociation (CID), High Energy Collision Dissociation (HCD), and Electron Transfer Dissociation (ETD) Fourier Transform MS/MS Degradome–Peptidome Identifications Using High Accuracy Mass Information

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. <i>J. Proteome Res</i>. <b>2011</b>), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20–40% more peptides than SEQUEST, Mascot, and MS_GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered >90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic–peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information, >1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components

Additional file 3: Table S3. of RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states

Long non-coding RNAs identified as expressed in neutrophils. (XLSX 341 kb

Additional file 8: Table S8. of RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states

Differentially expressed long non-coding RNAs in different groups comparison. (XLSX 56 kb

Additional file 1: Table S1. of RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states

Genes identified as expressed in neutrophils in all 9 subjects. (XLSX 893 kb

The relationship between average depth-coverage and variant detection sensitivity.

<p>The x-axis is the average depth-coverage. The y-axis is the theoretical fraction of genome where potential variants can be detected under the assumptions described above. The red dot marks 95% sensitivity at 13× coverage.</p

Additional file 6: Table S6. of RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states

Differential usage of exons in AD vs CRM comparison. (XLSX 25 kb

Sequencing and mapping statistics.

<p>Sequencing and mapping statistics.</p

Improving Collision Induced Dissociation (CID), High Energy Collision Dissociation (HCD), and Electron Transfer Dissociation (ETD) Fourier Transform MS/MS Degradome–Peptidome Identifications Using High Accuracy Mass Information

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. <i>J. Proteome Res</i>. <b>2011</b>), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20–40% more peptides than SEQUEST, Mascot, and MS_GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered >90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic–peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information, >1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components

Comparing platforms.

<p>Comparing platforms.</p

Depth-coverage statistics.

<p>Depth-coverage statistics.</p