Correlation of BCVA recovery with age, and higher-order aberrations data without cycloplegia after treatment for 6 months.

Correlation of BCVA recovery with age, and higher-order aberrations data without cycloplegia after treatment for 6 months.</p

Baseline characteristics and objective refraction error of studied subjects with cycloplegia.

Baseline characteristics and objective refraction error of studied subjects with cycloplegia.</p

Treatment and higher-order aberrations data of studied patients.

Treatment and higher-order aberrations data of studied patients.</p

Correlation of BCVA recovery with age, and higher-order aberrations data with cycloplegia after treatment for 6 months.

Correlation of BCVA recovery with age, and higher-order aberrations data with cycloplegia after treatment for 6 months.</p

Example to explain data collection: Left eye of a 5-year-old girl who underwent iDesign measurements after cycloplegia.

(A) Eye image and refraction error power (diopter), wavefront reconstruction based on Zernike polynomials for all-order aberrations root mean square (RMS) error (μ), higher-order aberrations (HOAs) rate (%), all-order aberrations effective blur (diopter), and quality check signal light; (B) Point spread function and HOAs effective blur (diopter) using Fourier reconstruction; (C) HOAs Wavefront error map and HOAs RMS error (μ) using Fourier Reconstruction; (D) Zernike table and Polar Zernike coefficients (μ) at aperture diameter of 4 mm.</p

Cassia grandis

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC–MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ∼12500 Swiss-Prot entries, leaving ∼7500 gene products yet to be confidently cataloged. We characterize these “PA-unseen” proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future

The State of the Human Proteome in 2012 as Viewed through PeptideAtlas

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC–MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ∼12500 Swiss-Prot entries, leaving ∼7500 gene products yet to be confidently cataloged. We characterize these “PA-unseen” proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future

The State of the Human Proteome in 2012 as Viewed through PeptideAtlas

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC–MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ∼12500 Swiss-Prot entries, leaving ∼7500 gene products yet to be confidently cataloged. We characterize these “PA-unseen” proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future

The State of the Human Proteome in 2012 as Viewed through PeptideAtlas

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC–MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ∼12500 Swiss-Prot entries, leaving ∼7500 gene products yet to be confidently cataloged. We characterize these “PA-unseen” proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future

The State of the Human Proteome in 2012 as Viewed through PeptideAtlas

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC–MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ∼12500 Swiss-Prot entries, leaving ∼7500 gene products yet to be confidently cataloged. We characterize these “PA-unseen” proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future