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Home"Analysis Of Dda Data Works I Want To Briefly Give You A Sense Of Generally How You'Ll Analyze Your D Da Data Although If You'Re Staying For The MexicoíS On Summer School Later In The Week You'Ll Get This In A Lot More Detail The First Step In Identifying Your Peptides Or The First Step In The Analysis Is To Identify Your Peptides From Your Ms/Ms Spectra And This Is A Process Called Peptide Spectral Matching And There'S Really Three Ways That People Do This The First Way Is A Database Search And This Is Where You Compare Your Experimental Spectra To All Theoretical Spectra Predicted From A Database Of Possible Peptides Based On The Genomic Sequence So A Database Search Requires That You'Ve Also Have Some Genomic Reference Sequence For That Organism The Second Way Is Called A Spectral Library Search And This Is Where You Compare Your Experimental Vectra To A Library Of All Spectra That You'Ve Identified Previously Probably From Another Database Search And The Last Method Is De Novo And This Is Where You Look At The Fragment Spectra And Try To Piece Together What The Amino Acid Sequence Should Be And After Each Of These Different Options I'Ve Listed A Few Different Software Tools That Perform Each Of These Types Of Analysis Now For Any Of These Peptide Identification Methods We Need A Way To Assign Statistics That We'Ve Assigned The Right Peptide To Our Spectra And We Do This With Something Called The Target Decoy Approach And That Means That You Search For Real Peptides That You Expect Should Be In Your Sample But You Also Search For Shuffled Or Reversed Fake Peptide Sequences Which Should Not Be There And Those Are Your Decoys This Allows You To Determine How Often Your Search Finds The Wrong Answers Which Is Called Your False Discovery Rate Or Fdr And The Way This Works Is You Determine Distributions Of Your Target Decode Or Your Target Hits In Green And Your Decoy Hits In Red And That Allows You To Set A Score Threshold Above Which You'Ll Accept Any Peptide Id At A Known Proportion Of Decoy Hits Often We Use 1% Fdr After You'Ve Identified Your Peptides And Assign Statistics To Those Identifications You Probably Need To Infer Proteins So Although In Bottom-Up Proteomics Were Actually Looking At Peptides We Need Statistically Rigorous Ways To Determine What Proteins We Found And It'S Important To Note That You Must Compute Your Protein And Peptide Fdr Separately So A 1% Peptide Fdr Will Almost Always Correspond To A Higher Protein Fdr And There'S Many Different Programs That Will Do This For You A Couple Examples Are Protein Profit And Mayu And There'S Actually Many Different Tools Well The Final Step Is To Quantify Your Peptides And Proteins And I'Ll Talk About That More On The Next Slide But I Want To Make The Point That There'S Many Different Tools That We'Ll Do Each Of These Steps That Are Developed Academically Or Commercially One Really Great Tool Is Max Font Because It Will Do All Of These Steps For You It Puts Everything Together But I'Ve Also Given You A Citation For An Example Of A Different Route That I Published Earlier This Year"

"Analysis Of Dda Data Works I Want To Briefly Give You A Sense Of Generally How You'Ll Analyze Your D Da Data Although If You'Re Staying For The MexicoíS On Summer School Later In The Week You'Ll Get This In A Lot More Detail The First Step In Identifying Your Peptides Or The First Step In The Analysis Is To Identify Your Peptides From Your Ms/Ms Spectra And This Is A Process Called Peptide Spectral Matching And There'S Really Three Ways That People Do This The First Way Is A Database Search And This Is Where You Compare Your Experimental Spectra To All Theoretical Spectra Predicted From A Database Of Possible Peptides Based On The Genomic Sequence So A Database Search Requires That You'Ve Also Have Some Genomic Reference Sequence For That Organism The Second Way Is Called A Spectral Library Search And This Is Where You Compare Your Experimental Vectra To A Library Of All Spectra That You'Ve Identified Previously Probably From Another Database Search And The Last Method Is De Novo And This Is Where You Look At The Fragment Spectra And Try To Piece Together What The Amino Acid Sequence Should Be And After Each Of These Different Options I'Ve Listed A Few Different Software Tools That Perform Each Of These Types Of Analysis Now For Any Of These Peptide Identification Methods We Need A Way To Assign Statistics That We'Ve Assigned The Right Peptide To Our Spectra And We Do This With Something Called The Target Decoy Approach And That Means That You Search For Real Peptides That You Expect Should Be In Your Sample But You Also Search For Shuffled Or Reversed Fake Peptide Sequences Which Should Not Be There And Those Are Your Decoys This Allows You To Determine How Often Your Search Finds The Wrong Answers Which Is Called Your False Discovery Rate Or Fdr And The Way This Works Is You Determine Distributions Of Your Target Decode Or Your Target Hits In Green And Your Decoy Hits In Red And That Allows You To Set A Score Threshold Above Which You'Ll Accept Any Peptide Id At A Known Proportion Of Decoy Hits Often We Use 1% Fdr After You'Ve Identified Your Peptides And Assign Statistics To Those Identifications You Probably Need To Infer Proteins So Although In Bottom-Up Proteomics Were Actually Looking At Peptides We Need Statistically Rigorous Ways To Determine What Proteins We Found And It'S Important To Note That You Must Compute Your Protein And Peptide Fdr Separately So A 1% Peptide Fdr Will Almost Always Correspond To A Higher Protein Fdr And There'S Many Different Programs That Will Do This For You A Couple Examples Are Protein Profit And Mayu And There'S Actually Many Different Tools Well The Final Step Is To Quantify Your Peptides And Proteins And I'Ll Talk About That More On The Next Slide But I Want To Make The Point That There'S Many Different Tools That We'Ll Do Each Of These Steps That Are Developed Academically Or Commercially One Really Great Tool Is Max Font Because It Will Do All Of These Steps For You It Puts Everything Together But I'Ve Also Given You A Citation For An Example Of A Different Route That I Published Earlier This Year"

Summary: Hybrid Mass Spectrometers in Proteomics and Metabolomics

Modern mass spectrometers used in proteomics and metabolomics are hybrid instruments that incorporate multiple mass spectrometry (MS) units. Typically, these include two quadrupoles (Q1 and Q2) followed by a final mass detector, which can be another quadrupole, an Orbitrap, or a time-of-flight (TOF) detector. The configuration allows for the selection of specific masses for fragmentation before detection.

Key Scanning Methods

Precursor Ion Scan (MS1):
- Q1 is set to select a broad mass range (e.g., 400 m/z for peptides).
- Q2 has gas off, allowing detection of intact peptides.
Fragment Ion Scan (MS2):
- Q1 selects a narrower mass range.
- Q2 collides selected peptides with inert gas to induce fragmentation before detection.

Acquisition Strategies

Data Dependent Acquisition (DDA):
- The scan sequence adapts based on detected data, leading to variability in scans across runs.
- Targeted DDA uses an inclusion list to monitor specific peptides, while untargeted DDA collects data based on the most abundant precursor ions.
Data Independent Acquisition (DIA):
- The scan sequence remains constant regardless of detected data.
- Targeted DIA methods include Selected Reaction Monitoring (SRM) and Parallel Reaction Monitoring (PRM), focusing on predefined masses.
- Untargeted DIA (SWATH) fragments larger mass ranges to capture all peptides of interest.

Targeted DDA Process

Initial Scan: Q1 is set to a wide mass range to capture all peptides.
Comparison: The mass spectrometer compares detected ions to the inclusion list.
Fragmentation: Matches are selected for fragmentation in Q2, and the process is repeated for all identified precursors.

Untargeted DDA Process

Similar to targeted DDA but focuses on the most abundant precursor signals without a predefined list, leading to a stochastic identification process.

Stochasticity in DDA

Repeated analyses of the same sample yield different protein identifications, with diminishing returns in unique identifications over multiple runs.

This overview highlights the operational principles and methodologies of hybrid mass spectrometers in proteomics and metabolomics, emphasizing the differences between DDA and DIA approaches

Notes on DDA Data Analysis

Overview

DDA (Data-Dependent Acquisition): Analyzing mass spectrometry (MS/MS) data to identify peptides.

Steps in Peptide Identification

Peptide Spectral Matching: Identifying peptides from MS/MS spectra.
- Database Search: Compare experimental spectra to theoretical spectra from a genomic database.
- Spectral Library Search: Compare to a library of previously identified spectra.
- De Novo: Analyze fragment spectra to deduce amino acid sequences.
Statistical Validation:
- Target-Decoy Approach:
  - Search for real peptides and shuffled (decoy) sequences.
  - Determine False Discovery Rate (FDR) using distributions of target and decoy hits.
Protein Inference:
- Compute protein and peptide FDR separately.
- Tools: Protein Prophet, Mayu.
Quantification:
- Quantify identified peptides and proteins.