Frequently asked questions.
Getting started with MD+ Discovery
What can MD+ Discovery do for me?
MD+ Discovery is an automated Mass Spectrometric analysis workflow that will tell you which proteins may be present or differentially abundant across your samples.
You don’t need to be an expert in Mass Spectrometry to make use of this workflow. In fact, it is designed to provide dependable, interpretable and publishable results that are supported by a detailed quality control report.
We do all this using state of the art analytical methods and require minimal inputs.
Examples of analyses you could perform with this service protein abundance in healthy/diseased tissues or observing downstream effects of gene removal on protein expression.
How do I get started?
At Mass Dynamics, we've made it simple to get started. If you have raw or converted files that have been produced using a ThermoFisher High Res (MS1) Mass Spectrometer, you're good to go.
If you have Bruker files, then mzml format will be required. Along with your MS data files, you will need to upload a FASTA file so that we understand the context of your experiment. If you don't have one, you can download one from the Uniprot website.
What if I don't have the required files?
In order to get the required data for this analytical workflow, you will need to process your samples using a Mass Spectrometer.
Luckily, there are hundreds, if not thousands of Mass Spectrometry facilities around the world that can help you do this.
Go to our community page to find an MS facility near you.
How long will Mass Dynamics take to process my experiment?
Your results should be available to you within a day. If we foresee any issues, a real human will be in contact to let you know. You can view progress online.
What types of experiments can MD+ Discovery process?
To date, we support Data-Dependant Acquisition, Label-Free Quantitative (DDA LFQ) methods and we will be introducing additional methods in the future.We recommend the following experiment attributes during the trial period:
* a DDA LFQ experiment
* A minimum of 1 condition
* A minimum of 3 MS raw data files
* Data acquired using a ThermoFisher High Res (MS1) Mass Spectrometer
Is it compatible with all Mass Spectrometry instruments’ raw data?
While Mass Dynamics will eventually be compatible with all instruments, to date ThermoFisher is compatible.
How does the MD+ Discovery workflow treat my MS data files?
First we perform a feature detection step to extract the raw MS1 features from your data. To ensure maximum accuracy we perform a quick database search to find as many high-confidence peptide identifications as possible. This recalibrates your data to account for drift associated with the Mass Spectrometer data acquisition process
To ensure that we can associate the correct features across multiple data files we link features together based on what has been identified in your data files. This allows us to confidently align and warp retention times. As a result we create a consensus map of every feature that has been detected in your data.
While the mass spectrometer’s fragmentation selection process is mostly stochastic and biased by intensity, we increase the number of ‘fragmented features’ by associating any MS1 feature that is included in the isolation window. This cloning process removes the distinction between a ‘selected’ feature and a feature that is co-fragmented. This allows us to retrieve more information from your data compared to classical approaches that typically limit searches to ‘selected’ features.
To detect and identify peptides that exist within your data, we run a secondary and more exhaustive search against your fasta database using tighter thresholds thanks to recalibrated data.
To determine the proteins that exist in your samples we run a protein inference step to reduce false discovery rates and increase the accuracy of identified proteins and peptides.
To drastically reduce the missing values in your data we perform an unbiased extraction of the MS1 data for every detected feature in the consensus map across all data files. We do this using a concept called Extracted Ion Chromatogram (EIC/XIC).
To ensure that we reduce false positives in the MS1 feature extraction, we create decoy features that are known to be wrong, and create a machine learning model. We test the model, and apply it across the entire extracted feature list. This thoroughly improves the accuracy of the feature detection and it also produces a measure of confidence that the MS1 feature actually exists.
The final step involves producing the quality control report, converting the data to a human-readable format and preparing all that is needed for you to take the driver’s seat and derive insights from your experiment.
How does the MD+ Discovery workflow differ from other workflows that do the same task?
MD+ Discovery differs in a number of fundamental ways:
* MD+ discovery requires minimal input parameters, allowing you to focus on the biology.
* MD+ discovery connects raw data to results, allowing you to avoid installing and maintaining one or more software tools to complete your analysis.
* MD+ discovery provides a comprehensive QC report that demonstrates if the data collection process has failed in a way that will affect your results.
Due to the complexity of both the data and workflow, it is impossible to retrieve identical results as other combinations of tools. Furthermore, it is important to keep in mind that reporting results at different false discovery (FDR) levels or using different FDR boundaries may contribute to varying results.
Why are there so few parameters needed?
Built for simplicity, we have limited the number of parameters required, and can activate particular parameters on request.
How are p-values calculated?
The p-values are calculated using ANOVA and the pairwise comparisons with Tukey’s post-hoc analysis, all p-values are corrected for multiple testing by “Benjamini-Hochberg” so they are in fact FDR values. This is a work-in-progress for trial purposes and an implementation of “*limma*” and “SAM” tests will be available shortly.
What is the identification False Discover Rate (FDR) cut off?
The workflow identification FDR cutoff is 1% and is applied to peptide-spectrum match (PSM), peptide and protein identification tables. The workflows' protein inference step is a work-in-progress and at this stage is considered conservative, and reporting much smaller numbers of proteins in comparison to state-of-the-art. A more comprehensive solution will be available shortly.
We report an FDR on the MS1 feature detection. Thanks to our machine learning processes, our workflow reports a measure of “confidence” that a feature is actually a real MS1 feature. Typical feature detection techniques do not provide a measure of accuracy of detection and matching.
What results files will MD+ Discovery create for me?
When Mass Dynamics has finished processing your files, you will be notified. Once you log in, you will be given access to your results files that you can download. This includes:
* A Quality Control (QC) report
* Feature Tables
* Peptide Intensity
* Peptide Table
* Protein Intensity
* Protein Table
* Features Identified
* Features All
Sharing and Data Storage
Can I share the results with my peers or team leaders?
You sure can. To ensure the right level of security, please let us know if you would like additional users added to Mass Dynamics so that password-sharing is not required. In the future we will make sharing even easier. And if you would like to use the results for any other purposes outside of the trial, please get in contact with us so the results can be validated.
What will happen with my data?
All results files that are produced by Mass Dynamics are owned by the submitter of the experiment (researcher). If you wish to use the workflow results for any public-facing purposes, please get in contact with MD so the results can be validated.
Slack and Mass Dynamics
Slack is a platform that connects teams with apps, services, and resources they need to get work done. This will be the main channel that the MD team will communicate with you.It's an open and public workspace so you can get yourself started by clicking here.