MD 1.0 is an automated Mass Spectrometry service that helps scientists extracts insights from complex protein mixtures by determining which proteins may be present or differentially abundant across your samples.

You can read more in our pre-published paper on BioRxiv.

There are two core parts to the service: an automated data analysis workflow to process raw or pre-processed (MaxQuant) Mass Spectrometry data and a web application to help analyse and share your results.

You don’t need to be an expert in Mass Spectrometry to make use of this workflow. In fact, it is designed to lower the barrier for all life scientists to access 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. 
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Examples of analyses you could perform with this service include protein abundance in healthy/diseased tissues or observing downstream effects of gene removal on protein expression. 

We've made it simple to get started. If you have .RAW files that have been produced using a ThermoFisher High Res (MS1) Mass Spectrometer, you're good to go.

If you have .RAW files that have been produced using a Bruker Mass Spectrometer, then a conversion to mzml format will be required.
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For .RAW file processing, 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.

We also accept MaxQuant output text files that were produced using both LFQ-DDA and TMT methods - see "Types of experiments" answer below.

We will include more MS formats, so please contact us to let us know what you use.

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. 

If you don't have any appropriate data, you can opt to use sample data.
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Go to our community page to find an MS facility near you.

It depends! If you are processing an experiment with MaxQuant output text files, your results will be ready in minutes.

If you are using .RAW MS files, your results should be available to you within a day.

If we foresee any processing issues, a real human will be in contact to let you know. You can view progress online.

To date, we support Label-Free Quantitative, Data-Dependant Acquisition (LFQ-DDA) for both MaxQuant output text files and .RAW MS files. For the Tandem Mass Tag (TMT) method, we support the upload of MaxQuant output text files.

We will be introducing additional methods in the future.

For .RAW files, we recommend the following experiment attributes:
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* an LFQ-DDA experiment
* A minimum of 1 condition
* A minimum of 3 MS raw data files
* Data acquired using a ThermoFisher High Res (MS1) Mass Spectrometer

For MaxQuant output text file processing, you will need the following:

* msms.txt
* peptides.txt
* modificationSpecificPeptides.txt
* proteinGroups.txt
* evidence.txt
* summary.txt
* parameters.txt

While Mass Dynamics will eventually be compatible with all instruments, to date ThermoFisher is compatible.

Bloom.J., Triantafyllidis.A., Quaglieri.A., Burton Ngov.P., Infusini.G., & Webb.A. (n.d.). Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data. J. Proteome Res. 2021, 20, 11, 5180–5188. DOI: 10.1021/acs.jproteome.1c00683

The volcano plot in the Analysis tab supports the Mass Dynamics protein quantification experience. It can be used to view more information about your proteins across sample comparisons, create candidate lists and use these lists to perform enrichment analysis with REACTOME.

In some cases, the volcano plot may fail to render. If this happens, please check that your browser is up to date. Should you still have this issue, please contact us.

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.

MD 1.0 differs in a number of fundamental ways:
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* Web- and subscription-based: No ‘seat’ or ‘license’ constraints. Seamless and instant information sharing enables true collaboration and better sharing of work amongst your team.
‍* Intuitive design: No manual handling between tools, modules or the setting of complex input parameters. A seamless experience supports high quality output, improved efficiencies and allows you to focus on the biology.
* Cloud-based service: No installation or IT maintenance needed. Access via web browser - anytime and anywhere and enjoy endless capacity to scale and meet your labs’ processing needs.
* Automation of scientific processing and statistics: No time-wasting on repetitive tasks. Scientific processing is done at lightning-fast speed without compromising efficacy. A comprehensive QC report is produced that demonstrates if the data collection process has failed in a way that will affect your results allowing you to focus on reviewing and reporting on scientific outcomes.
* Flexible pricing: No large lump sum license fees or ‘upgrade’ payments for new releases. Have the flexibility to pay monthly and scale up or down to align your usage and throughput.
* MS Manufacturer-agnostic: No manufacturer ‘lock-in’. You have the flexibility to use any MS setup required to fit your business and scientific needs.
* Human-powered: No long delays to get help. We thrive off collaborating with you and other customers. If you need help or have an idea on how to improve the service, you will talk to a real person.‍

Built for simplicity, we have limited the number of parameters required, and can activate particular parameters on request.

We use DIA-NN to make it easier for our users to run and leverage the tool built by V Demichev 2020 and follow recommendations laid out in the DIA-NN github page. In summary, we apply the Global Q-Value filter to be 0.01 for identification, both at the ProteinGroup and Peptide level. When it comes to the quantitative analysis, we consider “quantifiable” only proteins that have been identified in at least 50% of replicates in at least one condition.

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.

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
* MS2

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 publication purposes, please get in contact with us so the results can be manually verified.

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 manually verified.

Ingested raw data and results files will only be stored as per Terms of Use. It will then be deleted and not accessible by Mass Dynamics or the submitter of the experiment (researcher). Files can be downloaded and saved locally at any given point.