Compound Discoverer Software

Whether you are conducting single-sample analysis or extensive large-sample studies, Compound Discoverer software provides everything you need for small-molecule unknown data processing, including:

• Unknown peak detection using multi-factorial Peak Quality factors to identify and quantify with confidence
• Advanced statistical tools
• Interactive data visualization capabilities
• Compound annotation tools
• Integrated database and mass spectral libraries
• Biochemical pathway mapping
• Untargeted stable isotope labelling analysis
• Normalization tools for large studies

Regardless of study sample size, each sample contains a wealth of raw data points. Some of those data points are related to one another and many are not. Making sense of this complex, but high-quality , MS/MS, and MSⁿ information, requires data reduction to reach meaningful insights.

Workflows can be set up using drag-and-drop capabilities, using one of the multiple application-specific templates or editing one of those templates to make data processing quick and easy. Each processing step is accounted for by a ‘node’ within a given workflow tree, which can be connected to drive data processing and interpretation based upon your study requirements; new nodes can be created using a software developer kit or custom scripts like those from R or Python, and subsequently used with the Scripting Node, tailoring workflows to your needs.

The Compound Discoverer software interface streamlines review of results by showing the information most relevant to the questions being asked; each plot and table is linked so that your view is instantly updated to reflect the compound or sample(s) that you are reviewing.

Metabolomic studies can be very complex, so ensuring acquisition of high-quality, comprehensive data is challenging, as is analyzing that data to gain insights. Ensuring complete sample coverage typically requires extensive manual work to create inclusion and exclusion lists for Data Dependent Acquisition (DDA) experiments.

AcquireX, an automated workflow, allows direct interrogation of all sample components through improved MS/MS sampling with automated background ion exclusion and data acquisition that focuses on true sample components.

Combining AcquireX with other enabling tools for Compound Discoverer software dramatically reduces the number of compounds without MS/MS spectra and significantly increases the number of compounds with confident identification and ranked putative identifications.

Stable isotope labelling can assist with untargeted metabolomic studies, and Compound Discoverer software provides a range of data review and visualization tools to support this workflow. Compound Discoverer software automatically detects labelled compounds (isotopologues) based on formulas of unlabelled compounds found in reference file(s). Once processed, the exchange rate (or rate of incorporation) can be plotted to see the response across multiple files or overlaid onto Metabolika pathways.

Compound Discoverer software can perform a range of univariate and multivariate analyses as discussed in the statistical analysis and data normalization section.

• Poster: High-Throughput Comprehensive Coverage of Hydrophilic and Hydrophobic Metabolites in Beer Utilizing a Dual Separation/High Resolution Accurate Mass Spectrometry System
• Poster: Improved Ranking of Putative Candidates Through a Hybrid In Silico / Real Fragmentation Technique
• Poster: Improved Metabolome Coverage and Increased Confidence in Unknown Identification Through Novel Automated Acquisition Strategy Combining Sequential Injections and MSn

Compound Discoverer software includes structurally intelligent dealkylation/diarylation and general metabolism prediction capabilities that allow you to find, identify, and report metabolites of interest. Identification of impurities and degradation products follows similar workflows and relies on a range of software tools and customizable approaches to enable confident detection of related components in complex samples.

Used for structural annotation of fragmentation spectra, Fragment Ion Search (FISh) can localize the site of potential transformations in addition to enabling structural elucidation for unknowns.

The Compound Class Scoring node, provides another tool to ensure nothing is missed. It uses a set of representative fragments, created from one or more known molecules in a compound class, to identify other components that could be related or are from the same compound class.

Compound Discoverer software reduces the complexity of samples by reducing matrix interferences, as well as targeting specific compound classes through their related mass defects, so you can identify, detect, and review of complex datasets faster.

• Poster: Optimized Workflow for Structure Elucidation of Pharmaceutically Relevant Extractables and Leachables

Compound Discoverer software can be used to analyze the metabolic fate and structural composition of food impurities and degradation products as well as detect environmental contaminants in soil and water. Once unknown compounds are identified in environmental and food safety studies, they often require high-throughput screening using either quadrupole or high-resolution MS-based techniques. Compound Discoverer software allows you to export your data directly to a new or existing mzVault library or targeted list to be used with Thermo Scientific TraceFinder software for screening and quantitation to reduce the burden of method transfer within your organization.

• Brochure: Compound Discoverer Software - Compounding Insights
• App Note: How to screen and identify unexpected and unwanted compounds in food
• Poster: Routine Quantitative and Qualitative Methodologies for Food Pesticide Residue Laboratories Using Tandem and High Resolution Accurate Mass (HRAM) LC/MS Instrumentation

Compound Discoverer software detects unknown metabolites of drugs of abuse and structurally related designer drugs; for example many new drugs contain similar structures, and the Compound Class Scoring Node can be used to score detected compounds against common fragment ions, therefore aiding the ability to find new drugs based upon characteristic fragments. This information can transferred to screening methods to help you keep up with an ever-expanding array of new drugs and their metabolites. Once unknown compounds have been identified using any of the multiple workflows available within Compound Discoverer software, the data can be exported directly to a new or existing mzVault library, or a targeted list that can be used with Thermo Scientific TraceFinder software for screening and quantitation using either quadrupole or high-resolution MS-based techniques.

• Ebook: Identify Rapidly Evolving Novel Psychoactive Drug Substances Faster
• Presentation: Accelerate Unknown Detection in Emerging Drug Testing Using Thermo Scientific Compound Discoverer and mzCloud

* For Forensic Use Only

For the analysis of data acquired using Thermo Scientific GC-Orbitrap based mass spectrometers, there are two primary workflows, enabled using specific workflow nodes such as Electron Impact (EI) and Chemical Ionisation (CI) deconvolution nodes. GC-Orbitrap data can be analyzed using the extensive tools within Compound Discoverer to enable confident compound identification, or statistical analysis, for example.

Examples of two GC-based workflow trees; the first is an EI workflow that can be used to find biomarkers through statistical analysis and identify unknown compounds via library search, and the second is a CI workflow that can identify unknown compounds of interest through molecular formula determination and structural elucidation of MS/MS spectra.

When using GC-EI or -CI data within Compound Discoverer, the data analysis tools and relevant fields are easily accessed to ensure simple data review, and access to results.

The above shows GC-EI compound identification in the result view. On the upper right-hand side, a mirror plot can be seen between the deconvolved spectrum and the library spectrum. Highlighted in the second level table under Library Search Results are total score, delta mass of molecular ion and RI delta: Total score is a composite score that includes contribution from the HRF score and SI score; delta mass is the mass accuracy of the molecular ion if it is present in the deconvoluted spectrum; RI delta is the difference between the library RI and calculated RI. Based on the total score “94.9”, the less than 1 ppm delta mass of the molecular ion, and the RI delta value of one, there is very high confidence in this identification.

Data acquired using GC-electron impact (EI) and chemical ionization (CI) techniques from GC data can be processed using the same tools as IC- and LC-MS data, such as unknown compound identification and statistical analysis. Understanding and interpreting comprehensive GC EI and CI data sets requires the ability to confidently deconvolute spectral data, accounting for extensive fragmentation (from EI), or potential multiple molecular ions (from CI), then subsequent identification, and analysis.

Accurate deconvolution of EI data (above) is performed to identify all the features and bin them based on the apex retention time to form compounds. Second, the user can (optionally) calculate retention indices based on the retention times of n-alkanes adjacent to the analytical peaks, to help identify compounds when performing library searches. Deconvolved spectra are searched either against unit mass libraries, such as NIST, or high-resolution accurate mass libraries, such as GC Orbitrap libraries. Cross sample peak grouping allows grouping of the same compound across multiple samples in a batch to enable subsequent statistical or comparative analyses.

Similar to EI Deconvolution, the first step performs chromatographic peak deconvolution, which is followed by molecular ion identification. The algorithm looks for [M+H]+ pseudo-molecular ions for each deconvoluted compound, with each compound being assigned additional pseudo-molecular ions, such as [M+C2H5]+, [M+C3H5]+ and [M-H]+ for methane PCI; adduct patterns help to confirm molecular ion identification.

More unknowns can be confidently identified with MSⁿ and substructure spectral matching, utilizing the full power of structure retrieval from online databases or user provided structures.

The many precursor and MSⁿ fragmentation spectra are logically organized into Spectral Trees for each compound within mzCloud. Each level of a spectral tree symbolizes an MSⁿ stage, where the top level starts at n=1, or the precursor spectra. Each level can contain numerous spectra, as data are acquired using various different experimental conditions to ensure a broad and representative coverage of subsequent fragments, increasing the likelihood of high-quality search results.

A schematic representation of a spectral tree from mzCloud. The MS spectra are acquired for a given compound in multiple polarities (ESI +/-), and for a range of adducts. Each precursor is exhaustively fragmented using different fragmentation techniques (CID, HCD) and at multiple collision energies to produce collections of fragmentation spectra at each fragmentation level (MS², MS³, MS⁴ etc.), generating a comprehensive spectral tree of information for each library entry.

The extensive data for each library entry is critical for accurate compound identifications, matching experimentally obtained data to that of the library contents, with fit confidence and data visualization provided in the Compound Discoverer and Mass Frontier data analysis software packages. Additional tools include mzLogic, which uses the extensive fragmentation information to confidently identify unknowns that cannot be identified based upon the spectral library compound entries alone.

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Create, edit and search reaction pathways with Metabolika. With publication-quality graphical functionality to create and edit reaction pathways, and more than 370 curated and annotated biochemical pathways for a range of organisms included, you can easily share your pathway knowledge.

The information in Metabolika is also used for fragmentation prediction and mzLogic, further increasing the chances of unknown compound identification.

Additionally, for stable isotope labeling analyses, you can include your exchange rate (or rate of incorporation) in Metabolika to give a more comprehensive view of your pathway.

In addition to Metabolika, Compound Discoverer software supports both KEGG and BioCyc biological pathway databases. Compound mapping can be shown in two different ways: Context-specific, i.e., looking at a specific compound, you can see what pathways this compound was mapped to, or you can use the global view where you start from the list of pathways and visualize all compounds that were mapped to a given pathway. Detected compounds can be confirmed using mzCloud, for example, with the resulting data color-coded on the embedded pathways.

Your data has inherent value, as it is the knowledge that you acquire. mzVault provides you the capabilities to access and search the MS²-level spectral data from mzCloud off-line, or to store your own spectral library information. Spectral information, and your knowledge, can be automatically sent from Compound Discoverer into a new, or existing library, which can then be searched using Compound Discoverer or TraceFinder software, or edited using Thermo Scientific Mass Frontier software.

Even with extensive online structural databases, and mzLogic to propose a structure or sub-structure, unknowns may sometimes remain unknown. It can be useful to store this information alongside your libraries of previously identified, proprietary compounds, and use it to answer the question, “Have I seen this before?”

For many applications, Compound Discoverer software provides the means to confidently identify unknowns from novel environmental contaminants to designer drugs and metabolites. The next step for some of these applications can be higher throughput identification and/or quantitation using quadrupole or high-resolution MS with TraceFinder software, or further analysis with third party packages.

Good experimental design is critically important for any analysis, especially for statistical studies to ensure that any potential trends observed occur based upon real changes, rather than those which can be attributed to experimental effects. As such, there are protocols for large-scale studies where the use of pooled quality control (QC) samples are utilized to achieve normalization of these large-scale studies.

An extensive suite of powerful statistical tools within Compound Discoverer software are fully linked to help you understand what compounds/groups of data change and by how much.

Statistical analysis can be used across a range of different analyses from metabolomic, environmental, food safety and adulteration, forensics, clinical, impurities, and extractable and leachable studies; when using GC-EI and -CI data, the statistical plots chart individual compounds to aid the easy identification of features that are relevant to the analysis. The capability to perform a range of univariate and multivariate analyses from differential analysis, ANOVA, PCA through to PLS-DA, and combining the output from these tools with the results from compound identification through workflows in a highly graphical and interactive way provides deep insights into your data which can easily be reported and shared.

Compound Discoverer software offers multiple ways to visualize complex data sets and relationships, giving you the ability to add multiple plots across monitors to track and view these relationships and better understand your data.

Once complex data sets are thoroughly reviewed, and the components that give rise to differences are evaluated, more substantial analyses may be required in order to verify that the changes/differences are caused by the identified compounds. Checked compounds can easily be exported from Compound Discoverer software to a range of different outputs to facilitate additional analysis. For more information see the “Custom, local libraries and data transfer” section.

Exploring the relationships among your compounds can reveal additional information and insights into your data sets. With Molecular Networks, you can interactively explore relationships between compounds in your analysis based on transformation and spectral similarity, for example a range of Phase I and Phase II transformations.