PEAKS Studio Xpro

Complete & Vendor Neutral Solution for Proteomics with DDA & DIA Support

  • Peptide/Protein identification
    • de novo sequencing
    • Database search
    • Spectral Library Search
    • Post-translational modification (PTM) search with 500+ modification
    • Sequence variant and mutation search
  • Protein Quantification:
    • Label-free and Label-based: TMT (MS2, MS3) / iTRAQ, SILAC, 18O labelling, ICAT, User-Defined
    • Quantification results can be visualised using heat maps, correlation profiles, and extracted ion chromatograms (XICs).
  • Intuitive Visualisation for data, libraries, results:
    • Detailed and easy-to-use graphical user interface (GUI) to view, filter and validate results.
    • Spectral library viewer to assess the quality and validate library before use
    • Statistical calculations presented visually to assess quality of raw data and/or results
    • LC-MS/MS heat maps provide full visualisation of peptide features, MS/MS spectra acquisition, and identification position relative to mass over charge (m/z), retention time (RT), compensation voltages (CV), ion mobility (1/ko), and signal intensity.
  • Vendor neutral and compatible with latest MS technology:
    • Support ion mobility spectrometry proteomics (timsTOF Pro, FAIMS, HDMSe)
    • Algorithms fine tuned for each instrument and fragmentation type to ensure optimal accuracy and sensitivity
    • Comprehensive support of DDA and DIA for identification and quantification analyses

Proteomics is the study of proteins expressed in a given type of cell, tissue or organism under particular biological conditions at a given time. Shotgun (or bottom-up) proteomics is the most commonly used MS-based approach to study proteins by digesting proteins into peptides prior to MS analysis. PEAKS Studio is a software platform with complete solutions for discovery proteomics, including protein identification and quantification, analysis of post-translational modifications (PTMs) and sequence variants (mutations), and peptide/protein de novo sequencing.

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Software Overview

The Peptide Feature View

From data refinement to identification and quantification, the PEAKS X series is peptide feature-based. A peptide feature includes a series of corresponding m/z values, a retention time range, and intensities formed by different molecular isotypes.

In PEAKS identification results, users will find a “Feature” tab which presents all details of every peptide feature in the raw data, visually and tabularly. In the Peptide Feature table, PEAKS conveniently summarises feature details detected from LC-MS or LC-IMS/MS, including the identified sequence as determined by database search, library search, or de novo sequencing. Each feature is also linked to their corresponding protein view, spectrum view and LC-MS or LC-IMS/MS view for further inspection.

Thermo FAIMS DDA data analysed with PEAKS DB Search

Bruker timsTOF diaPASEF data analysed with PEAKS LIB Search

The Protein View

The Protein View presents protein profiling across complex biological samples. For each protein, the Sequence Coverage View displays a peptide map with spectrum annotation for validation. With PEAKS traditional de novo-assisted database search, users easily view  identified peptide sequence in blue, while a grey bar indicates a de novo only tag match.

Bruker timsTOF ddaPASEF data analysed with PEAKS DB Search

The Peptide View

The Peptide View provides a list of identified peptides with the abundance from MS1. For each modified peptide, the confidence of modification site (Ascore) is associated.

The Quantification View

With the add-on module of PEAKS Q, PEAKS Studio also determines relative protein abundance changes across a set of samples simultaneously and without the requirement for prior knowledge of the proteins involved.

Highly differentially expressed proteins between two groups are identified by statistical analysis tool (fold change >2, FDR <0.01) and displayed in a heatmap format.


Algorithms

PEAKS Studio uses the latest PEAKS Xpro algorithm for all analyses, including data loading/refinement, identification and quantification.

a. Feature-based identification workflow to increase sensitivity and maximize peptide identification efficiency

  • Support both DDA and DIA technology to improve reproducibility
  • Integrate database search and de novo sequencing to extend in-depth analysis
  • Resolve chimeric spectra to increase efficiency of peptide ID

Learn more about the advantages de novo sequencing brings to your research.

b. Fast, Accurate, and Easy to Use Spectral Library Search

  • Support DIA, SWATH, diaPASEF, FAIMS-DIA spectral library search.
  • Spectral Library editor and viewer compatible with PEAKS_LB, OpenMS_LB, text file libraries, and more.
  • Conduct thorough label-free or labelled quantitative analyses on Spectral Library Search results

c. Ion mobility proteomics quantification enabled to minimise missing-value and enhance accuracy


Licence Information

The PEAKS Studio licence can be scaled to address your lab’s requirements.

  • Desktop – 16 threads, capable of processing across up to 16 cores
  • Workstation – 32 threads, up to and across 32 cores

Supported Configuration

  • Microsoft Windows 32 or 64 bit
  • Quad-core processors
  • 16GB of RAM
  • More than 1GB of free disk space to install

Recommended Configuration

  • Microsoft Windows 64-bit
  • Intel Core i7/i9/Xeon Processor
  • Total 16 threads or more
  • 32GB of RAM or more

Large Data Configuration  (Workstation licence suggested)

  • Microsoft Windows 64-bit
  • Processor: above Xeon(R) or Intel i7, 24 Core, 3.2GHz
  • RAM: 64 – 128 GB
  • HD: SSD 1TB or  512GB SSD + 2.0TB HDD

Reference

  1. Tran NH, Qiao R, Xin L, Chen X, Liu C, Zhang X, Shan B, Ghodsi A, Li M. Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry. Nature Methods. 16(1), 63-66. 20/12/2018.
  2. Tran NH, Zhang X, Xin L, Shan B, Li M. De novo peptide sequencing by deep learning. Proceedings of the National Academy of Sciences of the United States of America. 114(29). 18/7/2017.
  3. Tran NH, Rahman MZ, He L, Xin L, Shan B, Li M. Complete De Novo Assembly of Monoclonal Antibody Sequences Scientific Reports. 6(31730). 26/08/2016.