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DIA-NN 2.0

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@vdemichev vdemichev released this 29 Jan 08:46
2.0
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Learn about vigilant mode.

We are excited to announce DIA-NN 2.0, the most significant milestone in the history of DIA-NN development.

Key Breakthroughs

  • Proteoform Confidence mode: DIA-NN 2.0 solves the long-standing challenge of DIA proteomics, combining the proteomic depth of DIA with DDA-like identification confidence. First, it features a major improvement in achieving peptidoform confidence: almost all identifications are now peptidoform-confident on modern instruments. Second, the new mode in DIA-NN also extends peptidoform confidence to protein sequences.

  • Fine-Tuning of Deep Learning Models for specific data and PTMs. This makes DIA-NN applicable to a wide range of PTM-focused applications.

Main advances

  • Phosphoproteomics and Ubiquitinomics: major improvement in identification numbers.
  • Scanning PASEF methods support: DIA-NN 2.0 implements a universal algorithm that automatically takes advantage of any kind of Q1 information present in the data, including various schemes based on window overlaps, with or without ion mobility dimension.
  • Tunable decoy models enable, for the first time, correct handling of peptidomics data that involves peptides with shared sequence patterns.

Improved algorithms

  • Major changes in DIA-NN architecture (new search logic, decoy generation, neural network module and calibration module), resulting in higher identification numbers.
  • Fold-change reduction of RAM usage when processing multiplexed data with large libraries.
  • Support for non-specific digests: with the new Proteoform confidence mode and major RAM usage reductions this finally makes sense for DIA. We envision applications to immunopeptidomics and protease specificity mapping.
  • Improved QuantUMS quantification for multiplexed DIA.

Reporting and documentation

  • DIA-NN's PDF report now comprises a set of per-run QC plots, which include distributions of PSMs over RT and IM dimensions, as well as information on peak widths and MS1 mass accuracy. We will add certain extra QC plots in the future. We will also be grateful for any feedback and suggestions from the proteomic community here: DIA-NN calculates a wide variety of QC metrics internally, it can report almost anything.
  • We have introduced a major update to the DIA-NN documentation, making it significantly more detailed as well as including some tips and best practices when it comes to bioinformatics.

Future roadmap

DIA-NN 2.0 fulfills all the major goals announced with the DIA-NN 1.9 release. In the future, we will switch from major releases to frequent minor updates, continuously incorporating feedback and suggestions from the proteomics community. In addition:

  • We plan to put stronger emphasis on leveraging experiment-specific deep learning models for boosting identification performance and data completeness.
  • We have some key improvements of protein quantification in works.
  • There are some new ways of doing DIA in works.
  • We will migrate to new Thermo libraries and add native .raw support on Linux
  • We consider changing the pipeline format to .json, allowing for easy editing of pipelines with scripts

Any updated information on DIA-NN will be posted here #1366.

Get DIA-NN

  • The attached binaries are for academic use (please see LICENSE.txt).
  • We also start the distribution of DIA-NN Enterprise for Industry. To purchase or get a trial license, please contact Aptila Biotech aptila.bio.
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