DIA-NN: Neural Networks & Interference Solutions in Proteomics

DIA-NN is a software suite built around deep neural networks that processes data-independent acquisition (DIA) mass spectrometry experiments, with a particular focus on correcting the signal interference that has long plagued this approach to measuring proteins in biological samples. First described in a 2019 Nature Methods publication, the tool has become one of the most widely used platforms in quantitative proteomics, consistently outperforming or matching competing software across a range of benchmarks and biological applications.¹ Understanding what makes DIA-NN distinctive requires a brief look at the measurement challenge it was designed to solve.

Why Interference Is the Central Problem in DIA Proteomics

In a DIA experiment, the mass spectrometer does not pick out individual peptides for analysis. Instead, it sweeps across the entire mass range and fragments everything that falls within each wide isolation window, cycling through these windows systematically so that no peptide is left behind.² This unbiased fragmentation is the great strength of DIA: unlike older data-dependent acquisition (DDA) methods, it captures information on virtually every peptide in a sample rather than sampling a semi-random subset. In head-to-head comparisons, DIA identifies substantially more proteins, quantifies them more completely, and produces more reproducible measurements than DDA.³

The trade-off is complexity. Because each isolation window co-fragments many peptides simultaneously, the resulting spectra are a composite of overlapping fragment ion signals. When you try to extract the signal for one peptide, fragments from other co-eluting peptides bleed into that measurement. This is fragment ion interference, and it is pervasive in wide-window DIA data. Conventional approaches handle interference by comparing each fragment ion’s chromatographic shape to the expected peak shape and discarding fragments that do not match well enough. A correlation threshold, often around 0.9 for quantification and 0.75 for detection, separates clean signals from contaminated ones.⁴ These statistical filters help, but they are blunt instruments. They either keep or discard entire fragment ions, with no way to learn the subtler patterns that distinguish a genuine peptide signal from interference artifacts in ambiguous cases.

How DIA-NN Uses Neural Networks to Score and Correct Signals

DIA-NN’s core innovation is replacing handcrafted scoring rules with deep neural networks trained to evaluate peptide-spectrum matches. Rather than relying on a fixed set of score thresholds, the neural networks learn from the data itself which combinations of features, including fragment ion intensities, retention time agreement, and peak shape consistency, are most predictive of a genuine identification. This allows the software to make more nuanced decisions about borderline signals that a rigid threshold system would either incorrectly accept or unnecessarily reject.¹

On top of this neural network scoring, DIA-NN implements dedicated signal correction strategies that address interference at the quantification stage. Instead of simply throwing out interfered fragment ions, the software applies corrections that attempt to recover accurate abundance measurements even from partially contaminated signals. The combination of smarter scoring and interference-aware quantification is what gives DIA-NN its characteristic ability to identify more peptides and proteins while maintaining or improving the accuracy of their measured abundances, particularly in fast chromatographic setups where co-elution is more severe.

Predicted Libraries and Library-Free Searching

Traditional DIA analysis requires a spectral library: a reference catalog of what each peptide’s fragmentation pattern looks like, typically built from prior DDA experiments. Building these libraries is time-consuming and limits your analysis to peptides you have already seen. DIA-NN supports a workflow that sidesteps this bottleneck entirely. It can generate predicted spectral libraries from a protein sequence database alone, using deep learning models to forecast each peptide’s fragmentation pattern and chromatographic retention time. These in silico libraries are then refined with empirical data from the DIA experiment itself, correcting any systematic biases in the predictions.⁵

This library-free mode is a practical game-changer. A researcher working on a non-model organism, or simply wanting to avoid the overhead of generating a DDA library, can point DIA-NN at a FASTA protein database and get comprehensive results. In benchmarks comparing library strategies, DIA-NN using in silico libraries achieved the best identification performance among the tools tested, reporting over 5,100 mouse proteins from a membrane proteome dataset.⁶ The ability to perform well without a pre-built library lowers the barrier to entry for labs that want to adopt DIA without investing heavily in library generation up front.

False Discovery Rate Control Across Versions

Identifying more peptides is only useful if those identifications are real. False discovery rate (FDR) control, making sure that only a small percentage of reported identifications are incorrect, is the credibility check for any proteomics search engine. A recent evaluation using mixtures of synthesized human proteins tested DIA-NN’s library-free search across three software versions and found that newer releases have tightened FDR control substantially. At the precursor level, average FDR dropped from about 0.54% in version 1.8.1 to roughly 0.39% in versions 1.9.2 and 2.1.0. At the protein level, the improvement was more dramatic: FDR fell from around 2.9% to about 1.8%. Critically, the newer versions also identified more peptides, meaning the gains in stringency did not come at the cost of sensitivity.⁷

This trend of improving both identification depth and statistical rigor across successive versions is worth noting because it is not guaranteed. Many software updates that boost identification numbers do so by loosening statistical filters, inadvertently inflating false positives. The DIA-NN development trajectory shows the opposite pattern: neural network refinements and scoring recalibrations have allowed the software to be more aggressive in finding real signals and more conservative in filtering out false ones simultaneously.

Head-to-Head Performance Against Other DIA Tools

Several independent benchmarks have compared DIA-NN against other leading DIA analysis platforms, with Spectronaut being the most frequent competitor. In a comprehensive analysis across six different datasets, DIA-NN significantly outperformed all other tools, with identification numbers exceeding those of the second-best tool (Spectronaut) by margins ranging from about 7% to 60% for peptides and from roughly 23% to 53% for unique proteins, depending on the dataset.⁸

The picture is not always one-sided. In a study comparing the two tools on lung adenocarcinoma biopsy samples, both Spectronaut and DIA-NN identified over 7,600 proteins, with roughly 7,180 proteins common to both platforms. The differences emerged in downstream analysis: DIA-NN reported more upregulated proteins in tumor versus surrounding tissue, while Spectronaut found more downregulated proteins.⁹ This matters because the choice of analysis tool can influence which biological signals a researcher ultimately detects, even when the raw identification numbers are similar. In practice, many labs run both tools on critical datasets or select one based on the specific instrument platform and experimental design they are using.

The benchmarking landscape also varies by library strategy. When using DDA-dependent spectral libraries, Spectronaut has achieved the highest coverage in some mouse proteome experiments. When using in silico predicted libraries, DIA-NN has the edge.⁶ This means the “better” tool partly depends on your experimental setup and how much effort you have invested in library generation.

QuantUMS and Quantification Quality Filtering

Identifying a protein is one thing; accurately measuring how much of it is present in different samples is another. DIA-NN introduced the QuantUMS algorithm specifically to improve quantification quality control. QuantUMS calculates multiple scores that assess how well different layers of evidence agree with each other, including the consistency between precursor-level (MS1) and fragment-level (MS2) signals. When researchers apply cutoffs to these scores, they can filter out proteins whose abundance measurements are unreliable, typically those present at very low levels or showing high variability between replicates.¹⁰

The practical effect is that QuantUMS gives researchers a tunable dial for the trade-off between proteome depth and quantitative precision. A study focused on discovering biomarker candidates might use lenient filters to cast a wide net, then tighten the QuantUMS cutoffs in a validation phase where accurate quantification matters more than coverage. This flexibility is particularly valuable in clinical and translational studies where downstream decisions hinge on reliable abundance changes.

Ion Mobility and dia-PASEF Support

A newer generation of mass spectrometers separates ions not just by mass and charge but also by their physical shape, using a technique called trapped ion mobility spectrometry (TIMS). The dia-PASEF acquisition method pairs this ion mobility separation with DIA, adding a third dimension of information that can further reduce interference. DIA-NN includes a dedicated ion mobility module that extracts mobility-separated data, performs two-dimensional peak-picking in the mobility-versus-mass space, and uses neural networks to assess whether the observed ion mobility of a candidate peptide matches expectations. This module was integrated directly into the DIA-NN suite to provide an accessible, end-to-end solution for dia-PASEF data.¹¹

Ion mobility effectively adds another layer of selectivity before the data ever reaches the software’s scoring algorithms. Two peptides that co-fragment because they share similar masses and elute at the same time may still have different ion mobilities, allowing their signals to be teased apart. For DIA-NN, this means fewer interfered signals to correct and cleaner input for the neural networks, which translates to deeper proteome coverage, especially from limited sample amounts.

Single-Cell Proteomics

Measuring proteins in individual cells pushes the limits of sensitivity, and the signal-to-noise challenges that DIA-NN was designed to handle become even more acute. In single-cell experiments, the amount of protein entering the mass spectrometer is vanishingly small, making it tempting for software to over-interpret noise as real peptide signals. A recent study evaluating DIA-NN’s match-between-runs feature (DIA-ME), which transfers identifications from higher-input reference samples to low-input single-cell runs, found that DIA-NN maintained a false positive rate of no more than 0.19% regardless of how aggressively it was matching. By comparison, the same study found that a competing tool’s matching process was less well controlled under equivalent conditions.¹²

This matters because single-cell proteomics is increasingly used to study cell-to-cell heterogeneity in tumors, immune responses, and developmental biology. If the analysis software inflates identifications at low input levels, the resulting biological conclusions are compromised. DIA-NN’s ability to maintain statistical rigor even at the single-cell scale has made it a default choice for many groups working in this space.

Clinical and High-Throughput Applications

One of DIA-NN’s design priorities was speed, which makes it suitable for clinical-scale proteomics studies involving hundreds or thousands of samples. A streamlined high-throughput plasma proteomics platform validated DIA-NN’s suitability for this kind of work by analyzing 300 plasma samples from 25 subjects across multiple 96-well plates. The study quantified 518 proteins with 98% data completeness, meaning that almost every protein was measured in almost every sample, and quality-control injections showed highly reproducible chromatograms throughout the run.¹³

Data completeness is a particularly important metric for clinical studies. If a protein is only measured in half the samples, it becomes difficult to compare patient groups or track changes over time. DIA’s inherent advantage over DDA in reproducibility, with intra-group correlation coefficients above 0.98 and coefficients of variation below 10% compared to DDA’s corresponding figures of 0.93 to 0.98 and above 15%, provides the foundation.³ DIA-NN’s fast processing times and interference correction then extract maximum value from that already-cleaner data, making the pipeline practical for biomarker discovery studies and longitudinal patient monitoring.

Phosphoproteomics and Modified Peptides

Protein phosphorylation, the addition of phosphate groups that switches proteins on and off in signaling pathways, is one of the most biologically important post-translational modifications. Analyzing it by DIA is technically demanding because phosphopeptides are often low in abundance and their fragmentation patterns can be harder to predict. A recent evaluation compared DIA-NN (both versions 1.9.2 and 2.3), FragPipe, and Spectronaut for DIA-based phosphoproteomics across different instrument platforms. On the Orbitrap Astral with short 15-minute gradients, Spectronaut achieved the highest phosphoproteome coverage, while DIA-NN and FragPipe showed less variability in quantification.¹⁴

This illustrates a recurring theme in DIA software comparisons: the “best” tool depends on what you are optimizing for. If you need the most phosphosite identifications possible for a discovery experiment, one tool may have the edge. If your priority is quantitative precision for comparing phosphorylation levels across conditions, another may be preferable. DIA-NN’s strengths in quantitative consistency make it well-suited for studies where the goal is to detect meaningful changes in phosphorylation rather than simply catalog as many sites as possible.

Immunopeptidomics and the HLA Ligand Atlas

A less conventional application of DIA-NN is in immunopeptidomics, which studies the short peptide fragments displayed on cell surfaces by the immune system’s HLA molecules. These peptides are what T cells scan to detect infected or cancerous cells, making them central to vaccine and immunotherapy development. Immunopeptidomics has traditionally relied on DDA methods, but a recent expansion of the HLA Ligand Atlas using DIA with DIA-NN processing demonstrated substantial advantages. The DIA approach confirmed about 70% of previously detected peptides while finding roughly 2,000 additional peptides that DDA had missed. Perhaps more importantly, the DIA data matrix had far fewer missing values, dropping from about 68% in DDA to roughly 37% in DIA, making it far more practical for comparing peptide presentation across different tissues.¹⁵

The additional peptides identified exclusively by DIA tended to be about an order of magnitude lower in intensity than those found by both methods, suggesting that DIA’s systematic sampling captures the low-abundance tail of the immunopeptidome that DDA’s stochastic sampling misses. For cancer immunotherapy, where the peptides of interest may be rare neoantigens derived from tumor-specific mutations, this improved sensitivity could mean the difference between finding a therapeutic target and missing it entirely. DIA-NN’s interference correction is especially relevant here because immunopeptides are not tryptic, they do not follow the predictable fragmentation rules that most proteomics software is optimized for, making accurate scoring of these non-standard peptides a genuine technical challenge.