Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

Domain: chemistry
Published: 2026-03-02
Editorial outcome: minor_revision

Abstract
Isobaric mass tags, such as isobaric tags for relative and absolute quantitation (iTRAQ) and
tandem mass tag (TMT), are widely utilized for peptide and protein quantification in multiplex
quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing
quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene,
protein, peptide, and post-translational modification site levels. We demonstrate the versatility of
TMT-Integrator using five publicly available TMT datasets: clear cell renal cell carcinoma (ccRCC)
whole proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis
Consortium, an E. coli dataset with 13 spike-in proteins, and two human cell lysate datasets
showcasing the latest advances with the Thermo Orbitrap AstrSal mass spectrometer and TMTpro
35-plex reagents. Integrated into the widely usedE FragPipe computational platform
(https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data
analysis workflows. We evaluated the performance of FragPipe coupled with TMT-Integrator
analysis pipeline against MaxQuant and Proteome Discoverer with multiple benchmarks,
facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe coupled with TMT-
Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, quantifies
more phosphorylated sites in the CccRCC phosphoproteome dataset, and overall delivers more
robust quantification performancIe compared to other tools.

Introduction
Tandem mass spectrometry (MS/MS) in combination with liquid chromatography (LC), known as
LC-MS/MS, has become a central technology in the field of proteomics [1, 2]. Isobaric mass tags,
such as isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT),
are isotopically-encoded chemical reagents composed of a mass reporter region (which
determines the mass channel), a balancer region, and a reactive group that attaches to peptide
N-termini and lysine residues [3-7]. In isobaric quantitative proteomics workflows, digested
peptides from different biological samples are labeled with reagents from different channels and
then combined for LC-MS/MS analysis. During mass spectrometry analysis, the reagents are
expected to dissociate such that the mass reporter regSions from each channel can be
distinguished in MS/MS spectra. Peptide and protein quanEtification across samples/channels is
then accomplished by comparing the reporter ion intensities within the MS/MS spectra. Since the
interference effect (e.g., co-fragmentation) are inevitable during the acquisition of tandem mass
spectra [8, 9], triple-stage mass spectrometry (MS/MS/MS) has been proposed to mitigate
potential ratio distortion [10-12]. Isobaric labeling enables simultaneous measurement of multiple
samples, reducing analysis time and eliminating potential run-to-run variation [13]. The
commercially available isobaric chCemical tags facilitate the simultaneous analysis (multiplexing)
of up to 35 experimental sampleIs [14]. The isobaric mass tag-based strategies are now routinely
applied as part of biological studies, from drug target identification [15] to interactome profiling
[16, 17] and single-cAell proteomics [18-20]. They have been extensively applied in multiple
proteogenomic studies such as those supported by the Clinical Proteomic Tumor Analysis
Consortium (CPTAC) [21-27].

There have been multiple publications describing various computational approaches developed
specifically for post-processing and differential protein analysis of TMT/iTRAQ proteomics data
[28-34], including MSstatsTMT [35] and PAW pipeline [36]. These tools focus on improving the
integration of quantitative data, acting as independent components to support isobaric
quantification. Still, most isobaric mass tag-based proteomics studies have so far used Proteome
Discoverer, largely because of its end-to-end analysis capabilities and graphical user interface
(GUI). However, at least in our own experience of working with CPTAC data, the application of
Proteome Discoverer and other tools such as MaxQuant [37], to large datasets consisting of many
TMT-labeled sample sets (called TMT plexes or plex sets) has been challenging. Thus, we sought
to create an option for the proteomics community that would enable fast and accurate analysis of
such data, process datasets with dozens or even hundreds of TMT plexes, and run on a variety
of computational platforms, from individual Windows desktops to high-performance Linux clusters
and cloud computing. This resulted in the creation of a computational platform, FragPipe, that
included our ultrafast database search engine MSFragger [38-40], deep-learning prediction and
scoring module MSBooster [41], reranking module Percolator [42], protein inference module
ProteinProphet [43], Philosopher for false-discovery rate (FDR) calculation [44], quantification
module IonQuant [45, 46], and multiple other tools [47-52] for comprehensive analysis of
proteomics data, executable via command line or an easy-to-use GUI. TMT-Integrator has been
a key component of the pipeline, enabling aggregation of peptide-spectrum match (PSM) tables
containing raw TMT reporter ion intensities extracted by IonQuant or Philosopher from one or
more TMT plexes, followed by PSM to higher-level roll-up and report generation. TMT-Integrator
not only leverages abundance information from PSMs to Ehigher-level quantification (e.g., post-
translational modification (PTM) sites, peptides, proteins, and genes) but also supports several
outlier removal and normalization options for downstream statistical analyses. We have used
TMT-Integrator for all proteomics and phosphoproteomics analyses as part of multiple CPTAC
landmark publications [21, 22, 24, 25, 53] and other studies [54].

Here, we present a more in-depth Cdescription of TMT-Integrator and evaluate the performance of
our entire analysis pipeline usinIg five proteomics datasets. The first two are the whole proteome
and phosphoproteome datasets from the clear cell renal cell carcinoma (ccRCC) study published
by the CPTAC [21]. EaAch of the two datasets contains 23 TMT 10-plex sets of patient tumor and
normal adjacent tissue samples, with replicates of two additional samples (a non-CPTAC kidney
tumor sample and an NCI-7 cell line mix sample) inserted into the dataset for quality control
purposes. The third dataset is a single TMT 10-plex E. coli sample [55] downloaded from
ProteomeXchange [56] with the identifier PXD005486, in which 13 standard proteins were spiked
in at different amounts. The other two datasets contain human cell lysates derived from diverse
cell lines in biological triplicates. One [57] was collected on an Astral mass spectrometer, while
the other [14] utilized the newly developed TMTpro 35-plex reagent which enables simultaneous
analysis of up to 35 samples. These five datasets were processed using built-in FragPipe TMT
workflows, and the identification and quantification results were evaluated using OmicsEV [58]
and R scripts. Based on the analyses of the ccRCC whole proteome data, our workflow identifies
and quantifies more proteins than MaxQuant and exhibits reduced batch effects and higher gene-
wise correlation with RNA data. FragPipe with TMT-Integrator shows performance similar to that
of the Proteome Discoverer and MaxQuant computational platforms in detecting spike-in proteins
in the E. coli sample. We also demonstrate that FragPipe with TMT-Integrator performs well in
analyzing Astral and TMTpro 35-plex data while ensuring precise and accurate quantification. In
addition to providing accurate quantification, TMT-Integrator can be easily applied to a range of
experimental designs, from individual single-shot samples to large-scale studies with many
multiplexed samples and fractions.

Results
Overview of the isobaric quantification workflow in FragPipe
The overview of the entire TMT data analysis workflow in FragPSipe is shown in Figure 1. It consists
of three main steps: (1) peptide identification from MS/MSE spectra using MSFragger; (2) PSM
validation, protein inference, FDR filtering, and quantification using various FragPipe modules; (3)
PSM table integration, analysis, and report generation using TMT-Integrator. The output files from
MSFragger are processed using MSBooster rescoring with Percolator [42] (alternatively,
PeptideProphet [59] alone) to compute the posterior probability of correct identification for each
PSM. The outputs are then processed by ProteinProphet [43] to assemble peptides into proteins
(protein inference). In the case of CPTM-enriched datasets, Percolator (or PeptideProphet) output
files are additionally processed Iusing PTMProphet [60] to calculate the localization probabilities
for each modification site. Then, all files are further processed by Philosopher to calculate protein-
level global FDR and TAMT plex-specific FDR at the PSM, ion, and peptide levels. The protein list
is filtered at a 1% protein-level FDR. By default, the picked FDR target-decoy strategy is used in
all TMT workflows [61]. In each TMT plex, the PSM lists are filtered using a sequential FDR
strategy, retaining only those PSMs that pass a TMT-plex-specific 1% PSM-level FDR and map
to proteins that also pass the global 1% protein-level FDR. For each PSM that passes these filters,
the corresponding precursor ion intensity is extracted using IonQuant (alternatively, as used in
earlier versions of the pipeline, using the label-free quantification module of Philosopher). Finally,
for every PSM corresponding to a TMT-labeled peptide, TMT reporter ion intensities are extracted
from the MS/MS scans (or MS/MS/MS scans for MS3-based data) using IonQuant (alternatively,
using Philosopher’s isobaric quantification command), which also calculates precursor ion purity
scores. All supporting information for each PSM, including protein, gene, and quantification
information, is summarized in the output psm.tsv files, one file for each TMT plex. TMT-Integrator
takes the PSM tables as input and exports an integrated report with columns for sample names
and rows for abundances at each user-specified level of data summarization (gene, protein,
peptide, PTM multi-site, and PTM single-site). The key steps of the TMT-Integrator algorithm (see
Methods) include additional filtering of PSMs, ratio-to-reference normalization (conversion of
PSM-level reporter ion intensities to ratios on the log2 scale), grouping of PSMs at each level,
outlier removal, PSM aggregation (i.e., quantification roll-up from PSMs to each higher data
summarization level), sample-wise ratio normalization, and conversion from ratio back to an
intensity-like quantity, hereafter referred to as “abundance”. At the end, TMT-Integrator generates
multiple output tables containing ratios (“ratio” labeled tables) and abundances (“abundance”
labeled tables). The tables are additionally labeled to indicate the level of data summarization (i.e.,
gene, protein, peptide, multi-site, and single-site) and the normalization method used (i.e., None,
MD for median centering normalization, and GN for global normalization by median absolute
deviation (MAD)-based variance scaling). For example, fileS names follow patterns such as
abundance_gene_MD.tsv and ratio_protein_None.tsv.

Ratio-based integration for analyzing samIples from multiple plex sets
Due to the limited multiplexity of isobEaric labeling reagents for simultaneously labeling and
analyzing samples in a single massL spectrometry experiment, multiple plex sets are usually
required to analyze samples on a scale that exceeds their multiplexity, and a channel in each plex
set is commonly used as theT reference channel, typically containing equal amounts of proteins
from pooled samples. The common reference channel helps bridge multiple plex sets and is used
for PSM normalization, commonly known as ratio-to-reference normalization, which compares
each sample to the reference within the same plex at the PSM level using MS2 reporter ion
intensities. This approach minimizes batch effects and technical variations, ensuring reliable
quantification when integrating data across plexes. However, the reference sample may not
always be included. To support both scenarios, we implemented two approaches in TMT-
Integrator to perform ratio-to-reference normalization, as shown in Figure 2a. One is the real
reference approach for data with real reference channels, and the other is the virtual reference
approach that involves creating a virtual reference channel by taking the average of the intensities
in each plex. Ratio-to-reference normalization is then performed against either the real or virtual
reference channel for each plex. The virtual approach is necessary when a separate reference
sample is not included. It can also facilitate combining data from different cohorts or studies.
With the help of the ratio-to-reference normalization, TMT-Integrator can efficiently integrate data
from multiplex experiments. To provide a comprehensive overview of this integration process, we
break down the whole process into three major steps from the perspective of quantitative data
transformation: (1) transforming the PSM intensity table into a PSM ratio-to-reference table,
(2) integrating PSM ratio tables from multiple plexes into a single ratio matrix at a specific level,
and (3) converting the ratio matrix into an abundance matrix by incorporating intensity data. For PTM
data, there is an additional step for generating single-site information from multi-site data (Fig.
2b).

TMT-Integrator starts by taking PSM tables from all plexes as input. It then extracts MS2 reporter
ion intensities and MS1 precursor intensities from the PSM taSbles. Reporter intensity values are
denoted as 𝐼 , where 𝑖 is the PSM identifier and 𝑗 is the chaEnnel identifier, and precursor intensity
𝑖𝑗
values are denoted as 𝐴𝑃𝑟. Channel intensities are normalized using the reference channel and
𝑖 P
log2-transformed to produce ratio tables for each p lex, where log-ratios are denoted as 𝑅 . At the
𝑖𝑗
same time, MS1 precursor intensities are transformed into reference-weighted abundances by
multiplying by a factor representing the pro portion of the reference channel intensity in the total
𝑟𝑒𝑓
reporter ion intensity (sum across all cLhannels), denoted as 𝐴 . PSMs from each plex are further
𝑖
grouped by entry index (representing a gene, protein, peptide, multi-site, or single-site) at a
specified level, and PSM ratTios are then aggregated based on the grouping information. There
are two options for ratio aggregation: the median ratio approach and the weighted median ratio
approach (see Methods). After aggregating PSM ratios to their respective indexes for each
channel, the aggregated ratios from all plexes are combined into an integrated ratio table. Ratio
values are denoted as 𝑀 , where 𝑛 is the index identifier and 𝑗 is the channel identifier. The ratio
𝑛𝑗
matrix is further converted to an abundance matrix by incorporating aggregated reference
𝑟𝑒𝑓
intensities (denoted as 𝑀 ) across plexes. These reference values are calculated as the sum
𝑛
of the weighted MS1 intensities of the top three most intense peptides in each plex for every group
(𝑖𝑛𝑑𝑒𝑥 ). In this matrix, abundance values are denoted as 𝐴 . In addition to using the MS1
𝑛 𝑛𝑗
intensity of the top three peptides, ratio-to-abundance conversion also supports using MS2
intensities from the reference channels and the summed intensities of all peptides.

For PTM data, a single-site matrix is generated from the multi-site matrix (Supplementary Fig.ure
1; Methods) by collapsing multiply-modified forms into singly-modified sites with the following
sequential logic: (1) keep PSMs with localized sites; (2) preferentially use the PSMs with a single
localized site when available; and (3) if a site is only observed together with other modified sites,
use the median value of all PSMs in which it is localized.

Robust and accurate quantification by TMT-Integrator in large-scale datasets
We first evaluated the performance of FragPipe with TMT-Integrator alongside MaxQuant, using
the ccRCC whole proteome dataset [21], which has 23 TMT 10-plex sets (each plex set contains
25 fractions). To simplify the comparison between the tools and with matching RNA data, the
analysis was performed using protein quantification data collapsed to gene symbol level (see
Methods). Both TMT-Integrator and MaxQuant were run with normalization to the actual reference
sample (the pooled sample) as well as with the virtual reference approach, referred to as “FP/MQ
RefRatio” and “FP/MQ VirtualRatio”, respectively (Table 1). FoSr both tools, unless otherwise noted,
we used the median ratio approach when aggregating ratiEos-to-reference from the PSM level to
the peptide/protein/gene level. An important difference between these two tools is that FragPipe
performs an additional step of converting from ratios-to-reference back to abundances, whereas
MaxQuant produces only ratio tables. For both tools, the output tables were computed with and
without an additional median centering (“MD”) of all protein quantifications in each sample. For
MaxQuant, this median centering was applied using additional scripts (Code Availability). We also
ran MaxQuant using its “No NormCalization” approach (“MQ NoNorm” and “MQ NoNorm MD”),
in which no ratio-to-reference convIersion was performed. Note that under this option – which is the
default option of MaxQuant - MaxQuant generates output tables with peptide/protein intensities,
not ratios. A
The number of quantified proteins (in total, and with less than 50% missing values across the
entire cohort) reported by TMT-Integrator is higher than that reported by MaxQuant (12210 vs.
10811 in total; 9521 vs. 8461 with less than 50% missing values; Table 1 and Fig. 3a). Of note,
10660 proteins (86%) are commonly detected by both tools. The entrapment database searching
experiment showed that TMT-Integrator’s high sensitivity was reliable, with a low false-discovery
proportion (FDP) similar to that of MaxQuant (Supplementary Data 2). We also found that the
proteins uniquely quantified by each tool were mostly low in abundance, demonstrating that
FragPipe with TMT-Integrator improves the detection of low-abundance proteins (Supplementary
Fig. 2a). Both tools are able to accurately separate tumor from normal samples (Fig.3b for
FragPipe and Supplementary Fig. 2b for MaxQuant). Using default settings for each pipeline,
FragPipe with TMT-Integrator (“FP RefRatio MD”) and MaxQuant (“MQ NoNorm”), both using
abundance quantities, show similar protein-level abundance correlations across eight replicate
runs of the same quality control (QC) sample; however, FragPipe demonstrates better correlation
across the replicates of the five NCI sample runs (Fig. 3c). The NCI samples are different from
the patient tissue samples and the QC tissue samples. Thus, this observation suggests that TMT-
Integrator with the normalization to reference approach is better able to account for the outlier
nature of the NCI samples. We also compared the protein-level quantification correlation using
ratio tables: the FragPipe ratio report with the default settings (“FP RefRatio MD”) and the
MaxQuant output with the normalization to reference settings alongside median-centering
normalization (“MQ RefRatio MD”). Both tools consistently showed overall good and similar
correlations for NCI and QC samples, indicating their comparable performance in achieving
reliable ratio-to-reference quantifications (Supplementary Fig. 2c).

In terms of protein-RNA correlation evaluation, FragPipe uEnder all settings, including the default
setting for datasets with multiple plexes and a common bridging sample (“FP RefRatio MD”),
demonstrates good correlation between the protein and RNA data, both sample-wise (protein-
RNA correlation within the same sample) and gene-wise (protein-RNA correlation across all
samples), as shown by the median correlation coefficients in Table 1 and the overall distributions
for selected settings in Fig. 3d. Of note, median centering significantly improved gene-wise
correlation, from 0.28 to 0.41, in FCragPipe (note that the sample-wise correlation is not affected
by the median centering). GivenI that observation, and because MaxQuant does not perform the
additional median centering, we applied the median centering to MaxQuant results ourselves (see
Methods). MaxQuant, Awith normalization to reference and median centering (“MQ RefRatio MD”),
shows a gene-wise protein-RNA correlation of 0.40, comparable to FragPipe under similar
settings (“FP RefRatio MD”). However, the importance of estimating protein abundances (as
compared to just using ratios-to-reference as the final output) becomes evident from inspecting
the sample-wise correlation between RNA and protein data. Unlike gene-wise correlation that can
be calculated using normalized values such as ratios, comparing RNA and protein abundance
within the same sample requires protein abundance estimation. Indeed, as shown in Table 1 and
Fig. 3d, TMT-Integrator computed abundances (obtained by converting the ratio back to intensity,
see Methods) show a notably higher median sample-wise abundance correlation, 𝑅 ,
𝑠𝑎𝑚𝑝𝑙𝑒−𝑤𝑖𝑠𝑒
compared to that using MaxQuant results: 0.46 (“FP RefRatio MD”) vs 0.19 (“MQ RefRatio MD”).
The ability of FragPipe with TMT-Integrator to report protein abundances enables additional
biological analyses. For example, as we described in our original ccRCC publication [21], we
observed higher sample-wise protein-RNA correlation in tumors associated with certain clinical
features such as high tumor grade or mutation status of key genes/proteins, and were then able
to link high sample-wise protein-RNA correlation to increased protein translation. Running
MaxQuant without normalization to reference (“MQ NoNorm MD”), which produces output tables
with intensities, does increase the sample-wise protein-RNA correlation to 0.41 (albeit still lower
than that from FragPipe with TMT-Integrator). However, it comes at the cost of a significant
decrease in gene-wise protein-RNA correlation, to 0.21 (Table 1, Fig. 3d).

We also used OmicsEV to evaluate the results with respect to the correlations within
(intracomplex) and between (intercomplex) protein complexes (based on CORUM [62]) using the
Kolmogorov-Smirnov (K-S) test statistic. The resulting “complex AUC” score (Table 1) measures
the ability of different processed datasets to capture biologically relevant information. The
rationale behind this evaluation is that the median correlation Sof the abundances of protein pairs
from different protein complexes is likely to be close to Ezero, while the median correlation of
protein pairs within the same protein complex is likely to be higher than zero. Thus, the K-S test
statistic can be used to measure the difference between within-complex and between-complex
correlation values. A higher K-S value indicates that the abundance correlations are higher (i.e.,
more consistent) within protein complexes and lower (i.e., more dissimilar) between protein
complexes.

OmicsEV also computes a KEGIG pathway membership prediction score (“Func AUC” score).
This method evaluates the biological signal in a data table by constructing co-expression networks
from each data table Aand assessing functional category predictions. For a chosen functional
category (in this case, from KEGG [63]), proteins/genes annotated to the category form the
positive set, while others form the negative set. Subsets of these proteins/genes are used as
seeds for random walks through the network to calculate scores for the remaining proteins/genes,
where higher scores indicate closer relationships to the seed proteins/genes. Table 1 shows the
“Func AUC” scores as metrics of prediction performance (the higher the better). FragPipe with
TMT-Integrator, using normalization to reference, shows stronger performance (a functional
prediction score of 0.82 for “FP RefRatio MD”) compared to that of MaxQuant (a score of 0.71 for
“MQ RefRatio MD”). This suggests that the FragPipe-generated protein table may better reflect
protein function than that of MaxQuant. Supplementary Figures 2d and 2e further support this
finding, showing that FragPipe has more KEGG categories (103) where the protein table exhibits
more biological signal than the RNA table, compared to MaxQuant.

As bridging samples may not be included in each plex due to experimental design constraints, we
have implemented an option to artificially generate a virtual reference channel, using the average
abundance of the TMT channels (see Methods). Using the virtual reference channel approach
(“FP VirtualRatio MD”) resulted in similar performance across all metrics as when using the real
pooled reference sample. This is an encouraging observation, suggesting the virtual reference
approach in TMT-Integrator can be used to process and harmonize data from different cohorts.
MaxQuant has a similar option (“MQ VirtualRatio”); however, it does not perform as well as TMT-
Integrator’s virtual approach according to our experiments (Table 1). We also explored the use of
an alternative, weighted median ratio, approach for aggregating PSM-level ratios-to-reference to
the protein level. The weighted median ratio approach in TMT-Integrator resulted in slightly worse
gene-wise correlation between the protein and RNA dataS compared to the median ratio
aggregation (0.39 vs 0.41). In MaxQuant, the weighted medEian ratio approach showed noticeably
worse results (Table 1; Supplementary Data 3).

The analysis described above is based data summarized from PSM to the protein level. One
advantage of this summarization is that inaccurately quantified peptides are removed from the
analysis as outliers during the aggregation from PSM to protein. As a drawback, it inevitably
results in the loss of biological infCormation, for example, due to the inability to quantify different
protein products of the same geIne (e.g., different splice isoforms) [64, 65]. Thus, TMT-Integrator,
in addition to summarization at the protein level, also creates peptide-level summary reports.
We compared peptide- anAd protein-level reports by examining the distribution of noise-to-signal
ratio (NSR) values, calculated as the ratio of the standard deviations of QC samples (five NCI and
eight QC samples) to those of patient samples. A lower NSR value indicates that a peptide or
protein has less variability in QC samples than in patient samples, which is expected and reflects
better performance. We observed higher NSR values for the NCI and QC technical replicates
when calculated using the abundances at the peptide level compared to the protein level (Fig.
3e).

We also compared the runtime of MaxQuant with that of FragPipe coupled with TMT-Integrator
(Supplementary Fig. 2f). The comparison shows that FragPipe with TMT-Integrator is significantly
faster than MaxQuant, demonstrating its scalability and suitability for large-scale multiplexed
datasets.

Evaluation of TMT-Integrator using a spike-in benchmark dataset
A publicly available E. coli dataset with 13 spike-in proteins was downloaded from
ProteomeXchange (identifier: PXD005486). It included 12 fractions acquired with MS2-level
quantification and 12 fractions acquired with MS3-level quantification. The analyses of both MS2
and MS3 data were analyzed separately using FragPipe coupled with TMT-Integrator, and the
results were compared with those from MaxQuant and Proteome Discoverer. As shown in Table
2, each of these three tools detected all 13 spike-in proteins in both MS2 and MS3 data, while
FragPipe coupled with TMT-Integrator detected the most E. coli proteins. For the performance
evaluation, only 12 spike-in proteins were used, with albumin excluded because its amount did
not follow the designed fold-change patterns.

First, we used the abundances of all E. coli proteins to investiSgate whether the median-centering
normalization improves quantitative accuracy. Given that tEhe E. coli sample amounts across ten
channels are equal, the expected protein ratios for all E. coli proteins should be 1. We therefore
calculated the protein ratios between any two of the ten channels (see Methods for details).
Comparing the ratio distributions of MS2 and MS3 data (with and without median centering) in
Figure 4a, we observed that FragPipe and MaxQuant had similar ratio distributions, and the
median-centered ratios were much closer to 1, indicating the importance of ratio normalization
prior to downstream statistical anCalysis. Therefore, the median-centered values will be used in
subsequent analyses unless otIherwise noted. It is also worth noting that the ratios reported by
Proteome Discoverer are close to 1 regardless of whether median centering is used because
peptide-level normalizAation is applied.

Next, we used the abundances of the 12 spike-in proteins to evaluate the performance of three
tools on MS2 and MS3 data. Detailed spike-in amounts for the 12 proteins in each channel are
described in [55]. In brief, 12 proteins were spiked into the E. coli samples at different amounts to
represent fold changes of 1, 2, 4, 5, 10, 20, and 40. Since these 12 proteins were injected in
known amounts, the reported protein abundance ratios should reflect the amounts added, with
better agreement between the theoretical and observed protein ratios indicating higher accuracy.
We calculated the spike-in protein ratios between each pair of the ten channels (Supplementary
Data 1). Figures 4b and 4c show the log2-transformed observed ratios and the theoretical ratios
for the seven groups in the MS2 and MS3 data, respectively. In the MS2 data, all investigated
tools show noticeable deviations from the expected ratios for all groups except for the group
representing a fold change of 1. The ratio suppression effect is more evident in Fig. 4d, which
plots the median observed ratios for the seven groups. Notably, in the MS2 data, the weighted
median ratio method in FragPipe consistently outperforms MaxQuant’s across all groups,
especially for the fold changes of 20 and 40. We also observed that the weighted median ratio
approach performed better than the median ratio approach. In contrast, the advantage is less
noticeable in the MS3 data. All methods show overall good agreement with the expected ratios,
except for the group with a ratio of 40. Note that we excluded the MaxQuant weighted median
ratio method from the MS3 data due to poor performance (Supplementary Fig. 3a). Among all
tools, Proteome Discoverer appears to be less accurate in the MS3 data, showing slightly larger
deviations and more outliers compared to MaxQuant and FragPipe (as clearly visible in Fig. 4c).

We further compared the median ratio and weighted median ratio approaches using the data from
both the E. coli spike-in datasets and the ccRCC whole prSoteome dataset discussed earlier.
Figure 4e shows that the ratio distributions of the E. coli pEroteins in the MS2 and MS3 data are
similar across the median ratio and the weighted median ratio approaches in FragPipe coupled
with TMT-Integrator. However, the median ratio approach exhibited less variation, with a narrower
interquartile range (IQR) compared to the weighted median ratio approach. This can also be seen
in the density plot (Supplementary Fig. 3b), and a similar trend was observed in the MaxQuant
results. For the ccRCC proteome data, we examined the differences in protein NSR values
between the median ratio and the weighted median ratio approaches (Supplementary Fig. 3c).
The median ratio approach perIformed better, resulting in more proteins with lower NSR values
than the weighted median ratio approach. Based on these results, we conclude that the weighted
median ratio approacAh may be more suitable for MS2 data and for situations in which it is
important to accurately quantify proteins with very large fold changes (e.g., in affinity purification
experiments comparing bait protein purification with negative controls). At the same time, the
median ratio approach is likely to be more suitable for common global proteome profiling
experiments and, therefore, is selected as the default option in TMT-Integrator.

Using the median ratio approach, we also evaluated two advanced PSM filtering options (i.e.,
“Best PSM” and “Outlier removal”) for their impact on the quantification accuracy. For each LC-
MS run, it is common to see multiple MS/MS spectra matching the same peptide. When the “Best
PSM” option is enabled, TMT-Integrator retains the PSM with the highest summed reporter ion
intensity for each peptide. This reduces the impact of low-quality PSMs, which could increase
variance and lead to underestimation of the quantification in median ratio-based aggregation. As
shown in Supplementary Figure 3d, enabling the “Best PSM” option improves the accuracy of
protein quantification in both MS2 and MS3 datasets. The “Outlier removal” option enhances
quantification reliability by removing outlier channel values within each PSM during aggregation,
rather than discarding the entire PSM. TMT-Integrator uses IQR filtering to keep channel values
within the IQR, preventing outliers from skewing the median estimation. Supplementary Figure 3e
shows that the “Outlier removal” option also improves quantification accuracy for proteins in both
datasets.

Evaluation of the phosphorylation site reports
Besides generating gene, protein, and peptide reports, TMT-Integrator also generates site-level
reports for specified modifications in PTM-enriched proteomic data, which have been used in
multiple studies [21, 24, 66]. In this process, TMT-Integrator uses PTM localization probabilities
from PTMProphet [60] in FragPipe and summarizes quantificSation information at multi-level and
single-level modification sites. The details on how the Emulti-site and single-site reports are
generated, along with the illustrations of multi-site and single-site indexes, are described in the
Methods section and Supplementary Figure 1.

To evaluate the performance of FragPipe with TMT-Integrator in analyzing PTM-enriched data,
we used the ccRCC phosphorylation-enriched dataset. As with the whole proteome dataset
discussed earlier, its phosphorylaCtion-enriched counterpart contains tumor and normal samples,
alongside five NCI samples andI eight QC samples. Figure 5a shows the number of quantified
phosphorylated features at different levels. A total of 9045 phosphorylated proteins corresponding
to 9037 genes were qAuantified, with 4148 proteins and 4145 genes quantified in all samples. At
the peptide (sequence) level, 48220 phosphorylated peptides were quantified, of which 9389 were
quantified in all samples and 24070 were quantified in more than 50% of the samples. At the
peptidoform level (considering all reported site configurations and counting localized and
unlocalized configurations as separate entries), there were 108310 phosphorylated peptidoforms
(multi-site report) and 60733 localized sites (single-site report), of which 17292 sites were
detected in more than 50% of the samples. As expected, the peptide-level report exhibits better
data completeness compared to the multi-site and single-site reports, reflecting the fact that in
many instances a phosphopeptide could be detected across many samples but confidently
localized sites are present only in a subset of these samples. This also explains why some studies
[21] chose to perform certain downstream analyses using phosphopeptides but not site-level data.
However, many downstream analyses of PTM-centric data require a single-site quantification
table. For example, PTM-SEA [67] uses quantification information from specific sites to check for
enriched sites in pathways and infer kinase activity. Thus, we decided to conduct a performance
evaluation and comparison with MaxQuant using the single-site reports.

We first performed principal component analysis (PCA) on the data from tumor and normal
samples. The single-site PCA plot (Fig. 5b) shows that the samples are clustered into two distinct
groups, clearly separating tumor and normal samples as expected, indicating strong differences
in site quantification between the two groups. Similarly, the PCA plots at other reporting levels
(Supplementary Fig. 4a) show a similar separation, suggesting that TMT-Integrator reports
provide reliable quantification at all levels. Since MaxQuant does not directly generate single-site
reports, we applied the same process used in TMT-Integrator to generate a single-site report from
the MaxQuant “Phospho (STY) Sites.txt” file (Code Availability). The generated MaxQuant single-
site data were median-centered, and the PCA plot (SuppElementary Fig. 4b) showed a similar
separation between tumor and normal samples. Figure 5c shows the overlap of phosphorylation
sites quantified by FragPipe and MaxQuant. 38732 phosphorylation sites were detected by both
software, and 22001 and 9570 sites were uniquely detected by FragPipe and MaxQuant,
respectively. As expected, uniquely quantified sites were detected in only a few samples, likely
reflecting their lower abundance. We then compared the data completeness of the sites quantified
in common and sites quantified unCiquely (Fig. 5d). For the common sites, FragPipe resulted in a
larger number of sites detecteId in all samples compared to MaxQuant, in agreement with a
previous report [68].

Next, we evaluated the consistency and robustness of phosphorylation site quantification using
replicates of the NCI and QC samples. We computed coefficient of variation (CV) and correlations
for the NCI and QC samples. Figure 5e and Supplementary Figure 4c show the number of
phosphorylation sites in different CV groups for NCI and QC samples, respectively. Compared to
MaxQuant, FragPipe quantified more sites in most CV groups. The ratio correlation was assessed
by fitting a linear regression to each pair of samples and evaluating the fit using the R-squared
(R²) metric. An essential feature of TMT-Integrator is that it produces both ratio and abundance
reports, and the difference in their data scales can result in slightly different linear-fit results. Thus,
we included both abundance and ratio reports for the R² calculation and compared them with
MaxQuant ratio data. Figure 5f shows that both FragPipe abundance and ratio reports have good
quantification consistency, with FragPipe-computed abundances getting the highest R².
Supplementary Figure 4d illustrates the linear fits for each pair of samples, including additional
Pearson correlation tests, demonstrating the high consistency of site-level quantification in
FragPipe. It is worth noting that both ratio and abundance reports have the same within-feature
variation, so the choice between them does not significantly affect PCA, CV, or differential
expression analysis. However, the abundance reports are more suitable for integrative analyses
with complementary proteomic or other omics data, such as label-free proteomic and
transcriptomic data, which are not ratio-based.

Evaluation of Astral and TMTpro 35-plex datasets
Advancements in mass spectrometry instrumentation and the multiplexing capability of isobaric
labeling have advanced largely in recent years. We utilized two datasets representing the latest
advances to showcase the versatility of FragPipe coupled with TMT-Integrator in analyzing data
from the cutting-edge instruments and isobaric labeling reageSnts. The first dataset comprises 18
cell lysate samples collected using an Orbitrap Astral massE spectrometer in both data-dependent
acquisition (DDA) and data-independent acquisition (DIA) modes, facilitating a direct comparison
of TMT and DIA quantification using the Astral instrument. The second dataset contains 32 cell
lysate samples labeled with TMTpro 35-plex reagents. Both datasets allow evaluation of the
accuracy and precision of the quantification using TMT-Integrator with protein data summarized
at the gene symbol level.

The Astral datasets from Liu eIt al. [57] were employed to assess FragPipe’s performance in
quantifying Astral data. Both TMT and DIA approaches have deep proteomic coverage, with
10353 proteins quantiAfied in the DIA data, 10031 proteins quantified in the TMT data, and 9522
proteins quantified by both approaches. Although the TMT method detected slightly fewer proteins,
it had fewer missing values across all samples. As shown in Figure 6a, TMT quantified 10024
proteins with no missing values and another seven proteins in more than 50% of the samples. In
contrast, DIA quantified 8169 proteins (78.9% of all proteins) with no missing values, 1689 in more
than 50% of the samples, and 495 proteins in less than or equal to 50% of the samples.

We then assessed the concordance of protein abundance between TMT and DIA. In Figure 6b,
we compared the log2 intensities of TMT and DIA measurements for each protein. The overall
agreement is indicated by a Spearman correlation coefficient of 0.84 and a linear fit with an R-
squared value of 0.7. We also compared the distribution of CVs. CVs were calculated from
triplicates of each condition (varying by cell line and treatment), and the combined CV distributions
from all cell lines are shown in Figure 6c and the CV distributions for each cell line are presented
in Supplementary Figure 5a. The median CV of the DIA data is slightly higher than that of the
TMT data (4.59% vs. 4.25%). Overall, we demonstrate that FragPipe coupled with TMT-Integrator
achieves deep proteome coverage and precise quantification by leveraging the fast acquisition
rate of the Astral mass spectrometer. Additionally, the FragPipe platform enables consistent
analysis of the data using both quantification strategies, TMT and DIA.

The TMTpro 35-plex dataset from Zuniga et al. [14] was used to illustrate that TMT-Integrator can
handle higher-order multiplexing data labeled with both deuterated and non-deuterated TMTpro
reagents. The original study noted that the deuterium isotopes cause a slight retention time shift
among co-eluting peptides, affecting reporter ion intensity accuracy and leading to batch effects.
They suggested using three bridge channels - two in the non-deuterated subplex and one in the
deuterated subplex - to normalize the intensities and remove Sthe batch effects. We propose that
TMT-Integrator’s virtual reference approach can achieve siEmilar results.

We started by comparing protein quantification distributions between deuterated and non-
deuterated channels using the ratio report. Figure 6d shows bell-shaped curves for all channels,
indicating both labeling types have similar quantification distributions with no noticeable
distortions. We used PCA to assess how effectively TMT-Integrator’s virtual approach manages
the effects of different labeling typeCs. Figure 6e reveals a clear separation between HCT and HEK
cell lines, with no distinction bIetween samples labeled with non-deuterated and deuterated
reagents. Additionally, three bridge samples from both labeling reagents cluster closely.
Consistent with PCA Aresults, unsupervised clustering (Supplementary Fig. 5b) separates HCT
and HEK cell lines, with deuterated and non-deuterated samples clustered together. The CV
distributions within each cell line (Supplementary Fig. 5c) also demonstrate high quantification
precision. We also assessed the consistency of the quantification between the two labeling types
by examining the fold changes in protein abundance between HEK and HCT cell lines. We
compared the log2 ratios of HEK versus HCT and fitted a regression line. The scatter plot in
Figure 6f shows a strong correlation (R-squared value of 0.94), indicating consistent quantification
between the two labeling types. Our results with the TMTpro 35-plex data demonstrate that TMT-
Integrator provides accurate and consistent quantification. Notably, the virtual reference approach
in TMT-Integrator does not require bridge samples, increasing the potential number of biological
samples profiled within each plex. A more in-depth investigation of the potential benefits of using
bridge samples will be conducted in the future as more data become available.

Methods
Datasets
ccRCC whole proteome and phosphoproteome datasets. The ccRCC proteome and
phosphoproteome datasets are available from the CPTAC Data Portal at https://cptac-data-
portal.georgetown.edu/study-summary/S044. A total of 110 ccRCC tumor samples, 84 paired
normal adjacent tissue (NAT) samCples, 8 aliquots of a non-CPTAC kidney tumor sample (labeled
“QC”), and 5 aliquots of the NCI-7 mix of cell lines (samples labeled “NCI”) were randomly
assigned to 23 TMT 10-plex sets in the proteome and phosphoproteome datasets. The last
channel in each TMT 1A0-plex set was reserved for bridging (reference) samples created from the
pool of all patient samples. Each plex set in the proteome dataset has 25 fractions, and each plex
set in the phosphoproteome dataset has 13 fractions, obtained using offline LC fractionation. As
described in the original publications [21, 66], 7 tumor samples were later determined to be of a
non-ccRCC subtype and removed from the final analysis. In addition, 5 NAT samples missing
RNA data and the QC and NCI quality control samples were also excluded. The evaluation of the
results was based on 103 ccRCC tumors and 79 NAT samples; where noted, additional analyses
were performed using the QC and NCI samples.

Spike-in dataset. The dataset is composed of 10 whole E. coli cell lysate samples with 12
recombinant human proteins plus bovine serum albumin spiked in to generate fold changes of 1,
2, 4, 5, 10, 20, and 40 among the TMT 10-plex labeled samples. Reporter ions were acquired at
both MS2 and MS3 levels, resulting in two sets of data, each containing 12 fractions. The dataset
was downloaded from ProteomeXchange under the identifier PXD005486.

Astral datasets. The Astral datasets contain 18 human cell lysate samples from four cell lines
(IHCF, HCT116, HeLa, and MCF7). The IHCF cell line was additionally treated with two
concentrations of H2O2, resulting in six distinct conditions in total, each in biological triplicate.
These samples were analyzed using a Thermo Fisher Scientific Orbitrap Astral mass
spectrometer with both TMT and DIA approaches. In the TMT experiment, the samples were
labeled with TMTpro 18-plex reagents, and the data were collected after the samples were
fractionated into 24 fractions. For the DIA experiment, the samples were analyzed using a 60-
minute gradient, and the data were collected with and without high field asymmetric ion mobility
spectrometry (FAIMS). Only the data without FAIMS were used in this study, as they yielded
better results as described in the original publication [57]. ThSe datasets were downloaded from
ProteomeXchange under the identifier PXD058918.

TMTpro 35-plex dataset. The TMTpro 35-plex dataset contains 32 human cell lysate samples
plus 3 bridge samples, of which 18 were labeled with non-deuterated TMTpro reagents and 17
were labeled with deuterated TMTpro reagents (TMTproD). For each type of labeling, there are
eight replicates each of the HCT116 and HEK293T cell lines. In addition, there are three common
references (bridge samples) derCived from equal amounts of pooled cell lysates across all
replicates of both cell lines: twoI in non-deuterated samples and one in deuterated samples. All
samples were fractionated into 12 fractions and analyzed using a Thermo Fisher Scientific
Orbitrap Exploris 480 mAass spectrometer. The dataset was downloaded from ProteomeXchange
under the identifier PXD054559.

FragPipe data processing
ccRCC whole proteome and phosphoproteome datasets. All LC-MS files in mzML format were
processed using FragPipe version 22, a newer version than that used in the previous publication.
Unless otherwise specified, the default FragPipe parameters were applied (“TMT10-bridge” or
“TMT10-phospho-bridge” workflows). MSFragger version 4.1 was used to search the spectra
against the reviewed human protein sequences downloaded from UniProt. Decoy and
contaminant protein sequences were added using Philosopher via FragPipe. “Stricttrypsin” was
selected as the enzyme, allowing up to two missed cleavages. The precursor mass tolerance was
set to 20 ppm, and the C12/C13 isotope errors were set to −1/0/1/2/3. Mass calibration and
parameter optimization [39] and MS/MS spectral deisotoping [40] were enabled. Cysteine
carbamidomethylation and lysine TMT labeling were specified as fixed modifications. Methionine
oxidation, protein N-terminal acetylation, peptide N-terminal TMT labeling, and serine TMT
labeling were specified as variable modifications. For phosphorylation-enriched data, the variable
modifications included phosphorylation of serine, threonine, and tyrosine, and methionine
oxidation. Cysteine carbamidomethylation, and peptide N-terminal and lysine TMT labeling were
specified as fixed modifications. C12/C13 isotope errors were set to 0/1/2. MSFragger search
results were processed by MSBooster [41] (except for the phosphoproteome data), Percolator
[42], PTMProphet (for the phosphoproteome data) [60], and ProteinProphet [43]. Resulting PSM
lists for each TMT plex were filtered at 1% PSM-level and protein-level FDR using Philosopher
[44]. Precursor ion intensity and TMT reporter ion intensities were extracted from the MS and
MS/MS scans, respectively, using IonQuant [45]. The precursoSr ion purity scores were calculated
by taking the ratio of the precursor ion intensity to the total iEntensity of all ions in the MS1 isolation
window. Both ccRCC whole proteome and phosphoproteome datasets contained pooled samples
as one of the channels in each plex, and the pooled samples were specified as reference samples.
FragPipe was also run with the virtual reference approach (i.e., without specifying a reference
sample), as noted in the main text.

Spike-in dataset. Spectral files froCm 24 fractions of the data (12 from the MS2 TMT dataset and
12 from the MS3 TMT dataset) Iwere converted from Thermo RAW to the mzML file format using
ProteoWizard [70]. The protein sequences for E. coli K12 (accession number UP000000625)
were downloaded from UniProt [71], and common contaminants were added. The reversed decoy
protein sequences were generated using Philosopher. Thirteen spike-in proteins along with their
reversed decoy sequences were manually added to the database, generating a total of 8828
protein sequences. The converted mzML files were searched against the database using
FragPipe version 22 as described above. TMT reporter ion intensities were extracted from either
MS/MS scans (MS2 dataset) or MS/MS/MS scans (MS3 dataset) using IonQuant. TMT-Integrator
was run using the virtual reference option.

Astral datasets. The reviewed human protein sequences were downloaded from UniProt
(accession number UP000005640) and appended with common contaminants. Decoy protein
sequences were added using Philosopher. For the TMT dataset, raw files (without conversion to
mzML) were searched against the database using FragPipe (version 23.0) with the built-in
TMT18-Astral workflow. Most search parameters stayed the same as those for the ccRCC whole
proteome dataset. In MSFragger settings, the TMT modification mass was set to 304.20715 Da.
MSBooster was disabled. In TMT-Integrator settings, instead of the default intensity-based filter,
a minimum resolution of 45000 and a minimum signal-to-noise ratio (SNR) of 1000 were applied
to filter the PSMs. The “Best PSM” option was disabled, the “Min intensity (percent)” was set to 0,
and the “Mass tolerance” was set to 10 ppm. For the DIA dataset, the raw files were converted
into mzML format as previously described. The mzML files were searched against the same
database using FragPipe (version 23.0) with the built-in DIA_SpecLib_Quant workflow. In short,
MSFragger-DIA [49] was used to search the DIA data directly, and the search results were
processed using MSBooster for deep learning-based score calculation, Percolator for rescoring
and posterior error probability calculation, ProteinProphet for protein inference, Philosopher for
FDR filtering, and EasyPQP for spectral library building. The spectral library was filtered to 1%
global peptide- and protein-level FDR. The resulting library waSs passed to DIA-NN to extract and
quantify precursors, peptides, and proteins from the DIA daEta.

TMTpro 35-plex dataset. The raw files were converted into mzML format and analyzed using
FragPipe (version 23.0) with the same human database as the Astral datasets. Most search
parameters were kept the same as those for the ccRCC whole proteome dataset, except that in
MSFragger, the TMT-labeling modification mass was set to 304.20715 Da. A new feature in TMT-
Integrator was developed to achiCeve accurate quantification from the TMTpro 35-plex dataset.
The original publication [14] notIed that the deuterium isotope effect can result in a slight shift in
retention time for co-eluting peptides, which can affect reporter ion-based quantification and
cause batch effects. AThey also demonstrated that this effect can be effectively mitigated by
treating non-deuterated and deuterated channels as two separate subplexes during normalization.
To ensure accurate quantification of TMTpro 35-plex data, we made an adjustment in TMT-
Integrator by splitting the 35 channels into non-deuterated and deuterated subplexes. This
adjustment allows TMT-Integrator to proceed with two subplexes in a manner similar to a typical
multiplex dataset without any changes to its core algorithm. To generate quantification reports
from this dataset using TMT-Integrator, we used the virtual reference approach, where a virtual
reference channel was created for each subplex by averaging intensities across channels within
that subplex for each PSM. PSM ratio-to-reference normalization was then conducted separately
for each subplex using its virtual reference channel, resulting in two sets of ratio data. The rest of
the processing followed the steps outlined in Figure 2, from PSM grouping and aggregation to
generating integrated ratio and abundance data.

MaxQuant data processing
Raw MS/MS data were processed using MaxQuant [37, 72] (version 2.6.4.0) with the Andromeda
search engine [73]. The databases were the same as those used in FragPipe. Decoy sequences
were not included in the database because MaxQuant generated decoys by itself. The enzyme
specificity was set to “Trypsin/P” and up to two missed cleavages were allowed. Carbamidomethyl
(C) was set as a fixed modification; “Oxidation (M)” and “Acetyl (protein N-term)” were set as
variable modifications. For the ccRCC phosphoproteome dataset, “Phospho (STY)” was also
added as a variable modification. For both ccRCC whole proteome and phosphoproteome
datasets, the type was set to “Reporter ion MS2” and the isobaric label was set to “10plex TMT”.
For the spike-in datasets, the type was set to “Reporter ion MS3” for the MS3 data and “Reporter
ion MS2” for the MS2 data. An FDR cutoff of 1% was used at the PSM and protein levels. For the
phosphopeptide identification in the ccRCC phosphoproteomiScs dataset, only PSMs passing 1%
FDR and with confidently localized phosphosites (localizaEtion probability > 0.75) were used for
downstream analysis. The MaxQuant protein table of the ccRCC whole proteome dataset was
collapsed to the gene symbol level (Code Availability) for the direct comparison with TMT-
Integrator gene reports. That is, for the proteins corresponding to the same gene, the median
abundances (for abundance tables) or ratios (for ratio tables) were taken. With respect to the
normalization approach, all MaxQuant options - “None”, “Ratio to reference channel”, and
“Weighted ratio to reference channCel” - were tested for the ccRCC whole proteome and the spike-
in datasets. The “Ratio to referenIce channel” was used for the ccRCC phosphoproteome dataset.
We used the real reference channels when running MaxQuant with “Ratio to reference channel”
and “Weighted ratio toA reference” approaches. We also applied the virtual reference approaches
to the ccRCC whole proteome dataset to compare the results with those from the FragPipe virtual
reference approach. The spike-in dataset does not contain any real reference channels. Thus, we
applied the virtual reference approach. Other parameters were set to the default values. We also
performed an additional median-centering normalization (Code Availability) during the
downstream analysis.

Proteome Discoverer data processing
Proteome Discoverer (PD) version 3.0 (Thermo Fisher Scientific) was used for analysis of the
spike-in dataset. The database was the same as that used by MaxQuant. The following search
parameters were used: the MS1 tolerance was set to 10 ppm, and the MS2 tolerance was set to
0.02 Da (MS2 dataset) or 0.6 Da (MS3 dataset). Carbamidomethylation of cysteines and TMT
labeling of lysines were set as static modifications. Oxidation of methionine, TMT labeling of
serines and peptide N-termini, and acetylation of protein N-termini were set as variable
modifications. Trypsin was set as the enzyme. Peptides of 7 to 50 residues in length with a
maximum of two missed cleavages were allowed. PSMs were subsequently processed using
Percolator [42], and the identified peptides and proteins were filtered at a 1% FDR threshold.
Quantification was performed using reporter ion intensities from MS/MS or MS/MS/MS spectra,
depending on the dataset. Protein quantification was done using unique and razor peptides, and
quantification was normalized using the total peptide amount option.

Result evaluation in OmicsEV
OmicsEV takes reports generated by TMT-Integrator or MaxQuant, along with a sample
annotation file, as input. The data quality evaluation compSonents implemented in OmicsEV
include missing values, batch effects, unsupervised clusterEing analysis, correlation analysis, and
phenotype and gene function prediction based on expression data. If the dataset contains protein
expression data, paired RNA expression data (if available, as in the ccRCC dataset) could also
be used in the evaluation. An example script used to analyze the data in this work is provided in
the Code Availability.

Noise-to-signal ratio in ccRCC
As described above, the ccRCIC datasets included 8 aliquots of a non-CPTAC kidney tumor
sample (“QC” samples) and 5 aliquots of the NCI-7 cell line mix (“NCI”), which were profiled as
part of the sample coAhort. These samples were used to evaluate the performance of different
pipelines using the CV and the NSR metrics. NSR was calculated for each protein or peptide as
the standard deviation across the 5 NCI (or 8 QC) samples divided by the standard deviation
calculated across the 182 patient samples (103 tumor and 79 normal). Smaller NSR values are
indicative of more accurate quantification measurements.

Entrapment database searches
The ccRCC whole proteome and spike-in datasets were used for entrapment database searches
using FragPipe coupled with TMT-Integrator, MaxQuant, and Proteome Discoverer. Entrapment
databases were constructed by randomly shuffling protein sequences from the previously
described databases. These databases were then used to evaluate and compare the FDP of
different tools using the method described by Wen et al. [74].

Runtime comparisons
The ccRCC whole proteome dataset, comprising 23 plexes with 25 fractions each (575 raw files
in total), was used to evaluate the speed of FragPipe with TMT-Integrator and MaxQuant on large-
scale isobaric labeling experiments. The analyses were run on two platforms: (1) a Windows
desktop with an Intel Xeon W-2235 CPU (3.80 GHz, 6 physical cores, 12 threads) and 128 GB of
RAM, and (2) a Linux server with an Intel Xeon Gold 6354 CPU (3.00 GHz, 36 physical cores, 72
threads) and 755 GB of RAM. FragPipe was run on both platforms using the mzML files converted
from the raw files, while MaxQuant was run with the raw files. Because MaxQuant could not finish
the analysis within a week using the Windows desktop, only the runtime on the Linux server is
reported. All search parameters are as previously described.

TMT-Integrator algorithm
TMT-Integrator takes PSM tables (Fig. 1a), one table for eaEch TMT plex, as input and exports an
integrated report with columns for sample names and rows for ratios or abundances at user-
specified levels (i.e., gene, protein, peptide, and modified site). The seven steps in TMT-Integrator
are explained as follows (Fig. 1b), concluding with the report generation at various levels (Fig. 1c).

Step 1. Filtering. PSMs are first removed from each PSM table if they meet any of the following
criteria: a lack of TMT modificationC on the peptide, zero intensity in the sample designated as the
reference sample (if specified), Iprecursor ion purity less than a user-specified threshold (0.5 by
default), or probability below the defined threshold (default 0.9). Of note, PSM probability filtering
is applied on top of PhAilosopher’s FDR filtering, through which most low-probability PSMs have
already been excluded from the PSM tables. Then, the reporter ion intensities across all channels
in the plex are summed, and the fraction of PSMs with the lowest summed intensities (by default,
the lowest 5% for the whole proteome and 2.5% for phosphoproteome data) is excluded. Since a
peptide may have multiple PSMs in the same sample/fraction, only PSMs with the maximum
summed reporter ion intensities are retained (i.e., the “Best PSMs” option). PSMs mapping to
contaminants are also filtered out. For Astral data, PSMs are removed if their summed SNR
across all channels is below a set value (default 1000). PSMs are also excluded if any channel
has a resolution below a set value (default 45000) and has SNR ≥ 1. By default, both unique and
razor peptides are used for the analysis (controlled by the “Peptide-Protein uniqueness” option in
the TMT-Integrator tab of FragPipe). In certain situations, it is useful to restrict the estimation of
protein-level expression to unique peptides only. However, this may lead to a significant loss of
peptides. TMT-Integrator also provides a less conservative option to remove only peptides
mapping to proteins from multiple gene symbols. An example of using this option as part of a
large-scale proteogenomics study can be found in [24].

Step 2. Reference sample normalization (Normalization I). For each PSM, the reporter ion
intensities are log2-transformed, and the reference sample intensity is subtracted from each
sample intensity. Thus, the data are converted, on a PSM-by-PSM basis, to log2-based ratios of
intensities with respect to the specified reference (referred to as ratios below). TMT-Integrator
supports two approaches for defining the reference for the ratio conversion (Fig. 2a): 1) the
“Reference sample” approach, in which the reference sample is one of the actual samples in the
TMT plex. Typically, it would be the common (often referred to as “bridge”) sample used in every
TMT plex to assist with normalizing abundances across multiSple TMT plexes. In the case of the
ccRCC dataset, for example, the reference sample was cEreated by pooling all samples in the
study. 2) the “Virtual reference” approach, which is useful when no reference sample is available
or specified by the user, in which case the normalization is done with respect to an internally
created virtual reference intensity. By default, the virtual reference intensity is calculated for each
PSM as the average of all reporter ion intensities in the corresponding spectrum. In addition, there
is an optional PSM normalization by retention time, which is not used in any built-in FragPipe
workflows. Specifically, after conCverting the intensities to ratios, each channel (except for the
reference channel when used foIr normalization) in a PSM table can be divided into ten retention
time bins, and the median ratio in the corresponding bin is then subtracted from each individual
ratio. A

Step 3. Grouping. After filtering, reference normalization (conversion to ratios), and optional
retention time-based normalization, the selected PSMs are grouped into various levels, including
gene, protein, peptide, and modification-site levels if PTM-level reports are requested (Figure 2b).
At the protein level, PSMs are grouped based on the protein to which they are assigned as unique
or razor peptides. At the gene level, PSMs are grouped based on the gene symbol of the
corresponding protein to which they are assigned as unique or razor peptides. Note that when
PTM reports are requested, the gene- and protein-level tables are derived only from PSMs
containing the user-specified PTM (e.g., in the case of phosphorylation, the protein level table
contains proteins in the phosphorylated form only). To generate peptide-level and site-level tables,
additional post-processing is applied to generate all non-conflicting PTM configurations (of the
specified PTM type) using a strategy similar to that described in [75] (Fig. 1c; Supplementary Fig.
1). In phosphoproteome or other PTM-enriched datasets, when PTMProphet is used for site
localization, confidently localized sites are defined as sites with a localization probability of 0.75
or higher. In the table, the same peptide sequences but with different PTM site configurations
(different localized site configurations or peptides with unlocalized sites) are first indexed as
separate entries (these tables are referred to as “multi-site” tables). “Single-site” tables will be
described below. In the peptide-level tables, different site-level configurations are combined into
a single peptide-level index, grouping PSMs with all site configurations together if they correspond
to the same peptide sequence. When PTMProphet is not used for computing the site localization
probability, the reports are generated based on site assignments from the MSFragger search
engine.

Step 4. Outlier Removal. After grouping, an entry (i.e., a gene/pSrotein/peptide/multi-site) may have
multiple PSMs grouped into that entry, and their abuEndance ratios will be subsequently
aggregated for quantification. To prevent spurious measurements introduced by technical noise
from affecting quantification estimation, an IQR-based outlier removal step is performed for each
PSM group in every sample separately. Specifically, for each sample, we extract abundance
ratios for that channel from the grouped PSMs of a given entry and calculate the first quantile (𝑄1)
and third quantile (𝑄3), and the interquartile range (IQR; i.e., 𝑄3−𝑄1) of these ratios. Ratios
outside the bounds of 𝑄1−1.5×C𝐼𝑄𝑅 and 𝑄3+1.5×𝐼𝑄𝑅 are set to NA and excluded from
downstream aggregation. ThusI, only a subset of reasonable abundance ratios is retained to
represent each entry in each sample, which helps enhance the robustness of quantification. The
outlier removal step isA performed independently at each level and only for groups containing at
least four PSMs.

Step 5. PSM Aggregation. TMT-Integrator provides two aggregation methods: median ratio
(default) and weighted median ratio. The median ratio method is a common approach that takes
the median ratio of all PSMs (after outlier removal) for the same protein (peptide/gene/multi-site)
as the protein (peptide/gene/multi-site) ratio. The weighted median ratio aggregation method
resembles that used in MaxQuant [37], and is implemented as follows. First, the precursor
intensity for each PSM in a PSM group is exponentiated and then normalized by the sum of these
exponentiated values within that group, generating a weighting factor for each PSM such that all
weights sum to 1. The PSMs are then sorted by their ratio to reference in ascending order. The
cumulative weight is incremented by each weighting factor as the sorted PSMs are traversed. The
ratio at which the cumulative weight first exceeds 0.5 is taken as the representative of the group.
Otherwise, the ratio with the highest weighting factor will be selected. The representative ratios
are used as the final values at the corresponding level of grouping (gene, protein, peptide, etc.).

Step 6. Normalization II. To improve comparability, consistency, and precision of quantification
across samples, two normalization methods, conventional sample-specific median centering (MD)
and global normalization (GN) by variance scaling, are used to normalize the ratios in the
integrated tables, separately at each level of the data summarization (i.e., gene, protein, peptide,
and multi-site). Median-centering normalization is performed on a per-sample basis. It shifts all
values by subtracting the sample-specific median. In contrast, global normalization further scales
all values using a factor calculated from the entire dataset on top of the median-centering
normalization. Both methods depend on the assumption that mSost entries are unchanged across
samples, allowing normalization based on the sample meEdian without being skewed by a few
highly variable proteins. In detail, considering a 𝑝 (number of entries, e.g., proteins) by 𝑛 (number
of samples) table of ratios (with the reference samples excluded), for each entry 𝑗 in sample 𝑖, 𝑅 ,
𝑖𝑗
the median ratio is computed, 𝑀 =𝑚𝑒𝑑𝑖𝑎𝑛(𝑅 ,𝑗 = 1...𝑝). The ratios (on the log2 scale) in each
𝑖 𝑖𝑗
sample are then median-centered, 𝑅𝑀𝐷 = 𝑅 −𝑀. As a result, in the MD ratio tables, the median
𝑖𝑗 E𝑖𝑗 𝑖
ratio of each sample is zero. To geneLrate the alternative, GN-normalized ratio tables, the MAD of
the median-centered values in each sample is calculated, 𝑀𝐴𝐷 = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑎𝑏𝑠(𝑅𝑀𝐷),𝑗 = 1…𝑝),
𝑖 𝑖𝑗
along with the global absolutTe deviation, 𝑀𝐴𝐷 = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑀𝐴𝐷,𝑖 = 1…𝑛). All median-centered
0 𝑖
ratios are then additionally scaled to equalize the ratio distributions across all samples, 𝑅𝐺𝑁 =
𝑖𝑗
𝑅𝑀𝐷
( 𝑖𝑗 )×𝑀𝐴𝐷 . In this approach, global normalization uses the sample median and MAD to
0
𝑀𝐴𝐷𝑖
rescale distributions. Unlike total intensity scaling, quantile normalization, or more complex
variance-stabilizing normalization (VSN), this MAD-based normalization is non-parametric, robust
to outliers, and adjusts only the overall location and spread while preserving distribution shape.
This makes it a simple and effective choice for general use. In FragPipe workflows, median-
centering is the default option.

Step 7. Conversion of ratios back to intensities (abundances). As the last step, the ratios
(unnormalized, MD-normalized, or GN-normalized) are converted back to the intensity scale using
the estimated intensity of each entry in the reference sample. By default, the intensity of entry 𝑖
measured in TMT plex 𝑘 (𝑘 = 1…𝑞), 𝑅𝑒𝑓 , is estimated using the weighted sum of the MS1
𝑖𝑘
intensities of the top 3 most intense peptides [76] quantified for that entry in TMT plex 𝑘. For each
PSM, the weighting factor is defined as the proportion of the reference channel TMT intensity
relative to the sum of all TMT channel intensities. The overall (based on all 𝑞 TMT plexes in the
dataset) reference intensity for entry 𝑖 is then estimated as 𝑅𝑒𝑓 = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑅𝑒𝑓 ,𝑘 = 1…𝑞). In
𝑖 𝑖𝑘
doing so, the missing intensity values (i.e., when there are no identified or quantified PSMs for
that entry in a particular TMT plex) are imputed using a global minimum intensity value. The final
intensity (on the log2 scale) of entry 𝑖 in sample 𝑗 is computed as 𝐴 = 𝑅𝑀𝐷+log (𝑅𝑒𝑓) in the
𝑖𝑗 𝑖𝑗 2 𝑖
case of MD-normalized ratios (and similarly for the unnormalized and GN-normalized tables).
TMT-Integrator also provides an option to use the total summed reporter ion intensity in place of
the MS1 precursor intensity.

In PTM studies, TMT-Integrator also provides single-site PTM rSeports generated by additional
processing of the multi-site reports described above (Fig. 1c; SSupplementary Fig. 1), as illustrated
here using phosphorylation as an example. Single-site representation is necessary for some
downstream analysis tools, such as PTM-SEA [67]. To generate the single-site reports, the entries
in the multi-site reports that do not contain any loc alized sites are removed. A new index format
that combines the protein accession number, modified amino acid, and the localized site position
within the protein sequence is created. The entries in the multi-site report are re-clustered using
this new single-site index. If a localized site is only observed in a singly phosphorylated form,
TMT-Integrator propagates the raCtio from that form to the single-site table. If a localized site is
observed in both singly and multiply phosphorylated forms, TMT-Integrator reports the ratio
observed in the singly Rphosphorylated form. If a localized site is observed in multiply
phosphorylated formsA, TMT-Integrator reports the median ratio of those forms. Similar to other
tables, TMT-Integrator converts the ratios back to the intensities using the same logic. For
example, localized sites on peptides in singly phosphorylated forms inherit intensities directly from
the corresponding entries in the multi-site table. For localized sites without singly phosphorylated
forms, the median intensity of those multiply phosphorylated forms is used to estimate the site-
level intensity.

When generating these final matrices, an additional filter is applied in TMT-Integrator to remove
entries for which the best (highest probability, across the entire dataset) supporting PSM has
probability below a certain threshold (allowing the application of an even higher stringency of
filtering than the FDR thresholds applied in the Philosopher filter command at the earlier stage;
controlled by the “Min best peptide probability” parameter in FragPipe).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents TMT-Integrator, a bioinformatics tool integrated into the FragPipe computational platform for analyzing isobaric quantitative proteomics data from TMT and iTRAQ experiments. It generates comprehensive reports at gene, protein, peptide, and post-translational modification (PTM) levels. The study evaluates TMT-Integrator’s performance using five datasets: an E. coli spike-in dataset, clear cell renal cell carcinoma (ccRCC) whole proteome and phosphoproteome datasets from CPTAC, and two human cell lysate datasets (Astral instrument and TMT 35-plex). Compared to MaxQuant and Proteome Discoverer, FragPipe with TMT-Integrator quantifies more proteins and phosphorylation sites, shows better correlation with RNA data, and reduces batch effects. It supports diverse experimental designs, including high-multiplexing and advanced instrumentation, with robust normalization methods (real and virtual reference approaches). The tool’s versatility, accuracy, and compatibility with downstream analyses make it a valuable asset for proteomics research.\n\nThe manuscript is very well-written, the methods address the questions, and the data support the conclusions. Full disclosure, this reviewer is a frequent and long-time user of software from the Nesvizhskii Lab, including the software highlighted herein. It must be stated that the MSFragger suite is a valuable asset to the mass spectrometry-based proteomics community and associated collaborators from numerous disciplines, specifically with respect to academic laboratories.\n\nThis paper is appropriate for and should be published in Nature Communications. That said, I do have several suggestions that will enhance the quality of this manuscript and that should be addressed before this paper can be published here.\n1) The study reports higher protein identification rates but does not assess TMT-Integrator’s sensitivity to low-abundance proteins. Perform a sub-analysis on the ccRCC dataset to compare the detection of low-abundance proteins (e.g., bottom quartile by intensity) between TMT-Integrator, MaxQuant, and Proteome Discoverer. This could reveal whether TMT-Integrator’s higher coverage extends to challenging low-abundance targets, strengthening its claimed advantages.\n2) Continuing with the higher protein identification rates, performing an entrapment database analysis would assess the specificity and FDR control of TMT-Integrator within FragPipe compared to MaxQuant and Proteome Discoverer. This analysis would use existing data and standard proteomics tools, requiring only a computational re-run. Such an analysis has recently been performed for DIA datasets: https://pubmed.ncbi.nlm.nih.gov/40524023/\n3) While the paper evaluates quantification consistency using NCI and QC samples, it could further quantify reproducibility by calculating coefficients of variation (CVs) for all proteins across technical replicates in the Astral and TMT 35-plex datasets. Adding this to Figure 6 would provide a more comprehensive view of TMT-Integrator’s precision across cutting-edge platforms.\n4) The ccRCC dataset analysis shows T_mt-Integrator handles missing values well. To strengthen this, simulate varying levels of missing data (e.g., 10%, 20%, 30% missing PSMs) in the E. coli or Astral datasets and evaluate the impact on quantification accuracy. This in silico analysis would demonstrate TMT-Integrator’s robustness to incomplete data, a common issue in proteomics.\n5) The paper highlights TMT-Integrator’s ability to handle large datasets but does not quantify its computational performance. Please measure and report the runtime and memory usage for processing the ccRCC and TMT 35-plex datasets using TMT-Integrator compared to MaxQuant and Proteome Discoverer on a standard computational platform (e.g., a desktop with specified specs). Adding a table or brief statement in the results would demonstrate TMT-Integrator’s scalability and practicality for large-scale studies, requiring only simple benchmarking.\n6) TMT-Integrator applies additional PSM filtering based on probability thresholds. To assess its impact, a subset of the ccRCC or Astral dataset could be re-analyzed with varying “min best peptide probability” thresholds (e.g., 0.7, 0.9, 0.95) and changes could be reported in protein/PTM identification rates and quantification accuracy. Including a supplementary figure or table would clarify how filtering affects performance, providing users with guidance on parameter optimization using existing data.", "reviewer_2": "Thank you for the opportunity to review the manuscript entitled “Analysis of Isobaric Quantitative Proteomic Data Using TMT-Integrator and the FragPipe Computational Platform” by Chang et al. As a FragPipe user, I always appreciate the opportunity to learn more about the tool. This manuscript, in particular, focuses on one of its modules — TMT-Integrator.\n\nGeneral comment.\nThe comparison with MaxQuant somewhat misses the point, as there is much more involved than just reporter ion integration. Differences in performance may stem from variations in MS/MS identification, peptide-to-protein mapping rules, or other filtering and preprocessing steps. I would argue that with a different dataset, a different comparison strategy, or evaluation metrics, MaxQuant could very well look like outperforming FragPipe. It's reminiscent of the perennial Mac vs. PC or Vim vs. Emacs debate.\n\nThe emphasis on MaxQuant versus Proteome Discoverer also seems somewhat misplaced, given that PD is likely more widely used for TMT-based workflows. In my opinion, a more informative direction would be to focus on the impact of different analytical options within FragPipe itself, so the users make more informed choices. While this is partially addressed through the comparison of median versus weighted summarization, additional comparisons, such as the effects of outlier removal or the choice between retaining all PSMs versus selecting only the best, would be valuable.\n\nThat said, FragPipe is undoubtedly a powerful tool, and I support any effort to publish detailed descriptions of it or its components.\n\nActionable issues:\n1. I like the idea of using prior knowledge of protein complex information to assess the sensibility of the data. However, the comparison presented in Table 1 is difficult to interpret, as it is based solely on point estimates of scores. For example, how meaningful is the difference between 0.87 and 0.86? Or between 0.87 and 0.62? It would be helpful to include confidence intervals, perhaps derived via bootstrapping, or p-values based on a statistical test such as the Kolmogorov–Smirnov (K-S) test, to better support these comparisons. Similar concerns apply to the “func_auc” metric. It appears to be redundant with “complex_KS,” and it's unclear whether it provides additional value. I recommend making the differences in “complex_KS” scores more statistically defensible and considering the removal of the “func_auc” metric if it does not contribute distinct insights. Please make “complex_KS” scores differences statistically defensible. Also consider removing redundant “func_auc” metric.\n2. The sentence starting on line 487 reads: “However, when the goal of the analysis is to quantitatively integrate PTM site data with proteomic or transcriptomic data (which are in absolute abundance scale), the use of abundance reports (instead of ratios) is recommended.” This statement is problematic. It is unclear which data type is being referred to as having an absolute abundance scale—proteomic or transcriptomic. I cannot speak for transcriptomics, but in proteomics, data are typically reported on a relative scale. Even in cases where efforts are made to approximate absolute quantities, the definition and accuracy of “absolute” are highly debatable.\nUnless a scientifically sound example can be provided, such as integration of absolute PTM measurements with absolute proteomic or transcriptomic data, I suggest either removing this recommendation or clearly qualifying it. Vague or unsupported guidance can be misleading, especially given that a significant portion of the field may adopt such recommendations without thinking twice.\n3. Line 566. The statement “we demonstrated that FragPipe with TMT-Integrator offers several advantages over existing platforms like MaxQuant and Proteome Discoverer” could benefit from a more balanced tone. In any objective comparison, there are typically both advantages and disadvantages. Personally, I prefer FragPipe over MaxQuant due to its speed and flexibility. As for Proteome Discoverer, its commercial nature limits accessibility, so I do not actively consider it. In my opinion, one of FragPipe’s greatest strengths is the breadth of configurable options that can be tailored to diverse experimental needs.\nThat said, making a blanket statement that FragPipe simply outperforms MaxQuant and PD feels overly assertive. As I mentioned earlier, it is entirely plausible that, under different datasets or evaluation criteria, MaxQuant or PD might outperform FragPipe. Therefore, the claim as currently phrased does not seem fully objective.\n4. The description of the outlier removal algorithm is quite brief and would benefit from additional detail. For example, lines 872–873 leave it unclear whether outlier removal is applied to individual PSM reporter ion values within specific samples, or whether the entire PSM is removed across all samples in the plex if an outlier is detected. Clarifying this distinction is essential for understanding the impact of this filtering step on the data.\nAdditionally, the rationale behind the outlier removal strategy should be elaborated. Is the goal to eliminate potential false-positive identifications or something else?\n5. Lines 881–882. The phrase “generating a weighting factor for each PSM” is unclear as currently written. How exactly is this weighting factor calculated? Is it based on the summed reporter ion intensity, signal-to-noise ratio, or some other metric? Providing a brief explanation or pointing to the relevant equation or section in the methods would clarify this important step in the quantification process.\n6. The term “GN normalization” used on line 896 may not be ideal. As described, the procedure seems more akin to variance scaling rather than normalization per se. Additionally, both normalization methods presented, MD and GN, rely on global assumptions about the proteome as a whole, which is worth explicitly stating.\nWhile median normalization (MD) is a well-established and relatively intuitive method, the “global normalization” procedure (GN) could benefit from either a more descriptive name or a clearer explanation of its rationale and implementation.\nProviding more detail about the underlying assumptions and how this method differs conceptually from other global scaling strategies would help the reader better understand its purpose and applicability."}, "author_response": "We thank the reviewer for the summary and positive feedback. We have addressed all the comments. Please find our point-by-point responses below. Our responses are presented in bold font, and quoted sentences are formatted in italic bold font.\n1) The study reports higher protein identification rates but does not assess TMT-Integrator’s sensitivity to low-abundance proteins. Perform a sub-analysis on the ccRCC dataset to compare the detection of low-abundance proteins (e.g., bottom quartile by intensity) between TMT-Integrator, MaxQuant, and Proteome Discoverer. This could reveal whether TMT-Integrator’s higher coverage extends to challenging low-abundance targets, strengthening its claimed advantages.\nResponse: We thank the reviewer for the suggestion. To evaluate TMT-Integrator’s sensitivity to low-abundance proteins/genes, we used both the ccRCC proteome and spiked-in datasets to perform a comparison among different tools.\nFor the ccRCC proteome dataset, we looked at the NCI and QC samples as their protein abundances were consistent across replicates. We calculated the average protein abundance and compared the distributions with FragPipe and MaxQuant. As shown in the figure, the proteins uniquely quantified by either tool tended to be low-abundance, while the commonly quantified proteins were more abundant. It also shows that FragPipe with TMT-Integrator is better at detecting low-abundance proteins. A similar pattern was observed with the spiked-in dataset.\nWe have included this comparison in Supplementary Figure 2a and revised the manuscript accordingly.\n“We also found that the proteins uniquely quantified by each tool were mostly low abundance, demonstrating that FragPipe with TMT-Integrator improves the detection of low-abundance proteins (Supplementary Figure 2a).”\n2) Continuing with the higher protein identification rates, performing an entrapment database analysis would assess the specificity and FDR control of TMT-Integrator within FragPipe compared to MaxQuant and Proteome Discoverer. This analysis would use existing data and standard proteomics tools, requiring only a computational re-run. Such an analysis has recently been performed for DIA datasets: https://pubmed.ncbi.nlm.nih.gov/40524023/\nResponse: We thank the reviewer for the suggestion. We conducted an entrapment database search using both the ccRCC proteome and spike-in datasets using FragPipe coupled with TMT-Integrator, MaxQuant, and Proteome Discoverer. We used FDRbench, the work mentioned by the reviewer, to create entrapment databases by randomly shuffling protein sequences.\nIn the ccRCC dataset, FragPipe identified 12015 target proteins and 97 entrapment proteins, with a lower bound FDP of 0.80% and an upper bound of 1.6%. MaxQuant identified 10726 target proteins and 56 entrapment proteins, resulting in an FDP range of 0.52% to 1.04%. At the peptide level, FragPipe identified 249873 target peptides and 341 entrapment peptides with a lower bound FDP of 0.14% and an upper bound of 0.27%, while MaxQuant identified 191947 target peptides and 221 entrapment peptides with a lower bound FDP of 0.12% and an upper bound of 0.23%.\nIn the spiked-in dataset, FragPipe showed similar results at both protein and peptide levels. Proteome Discoverer identified the fewest entrapment proteins and peptides, leading to a slightly lower FDP compared to the other tools. Overall, all three platforms produced comparable FDPs at protein and peptide levels.\nWe have included the FDP results in Supplementary Table 2, and added the following sentences to the revised manuscript:\n“The entrapment database searching experiment showed that TMT-Integrator’s high sensitivity was reliable, with a low false-discovery proportion (FDP) similar to that of MaxQuant (Supplementary Table 2).”\n“Entrapment database searches\nThe ccRCC whole proteome and spike-in datasets were used for entrapment database searches with FragPipe with TMT-Integrator, MaxQuant, and Proteome Discoverer. Entrapment databases were constructed by randomly shuffling protein sequences of the previously described databases. These databases were then used to evaluate and compare the FDP of different tools using the method in Wen et al. [74]. ”\n3) While the paper evaluates quantification consistency using NCI and QC samples, it could further quantify reproducibility by calculating coefficients of variation (CVs) for all proteins across technical replicates in the Astral and TMT 35-plex datasets. Adding this to Figure 6 would provide a more comprehensive view of TMT-Integrator’s precision across cutting-edge platforms.\nResponse: The CVs for the Astral dataset have already been shown in Figure 6c. We added a new Supplementary Figure 5, including the CV distributions for each cell type, an additional unsupervised clustering heatmap for the TMT 35-plex dataset, and the CV distributions of each cell type in the TMTpro 35-plex dataset. The manuscript has been revised accordingly with the following sentences:\n“the combined CV distributions from all cell lines are shown in Figure 6c, and the CV distributions for each cell line are presented in Supplementary Figure 5a.”\n“Consistent with the PCA results, unsupervised clustering (Supplementary Figure 5b) separates HCT and HEK cell lines, with deuterated and non-deuterated samples clustered together. The CV distributions within each cell line (Supplementary Figure 5c) also demonstrate high quantification precision.”\n4) The ccRCC dataset analysis shows TMT-Integrator handles missing values well. To strengthen this, simulate varying levels of missing data (e.g., 10%, 20%, 30% missing PSMs) in the E. coli or Astral datasets and evaluate the impact on quantification accuracy. This in silico analysis would demonstrate TMT-Integrator’s robustness to incomplete data, a common issue in proteomics.\nResponse: The incomplete data is indeed a common issue in proteomics. However, in isobaric labeling experiments, single-plex data generally have low levels of missing values, while multiplexed data commonly have more missing values. For reference, in the single-plex TMT datasets evaluated in our study, the spike-in TMT-10, Astral TMT-18, and TMT-35 datasets have only 1.69%, 2.71%, and 1.36% missing values at PSM level, respectively.\nFor the multiplexed ccRCC datasets, the median missing value rate per-plex is 1.30% (ranging from 0.91% to 1.6%) for the proteome dataset and 0.16% (ranging from 0.04% to 0.66%) for the phosphoproteome dataset across 23 plexes. However, when integrating data from all plexes and aggregate PSMs to the peptide, protein, gene, and site levels, the proportion of missing values increases substantially. As shown in Figure 5a, our classification of quantifications based on data completeness reflected the missing value patterns in the final integrated reports.\nSince the single-plex isobaric labeling datasets already exhibit low levels of missing values, we feel that the simulation analyses performed with single-plex data would not add much new information. At the same time, expanding the analysis to complex multi-TMT datasets such as CPTAC data would go beyond the score of this paper.\n5) The paper highlights TMT-Integrator’s ability to handle large datasets but does not quantify its computational performance. Please measure and report the runtime and memory usage for processing the ccRCC and TMT 35-plex datasets using TMT-Integrator compared to MaxQuant and Proteome Discoverer on a standard computational platform (e.g., a desktop with specified specs). Adding a table or brief statement in the results would demonstrate TMT-Integrator’s scalability and practicality for large-scale studies, requiring only simple benchmarking.\nResponse: We thank the reviewer for this valuable suggestion. We agree that reporting computational performance will better showcase TMT-Integrator’s advantages in handling large-scale, multiplexed datasets. To address this, we performed a benchmarking analysis using the ccRCC proteome dataset, which consists of 23 plexes with 25 fractionations each - totaling 575 raw files. In this comparison, we focused on FragPipe/TMT-Integrator and MaxQuant as evaluated in our manuscript. We tested the speed on two platforms: (1) a Windows PC with an Intel Xeon W-2235 CPU (3.80 GHz, 6 physical cores, 12 threads) and 128 GB RAM, and (2) an HPC Linux server with an Intel Xeon Gold 6354 CPU (3.00 GHz, 36 physical cores, 72 threads) and 755 GB RAM. All 575 raw files were processed using the same database and software versions as in the manuscript. The figure below shows the total processing times for both FragPipe/TMT-Integrator and MaxQuant. MaxQuant could not finish the analysis on the Windows PC within a week. Thus, we only reported MaxQuant's runtime on the Linux server.\nOn the HPC platform, FragPipe completed the analysis in 8.63 hours, while MaxQuant took 44.09 hours - about five times longer than FragPipe. On the Windows PC, FragPipe took 15.21 hours, still 2.9 times faster than MaxQuant on the HPC. We believe this comparison highlights TMT-Integrator’s efficiency and suitability for large-scale multiplexed datasets.\nFor the TMT 35-plex dataset (12 fractionated raw files), we focused on reporting the runtime of our tool as evaluated in the manuscript. Since the entire workflow took only 13.59 minutes, which is typical for FragPipe on a single-plex dataset, we did not include this runtime result in the revised manuscript.\nWe have added the following sentences to the Results and Methods sections:\n“We also compared the runtime of MaxQuant and FragPipe with TMT-Integrator (Supplementary Figure 2f). The comparison shows that FragPipe with TMT-Integrator is significantly faster than MaxQuant, demonstrating its scalability and suitability for large-scale multiplexed datasets.”\n“Runtime comparisons\nThe ccRCC whole proteome dataset, comprising 23 plexes with 25 fractionations each (575 raw files in total), was used to evaluate the speed of FragPipe with TMT-Integrator and MaxQuant on large-scale isobaric labeling experiments. The analyses were run on two platforms: (1) a Windows desktop with an Intel Xeon W-2235 CPU (3.80 GHz, 6 physical cores, 12 threads) and 128 GB RAM, and (2) a Linux server with an Intel Xeon Gold 6354 CPU (3.00 GHz, 36 physical cores, 72 threads) and 755 GB RAM. FragPipe was run on both platforms using the mzML files converted from the raw files, while MaxQuant was run with the raw files. Because MaxQuant could not finish the analysis within a week using the Windows desktop, only the runtime on the Linux server is reported. All search parameters are as previously described.”\n6) TMT-Integrator applies additional PSM filtering based on probability thresholds. To assess its impact, a subset of the ccRCC or Astral dataset could be re-analyzed with varying “min best peptide probability” thresholds (e.g., 0.7, 0.9, 0.95) and changes could be reported in protein/PTM identification rates and quantification accuracy. Including a supplementary figure or table would clarify how filtering affects performance, providing users with guidance on parameter optimization using existing data.\nResponse: These advanced PSM filtering options are intended for experienced users or developers to test parameters in certain (typically non-standard) cases. Since default settings have already proven effective in numerous datasets, we recommend that users avoid changing these advanced parameters. As our current work focuses on the main features of TMT-Integrator (such as ratio-to-reference normalization, PSM aggregation, median normalization, and ratio-to-abundance conversion), we chose not to include an assessment of the advanced parameters. We provide those options in the software to enable maximum flexibility, however, we are concerned that an extensive comparison of all possible settings in the manuscript would only confuse general readers.\n\nResponse to Reviewer 2:\nWe thank the reviewer for the valuable feedback and kind support. In this manuscript, we focus on the main features of TMT-Integrator, including ratio-to-reference normalization, PSM aggregation, median normalization, and ratio-to-abundance conversion. During the revision, we added performance evaluations such as FDR assessment using entrapment database search and runtime comparison, as well as the evaluation of two advanced options – “Best PSM” and “Outlier removal” - as suggested by the reviewer. Please find our point-to-point response to the comments in the following. Our responses are presented in bold font, and quoted sentences are formatted in italic bold font.\nWe have added the following sentences for the evaluations of the “Best PSM” and “Outlier removal” options to the Results section, “Evaluation of TMT-Integrator using a spike-in benchmark dataset”:\n“Using the median ratio approach, we also evaluated two advanced PSM filtering options (i.e., “Best PSM” and “Outlier removal”) for their impact on the quantification accuracy. For each LC-MS run, it is common to see multiple MS/MS spectra matching to a same peptide. When the “Best PSM” option is enabled, TMT-Integrator retains the PSM with the highest summed reporter ion intensity for each peptide. This reduces the impact of low-quality PSMs, which could increase variance and lead to underestimation of the quantification in median ratio-based aggregation. As shown in Supplementary Figure 3d, enabling the “Best PSM” option improves the accuracy of the protein quantification in both MS2 and MS3 datasets. The “Outlier removal” option enhances quantification reliability by removing outlier channel values within each PSM during aggregation, rather than discarding the entire PSM. TMT-Integrator uses IQR filtering to keep channel values within the IQR, preventing outliers from skewing the median estimation. Supplementary Figure 3e shows that the “Outlier removal” also improves quantification accuracy for the proteins in both datasets.”\nActionable issues:\n1. I like the idea of using prior knowledge of protein complex information to assess the sensibility of the data. However, the comparison presented in Table 1 is difficult to interpret, as it is based solely on point estimates of scores. For example, how meaningful is the difference between 0.87 and 0.86? Or between 0.87 and 0.62? It would be helpful to include confidence intervals, perhaps derived via bootstrapping, or p-values based on a statistical test such as the Kolmogorov–Smirnov (K-S) test, to better support these comparisons. Similar concerns apply to the “func_auc” metric. It appears to be redundant with “complex_KS,” and it's unclear whether it provides additional value. I recommend making the differences in “complex_KS” scores more statistically defensible and considering the removal of the “func_auc” metric if it does not contribute distinct insights. Please make “complex_KS” scores differences statistically defensible. Also consider removing redundant “func_auc” metric.\nResponse: We thank the reviewer for the comment. First, there is a typo: “complex_KS” should be “Complex AUC”. We have corrected it. In our analysis of the ccRCC proteome dataset, we compared nine data tables that differed in software, quantification type, aggregation and normalization methods. We used OmicsEV for the assessment as it allows systematic comparison of multiple data tables across eight quality metrics, including the “Complex AUC” and “Func AUC”.\nThe \"Complex AUC\" and \"Func AUC\" are complementary metrics. “Complex AUC” measures how well the data preserves correlations within protein complexes, focusing on specific interaction-based groupings. In contrast, the “Func AUC” assesses how well the data predicts broader functional categories (such as KEGG pathways) and captures a wider range of biological associations, not just protein complexes. While some overlap exists, most functional categories are not protein complexes. Also, the “Complex AUC” only evaluates co-regulation within protein complexes, not general functional associations. Since each of them provides unique and valuable insight into the quality of the data, we chose to keep both in the manuscript.\nRegarding the value differences, Table 1 only shows the summary results from OmicsEV. More detailed descriptions and visualizations, including distributions of the complex correlations (see the figure below), are available in Supplementary File 1. We hope the reviewer will find the standard outputs and methods from the published OmicsEV tool, as presented in the main figures and the supplementary files, to be sufficient given the scope of this manuscript.\n2. The sentence starting on line 487 reads: “However, when the goal of the analysis is to quantitatively integrate PTM site data with proteomic or transcriptomic data (which are in absolute abundance scale), the use of abundance reports (instead of ratios) is recommended.” This statement is problematic. It is unclear which data type is being referred to as having an absolute abundance scale—proteomic or transcriptomic. I cannot speak for transcriptomics, but in proteomics, data are typically reported on a relative scale. Even in cases where efforts are made to approximate absolute quantities, the definition and accuracy of “absolute” are highly debatable.\nUnless a scientifically sound example can be provided, such as integration of absolute PTM measurements with absolute proteomic or transcriptomic data, I suggest either removing this recommendation or clearly qualifying it. Vague or unsupported guidance can be misleading, especially given that a significant portion of the field may adopt such recommendations without thinking twice.\nResponse: We apologize for any confusion. In our context, “absolute abundance” does not refer to the absolute quantity, which needs certain technology to measure. Instead, it describes raw intensity-like quantities that capture magnitude information and are distinct from ratios. To clarify, we have revised the manuscript to include the following statements. We have also removed the word “absolute” from the whole manuscript.\n“conversion from ratio back to intensity-like quantity, which we hereafter refer to as “abundance”.”\nWe also appreciate the reviewer’s feedback about providing precise recommendations and have revised the manuscript to clarify this point:\n“It is worth noting that both ratio and abundance reports have the same within-feature variation, so the choice between them does not significantly affect PCA, CV, or differential expression analysis. However, the abundance reports are more suitable for integrative analyses with complementary proteomic or other omics data, such as label-free proteomic or transcriptomic data, which are not ratio-based.”\n3. Line 566. The statement “we demonstrated that FragPipe with TMT-Integrator offers several advantages over existing platforms like MaxQuant and Proteome Discoverer” could benefit from a more balanced tone. In any objective comparison, there are typically both advantages and disadvantages. Personally, I prefer FragPipe over MaxQuant due to its speed and flexibility. As for Proteome Discoverer, its commercial nature limits accessibility, so I do not actively consider it. In my opinion, one of FragPipe’s greatest strengths is the breadth of configurable options that can be tailored to diverse experimental needs.\nThat said, making a blanket statement that FragPipe simply outperforms MaxQuant and PD feels overly assertive. As I mentioned earlier, it is entirely plausible that, under different datasets or evaluation criteria, MaxQuant or PD might outperform FragPipe. Therefore, the claim as currently phrased does not seem fully objective.\nResponse: We thank the reviewer for the suggestion. We revised the text with the following changes in the Discussion section:\n“In this study, we presented TMT-Integrator, an isobaric labeling quantification tool designed to summarize PSM data at various levels, including gene, protein, peptide, and PTM sites. Central to its algorithm is the ratio-based integration, normalizing reporter ion intensities to a reference sample (ratios-to-reference), and converting the ratios back to intensity-like abundance values using MS1 precursor intensities. TMT-Integrator is fully integrated into FragPipe computational platform, leveraging the fast MSFragger database search engine and IonQuant quantification tool to enable efficient isobaric labeled peptide identification and quantification. We provided a comprehensive evaluation of FragPipe with TMT-Integrator as a robust computational platform for isobaric quantitative proteomics using a variety of datasets, including large-scale clinical studies like ccRCC, controlled experiments such as the spike-in tests, and recent advances like the Astral instrument and TMTpro 35-plex reagents.\nIn our tests, FragPipe with TMT-Integrator has consistently delivered high coverage and accurate quantification across diverse sample cohorts, effectively handling the complexities inherent in multiplexed proteomic experiments. FragPipe with TMT-Integrator demonstrated high efficiency, handling well large datasets and completing analyses fast. In addition, TMT-Integrator is capable of processing data from the latest instruments, such as Orbitrap Astral, and reagents, such as TMTpro 35-plex.”\n4. The description of the outlier removal algorithm is quite brief and would benefit from additional detail. For example, lines 872–873 leave it unclear whether outlier removal is applied to individual PSM reporter ion values within specific samples, or whether the entire PSM is removed across all samples in the plex if an outlier is detected. Clarifying this distinction is essential for understanding the impact of this filtering step on the data.\nAdditionally, the rationale behind the outlier removal strategy should be elaborated. Is the goal to eliminate potential false-positive identifications or something else?\nResponse: We thank the reviewer for the comment. Unlike other PSM filtering options that remove an entire PSM, the outlier removal is performed separately for each report ion channel corresponding to a sample. The purpose is to prevent spurious measurements introduced by technical noise from distorting quantification during median-based aggregation. We have revised the Methods section for “Step 4. Outlier Removal” to clarify this point:\n“Step 4. Outlier Removal. After grouping, an entry (i.e., a gene/protein/peptide/multi-site) may have multiple PSMs grouped to that entry, and their abundance ratios will be subsequently aggregated for quantification. To prevent spurious measurements introduced by technical noise from affecting quantification estimation, an IQR based outlier removal step is performed for each PSM group in every sample separately. Specifically, for each sample, we extract abundance ratios for that channel from the grouped PSMs of a given entry and calculate the first quantile (Q1), the third quantile (Q3), and the interquartile range (IQR, i.e., Q3-Q1) of these ratios. Ratios outside of the boundaries of Q1-1.5×IQR and Q3+1.5×IQR are set to NA and excluded from downstream aggregation. Thus, only a subset of reasonable abundance ratios is retained to represent each entry in each sample, which helps enhance the robustness of quantification. The outlier removal step is performed independently at each level, and only for groups containing at least four PSMs.”\n5. Lines 881–882. The phrase “generating a weighting factor for each PSM” is unclear as currently written. How exactly is this weighting factor calculated? Is it based on the summed reporter ion intensity, signal-to-noise ratio, or some other metric? Providing a brief explanation or pointing to the relevant equation or section in the methods would clarify this important step in the quantification process.\nResponse: We have revised the manuscript to include more details on how the weighting factor is calculated:\n“First, the precursor intensity for each PSM in a PSM group is exponentiated and then normalized by the sum of these exponentiated values within that group, generating a weighting factor for each PSM such that all weights sum to 1.”\n6. The term “GN normalization” used on line 896 may not be ideal. As described, the procedure seems more akin to variance scaling rather than normalization per se. Additionally, both normalization methods presented, MD and GN, rely on global assumptions about the proteome as a whole, which is worth explicitly stating.\nWhile median normalization (MD) is a well-established and relatively intuitive method, the “global normalization” procedure (GN) could benefit from either a more descriptive name or a clearer explanation of its rationale and implementation.\nProviding more detail about the underlying assumptions and how this method differs conceptually from other global scaling strategies would help the reader better understand its purpose and applicability.\nResponse: We thank the reviewer for the comment. “GN normalization” is a global variance scaling method and it serves as an alternative to the median normalization. Both methods were introduced to adjust quantification and improve comparability, consistency, and precision among samples, so we refer to both as normalization methods.\nWe agree that “GN” may not clearly describe its specific function, however, its use is based on historical reasons. This term has been established during the initial development of TMT-Integrator and has been referenced in several published studies. For consistency, we continue to use “GN” to refer to the global normalization.\nTo improve clarity, we have revised the Results section to explain the normalization methods: “(None, MD for median centering normalization, GN for global normalization by median absolute deviation (MAD)-based variance scaling)”. We have also added the details in the Methods section of “Step 6. Normalization II” as follows:\n“Median-centering normalization is performed on a per-sample basis. It shifts all values by subtracting the sample-specific median. In contrast, global normalization further scales all values using a factor calculated from the entire dataset on top of the median-centering normalization.”\nWe also agree that both normalization methods assume that most proteins remain stable across samples. To clarify this point, we have revised it in the Methods section with the following statement:\n“Both methods depend on the assumption that most entries are unchanged across samples, allowing normalization based on the sample median without being skewed by a few highly variable proteins.”\nTo further clarify how our GN normalization differs from other global scaling methods, we have also added:\n“In this approach, global normalization uses the sample median and MAD to rescale distributions. Unlike total intensity scaling, quantile normalization, or more complex variance-stabilizing normalization (VSN), this MAD-based normalization is non-parametric, robust to outliers, and adjusts only the overall location and spread while preserving distribution shape. This makes it a simple and effective choice for general use.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have responded thoughtfully and thoroughly to prior reviewer comments, and the revised manuscript is significantly improved.\nAs mentioned in my original review, this manuscript presents a well-designed and comprehensive computational framework for isobaric quantitative proteomics data, integrating TMT-Integrator with the FragPipe ecosystem. The authors demonstrate robust handling of reporter ion quantification, normalization, missing data, and statistical analysis, supported by thorough benchmarking across multiple datasets. Algorithms, statistical approaches, and comparisons are thoughtfully chosen and well validated. The integration into FragPipe is technically solid and practically useful.\nIn my opinion, the work is now complete, technically sound, and ready for publication in Nature Communications.", "reviewer_2": "The authors have satisfactorily addressed all of my comments and concerns. I have no further issues to raise."}, "author_response": ""}]

Decision letter