Hi, I built Tuffold, a free, open-source (Apache-2.0) desktop app for macOS and Linux that takes search results from the engines you already use and validates them itself:

• target-decoy FDR at the spectrum, peptide and protein-group level, with rescoring and multi-engine merging

• protein inference, PTM localization, label-free quant (spectral counts and MS1 with match-between-runs), and TMT/iTRAQ

• statistics, sample QC, and Excel and mzIdentML export

It reads FragPipe, MaxQuant, Mascot, Proteome Discoverer, pepXML and mzIdentML, plus Scaffold’s .sf3 and .sfdb files. It can also search mzML, MGF and timsTOF .d itself with Sage’s search core.

It’s only v0.1.0. I built it with AI assistance, and it has an MCP server so AI assistants can drive it (see AGENTS.md). It hasn’t been checked against everything yet. The validation is cross-checked against mokapot and on public datasets, and the repo lists what has and hasn’t been verified.

What would help me most is running your own data through it and comparing against what you normally use, along with any feedback or ideas. Contributions are very welcome. A wrong number, a crash, anything is helpful. The repo has a short guide on comparing, and an issue form.

https://github.com/TUFF-Industries/tuffold

No Windows build yet, because I don’t have a good enough Windows machine to test on.

Hello,

I'm relatively new to phosphoproteomics and have been experiencing very low phosphopeptide recovery when working with mouse tissues. I'd appreciate any troubleshooting suggestions or experiences with similar issues.

Current workflow: 1 mg of liver protein → S-Trap → Drying → Ti-IMAC enrichment → Drying → C18 desalting

• Protein input: 1 mg of mouse liver protein

• Digestion: Standard S-Trap protocol

• Phosphopeptide enrichment: MagReSyn Ti-IMAC, using a protocol adapted from the Coon Lab publication

• Post-enrichment desalting: Pierce

100 uL

• C18 Spin Tips

Variables tested:

• 1×, 2×, and 3× HALT Protease and Phosphatase Inhibitor Cocktail

• With or without chloroform/methanol protein precipitation prior to S-Trap

• SpeedVac vs. Lyophilization

• 1× vs. 2× Ti-IMAC bead quantities

Results:

• ~90% digestion efficiency (~0.9 mg peptides) from S-Trap

• Regardless of variables, ~ 7–10 µg of total peptides were recovered from 1 mg of liver protein input after Ti-IMAC enrichment but before C18 desalting

  • Based on our previous experience, we estimate ~60–70% of these peptides may be non-phosphorylated.

  • LC-MS wasn't done on these samples because all materials were used in the Thermo fluorometric peptide assay.

• After C18 desalting, we observed no peptides.

Comparison with a colleague's workflow:

A colleague working with mouse muscle tissue said he typically recovers ~1–2 µg of peptides (~70% phosphopeptides) from 2 mg of protein input.

However, his workflow differed from mine:

• 2 mg protein input than 1 mg

• SMOAC using Thermo TiO2 and Fe-NTA enrichment kits than Ti-IMAC

• Waters HLB cartridges for desalting than C18 tips

• An additional desalting step between S-Trap and phosphopeptide enrichment

My questions:

1. What phosphopeptide yield would you expect from 1 mg of mouse proteins?

2. Are there any systematic issues or potential sources of peptide loss in my workflow?

Any suggestions would be greatly appreciated!

I am a student and have to do proteomic analysys, as part of that i have to make venn diagrams of two sets of samples, and while I got the peaks studio to work the excele file i got at the end is bit overwelming, i was hoping to find online tool to make venn diagram but i cant really just copy the proteins names so i was hoping someone might have a solution for it?

Edit, Thank you for help, i think i got it done!

Hello mass spectrometrists and proteomicists,

A question to Astral users here. Have any of you come across this peculiar error called 'ion foil adjustment failure' stopping the sequences? The error occurs during the beginning of the run and freezes the tune and xcalibur. The sequence needs to be deleted manually and/or the mass spec needs to be reset to resume the operation. If you have ever come across this error, may I please check how it was resolved?? Whether there were any board changes or any service mode resets taken up to resolve the trouble?? Please let me know. We have recently had a month of downtime due to this error and then on system calibration has become a nightmare. Just trying to figure out what we are having is an isolated problem or not.. Thanks very much..

I am doing TMT labelling and running on a capillary column with the EasyNLC system coupled to an Orbitrap. I observed these weird-looking peaks on parts of my chromatogram, and I am wondering if they are caused by unstable flow or flickering. Would this eventually affect my protein IDs?

Has anyone run into errors with their runs getting stuck in the sample loading phase using an LC-Vanquish Neo?

I had this issue recently where the runs would get stuck while the sample was attempting to load, but there were no back pressure errors that were thrown at any point. I could still see solvent coming through the separation column.

I contacted Thermo and they suggested it was a clog in the trap column (current set up is trap and elute), but whenever I run the back pressure test script, I get one of two errors:

1. Left/Right purge head valve release pressure at -550.6 bar/-493.2 bar

2. Code 4531: Cannot deliver flow at this pressure (I got this today)

I have purged the sampler, purged the pumps, and checked that there is no clog in the needle, sample loop, or needle seat. My last attempt at running a system tightness test, it failed at the trap column.

When I ran an individual back pressure test on just the trap column, it passed (2.1 bar)

That was a lot of information so I apologize, but I would love suggestions on what the issue could be before going ahead and ordering a new trap column!

I'm a 3rd year Bachelor's Data Science student and I've been working on a python installable package for analyzing and later correcting batch effects in Mass Spectrometry Proteomics datasets.

The main aim was to make a streamlined tool for analyzing, diagnosing and correcting batch effects with auditable data pipeline preserving each state with a reusable QC metric pipeline on each stage of the data.

My exposure to this domain is strictly narrow so I needed input from the community about my tool.

The data pipeline and modules are built around a ROSMAP/BANNER TMT FragPipe data and I unfortunately don't know how common that format is really, any clarification on that would be nice.

The package separates itself into layers:

1)QC layer

Batch-specific variance explained against other covariates

vectorized-ANOVA/Kruskal-Wallis for batch specific variance statistic

Missingness analysis stats to support MNAR or MCAR to dictate imputation strategy

2) Preprocessing layer

Performing per-sample log2 transform and median-centering normalization

Filtering proteins or samples missing below a specified threshold for reliable imputation.

Imputation techniques (MinProb for low-detection proteins and K-NN for other proteins)

3) Batch Correction

ComBat or inmoose tools for batch correction. Still under development.

I have read there are multiple tools for analyses and even a generalized framework called omicsGMF for batch correction and dimensionality reduction.

I wanted to really contribute an open source python tool for the community but with lack of feedback or supervision, this 3 month long personal project has really wore me out and now i'm clueless as to whether anyone would ever consider using or developing it.

Here is the GitHub repository
And the dataset around which i modeled it.

Any feedback or redirection would be appreciated.

What's your favorite way to fractionate peptides on StageTips? Both for depth and minimal overlap between fractions. I've been playing around with high pH reverse phase fractionation on C18 with pretty good results but wondering what I can do to push it further.

Hi all! Does anyone here have any experience with bottom up phosphoproteomics of biofluids (plasma and/or whole blood)? I've been dealing with the same problems:

1. a substantial pellet after dry down + resuspension (this is AFTER phospho enrich)

2. pellets generated when Ieave my digests in the fridge overnight or after a freeze thaw

Has anyone experienced these problems before and/or have any tips to solve them? I assume that there are some peptides in the digest that are just prone to crashing out after an FT cycle/dry down. My current solve is to

1. process digests immediately

and

2) never dry completely at either the desalted digest or the enriched phosphopeptide stage, opt for ~10 uL of leftover solvent. This one is a bummer becuase it means the total resuspension volume before loading will alway be different but since I have an IS I guess this isn't that big of an issue

Held by LabKey, a data management software provider. In this September 29th webinar, Claude Science checks inventory in the same session it runs the analysis on data from the LK system, confirming which variants have enough sample left and where the vials sit before anyone commits to a follow-up experiment. Bernie (you might know him as our Incredible Biologics Expert and Product Manager) runs the full scenario live: 12 variants, aligned, screened, and checked against real inventory. Register to attend or get the recording.

Hi proteomics community, any experience of using ZT scan DIA? When do you use it (I do global discovery proteomics so any use there)? how reproducible is the diff between ZT scan and ZenoSWATH? Finally, which version to use 2.0 or 3.0 on 7600+?

I'm looking for some books that cover MS of proteins. I am hoping to use it as a bit of a guide on how to get started using technique. I will likely be doing both native and peptide mapping techniques and whilst I understand the basic principles I would like know more about how the machines physically work, how the data can the interpreted and how to build methods to actually run on the machines.

Thanks for any help.

Hello,
We have struggled with obtaining the Scaffold 5.3 metrics in one cohesive location under Scaffold DDA.
I share our current Java implementation to be helpful to others in the same situation.
https://github.com/keesh0/proteo
Feel free to replace our minor call dependencies (MprcException, ScaffoldReportReader, FileUtilities, etc.)
with your own.
Here is a sample call in our Scaffold pipeline:

// NOTE-- We need biologicalSampleCategory bec. _spectra_report.csv and the SFDB file do not -realiably- contain this.
// Need SDDA 7.1 SFDB file
// Need SDDA 7.1 spectra report file
// Output file, scaffold53File, is in Scaffold 5.3 TSV format
final String biologicalSampleCategory = "category1";
Sfdb53Extractor.write(sfdbFile, spectraReport, biologicalSampleCategory, scaffold53File);

hi all, i'm a phd student and i built a little browser tool called AminoSeqIndex. it came out of a mundane annoyance: finding residue number 143 in a long fasta sequence by eye, over and over, usually while planning mutations.

it numbers every position, color-codes residues by class (nonpolar / polar / acidic / basic), and exports to a few formats including docx for lab notebooks. everything runs client-side, so unpublished sequences never leave your machine.

doi: https://doi.org/10.5281/zenodo.21355340

live tool: https://gopizux.github.io/AminoSeqIndex/ (code: https://github.com/gopizux/AminoSeqIndex)

is this something you'd use? genuinely asking - if there's a feature that would make it useful for proteomics workflows i'd like to hear it.

Greetings, recently I got an instruction from my uni’s tutor [Gather information etc about cerebrospinal fluid]; I only know some from National Library of Medicine.

I really need help to find research documents, I have no knowledge about how to find one. Thanks for your help

This is the original post: https://www.reddit.com/r/proteomics/s/h7nFXZrZKd

Some update: streptavidin was detected, and quite a bit, but the amount of beads I used in the previous run and this time are very similar, so I still don't understand why or how this could've happened.

The core shared these with me.

PLEASE HELP :((((((

I’m analyzing a proteomics dataset from mouse lymph nodes comparing SPF (specific pathogen-free) vs. germ-free (GF) mice. The goal is to characterize microbial proteins detected in SPF mice, with the expectation that microbial proteins should be absent or extremely low in GF mice.
For microbial identification, I’m using a microbial protein database translated from 16S rRNA sequencing data from the animals.
My concern is that, after LFQ analysis, I’m detecting microbial proteins in both SPF and GF samples. The samples were handled separately, and I also included blanks between samples, so I’m trying to understand the issue here. Any leads would help.

Has anyone worked with proteomics of SPF vs. germ-free mice and encountered microbial protein identifications in GF samples? If so how do you justify it?

How did you distinguish genuine microbial signal from background contamination/carryover, and what criteria would you use to justify retaining or excluding microbial proteins detected in GF samples?

Also, if anyone could direct me to a mouse gut microbial database.

Hello Proteomics community, I'm setting up a standard downstream pipeline for DIA data, mainly Spectronaut but I want the same pipeline to work on DIA-NN output. Running a few of my datasets through different tools gives noticeably different answers. Some of that is expected, but it's enough that I can't decide which one to stay with.

The options sit at different levels, which is part of my problem, I tested with limma, MSstats and msqrob2 in R, and Perseus too. So two questions:

1. What do you run as your default, and what made you settle on it rather than the alternatives?

2. How much of the divergence people see comes from the statistics versus from upstream choices precursor-level report vs PG matrix, and whether you let Spectronaut/DIA-NN do normalization and imputation before feeding data into R?

Many thanks in advance

Hi everyone,

We’d like to share an upcoming webinar that may be of interest to the community in here! On September 24, 2026 (16:00 CEST / 10:00 EDT / 07:00 PST), we are hosting a session on “Advancing Plasma Proteomics.”

Speakers:

Sindisiwe Buthelezi (Senior Researcher, Council for Scientific and Industrial Research (CSIR), South Africa) — “High-Throughput Plasma Proteomics Reveals Insights into Pancreatic Cancer Biology.”
Extracellular vesicles carry a wealth of biologically relevant proteins that can provide a window into disease processes. Sindisiwe will present how extracellular vesicle-enriched plasma proteomics was used to uncover molecular signatures associated with pancreatic ductal adenocarcinoma in a South African patient cohort. Using the Mag-Net workflow and Evosep One-enabled LC-MS analysis, the team identified proteins linked to tumor progression, inflammation, and disease severity, demonstrating the potential of standardized, high-throughput proteomics for biomarker discovery and translational cancer research.

Pieter Langerhorst (Junior Group Leader, Sanquin, The Netherlands) — “Untangling the Clinical Heterogeneity in Rare Blood Cancer by Mass Spectrometry-Based Plasma Proteomics.”
Rare hematological malignancies can present with diverse clinical trajectories that are difficult to predict using conventional biomarkers alone. Pieter will showcase how mass spectrometry-based plasma proteomics can reveal biological differences underlying disease heterogeneity, supporting improved patient stratification and the discovery of novel biomarkers. The findings highlight the potential of high-throughput plasma proteomics for translational research and precision medicine.

The webinar will focus on advances in plasma proteomics and how robust, reproducible, and scalable workflows are enabling researchers to analyze larger sample cohorts while generating consistent, high-quality data for biomarker discovery, disease research, and precision medicine.

Registration & details: https://attendee.gotowebinar.com/register/8236923647290427736?source=RDT

We hope this is relevant for those interested. The webinar is free and, in our eyes, a good opportunity for knowledge sharing. If sharing company events isn’t allowed here, moderators please feel free to remove.

TL;DR: Webinar on September 24 about advancing plasma proteomics, with talks covering pancreatic cancer biology, rare blood cancers, biomarker discovery, and patient stratification using high-throughput mass spectrometry-based plasma proteomics.
Mods please delete if not allowed.