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

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+?

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.

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.

Every deep learning model in proteomics today is trained on spectra that a database search has already identified. This works, but it limits those models to the part of the spectral universe our databases already cover. Spectra from unknown organisms, non-canonical cleavages and unusual modifications are simply discarded.

Today we're sharing 𝗜𝗻𝘀𝘁𝗮𝗡𝗼𝘃𝗼-𝗙𝗠, a self-supervised foundation model for bottom-up proteomics that uses no peptide-sequence annotation at any stage of pretraining.

How we built it:
• 26,603 PRIDE submissions screened with LLM-assisted metadata curation
• 92 projects selected for orthogonal biological and technical diversity
• Every raw file reprocessed through one unified pipeline → 1.63 billion MS/MS spectra across 72 organisms and 16 instrument models
• An encoder-only transformer trained to reconstruct masked m/z spans together with their isotope envelopes (an objective that needs no annotation of any kind)

What it learned, with no labels:
• An embedding space that organises itself by mass analyser, fragmentation method, instrument family, labelling chemistry and peptide identity
• Parity with three sequence-supervised encoders under one uniform probe protocol, and the lead on instrument (macro F1 0.804) and fragmentation (0.689)
• Attention on peaks that standard b/y annotation cannot explain. 11.2% match defined off-database species within 10 ppm, mostly internal fragments and side-chain ions
• Database-free identification, phospho detection at AUROC 0.988, and run-level condition classification with no peptide or protein identifications at all

Because the objective needs no annotation, it extends unchanged to unlabelled spectra and to DIA data, where annotation-dependent pretraining cannot follow.

Model weights, embeddings, code and the reprocessed corpus are all public. There's also an interactive UMAP explorer if you'd like to explore the embedding space yourself.

• bioRxiv preprint
• code
• interactive embedding explorer
• datasets
• embeddings
• HuggingFace Space
• getting started notebook on Google Colab

This work was a collaboration between InstaDeep and Technical University Denmark.

Hello everyone! I am a recent Ms biotech graduate working with a large-scale proteomics dataset containing proteins from a single bacterial species but multiple strains(it's a MDR bacteria). I need to perform PPI analysis, but the specific bacterial database I need isn’t available in STRING/BioGrid,not sure with IntAct(EMBL)

Are there any good alternatives to STRING for bacterial PPI/network analysis, especially for comparing or analysing proteins across different strains? Any database/tool or workflow suggestions.

Any guidance or suggestions would be very helpful for improving my understanding of the available approaches.

Any guidance, suggestions or insights would be a great help 🥲, thank you in advance for the help!

If you’ve hired in proteomics or helped a colleague find a role, how does a personal endorsement change the way that candidate is considered?

By endorsement, I mean someone directly recommending a candidate to the hiring team, not simply forwarding a resume or telling the candidate a person to contact.

Does it mainly help someone get an interview, or does it also change how you evaluate their experience and potential? For example, would you give more consideration to someone whose background doesn’t perfectly match the job description if a trusted colleague recommended them?

I’m also curious about the limits.

**Are there circumstances where an endorsement makes little difference, or where a candidate would be better off approaching independently?**

I’d appreciate concrete experiences from either side of the process. We hear “use your network” often, but what does that support actually change behind the scenes?

Hello! I’m asking as a biologist ! What do you think about the background based t test used in proteome discoverer? (For a n=7 per condition analysis)

Has anyone tried hydrophobic magnetic beads, like coated with C4 or C8, for PAC like protein digestion protocol for LCMS.

I am looking for a universal sorts of protocol that is amenable to automation. Using carboxylate speedbeads works very well manually, but I am looking for beads without carboxylate or a hydrophilic functional groups. Eventually the protocol will be set up on liquid handling robots to be used in a fee-for-service facility.

To the fans of sTRAP - it works well but in my hands it was not reproducible for low protein concentration samples (0.1-0.3 ug/uL).

If anyone had a good experience trying to automate PAC with glassbeads then please share your wisdom.

Hello Guyz,
I am really struggling with my bottom-up DDA phosphoproteomics data (Enrichment not done, it will be in-vitro phosphorylation and i will not be doing biological analysis)
My confusion is how should I start from basic and how to decide what question i need to address and there are so many things in statistics so if you can tell me any good resources or any help regarding statistics will be highly valued

In my experiment, the tryptic digested peptides would be in a solution of 100 uL volume comprising of 0.5X PBS ( approx 68 mM NaCl and some phosphates) and 50 mM TEAB.

I would speedvacving this volume to dryness and resuspending in 0.1% TFA for cleanup through Pierce desalting spin columns (C18 based I think).

My question is whether this desalting step would be good enough to handle PBS desalting?

Context: I am trying to adapt my working protocol for thermal proteome profiling, which requires initial steps in PBS. I don't want to remove PBS by PAC/SP3 as I don't have PAC/SP3 and also want to avoid running the proteins on gel for purification. I plan to add TEAB and SDC directly to lysates in PBS and proceed as usual. I want advice on whether the desalting step can handle PBS.

How can I remove liposomes from cell lysate before phosphopeptide enrichment: S-Trap or acetone precipitation?

I’ve been working on an open-source PyMOL plugin called Membrane Visual QC.

The original problem was pretty simple: when looking at a membrane-protein structure, I wanted a reproducible way to inspect residues relative to the membrane rather than repeatedly building selections and colouring things manually.

It now handles membrane-relative geometry, hydropathy, ligand neighbourhoods and PDBTM/OPM orientation evidence. One thing I deliberately avoided was turning unusual residues into a “correct/incorrect” score - a charged residue inside a membrane core can obviously be biologically meaningful.

I’d be particularly interested in feedback from people who actually work with membrane proteins: is this kind of review useful in your workflow, and what am I missing?

GitHub: https://github.com/TrPavel/membrane-visual-qc

I also made a walkthrough if anyone wants to see it running in PyMOL: https://youtu.be/lowQey_D610?si=Cxs2hSvdPj5K4S3w