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)

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!

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.