Find Your
Golden Peptides
We unlock peptidomics gold from past & present DIA-MS datatasets
We are a computationally-focused biomarker discovery lab for DIA-MS datasets. We collaborate closely with both LC-MS instrument labs and biomedical research labs.
Using our DIA-MS algorithmic innovation coupled to advanced parallel computing (preprint), we unlock your past and present DIA-MS datasets' peptidomics gold in record time.
In brief, your lab may have collected many different DIA-MS projects over the years that are "generating minimal additional value". If the hidden value from your lab's datasets were to be unlocked quickly — as speed is essential for patent priority reasons — it could be a treasure trove of astronomical value.
With regards to our key innovation, we have discovered a computational way to quantify the ~75-95% of peptides in DIA-MS datasets (an up to ~2000% increase in quantifiable peptides) that are currently being unused.
Even more valuably, these up-to ~2000% more quantified peptides contain the unpredicted sequences and unexplored PTMs that are far more likely to separate your study conditions.
We are rapidly seeking collaborators with a unique combination of three traits: (a) highly collaborative; (b) ability & desire to move quickly; and, (c) ability & desire to go big.
We are a computationally-focused biomarker discovery lab for DIA-MS datasets. We collaborate closely with both LC-MS instrument labs and biomedical research labs.
Using our DIA-MS algorithmic innovation coupled to advanced parallel computing (preprint), we unlock your past and present DIA-MS datasets' peptidomics gold in record time.
In brief, your lab may have collected many different DIA-MS projects over the years that are "generating minimal additional value". If the hidden value from your lab's datasets were to be unlocked quickly — as speed is essential for patent priority reasons — it could be a treasure trove of astronomical value.
With regards to our key innovation, we have discovered a computational way to quantify the ~75-95% of peptides in DIA-MS datasets (an up to ~2000% increase in quantifiable peptides) that are currently being unused.
Even more valuably, these up-to ~2000% more quantified peptides contain the unpredicted sequences and unexplored PTMs that are far more likely to separate your study conditions.
We are rapidly seeking collaborators with a unique combination of three traits: (a) highly collaborative; (b) ability & desire to move quickly; and, (c) ability & desire to go big.
Peptides containing unpredicted sequences or unexpected PTMs are up to ~2000% more numerous than peptides identified from a FASTA library search space.
Peptides containing unpredicted sequences or unexpected PTMs are up to ~2000% more numerous than peptides identified from a FASTA library search space.
Far more valuably, these unexpected PTMs or unpredicted sequences are far more likely to separate your study conditions.
Far more valuably, these unexpected PTMs or unpredicted sequences are far more likely to separate your study conditions.
Please talk to our engineers:
Please talk to our engineers:
Our Innovations:
The best way to learn about our innovations (including our most recent R&D) is through an interactive web call with plenty of two-way Q&As. The preprint is a good resource too. For a quick overview, please click on the four figures to your right.
Global XIC Deconvolution
01
PROBLEM
DIA Produces Chimeric XICs
Two co-eluting peptides from a single sample
02
PROPOSED SOLUTION
LC has Natural Variance
Pairs of peptides that co-elute in one subset of samples do not exactly co-elute in another subset of samples
03
MULTIPARTITE MATCHING
Deconvolute *MS2* Fragments Computationally
Match fragment accross samples to create one clean spectra per peptide
04
QUANTIFICATION & AI
Quantify & Create Predictive Panel
Quantify all analytes in MS and use AI to create predictive panel
Our Innovations:
The best way to learn about our innovations (including our most recent R&D) is through an interactive web call with plenty of two-way Q&As. The preprint is a good resource too. For a quick overview, please click on the four figures to your right.
Global XIC Deconvolution
01
PROBLEM
DIA Produces Chimeric XICs
Two co-eluting peptides from a single sample
02
PROPOSED SOLUTION
LC has Natural Variance
Pairs of peptides that co-elute in one subset of samples do not exactly co-elute in another subset of samples
03
MULTIPARTITE MATCHING
Deconvolute *MS2* Fragments Computationally
Match fragment accross samples to create one clean spectra per peptide
04
QUANTIFICATION & AI
Quantify & Create Predictive Panel
Quantify all analytes in MS and use AI to create predictive panel
Ready to find peptides that separate your study conditions, publish or patent the results, and make science history?
Unlock Astronomical Value From All Your Past & Present Datasets, Rapidly
Unlock Astronomical Value From All Your Past & Present Datasets, Rapidly
We can quantify the peptides that contain your unpredicted sequences and unexpected PTMs (the ~75- 95% peptides in your MS, an up-to ~2000% increase). Far more valuably, these peptides are way more likely to separate your study conditions.
We can quantify the peptides that contain your unpredicted sequences and unexpected PTMs (the ~75- 95% peptides in your MS, an up-to ~2000% increase). Far more valuably, these peptides are way more likely to separate your study conditions.
We are actively seeking collaborators that are interested in moving quickly, thinking big, and collaborating beautifully.
We aim to dramatically accelerate timelines or improve quality of either (a) publications, (b) grant submissions or budget requests, (c) novel discoveries with potential patent submissions, or (d) some combination of the previous three.
We are actively seeking collaborators that are interested in moving quickly, thinking big, and collaborating beautifully.
We aim to dramatically accelerate timelines or improve quality of either (a) publications, (b) grant submissions or budget requests, (c) novel discoveries with potential patent submissions, or (d) some combination of the previous three.
(C) Copyright GoldenHaystack Lab 2025. All Rights Reserved.
(C) Copyright GoldenHaystack Lab 2025. All Rights Reserved.