Further improvement of feature extraction LFQ to reduce missing data

smith-chem-wisc / MetaMorpheus

Proteomics search software with integrated calibration, PTM discovery, bottom-up, top-down and LFQ capabilities

MIT License

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Further improvement of feature extraction LFQ to reduce missing data #1670

Open Peer2011 opened 5 years ago

Peer2011 commented 5 years ago

Would it be possible to further reduce missing data analogous to Ionstar improving retention time alignment algorithm and feature extraction? I love this piece of software.

acesnik commented 5 years ago

Could you provide a reference for lonestar? I haven't heard of that before.

Thanks! -AC

trishorts commented 5 years ago

LOL -- ionstar

acesnik commented 5 years ago

https://www.pnas.org/content/115/21/E4767

rmillikin commented 5 years ago

I think it is certainly possible to improve match between runs in terms of accuracy and data completeness. It is quite a difficult problem to solve though. I believe ionstar relies on the samples not being fractionated for retention time alignment and feature mapping. We do support fractionated data though, so we would need to find a broader solution to the problem. So we do have plans for this but honestly it is a very large topic.

Peer2011 commented 5 years ago

I am analyzing large cohorts. Missing data problem therefore is a big problem which I would like to reduce. As far as I understood Ionstar relies on the retention time algorithm of Thermo sieve which however as far as I understood is discontinued. Would be so nice if one could reduce missing values in label free.