pedrobeltrao.bsky.social
Associate professor at ETH Zurich, studying the cellular consequences of genetic variation. Affiliated with the Swiss Institute of Bioinformatics and a part of the LOOP Zurich.
101 posts
2,360 followers
537 following
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Charlie Boone playing the ukulele somehow fits
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Many congratulations Arne!
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Some of these are so specific and have such large budgets. There should be some top down pan European research initiates, specially on infrastructure of common interest, but for the rest I really doubt it is money well spent.
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Do you give your group members pro accounts? It is a current discussion in the group. It has gotten me thinking about the need for bioinformatics programme courses on how to best use AI in programming. Such skills will also matter in the non academic job market for group members.
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Push back for the rest of us :)
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Ludwig Princeton’s @skinnider.bsky.social discusses AI’s potential to revolutionize cancer metabolism research possibilities of mass spectrometry and computational capabilities in the lab setting. youtu.be/P8diwXtsbJs
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Switzerland will pay into the program but it does mean higher competition. In the end, the extra 500 million euro is a very small percentage increase in budget. Lets just hope the momentum translates to higher science and innovation funding, in particular for ERC, in the next EU budget cycle.
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Thanks Natali. We didn't try, but the compilation of the proteomics data should really help in defining tissue of origin for the Olink (or other platform) plasma proteomic datasets.
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While I believe the associations are already very useful they need to improve in accuracy and specificity (cell-types). We are committed to doing this with ongoing experiments and computational method development. An initial portal for this is at www.ppiatlas.com
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This is an @opentargets.org project, in collaboration with @intact-ebi.bsky.social and several groups here at @imsb-eth.bsky.social, including @berndwollscheid.bsky.social and alumni @marcvoo.bsky.social @afossati.bsky.social. All data/code is available at www.ebi.ac.uk/biostudies/s...
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The major application we focused on was combining the brain derived protein associations with public pull-down data and co-fractionation data generated at ETH to re-prioritize candidate disease genes in GWAS linked loci.
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Given the limitations in accuracy, these associations are best used in system level analyses such as comparing gene sets and/or to combine with other data such as pull-downs. As an example, we can derive trait-to-trait and trait-to-compartment distances in a tissue specific manner.
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These predictions are not better than a general predictor like STRING that incorporates data of higher precision but we can show replication in a tissue specific manner. Surprisingly, we don't think that the majority of the difference in associations is explainable by tissue specific expression
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Diederik compiled and harmonized, 7,811 public proteomic samples from 11 human tissues, confirming that co-expression networks are more accurately derived from protein abundance measurements (when compared to mRNA and even co-fractionation datasets).