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Taylor Reiter @ReiterTaylor

Scientist @ArcadiaScience. @Harvard, @ucdavis, @CUAnschutz alum. Computational biologist and sequencing data enthusiast.

Joined October 2012
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Introducing peptigate (peptide investigate), a workflow that predicts and annotates peptides from transcriptome assemblies. It predicts small ORF-encoded peptides, cleavage peptides, and ribosomally synthesized and posttranslationally modified peptides.🧵 github.com/Arcadia-Science/p…

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If you want to learn more about peptigate, read our pub or take a look at the GitHub repo. research.arcadiascience.com/… github.com/Arcadia-Science/p…

Predicting bioactive peptides from transcriptome assemblies with the peptigate workflow

Peptigate predicts bioactive peptides from transcriptomes. It integrates existing tools to predict sORF-encoded peptides, cleavage peptides, and RiPPs, then annotates them for bioactivity and other...

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Thank you to all of my colleagues @ArcadiaScience who helped put this project together – @emilycpierce, @seemaychou, @AdairLBorges, @mertcelebi_, @kchev, Audrey Bell, @thecrispress, and @RobertRoth57.
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Have you ever wondered whether a transcript is coding or noncoding? Or whether a predicted open reading frame encodes a functional protein? We recently had this question for very short open reading frames (sORFs). Enter the Python package plmutils 🧵 research.arcadiascience.com/…

Using protein language models to predict coding and non-coding transcripts with plm-utils

We explored the use of embeddings from protein language models to distinguish between genuine and putative coding open reading frames (ORFs). We found that an embeddings-based approach (shared as a...

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This was a combined effort between many scientists @ArcadiaScience. @Gilad had the initial idea to use ESM embeddings to improve prediction accuracy, while @kchev implemented the code and performed some of the validation.
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Learn more about plm-utils by reading our pub, “Using protein language models to predict coding and noncoding transcripts,” or see the GitHub repository. research.arcadiascience.com/… github.com/Arcadia-Science/2…

Using protein language models to predict coding and non-coding transcripts with plm-utils

We explored the use of embeddings from protein language models to distinguish between genuine and putative coding open reading frames (ORFs). We found that an embeddings-based approach (shared as a...

thestacks.org
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Taylor Reiter retweeted
Prachee Avasthi
@PracheeAC
1 Nov 2023
🚨Another new job alert @ArcadiaScience! I know some of you have been waiting for this kind of role and there are openings on both the discovery and translation side of the org! Apply now!
Arcadia Science@ArcadiaScience
1 Nov 2023
New open search! We are looking for project scientists to help us pilot high-risk, high-reward ideas: jobs.lever.co/arcadiascience…
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