Estimating tissue-specific peptide abundance from public RNA-Seq data
Several novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides’ source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1082168/full |
| _version_ | 1797955258803027968 |
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| author | Angela Frentzen Jason A. Greenbaum Haeuk Kim Bjoern Peters Bjoern Peters Zeynep Koşaloğlu-Yalçın |
| author_facet | Angela Frentzen Jason A. Greenbaum Haeuk Kim Bjoern Peters Bjoern Peters Zeynep Koşaloğlu-Yalçın |
| author_sort | Angela Frentzen |
| collection | DOAJ |
| description | Several novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides’ source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the abundance levels of the peptides’ source proteins. However, such expression data is often not directly available to users, and retrieving the expression level of a peptide’s source antigen from public databases is not trivial. We have developed the Peptide eXpression annotator (pepX), which takes a peptide as input, identifies from which proteins the peptide can be derived, and returns an estimate of the expression level of those source proteins from selected public databases. We have also investigated how the abundance level of a peptide can be best estimated in cases when it can originate from multiple transcripts and proteins and found that summing up transcript-level expression values performs best in distinguishing ligands from decoy peptides. |
| first_indexed | 2024-04-10T23:30:14Z |
| format | Article |
| id | doaj.art-08492a75c0094fa2a383671cbac795f1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-10T23:30:14Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-08492a75c0094fa2a383671cbac795f12023-01-12T06:25:21ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-01-011410.3389/fgene.2023.10821681082168Estimating tissue-specific peptide abundance from public RNA-Seq dataAngela Frentzen0Jason A. Greenbaum1Haeuk Kim2Bjoern Peters3Bjoern Peters4Zeynep Koşaloğlu-Yalçın5Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United StatesDepartment of Medicine, University of California, San Diego, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United StatesSeveral novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides’ source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the abundance levels of the peptides’ source proteins. However, such expression data is often not directly available to users, and retrieving the expression level of a peptide’s source antigen from public databases is not trivial. We have developed the Peptide eXpression annotator (pepX), which takes a peptide as input, identifies from which proteins the peptide can be derived, and returns an estimate of the expression level of those source proteins from selected public databases. We have also investigated how the abundance level of a peptide can be best estimated in cases when it can originate from multiple transcripts and proteins and found that summing up transcript-level expression values performs best in distinguishing ligands from decoy peptides.https://www.frontiersin.org/articles/10.3389/fgene.2023.1082168/fullRNA-SeqRNA sequencingpeptide (pep)predictionligandstool |
| spellingShingle | Angela Frentzen Jason A. Greenbaum Haeuk Kim Bjoern Peters Bjoern Peters Zeynep Koşaloğlu-Yalçın Estimating tissue-specific peptide abundance from public RNA-Seq data Frontiers in Genetics RNA-Seq RNA sequencing peptide (pep) prediction ligands tool |
| title | Estimating tissue-specific peptide abundance from public RNA-Seq data |
| title_full | Estimating tissue-specific peptide abundance from public RNA-Seq data |
| title_fullStr | Estimating tissue-specific peptide abundance from public RNA-Seq data |
| title_full_unstemmed | Estimating tissue-specific peptide abundance from public RNA-Seq data |
| title_short | Estimating tissue-specific peptide abundance from public RNA-Seq data |
| title_sort | estimating tissue specific peptide abundance from public rna seq data |
| topic | RNA-Seq RNA sequencing peptide (pep) prediction ligands tool |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1082168/full |
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