{"id":231,"date":"2021-08-19T14:09:01","date_gmt":"2021-08-19T14:09:01","guid":{"rendered":"https:\/\/gfellerlab.org\/?page_id=231"},"modified":"2024-04-20T09:32:05","modified_gmt":"2024-04-20T09:32:05","slug":"computational-tools","status":"publish","type":"page","link":"https:\/\/gfellerlab.org\/index.php\/computational-tools\/","title":{"rendered":""},"content":{"rendered":"\n
<\/span>
\n

Home<\/a><\/p>\n\n\n\n

Computational Tools<\/p>\n<\/div><\/div>\n\n\n\n

\n

MixTCRpred<\/h3>\n\n\n\n
\n
\n

Predictions of TCR-epitope interactions<\/p>\n\n\n\n

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.<\/a> Croce G<\/strong>, Bobisse S, Moreno DL,<\/strong> Schmidt J, Guillaume P, Harari A, Gfeller D<\/strong>. (2024) Nature Communication<\/em>s, 5(1):3211.<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

EPIC-ATAC<\/h3>\n\n\n\n
\n
\n

Repository for the R package EPIC-ATAC, to Estimate the Proportion of Immune and Cancer cells from bulk ATAC-Seq data.<\/p>\n\n\n\n

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data<\/a>. Gabriel AA<\/strong>, Racle J, <\/strong>Falquet M, Jandus C,\u00a0Gfeller D<\/strong>.<\/strong> (2023)\u00a0BioRxiv<\/em>, 10.1101\/2023.10.11.561826.<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

EPIC<\/h3>\n\n\n\n
\n
\n

Repository for the R package EPIC, to Estimate the Proportion of Immune and Cancer cells from bulk gene expression data.<\/p>\n\n\n\n

EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data<\/a><\/a>.<\/a> Racle, J.<\/strong>, &\u00a0Gfeller, D.<\/strong>\u00a0<\/strong>(2020).\u00a0Methods in molecular biology (Clifton, N.J.)<\/em>,\u00a02120<\/em>, 233\u2013248. https:\/\/doi.org\/10.1007\/978-1-0716-0327-7_17 <\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
Server<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

SuperCell<\/h3>\n\n\n\n
\n
\n

Coarse-graining of large single-cell RNA-seq data into super-cells.<\/p>\n\n\n\n

Metacells untangle large and complex single-cell transcriptome networks<\/a>. Bilous M, Tran L, Cianciaruso C, Gabriel A, Michel H, Carmona, S, Pittet MJ, Gfeller D., 2022. BMC Bioinformatics<\/em>, 23, 336.<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

MixMHCpred<\/h3>\n\n\n\n
\n
\n

MixMHCpred2.1 is a predictor of HLA-I ligand displayed at the cell surface.<\/p>\n\n\n\n

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.<\/a> Bassani-Sternberg, M., Chong, C., Guillaume, P., Solleder, M.<\/strong>, Pak, H., Gannon, P. O., Kandalaft, L. E., Coukos, G., & Gfeller, D.<\/strong> (2017). PLoS computational biology<\/em>, 13<\/em>(8), e1005725. https:\/\/doi.org\/10.1371\/journal.pcbi.1005725<\/p>\n\n\n\n

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.<\/a> Gfeller, D.<\/strong>, Guillaume, P., Michaux, J., Pak, H. S., Daniel, R. T., Racle, J.<\/strong>, Coukos, G., & Bassani-Sternberg, M. (2018). Journal of immunology (Baltimore, Md. : 1950)<\/em>, 201<\/em>(12), 3705\u20133716. https:\/\/doi.org\/10.4049\/jimmunol.1800914<\/p>\n\n\n\n

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2. 2 and PRIME2. 0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes<\/a>. Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. (2023), Cell Systems<\/em>, 14, 72.<\/p><\/p>\n\n\n\n

<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

PRIME<\/h3>\n\n\n\n
\n
\n

PRedictor of IMmunogenic Epitopes<\/p>\n\n\n\n

Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting.<\/a> Schmidt, J., Smith, A. R., Magnin, M., Racle, J.<\/strong>, Devlin, J. R., Bobisse, S., Cesbron, J., Bonnet, V., Carmona, S. J., Huber, F., Ciriello, G., Speiser, D. E., Bassani-Sternberg, M., Coukos, G., Baker, B. M., Harari, A., & Gfeller, D.<\/strong> (2021). Medicine<\/em>, 2<\/em>(2), 100194. https:\/\/doi.org\/10.1016\/j.xcrm.2021.100194<\/p>\n\n\n\n

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2. 2 and PRIME2. 0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes<\/a>. Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. (2023), Cell ystems<\/em>, 14, 72.<\/p><\/p>\n\n\n\n

<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
Server<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

MixMHC2pred<\/h3>\n\n\n\n
\n
\n

MixMHCpred2.1 is a predictor of HLA-I ligand displayed at the cell surface.<\/p>\n\n\n\n

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.<\/a> Racle, J.<\/strong>, Michaux, J., Rockinger, G. A., Arnaud, M., Bobisse, S., Chong, C., Guillaume, P., Coukos, G., Harari, A., Jandus, C., Bassani-Sternberg, M., & Gfeller, D.<\/strong> (2019). Nature biotechnology<\/em>, 37<\/em>(11), 1283\u20131286. https:\/\/doi.org\/10.1038\/s41587-019-0289-6<\/p>\n\n\n\n

Machine learning predictions of MH<\/a>C-II specificities reveal alternative binding mode of class II epitopes.<\/a> Racle J<\/strong>, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G<\/strong>, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D<\/strong>. (2023) Immunity<\/em>, 2023, 56, 1-17.<\/p><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
Server<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

MixMHCp<\/h3>\n\n\n\n
\n
\n

Tool for motif deconvolution in large HLA-I ligand datasets.<\/p>\n\n\n\n

Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.<\/a> Bassani-Sternberg, M., & Gfeller, D.<\/strong> (2016). Journal of immunology (Baltimore, Md. : 1950)<\/em>, 197<\/em>(6), 2492\u20132499. https:\/\/doi.org\/10.4049\/jimmunol.1600808<\/p>\n\n\n\n

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.<\/a> Gfeller, D.<\/strong>, Guillaume, P., Michaux, J., Pak, H. S., Daniel, R. T., Racle, J.<\/strong>, Coukos, G., & Bassani-Sternberg, M. (2018). Journal of immunology (Baltimore, Md. : 1950)<\/em>, 201<\/em>(12), 3705\u20133716. https:\/\/doi.org\/10.4049\/jimmunol.1800914<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
Server<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

MoDec<\/h3>\n\n\n\n
\n
\n

Tool for Motif Deconvolution in large HLA-II ligand datasets without the need of prior alignment.<\/p>\n\n\n\n

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.<\/a> Racle, J.<\/strong>, Michaux, J., Rockinger, G. A., Arnaud, M., Bobisse, S., Chong, C., Guillaume, P., Coukos, G., Harari, A., Jandus, C., Bassani-Sternberg, M., & Gfeller, D.<\/strong> (2019). Nature biotechnology<\/em>, 37<\/em>(11), 1283\u20131286. https:\/\/doi.org\/10.1038\/s41587-019-0289-6<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
\n

PhosMHCpred<\/h3>\n\n\n\n
\n
\n

PhosMHCpred is a predictor for HLA-I – phosphorylated ligand interactions.<\/p>\n\n\n\n

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands.<\/a> Solleder, M.<\/strong>, Guillaume, P., Racle, J.<\/strong>, Michaux, J., Pak, H. S., M\u00fcller, M., Coukos, G., Bassani-Sternberg, M., & Gfeller, D.<\/strong> (2020). Molecular & cellular proteomics : MCP<\/em>, 19<\/em>(2), 390\u2013404. https:\/\/doi.org\/10.1074\/mcp.TIR119.001641d.<\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n
Github<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

MixTCRpred Predictions of TCR-epitope interactions Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Croce G, Bobisse S, Moreno DL, Schmidt J, Guillaume P, Harari A, Gfeller D. (2024) Nature Communications, 5(1):3211. EPIC-ATAC Repository for the R package EPIC-ATAC, to Estimate the Proportion of Immune and Cancer cells from bulk …<\/p>\n

Read More »<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"class_list":["post-231","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/pages\/231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/comments?post=231"}],"version-history":[{"count":102,"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/pages\/231\/revisions"}],"predecessor-version":[{"id":1214,"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/pages\/231\/revisions\/1214"}],"wp:attachment":[{"href":"https:\/\/gfellerlab.org\/index.php\/wp-json\/wp\/v2\/media?parent=231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}