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GAGAM: A Genomic Annotation-Based Enrichment of scATAC-seq Data for Gene Activity Matrix

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology to assess the epigenetic landscape of thousands of cells. However, the current great sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. In particular, the most common way to interpret the raw scATAC-seq data is through peak-calling, generating the peaks as features. Nevertheless, this method is dataset-dependent because the peaks are related to the given dataset and can not be directly compared between different experiments. For this reason, this study wants to improve on the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing a Genomic-Annotated Gene Activity Matrix (GAGAM), which aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features solves the problem of the feature dataset dependency allowing for the link of gene accessibility and expression. The latter is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs.

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Author information

Authors and Affiliations

  1. Control and Computer Engineering Department, Politecnico di Torino, 10129, Turin, Italy

    Lorenzo Martini, Roberta Bardini, Alessandro Savino & Stefano Di Carlo

Authors
  1. Lorenzo Martini
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  2. Roberta Bardini
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  3. Alessandro Savino
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  4. Stefano Di Carlo
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Corresponding authors

Correspondence to Lorenzo Martini , Roberta Bardini , Alessandro Savino or Stefano Di Carlo .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Martini, L., Bardini, R., Savino, A., Di Carlo, S. (2022). GAGAM: A Genomic Annotation-Based Enrichment of scATAC-seq Data for Gene Activity Matrix. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_2

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