Dr Pedro Madrigal

Bioinformatics Engineer

Contact information

Research interests

Bioinformatics workflows, Statistical analysis of 'omics data, AI/ML, Scientific programming, Software development, Transcriptomics, Epigenomics, Space omics

Keywords

Bioinformatics, Computational biology, High-throughput sequencing

Publications

Madrigal et al. Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome. Microbiome 2022. https://doi.org/10.1186/s40168-022-01332-w

Overbey et al. Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight. Cell Reports Methods, 2022. https://doi.org/10.1016/j.crmeth.2022.100325

Deane CS, Space Omics Topical Team, da Silveira WA, Herranz R. Space omics research in Europe: contributions, geographical distribution and ESA member state funding schemes. iScience 2022. https://doi.org/10.1016/j.isci.2022.103920

Thematic Chapter: Engineering, Robotics, data and AI, in "Why Space: The opportunity for Health and Life Science Innovation", UK Space Life and Biomedical Sciences (LABS) Association, 2021.

Madrigal et al. Revamping Space-omics in Europe. Cell Systems 2020. https://doi.org/10.1016/j.cels.2020.10.006

Cremona MA, Xu H, Makova KD, Reimherr M, Chiaromonte F, Madrigal P. Functional data analysis for computational biology. Bioinformatics. 2019 Sep;35(17) 3211-3213. https://doi.org/10.1093/bioinformatics/btz045.

Cuomo AS, Seaton DD, McCarthy DJ, Martinez I, Bonder MJ, Garcia-Bernardo J, Amatya S, Madrigal P, Isaacson A, Buettner F, Knights A, Natarajan KN, Vallier L, Marioni JC, Chhatriwala M, Stegle O, HipSci Consortium. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Commun 2020. https://doi.org/10.1038/s41467-020-14457-z

Madrigal P. fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics. 2017 Mar;33(5) 746-748. https://doi.org/10.1093/bioinformatics/btw724.

Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016 Jan;17 13. https://doi.org/10.1186/s13059-016-0881-8.

Madrigal P, Krajewski P. Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform. BioData Mining. 2015;8 20. https://doi.org/10.1186/s13040-015-0051-7.

Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, Madrigal P, Taslim C, Zhang J. Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol. 2013;9(11) e1003326. https://doi.org/10.1371/journal.pcbi.1003326.

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

  • Supports and connects the growing data science and AI research community 
  • Builds research capacity in data science and AI to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Acts as a gateway for external organisations 

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