Miguel Pinheiro is a senior bioinformatician at the University of St Andrews where he has worked since 2012.
Miguel Pinheiro (MMP) began his career in bioinformatics in 2002 at the University of Aveiro, Portugal. He was involved in several projects including a study on the primary structure of mRNA that produced several publications, for which he developed software with mathematical, statistical and visual models providing a set of tools for processing data and genome analysis.
MMP also developed a neonatal screening system for metabolic diseases of the newborn in cooperation with the Medical Genetic Institute (IGM) in Porto, Portugal. The system stores mass spectrometer (MS/MS) information and makes statistical analysis of the data to detect newborns at risk. This system is fully operational in several hospitals in Portugal, Spain, Porto Rico and Colombia. In 2013 he developed a new version of this software to integrate new functionalities.
In 2008 MMP began working at the Bioinformatic unit of Biocant (A Portuguese Science Park devoted to Biotechnology) developing and improving data analysis pipelines, working in new-assembly, genome annotation, metagenomics and transcriptomics with data from an onsite Illumina 454 sequencer. He developed a pipeline in the Perl, a programming language, for the annotation of transcriptome data that integrates with a website for visualization of information supported in the mySQL open source database. This pipeline was successfully applied in several transcriptome projects.MMP also developed a pipeline for metagenomics analysis for several projects in Biocant.
In 2012 MMP joined The University of St Andrews where he is involved in several projects including re-sequencing the genomes of several bacteria and parasites including P. knowlesi for the malaria group. He performs genome-wide association studies (GWAS) in humans, transcriptomics and supports several groups in Medicine and Biology school.
Miguel Pinheiro’s main contribution to the malaria project is the development of new algorithms for next generation sequencing data and new strategies for genomic data analysis as well parallel computation.