Dr. João Pedro Saraiva

Contact

Dr. João Pedro Leonor Fernandes Saraiva
Post doctoral associate

Working Group Microbial Data Science

Department of Environmental Microbiology
Helmholtz Centre for Environmental Research - UFZ
Permoserstr. 15, 04318 Leipzig, Germany

Phone +49 341 235-1688
joao.saraiva@ufz.de
João Pedro Saraiva

Scientific Career

09/2017 present

Post Doctoral Associate - Microbial Systems Bioinformatics group, Department of Environmental Microbiology, Helmholtz Center for Environmental Research - UFZ, Leipzig, Germany

02/2014 07/2018

PhD student- Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll Institute, Network Modelling, Jena, Germany

01/2013 12/2013

Research Associate - Institute of Biotechnology and Bioengineering, University of Minho, Braga, Portugal

Education

PhD

2014
2017

Bioinformatics

Leibniz Institute for Natural Product Reasearch and Infection Biology, Hans Knöll Institute, Jena, Germany

MSc

2009
2012

Bioinformatics

Institute of Biotechnology and Bioengineering, Braga, Portugal
Dipl.

2000
2006

Biotechnology Engineering

Polytechnic Institute of Bragança, Bragança, Portugal

Research interests

My primary research interest is in the field of complex microbial systems. The advancements in high throughput sequencing technologies has greatly increased our ability to generate genomic data. This exponential increase in microbial big data now demands the development of novel concepts and strategies to better characterize and study microbial community composition and microbial interactions.

Developing and implementing novel concepts focused on microbial communities is the overall goal of my research. How to generate models that use (meta)genomic data to answer questions related to complex microbial communities and interspecies interactions is the main driver of my research activities. Because natural microbial communities are highly complex, answering these questions require careful design and planning to reflect its true scenario.

Currently I am focused on improving genome recovery of microbial eukaryotes from metagenomic datasets. The lack of reference genomes, higher complexity of eukaryotes (e.g., presence of repeat-rich regions) and limited tools for eukaryote MAG recovery makes this a challenge.

Publications

  1. Rocha, U. N. da et al. MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenome. Preprint at https://doi.org/10.21203/rs.3.rs-1431837/v1 (2022).
  2. Saraiva, J. P., Bartholomäus, A., Toscan, R. B., Baldrian, P. & da Rocha, U. N. Recovery of 447 Eukaryotic bins reveals major challenges for Eukaryote genome reconstruction from metagenomes. bioRxiv 2022.04.07.487146 (2022) doi:10.1101/2022.04.07.487146.
  3. da Rocha, U. N. et al. MuDoGeR: Multi-Domain Genome Recovery from metagenomes made easy. bioRxiv 2022.06.21.496983 (2022) doi:10.1101/2022.06.21.496983.
  4. Liu, B. et al. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. Microbiome 10, 48 (2022).
  5. Oliveira Monteiro, L. M. et al. PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning. Environmental Microbiome 17, 7 (2022).
  6. Saraiva, J.P., Bartholomäus, A., Kallies, R., Gomes, M., Bicalho, M., Kasmanas, J.C., Vogt, C., Chatzinotas, A., Stadler, P., Dias, O., da Rocha, U.N. OrtSuite: From genomes to prediction of microbial interactions within targeted ecosystem processes (2021), Life Science Alliance, 4 (12), art. no. e202101167.
  7. Tláskal, V., Brabcová, V., Větrovský, T., López-Mondéjar, R., Monteiro, L.M.O., Saraiva, J.P., da Rocha, U.N., Baldrian, P., Metagenomes, metatranscriptomes and microbiomes of naturally decomposing deadwood (2021), Scientific Data, 8 (1), art. no. 198.
  8. Keller-Costa, T., Lago-Lestón, A., Saraiva, J.P., Toscan, R., Silva, S.G., Gonçalves, J., Cox, C.J., Kyrpides, N., Nunes da Rocha, U., Costa, R., Metagenomic insights into the taxonomy, function, and dysbiosis of prokaryotic communities in octocorals, (2021) Microbiome, 9 (1), art. no. 72.
  9. Tláskal, V., Brabcová, V., Vetrovský, T., Jomura, M., López-Mondéjar, R., Monteiro, L.M.O., Saraiva, J.P., Human, Z.R., Cajthaml, T., da Rocha, U.N., Baldrian, P., Complementary roles of wood-inhabiting fungi and bacteria facilitate deadwood decomposition (2021), mSystems, 6 (1), art. no. e01078-20.
  10. Saraiva, J.P., Worrich, A., Karakoç, C., Kallies, R., Chatzinotas, A., Centler, F., da Rocha, U.N., Mining synergistic microbial interactions: A roadmap on how to integrate multi‐omics data (2021), Microorganisms, 9 (4), art. no. 840.
  11. Lian, S. et al. Biotransformation of hexachlorocyclohexanes contaminated biomass for energetic utilization demonstrated in continuous anaerobic digestion system. J. Hazard. Mater. 384, 121448 (2020). 
  12. Corrêa, F. B., Saraiva, J. P., Stadler, P. F. & da Rocha, U. N. (2020). TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. Nucleic Acids Research, 48(D1), D626-D632.
  13. Saraiva, J. et al. iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6. Front. Microbiol. 10, 1283 (2019).
  14. Rosado, P. M. et al. Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).
  15. Saraiva, J. & Koenig, R. Infection-specific human immune responses: fungi vs. bacteria. in INFECTION 43, S10–S10 (SPRINGER HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, 2015).
  16. Leonor Fernandes Saraiva J. P., Zubiria-Barrera C., Klassert T. E., Lautenbach M. J. Blaess, M., Claus R. A., König R. (2017). Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes. Frontiers in Microbiology, 8, 2366
  17. Saraiva JP, Oswald M, Biering A, Röll D, Assmann C, Klassert T, Blaess M, Czakai K, Claus R, Löffler J, Slevogt H, König R. (2017) Fungal biomarker discovery by integration of classifiers. BMC Genomics 18, 601.
  18. Saraiva J, Oswald M, Biering A, Assmann C, Klassert T, Blaess M, Czakai K, Claus R, Löffler J, Slevogt H, König R (2016) Integrating classifiers across datasets improves consistency of biomarker predictions in sepsis. In: Proceedings of the 6th IFAC Conference on Foundations of Systems Biology in Engineering, Magdeburg, 10/09/2016-10/12/2016, pp. 95-102.Elsevier ScienceDirect.