No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects

A. Danchin (1, 2) , C. Ouzounis (3) , T. Tokuyasu (4) , Jean-Daniel Zucker (5, 1)

1 IHU ICAN - Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière]
2 Li Kashing Faculty of Medicine [Hong Kong]
3 CERTH - Centre for Research and Technology Hellas
4 SIAT - Shenzhen Institute of Advanced Technology [Shenzhen]
5 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]

Genomics update No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects Antoine Danchin, 1,2, * Christos Ouzounis, 3 Taku Tokuyasu 4 and Jean-Daniel Zucker 1 1Integromics, Institute of Cardiometabolism and Nutrition, H^opital de la Pitie-Salp^etriere, 47 Boulevard de l’H^opital, 75013 Paris, France. 2School of Biomedical Sciences, Li KaShing Faculty of Medicine, Hong Kong University, 21 Sassoon Road, Pokfulam, Hong Kong. 3Biological Computation and Process Laboratory, Centre for Research and Technology Hellas, Chemical Process and Energy Resources Institute, Thessalonica 57001, Greece. 4Shenzhen Institutes of Advanced Technology, Institute of Synthetic Biology, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, China. Summary Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs auto- mated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process dis- courages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that sup- port multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

Domaines: Génétique; Analyse de données, Statistiques et Probabilités [physics.data-an]

A. Danchin, C. Ouzounis, T. Tokuyasu, Jean-Daniel Zucker. No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects. Microbial Biotechnology, 2018, 11 (4), p. 588-605. ⟨10.1111/1751-7915.13284⟩. ⟨hal-02932343⟩