Genome Interpretation (GI) is an umbrella term describing the scientific efforts oriented towards modelling and understanding the relationship between genotype and phenotype in living organisms. Being able to uncover the information encoded in our genome and connecting it to the phenotypes observed at the individual level would constitute a crucial advancement for medicine, genetics and molecular biology, leading to breakthroughs in contexts like precision/stratified medicine, but also agriculture and industry.
Various computational and statistical efforts focusing on specific aspects of GI have been proposed in the last decades. However, the growing availability of genomic data makes the applicability of novel and fully fledged Machine Learning (ML) methods (e.g., Deep Learning) an increasingly more realistic option thus relaxing some of the assumptions and limitations of the methods developed so far.
In the last decade, the bioinformatics community has addressed various specific aspects related to the Genome Interpretation (GI) problems. The classical attempts in this sense included Genome Wide Association Studies (GWAS), Polygenic Risk Scores (PRS), variant effect/pathogenicity predictors designed to assign a functional impact to coding variants and variant-prioritization tools designed to rank the most likely causative variants for a target phenotype. In the context of plant and animal sciences, SNP marker-based methods for the Genomic Prediction for plants and animal breeding (e.g. BLUP) have been widely used.
The growing data availability, however, now enables the development of computational methods attempting to directly model the phenotype produced by a given genome or exome. These tools take a certain organism's sequencing data as input and directly aim at the prediction of its phenotypes. We envision this “genomes in/phenotypes” out flavor of GI as spanning a spectrum of complexity. At the "simpler" end of the spectrum we have methods aiming at the binary prediction or regression of the presence/absence of a certain phenotype (e.g. on cases/controls studies), while at the "broader end" of the spectrum we have methods able to perform a multi-phenotypic prediction given a certain genome/exome (e.g. predicting the Human Phenotype Ontology terms related to a human genome/exome)
The goal of this Research Topic is to generate a collection of high-quality papers describing computational methods for GI and/or the investigation of the molecular mechanisms leading from genotype to phenotype.
We welcome submissions of Original Research Articles describing:
? novel computational methods for GI (e.g. statistical and/or Machine Learning) or novel application of existing methods to GI in the broader sense
? The methods could target GI on any organism (e.g. humans, plants, yeast, cattle, bacteria).
? These approaches could be based on any kind of genetic/molecular data (e.g. WES, WGS, SNP arrays, gene panels, …).
We invite submissions of Original Research Articles and:
? a limited number of perspective papers and reviews on GI approaches, trends and recent advancements in the field
? Database papers including samples annotated with both genotype and phenotypic detailed information on any organism. Ideally the database should be useful to benchmark and develop future GI methods.
The Editors would like to thank MD Nora Verplaetse, who was involved in the preparation of the Research Topic proposal.
Genome Interpretation (GI) is an umbrella term describing the scientific efforts oriented towards modelling and understanding the relationship between genotype and phenotype in living organisms. Being able to uncover the information encoded in our genome and connecting it to the phenotypes observed at the individual level would constitute a crucial advancement for medicine, genetics and molecular biology, leading to breakthroughs in contexts like precision/stratified medicine, but also agriculture and industry.
Various computational and statistical efforts focusing on specific aspects of GI have been proposed in the last decades. However, the growing availability of genomic data makes the applicability of novel and fully fledged Machine Learning (ML) methods (e.g., Deep Learning) an increasingly more realistic option thus relaxing some of the assumptions and limitations of the methods developed so far.
In the last decade, the bioinformatics community has addressed various specific aspects related to the Genome Interpretation (GI) problems. The classical attempts in this sense included Genome Wide Association Studies (GWAS), Polygenic Risk Scores (PRS), variant effect/pathogenicity predictors designed to assign a functional impact to coding variants and variant-prioritization tools designed to rank the most likely causative variants for a target phenotype. In the context of plant and animal sciences, SNP marker-based methods for the Genomic Prediction for plants and animal breeding (e.g. BLUP) have been widely used.
The growing data availability, however, now enables the development of computational methods attempting to directly model the phenotype produced by a given genome or exome. These tools take a certain organism's sequencing data as input and directly aim at the prediction of its phenotypes. We envision this “genomes in/phenotypes” out flavor of GI as spanning a spectrum of complexity. At the "simpler" end of the spectrum we have methods aiming at the binary prediction or regression of the presence/absence of a certain phenotype (e.g. on cases/controls studies), while at the "broader end" of the spectrum we have methods able to perform a multi-phenotypic prediction given a certain genome/exome (e.g. predicting the Human Phenotype Ontology terms related to a human genome/exome)
The goal of this Research Topic is to generate a collection of high-quality papers describing computational methods for GI and/or the investigation of the molecular mechanisms leading from genotype to phenotype.
We welcome submissions of Original Research Articles describing:
? novel computational methods for GI (e.g. statistical and/or Machine Learning) or novel application of existing methods to GI in the broader sense
? The methods could target GI on any organism (e.g. humans, plants, yeast, cattle, bacteria).
? These approaches could be based on any kind of genetic/molecular data (e.g. WES, WGS, SNP arrays, gene panels, …).
We invite submissions of Original Research Articles and:
? a limited number of perspective papers and reviews on GI approaches, trends and recent advancements in the field
? Database papers including samples annotated with both genotype and phenotypic detailed information on any organism. Ideally the database should be useful to benchmark and develop future GI methods.
The Editors would like to thank MD Nora Verplaetse, who was involved in the preparation of the Research Topic proposal.
