The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, there is no corresponding standard for exchanging phenotypic data, and this has impeded the sharing of phenotypic information for computational analysis. Here, we introduce the Global Alliance for Genomics and Health (GA4GH) Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis and research of all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases. To support translational research, diagnostics, and personalized healthcare, phenopackets are designed to be used across a comprehensive landscape of applications including biobanks, databases and registries, clinical information systems such as Electronic Health Records, genomic matchmaking, diagnostic laboratories, and computational tools. The Phenopacket schema is a freely available, community-driven standard that streamlines exchange and systematic use of phenotypic data and will facilitate sophisticated computational analysis of both clinical and genomic information to help improve our understanding of diseases and our ability to manage them.

GRAND: a database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Discovery of small molecule pathway regulators by image profile matching

Identifying chemical regulators of biological pathways is currently a time-consuming bottleneck in developing therapeutics and small-molecule research tools. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to a disease of interest. Here, we instead use a broad, virtual screening approach that matches compounds to pathways based on phenotypic information in public data. Our computational strategy efficiently uncovered small molecule regulators of three pathways, containing p38ɑ (MAPK14), YAP1, or PPARGC1A (PGC-1α). We first selected genes whose overexpression yielded distinct image-based profiles in the Cell Painting assay, a microscopy assay involving six stains that label eight cellular organelles/components. To identify small molecule regulators of pathways involving those genes, we used publicly available Cell Painting profiles of 30,616 small molecules to identify compounds that yield morphological effects either positively or negatively correlated with image-based profiles for specific genes. Subsequent assays validated compounds that impacted the predicted pathway activities. This image profile-based drug discovery approach could transform both basic research and drug discovery by identifying useful compounds that modify pathways of biological and therapeutic interest, thus using a computational query to replace certain customized labor- and resource-intensive screens.

GRAND: A database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12,468 genome-scale networks covering 36 human tissues, 28 cancers, 1,378 unperturbed cell lines, as well as 173,013 TF and gene targeting scores for 2,858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

An Instance Segmentation-Based Method to Obtain the Leaf Age and Plant Centre of Weeds in Complex Field Environments

Leaf age and plant centre are important phenotypic information of weeds, and accurate identification of them plays an important role in understanding the morphological structure of weeds, guiding precise targeted spraying and reducing the use of herbicides. In this work, a weed segmentation method based on BlendMask is proposed to obtain the phenotypic information of weeds under complex field conditions. This study collected images from different angles (front, side, and top views) of three kinds of weeds (Solanum nigrum, barnyard grass (Echinochloa crus-galli), and Abutilon theophrasti Medicus) in a maize field. Two datasets (with and without data enhancement) and two backbone networks (ResNet50 and ResNet101) were replaced to improve model performance. Finally, seven evaluation indicators are used to evaluate the segmentation results of the model under different angles. The results indicated that data enhancement and ResNet101 as the backbone network could enhance the model performance. The F1 value of the plant centre is 0.9330, and the recognition accuracy of leaf age can reach 0.957. The mIOU value of the top view is 0.642. Therefore, deep learning methods can effectively identify weed leaf age and plant centre, which is of great significance for variable spraying.

A Revised Phylogeny of the Mentha spicata Clade Reveals Cryptic Species

The genus Mentha is taxonomically and phylogenetically challenging due to complex genomes, polyploidization and an extensive historical nomenclature, potentially hiding cryptic taxa. A straightforward interpretation of phylogenetic relationships within the section Mentha is further hindered by dominant but outdated concepts on historically identified hybrid taxa. Mentha spicata is traditionally considered to be of hybrid origin, but the evidence for this is weak. Here, we aim to understand the phylogenetic relationships within the section Mentha using large sample sizes and to revisit the hybrid status and identity of M. spicata. We show that two of three traditional species in the subsection Spicatae are polyphyletic, as is the subsection as a whole, while the real number of cryptic species was underestimated. Compared to previous studies we present a fundamentally different phylogeny, with a basal split between M. spicata s.s. and M. longifolia s.s. Cluster analyses of morphological and genotypic data demonstrate that there is a dissociation between morphologically and genotypically defined groups of samples. We did not find any evidence that M. spicata is of hybrid origin, and we conclude its taxonomic status should be revised. The combination of genetic and phenotypic information is essential when evaluating hyperdiverse taxonomic groups.

phenopype: a phenotyping pipeline for Python

Digital images are a ubiquitous way to represent phenotypes. More and more ecologists and evolutionary biologists are using images to capture and analyze high dimensional phenotypic data to understand complex developmental and evolutionary processes. As a consequence, images are being collected at ever increasing rates, already outpacing our abilities for processing and analysis of the contained phenotypic information. phenopype is a high throughput phenotyping package for the programming language Python to support ecologists and evolutionary biologists in extracting high dimensional phenotypic data from digital images. phenopype integrates existing state-of-the-art computer vision functions (using the OpenCV library as a backend), GUI-based interactions, and a project management ecosystem to facilitate rapid data collection and reproducibility. phenopype offers three different workflow types that support users during different stages of scientific image analysis (prototyping, low-throughput, and high-throughput). In the high-throughput workflow, users interact with human-readable YAML configuration files to effectively modify settings for different images. These settings are stored along with processed images and results, so that the acquired phenotypic information becomes highly reproducible. phenopype combines the advantages of the Python environment, with its state-of-the-art computer vision, array manipulation and data handling libraries, and basic GUI capabilities, which allow users to step into the automatic workflow when necessary. Overall, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection.

Estimation of breeding value for bodyweight of grasscutters

Animal breeders are interested in the genetic worth or total genetic merit of an animal for a given trait. The value of an animal in a breeding program for a particular trait is called the breeding value. The aim of this study was to predict the breeding values for bodyweight of grasscutters at 4, 6 and 8 months of age using univariate animal model. Four families of grasscutters with five grasscutters per family were used for the study. Families 3 and 4 had higher bodyweight at 4 and 6 months compared with families 1 and 2. Family 4 had the highest bodyweight at 8 month and family 2 had the least. The estimated breeding values (EBV) for bodyweight of grasscutters ranged from -0.06kg to 0.45kg at 4 month, -0.05kg to 0.45 kg at 6 month and -0.04kg to 0.55kg at 8 month. The reliability of the EBV (%) ranged from 51.00 to 62.50, 22.25 to 43.81 and 25.84 to 49.00 at 4, 6 and 8 months of age respectively. This implies that the correlations between estimated breeding value and true genetic merit were medium to high in magnitude. The reliability of the EBV could be improved further through collecting more phenotypic information on the animal and its relatives and by improving the heritability of the trait. Les éleveurs s'intéressent à la valeur génétique ou au mérite génétique total d'un animal pour un trait donné. La valeur d'un animal dans un programme d'élevage pour un trait particulier est appelée valeur de reproduction. Le but de cette étude était de prédire les valeurs de reproduction du poids corporel des coupe-herbes à l'âge de 4, 6 et 8 mois à l'aide d'un modèle animal univarié. Quatre familles de coupe-herbes avec cinq coupes-herbes par famille ont été utilisées pour l'étude. Les familles 3 et 4 avaient un poids corporel plus élevé à 4 et 6 mois comparativement aux familles 1 et 2. Famille 4 avait le poids corporel le plus élevé à 8 mois et la famille 2 avait le moins. Les valeurs de reproduction estimées (le 'EBV') pour le poids corporel des coupe-herbes allaient de -0.06 kg à 0,45 kg à 4 mois, -0.05 kg à 0.45 kg à 6 mois et -0.04 kg à 0.55 kg à 8 mois. La fiabilité de l'EBV (%) 51.00 à 62.50, 22.25 à 43.81 et 25.84 à 49.00 à 4, 6 et 8 mois respectivement. Cela implique que les corrélations entre la valeur de reproduction estimée et le véritable mérite génétique étaient de taille moyenne à élevée. La fiabilité de l'EBV pourrait être encore améliorée en recueillant plus d'informations phénotypique sur l'animal et ses parents et en améliorant l'hérabilité du trait.