ATLAS : Eina informàtica per a estudis de traçabilitat

Avui dia, quan es fan estudis genètics, es genera una cascada de dades que només és comprensible després que un ordinador realitzi càlculs vertiginosos. Per aquest motiu, investigadors de la Facultat de Veterinària de la UAB han dissenyat un programa informàtic, l'ATLAS, que facilita els estudis de traçabilitat de la cadena alimentària.Hoy en día, el progreso científico depende en gran medida de las herramientas informáticas. En estudios genéticos, por ejemplo, se genera una cascada de datos sólo comprensibles después de vertiginosos cálculos que realiza un ordenador. Esto lo tienen muy claro en la Facultad de Veterinaria, por ello han diseñado un programa informático que facilita su tarea, ATLAS,en particular en los estudios de trazabilidad de la cadena alimenticia.Today scientific progress depends to a great extent on computer tools. In genetic studies, for example, a cascade of data is generated, which is only comprehensible after vertiginous calculations on the part of a computer. The Faculty of Veterinary Science is well aware of this and has, therefore, designed a computer program to facilitate its work. ATLAS is particularly useful in traceabilty studies on the food chain

Breeding beyond genomics

Peer reviewe

Aplicacions de l''aprenentatge profund' (Deep Learning) per a la millora genètica dels poliploides

Un article recentment publicat per autors del Centre de Recerca en Agrigenòmica (CRAG) i de la Universitat de Florida mostra com els mètodes computacionals moderns poden ajudar en la millora genètica d'espècies vegetals amb més de dos jocs de cromosomes.Un artículo recientemente publicado por autores del Centre de Recerca en Agrigenòmica (CRAG) y de la Universidad de Florida muestra cómo los métodos computacionales modernos pueden ayudar en el mejoramiento genético de las especies vegetales con más de dos juegos de cromosomas.An article recently published by CRAG (Centre de Recerca en Agrigenòmica) and researchers of the University of Florida shows how modern computational methods can help in the genetic improvement of plant species with more than two sets of chromosomes

Estimating conformational traits in dairy cattle with deepAPS : A two-step deep learning automated phenotyping and segmentation approach

Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R2 = 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com), we estimated high heritabilities (ranging from h2 = 0.18-0.82), indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations (50) to train this partially supervised machinelearning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https://github.com/lauzingaretti/deepaps

The genetic variability of pigs is greater than was thought

L'espècie porcina és molt més variable del que es pensava. Els investigadors han trobat una variabilitat genètica per al gen FABP4 (que està implicat en la quantitat de grasa que deposita l'animal) deu vegades superior a la trobada a l'espècie humana i similar a la de les espècies silvestres, no domesticades. A més, han descobert que el porc ibèric és molt variable.La especie porcina es mucho más variable de lo que se pensaba. Los investigadores han encontrado una variabilidad genética para el gen FABP4 (que está implicado en la cantidad de grasa que deposita el animal) diez veces superior a la encontrada en la especie humana y similar a la de las especies silvestres, no domesticadas. Además, han descubierto que el cerdo Ibérico es muy variable.The porcine species is much more variable than was once thought. Researchers have found a genetic variability for the gene FABP4 (which is involved in the quantity of fat that the animal deposits) ten times greater than that found in humans and similar to that of wild, undomesticated species. In addition they have discovered that the Iberian pig is very variable

SeqBreed : a python tool to evaluate genomic prediction in complex scenarios

Background: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. Results: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. Conclusions: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed

Bayes factors for detection of quantitative trait loci

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor

kernInt : A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt