Biology subjects

Cabello, S.

Publications and source records attributed to Cabello, S..

2 recordsLinked to original sources

bioRxiv2022

ifCNV: a novel isolation-forest-based package to detect copy number variations from NGS datasets

Copy number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms has progressed in recent years. However, only a few tools have taken advantage of machine learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. Furthermore, in general, most CNV software that is developed for specific data types has sub-optimal reliability for routine practice. In addition, the most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task which dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customisable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity and accuracy. ifCNV is a publicly available open-source software that allows the detection of CNVs in many clinical situations. Key pointsO_LICopy number variation detection C_LIO_LIMachine learning C_LIO_LILocalisation scoring C_LIO_LIBenchmark on various clinical situations and on various datasets C_LIO_LIEasy-to-use R and Python open-source Package C_LI

bioinformatics↗

bioRxiv2020

Predicting microbiomes through a deep latent space

MotivationMicrobial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features. ResultsIntegrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity ({inverted question}0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions. AvailabilitySoftware, results, and data are available at https://github.com/jorgemf/DeepLatentMicrobiome

bioinformatics↗