Biology subjects

Bento, J.

Publications and source records attributed to Bento, J..

4 recordsLinked to original sources

bioRxiv2022

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

bioinformatics↗

bioRxiv2020

Boundary-Forest Clustering: Large-Scale Consensus Clustering of Biological Sequences

Bacterial species with large sequence diversity enable studies focused on comparative genomics, population genetics and pan-genome evolution. In such analyses it is key to determine whether sequences (e.g. genes) from different strains, are the same or different. This is often achieved by clustering orthologous genes based on sequence similarity. Importantly, one limitation of existing pan-genome clustering methods is that they do not assign a confidence score to the identified clusters. Given that clustering ground truth is unavailable when working with pan-genomes, the absence of confidence scores makes performance evaluation on real data an open challenge. Moreover, most pan-genome clustering solutions do not accommodate cluster augmentation, which is the addition of new sequences to an already clustered set of sequences. Finally, the pan-genome size of many organisms prevents direct application of powerful clustering techniques that do not scale to large datasets. Here, we present Boundary-Forest Clustering (BFClust), a method that addresses these challenges in three main steps: 1) The approximate-nearest-neighbor retrieval method Boundary-Forest is used as a representative selection step; 2) Downstream clustering of the representatives is performed using Markov Clustering (MCL); 3) Consensus clustering is applied across the Boundary-Forest, improving clustering accuracy and enabling confidence score calculation. First, MCL is favorably benchmarked against 6 powerful clustering methods. To explore the strengths of the entire BFClust approach, it is applied to 4 different datasets of the bacterial pathogen Streptococcus pneumoniae, and compared against 4 other pan-genome clustering tools. Unlike existing approaches, BFClust is fast, accurate, robust to noise and allows augmentation. Moreover, BFClust uniquely identifies low-confidence clusters in each dataset, which can negatively impact downstream analyses and interpretation of pan-genomes. Being the first tool that outputs confidence scores both when clustering de novo, and during cluster augmentation, BFClust offers a way of automatically evaluating and eliminating ambiguity in pan-genomes. Author SummaryClustering of biological sequences is a critical step in studying bacterial species with large sequence diversity. Existing clustering approaches group sequences together based on similarity. However, these approaches do not offer a way of evaluating the confidence of their output. This makes it impossible to determine whether the clustering output reflect biologically relevant clusters. Most existing methods also do not allow cluster augmentation, which is the quick incorporation and clustering of newly available sequences with an already clustered set. We present Boundary-Forest Clustering (BFClust) as a method that can generate cluster confidence scores, as well as allow cluster augmentation. In addition to having these additional key functionalities and being scalable to large dataset sizes, BFClust matches and outperforms state-of-the-art software in terms of accuracy, robustness to noise and speed. We show on 4 Streptococcus pneumoniae datasets that the confidence scores uniquely generated by BFClust can indeed be used to identify ambiguous sequence clusters. These scores thereby serve as a quality control step before further analysis on the clustering output commences. BFClust is currently the only biological sequence clustering tool that allows augmentation and outputs confidence scores, which should benefit most pan-genome studies.

bioinformatics↗

bioRxiv2019

Entropy of a bacterial stress response is a generalizable predictor for fitness and antibiotic sensitivity.

Genes implicated in bacterial stress responses have been used to construct models that infer the growth outcome of a bacterium in the presence of antibiotics with the objective to develop novel diagnostic methods in the clinic. Current models are trained on data specific to a species or type of stress, making them potentially limited in their application. It is unclear if a generalizable response-signature exists that can predict bacterial fitness independent of strain, species or type of stress. Here we present a substantial RNA-Seq and experimental evolution dataset for 9 strains and species, under multiple antibiotic and non-antibiotic stress conditions. We show that gene panel-based models can accurately predict antibiotic mechanism of action, as well as the fitness outcome of Streptococcus pneumoniae in the presence of antibiotics or under nutrient depletion. However, these models quickly become species-specific as gene homology is limited. Instead, we define a new concept, transcriptomic entropy, which we use to quantify the amount of transcriptional disruption that occurs in a bacterium when responding to the environment. With entropy at the center, we train a suite of predictive (machine learning) models enabling generalizable fitness and antibiotic sensitivity predictions. These entropy-based models that predict bacterial fitness are validated for 7 Gram-positive and -negative species under antibiotic and non-antibiotic conditions indicating that transcriptomic entropy can be used as a generalizable stress signature. Moreover, rather than being a binary indicator of fitness, an entropy-based model was developed and validated to predict the minimum inhibitory concentration of an antibiotic. Lastly, we show that the inclusion of a varied-set of multi-omics features of a bacterial stress response further enhances fitness predictions by reducing ambiguity. By demonstrating the feasibility of generalizable predictions of bacterial fitness, this work establishes the fundamentals for potentially new approaches in infectious disease diagnostics, including antibiotic susceptibility testing.\n\nSignificance statementAccurate predictions of bacterial fitness outcome could potentially have clinical diagnostic value, such as predicting optimum antibiotic choice and dosage for treating infectious diseases. Existing models of fitness predictions rely mainly on gene panel approaches, which may be species- and stress-specific due to a lack of gene and response conservation. In order to overcome this limitation, we generated a substantial experimental dataset and identified entropy as a universal stress response signature that quantifies the level of transcriptional disruption that is indicative of fitness outcome under a stressful condition. We present and validate for Gram-positive and negative species a suite of entropy-based models that enable accurate predictions of fitness outcome and the level of antibiotic sensitivity in a species and stress-type independent manner.

systems biology↗

bioRxiv2018

Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning

Whether a bacterial pathogen establishes an infection and/or evolves antibiotic resistance depends on successful survival while experiencing stress from for instance the host immune system and/or antibiotics. Predictions on bacterial survival and adaptive outcomes could thus have great prognostic value. However, it is unknown what information is required to enable such predictions. By developing a novel network-based analysis method, a bacterium's phenotypic and transcriptional response can be objectively quantified in temporal 3D-feature space. The resulting trajectories can be interpreted as a degree of coordination, where a focused and coordinated response predicts bacterial survival-success, and a random uncoordinated response predicts survival-failure. These predictions extend to both antibiotic resistance and in vivo infection conditions and are applicable to both Gram-positive and Gram-negative bacteria. Moreover, through experimental evolution we show that the degree of coordination is an adaptive outcome - an uncoordinated response evolves into a coordinated response when a bacterium adapts to its environment. Most surprisingly, it turns out that phenotypic and transcriptional response data, network features and genome plasticity data can be used to train a machine learning model that is able to predict which genes in the genome will adapt under nutrient or antibiotic selection. Importantly, this suggests that deterministic factors help drive adaptation and that evolution is, at least partially, predictable. This work demonstrates that with the right information predictions on bacterial short-term survival and long-term adaptive outcomes are feasible, which underscores that personalized infectious disease diagnostics and treatments are possible, and should be developed.

microbiology↗