Biology subjects

Fernandez-Torras, A.

Publications and source records attributed to Fernandez-Torras, A..

3 recordsLinked to original sources

bioRxiv2024

Integration of diverse bioactivity data into the Chemical Checker compound universe

Chemical signatures encode the physicochemical and structural properties of small molecules into numerical descriptors, forming the basis for chemical comparisons and search algorithms. The increasing availability of bioactivity data has improved compound representations to include biological effects, although bioactivity descriptors are often limited to a few well-documented molecules. To address this issue, we implemented a collection of deep neural networks able to leverage the experimentally determined bioactivity data associated to small molecules and infer the missing bioactivity signatures for any compound of interest. However, unlike static chemical descriptors, these bioactivity signatures dynamically evolve with new data and processing strategies. Here, we present a computational protocol to modify or generate novel bioactivity spaces and signatures, describing the main steps needed to leverage diverse bioactivity data with the current knowledge, as catalogued in the Chemical Checker (CC), using the predefined data curation pipeline. We illustrate the functioning of the protocol through four specific examples, including the incorporation of new compounds to an already existing bioactivity space, a change in the data pre-processing without altering the underlying experimental data, and the creation of two novel bioactivity spaces from scratch, which are completed in under 9 hours using GPU computing. Overall, this protocol offers a guideline for installing, testing and running the CC data integration approach on user-provided data, with the aim of extending the annotation presented for a limited number of small molecules to a larger chemical landscape. Key pointsO_LIThe Chemical Checker is a large collection of processed, harmonized and integrated bioactivity signatures for over 1 million small molecules. Data are organized into 5 levels of increasing biological complexity and curation degrees: from chemical properties to clinical outcomes and from raw data representing explicit knowledge to embedded signatures inferred from observed bioactivity patterns. C_LIO_LIThe Chemical Checker package provides a predefined and versatile framework to integrate user-provided data to the Chemical Checker universe of small molecules and generate novel customized bioactivity signatures. C_LI Key referencesDuran-Frigola et al. "Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker." Nature Biotechnology 38.9 (2020): 1087-1096. Bertoni et al. "Bioactivity descriptors for uncharacterized chemical compounds." Nature Communications 12.1 (2021): 3932.

bioinformatics↗

bioRxiv2022

Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., drug treats disease, gene interacts with gene). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

bioinformatics↗

bioRxiv2018

Encircling the regions of the pharmacogenomic landscape that determine drug response

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for a priori, hypothesis-driven assumptions about drug action. In particular, transcriptomic signatures of drug sensitivity may guide drug repositioning, the discovery of synergistic drug combinations and suggest new therapeutic biomarkers. However, the inherent complexity of transcriptomic signatures, with thousands of genes differentially expressed, makes them hard to interpret, giving poor mechanistic insights and hampering translation to the clinics. Here we show how network biology can help simplify transcriptomic drug signatures, filtering out irrelevant genes, accounting for tissue-specific biases and ultimately yielding functionally-coherent, less noisy drug modules. We successfully analyzed 170 drugs tested in 637 cancer cell lines, proving a broad applicability of our approach and evincing an intimate relationship between modules gene expression levels and drugs mechanisms of action. Further, we have characterized multiple aspects of our transcriptomic modules. As a result, the drugs included in this study are now annotated well beyond the reductionist (target-centered) view.\n\nAuthor SummaryLarge scale pharmacogenomics studies performed with hundreds of cell lines offer a means to link the molecular features of the cells to their response to drug treatments. Unfortunately, simple drug-gene correlations are usually not enough to consistently identify what gene expression patterns will determine drug sensitivity, as the tissue of origin of the cells, together with the expression of e.g. membrane transporters can greatly confound the analysis. To ameliorate these biases, we have devised a network-based strategy that selects genes that are both well correlated to drug response and closeby in the human protein interaction network. Reassuringly, we have confirmed that our identified drug sensitivity modules are tightly connected to the mechanisms of action of the drugs. Moreover, while our modules have no more than 100 genes, they retain the predictive power of the much larger gene signatures that are typically obtained by drug-gene correlations alone. Here, we release the characteristic modules for almost 200 drugs, in a format that is suitable for downstream bioinformatics analyses such as gene-set enrichment analysis.

bioinformatics↗