Biology subjects

Fonseca, N.

Publications and source records attributed to Fonseca, N..

5 recordsLinked to original sources

bioRxiv2025

Chart Builder: An Interactive Tool for User Driven Data Visualization in the Electron Microscopy Data Bank

The cryogenic sample-electron microscopy (cryoEM) field has generated significant amounts of 3D Electron Microscopy (3DEM) volumetric data and associated metadata, now comprehensively archived in the Electron Microscopy Data Bank (EMDB - www.emdatabank.org) and the Electron Microscopy Public Image Archive (EMPIAR - www.empiar.org). Harnessing the full potential of these resources requires robust, flexible, and publicly accessible tools for data exploration, analysis and retrieval. Here, we present Chart Builder, an interactive web-based platform that enables researchers to create customizable, publication-quality visualizations directly from archival metadata, validation assessments, and cross-reference annotations. Chart Builder integrates the same query-driven and flexible Solr search system as EMDB search, into a user interface with tools to assist users to filter, group, and compare data without programming expertise. It supports multiple chart types (including line, bar, area, scatter (2D and 3D), histogram, bubble, pie, geographic and Venn diagrams) with customizable axes, data series, and statistical operators. Users can apply global filters, define temporal, categorical, or custom query-based axes, and explore multi-dimensional relationships interactively. Chart data-points are linked to their underlying datasets, such that visualisation interaction opens entry-level or archive-level search results for inspection and datasets from charts may be exported in several ways. Findability, accessibility, interoperability and reusability of data are facilitated by these direct access and export mechanisms, including HTML embedding, persistent URL sharing and chart/data download options. By combining interactivity and ease of use with up-to-date access to the EMDB and EMPIAR archive metadata, both computational and experimental communities may explore and visualize current metadata and export to formats for further analysis or as publication-ready figures. Chart Builder promotes community-driven data analysis and empowers users to evaluate trends in the biological 3DEM field. Chart Builder is freely accessible and fully integrated into the EMDB website at https://www.ebi.ac.uk/emdb/statistics/builder/.

biophysics↗

bioRxiv2022

EMPIAR: The Electron Microscopy Public Image Archive

Public archiving in structural biology is well established with the Protein Data Bank (PDB; wwPDB.org) catering for atomic models and the Electron Microscopy Data Bank (EMDB; emdb-empiar.org) for 3D reconstructions from cryo-EM experiments. Even before the recent rapid growth in cryo-EM, there was an expressed community need for a public archive of image data from cryo-EM experiments for validation, software development, testing and training. Concomitantly, the proliferation of 3D imaging techniques for cells, tissues and organisms using volume EM (vEM) and X-ray tomography (XT) led to calls from these communities to publicly archive such data as well. EMPIAR (empiar.org) was developed as a public archive for raw cryo-EM image data and for 3D reconstructions from vEM and XT experiments and now comprises over a thousand entries totalling over 2 petabytes of data. EMPIAR resources include a deposition system, entry pages, facilities to search, visualise and download datasets, and a REST API for programmatic access to entry metadata. The success of EMPIAR also poses significant challenges for the future in dealing with the very fast growth in the volume of data and in enhancing its reusability.

bioinformatics↗

bioRxiv2020

League of Brazilian Bioinformatics: a competition framework to promote scientific training

Backgroundthe scientific training to become a bioinformatician includes multidisciplinary abilities, which increase the challenges to professional development. Competition frameworkin order to improve and promote the ongoing training of the Brazilian bioinformatics community, we organize a national competition, with the main goal to develop human resources and abilities in Computational Biology at the national level. The competition framework was designed in three phases: 1) a one-day challenge composed of 60 multiple-choice questions covering Biology, Computer Science, and Bioinformatics knowledge; 2) five Computational Biology challenges to be solved in three days; and 3) development of an original project evaluated during the 15th X-meeting. Resultsthe first edition of the League of Brazilian Bioinformatics (LBB) counted 168 competitors and 59 groups, distributed into undergraduate students (14.4%), graduate students (12.6% master and 16.8%, Ph.D.), and other professional fields. The first phase selected 46 teams to proceed in the competition, while the second phase selected the three top-performing teams. Conclusionduring the competition, we were able to stimulate teamwork in the main areas of Bioinformatics, with the engagement of all research-level competitors. Furthermore, we identified opportunities to deliver and offer better training to the community and we intend to apply the acquired experience in the second edition of the LBB, which will occur in 2021. Supplementary informationSupplementary data are available at Bioinformatics

scientific communication and education↗

bioRxiv2017

Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

Cancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-Seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

cancer biology↗

bioRxiv2017

Transcription factor activities enhance markers of drug response in cancer

Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

cancer biology↗