Biology subjects

Raimundo, F.

Publications and source records attributed to Raimundo, F..

4 recordsLinked to original sources

bioRxiv2024

Targeting a shared neoepitope derived from non-canonical translation of c-MYC oncogene in cancer cells

Cancer cells rely on alternative modes of translation for protein synthesis, promoting internal ribosome entry site (IRES)-dependent translation of mRNA encoding pro-oncogenic factors. Furthermore, ribosomes translate mRNA with lower fidelity in tumor cells. We proposed that these translational modifications in cancer produce shared tumor-specific epitopes derived from IRES-containing oncogenes. To identify such neoepitopes, we developed an in silico-based method that we applied to c-MYC. We showed that the non-canonical translation of c-MYC mRNA in cancer cells, involving a (+1) ribosomal frameshift, generates a shared neoepitope which induces high-avidity T cells able to kill tumor cells in vitro and in vivo while sparing normal cells. Our data provide preclinical rationale for developing immunotherapies targeting this c-MYC-derived neoepitope and validate a new type of shared translation-associated neoantigens.

immunology↗

bioRxiv2022

Best practices for single-cell histone modification analysis

BackgroundSingle-cell histone post translation modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues, and are likely to unlock our understanding of various epigenetic mechanisms involved in development or diseases. Running an scHTPM experiment and analyzing the data produced remains, however, a challenging task since few consensus guidelines exist currently regarding good practices for experimental design and data analysis pipelines. MethodsWe perform a computational benchmark to assess the impact of experimental parameters and of the data analysis pipeline on the ability of the cell representation produced to recapitulate known biological similarities. We run more than ten thousands experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. ResultsThe analysis of the benchmark results allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation, and that using fixed-size bin counts outperforms annotation-based binning; that dimension reduction methods based on latent semantic indexing outperform others; and that feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed.

bioinformatics↗

bioRxiv2021

Machine learning for single cell genomics data analysis

Single-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data, machine learning (ML)-based model are increasingly used due to their flexibility, scalability, and impressive success in other fields. In recent years, we have seen a surge of new ML-based method development for low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference or multimodal data integration. To help readers navigate this fast-moving literature, we survey in this review recent advances in ML approaches developed to analyze single-cell omics data, focusing mainly on peer-reviewed publications published in the last two years (2019-2020).

bioinformatics↗

bioRxiv2020

The importance and difficulty to tune parameters of methods for single-cell RNA-seq dimensionality reduction

BackgroundMany computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering or differential analysis, often relying on default parameters. Yet given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need. ResultsHere, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it. ConclusionsWe find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA and scVI can reach better performance but after parameter tuning.

bioinformatics↗