Publications de l’équipe

Résumé

Résumé

Finding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks.

Résumé

Résumé

Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other.

Résumé

Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

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Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.

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The development of malaria parasites throughout their various life cycle stages is coordinated by changes in gene expression. We previously showed that the three-dimensional organization of the Plasmodium falciparum genome is strongly associated with gene expression during its replication cycle inside red blood cells. Here, we analyze genome organization in the P. falciparum and P. vivax transmission stages. Major changes occur in the localization and interactions of genes involved in pathogenesis and immune evasion, host cell invasion, sexual differentiation, and master regulation of gene expression. Furthermore, we observe reorganization of subtelomeric heterochromatin around genes involved in host cell remodeling. Depletion of heterochromatin protein 1 (PfHP1) resulted in loss of interactions between virulence genes, confirming that PfHP1 is essential for maintenance of the repressive center. Our results suggest that the three-dimensional genome structure of human malaria parasites is strongly connected with transcriptional activity of specific gene families throughout the life cycle.

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Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.

Résumé

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.