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4. Comprehensive locus-specific L1 DNA methylation profiling reveals the epigenetic and transcriptional interplay between L1s and their integration sites

Author

Lanciano, Sophie, primary, Philippe, Claude, additional, Sarkar, Arpita, additional, Pratella, David, additional, Domrane, Cécilia, additional, Doucet, Aurélien J., additional, van Essen, Dominic, additional, Saccani, Simona, additional, Ferry, Laure, additional, Defossez, Pierre-Antoine, additional, and Cristofari, Gael, additional

Published
2023
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6. A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Author

Pratella, David, Ait-El-Mkadem Saadi, Samira, Bannwarth, Sylvie, Paquis-Fluckinger, Véronique, and Bottini, Silvia

Subjects
QH301-705.5, High-Throughput Nucleotide Sequencing, rare diseases, Review, personalized medicine, Prognosis, artificial intelligence, Machine Learning, Chemistry, autoencoders, Humans, Neural Networks, Computer, Biology (General), QD1-999, Algorithms
Abstract

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

Published
2021

7. NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework

Author

Duboc, Véronique, Pratella, David, Milanesio, Marco, Boudjarane, John, Descombes, Stéphane, Paquis-Flucklinger, Véronique, Bottini, Silvia, Hôpital Archet 2 [Nice] (CHU), Université Côte d'Azur (UCA), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Numerical modeling and high performance computing for evolution problems in complex domains and heterogeneous media (NACHOS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut de Recherche sur le Cancer et le Vieillissement (IRCAN), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (JAD), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), and Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
bioinformatic pipeline, AcademicSubjects/SCI01060, Noninvasive Prenatal Testing, prenatal testing, Aneuploidy, benchmark, fetal aneuploidies prediction, fetal fraction, Pregnancy, Prenatal Diagnosis, Problem Solving Protocol, Humans, Female, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Cell-Free Nucleic Acids, Retrospective Studies
Abstract

International audience; Abstract Noninvasive prenatal testing (NIPT) consists of determining fetal aneuploidies by quantifying copy number alteration from the sequencing of cell-free DNA (cfDNA) from maternal blood. Due to the presence of cfDNA of fetal origin in maternal blood, in silico approaches have been developed to accurately predict fetal aneuploidies. Although NIPT is becoming a new standard in prenatal screening of chromosomal abnormalities, there are no integrated pipelines available to allow rapid, accurate and standardized data analysis in any clinical setting. Several tools have been developed, however often optimized only for research purposes or requiring enormous amount of retrospective data, making hard their implementation in a clinical context. Furthermore, no guidelines have been provided on how to accomplish each step of the data analysis to achieve reliable results. Finally, there is no integrated pipeline to perform all steps of NIPT analysis. To address these needs, we tested several tools for performing NIPT data analysis. We provide extensive benchmark of tools performances but also guidelines for running them. We selected the best performing tools that we benchmarked and gathered them in a computational pipeline. NiPTUNE is an open source python package that includes methods for fetal fraction estimation, a novel method for accurate gender prediction, a principal component analysis based strategy for quality control and fetal aneuploidies prediction. NiPTUNE is constituted by seven modules allowing the user to run the entire pipeline or each module independently. Using two cohorts composed by 1439 samples with 31 confirmed aneuploidies, we demonstrated that NiPTUNE is a valuable resource for NIPT analysis.

Published
2021

9. NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework.

Author

Duboc, Véronique, Pratella, David, Milanesio, Marco, Boudjarane, John, Descombes, Stéphane, Paquis-Flucklinger, Véronique, and Bottini, Silvia

Subjects
*PRENATAL diagnosis, *CELL-free DNA, *PIPELINE inspection, *PYTHON programming language, *MEDICAL screening, *PRINCIPAL components analysis, *DATA analysis, *QUALITY control
Abstract

Noninvasive prenatal testing (NIPT) consists of determining fetal aneuploidies by quantifying copy number alteration from the sequencing of cell-free DNA (cfDNA) from maternal blood. Due to the presence of cfDNA of fetal origin in maternal blood, in silico approaches have been developed to accurately predict fetal aneuploidies. Although NIPT is becoming a new standard in prenatal screening of chromosomal abnormalities, there are no integrated pipelines available to allow rapid, accurate and standardized data analysis in any clinical setting. Several tools have been developed, however often optimized only for research purposes or requiring enormous amount of retrospective data, making hard their implementation in a clinical context. Furthermore, no guidelines have been provided on how to accomplish each step of the data analysis to achieve reliable results. Finally, there is no integrated pipeline to perform all steps of NIPT analysis. To address these needs, we tested several tools for performing NIPT data analysis. We provide extensive benchmark of tools performances but also guidelines for running them. We selected the best performing tools that we benchmarked and gathered them in a computational pipeline. NiPTUNE is an open source python package that includes methods for fetal fraction estimation, a novel method for accurate gender prediction, a principal component analysis based strategy for quality control and fetal aneuploidies prediction. NiPTUNE is constituted by seven modules allowing the user to run the entire pipeline or each module independently. Using two cohorts composed by 1439 samples with 31 confirmed aneuploidies, we demonstrated that NiPTUNE is a valuable resource for NIPT analysis. [ABSTRACT FROM AUTHOR]

Published
2022
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11. Recent computational developments on CLIP-seq data analysis and microRNA targeting implications.

Author

Bottini, Silvia, Pratella, David, Grandjean, Valerie, Repetto, Emanuela, and Trabucchi, Michele

Subjects
*RNA, *TRANSCRIPTOMES, *CELLS, *TISSUES, *GENOMES
Abstract

C ross- L inking I mmuno p recipitation associated to high-throughput seq uencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies. [ABSTRACT FROM AUTHOR]

Published
2018
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