Nicolas Scalzitti

Nicolas Scalzitti
Michigan State University | MSU · Beacon Center for the Study of Evolution in Action

PhD

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10
Publications
947
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44
Citations

Publications

Publications (10)
Preprint
Transcription factors (TF) regulate gene activity in eukaryotic cells by binding specific regions of genomic DNA. In fungi, the most abundant TF class contains a fungal-specific ‘GAL4-like’ Zn2C6 DNA binding domain (DBD), while the second class contains another fungal-specific domain, known as ‘fungal_trans’ or Middle Homology Domain (MHD), whose f...
Article
Full-text available
Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide rang...
Thesis
Full-text available
Les projets de séquençage à haut débit produisent une énorme quantité de données biologiques brutes. Cependant, elles sont difficilement exploitables si elles ne sont pas annotées. Pour traiter ces données, des programmes d’annotation de génomes ont été développés, mais ces derniers sont encore trop sujet aux erreurs de prédiction, faisant de l’ann...
Article
Full-text available
Background Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction alg...
Preprint
Full-text available
Background. Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon-intron structures. Even the best eukaryotic gene prediction al...
Preprint
Full-text available
Background. Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon-intron structures. Even the best eukaryotic gene prediction al...
Preprint
Full-text available
Background: Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon-intron structures. Even the best eukaryotic gene prediction al...
Article
Full-text available
Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality...
Preprint
Full-text available
Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality,...
Preprint
Full-text available
Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality,...

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