Biology subjects

Ouangraoua, A.

Publications and source records attributed to Ouangraoua, A..

5 recordsLinked to original sources

bioRxiv2024

Non-canonical altPIDD1 protein: unveiling the true major translational output of the PIDD1 gene

Proteogenomics has enabled the detection of novel proteins encoded in non-canonical or alternative open reading frames (altORFs) in genes already coding a reference protein. Reanalysis of proteomic and ribo-seq data revealed that the p53-induced death domain-containing protein (or PIDD1) gene encodes a second 171 amino acid protein, altPIDD1, in addition to the known 910 amino acid-long PIDD1 protein. The two ORFs overlap almost completely, and the translation initiation site of altPIDD1 is located upstream of PIDD1. AltPIDD1 has more translational and protein level evidence than PIDD1 across various cell lines and tissues. In HEK293 cells, the altPIDD1 to PIDD1 ratio is 40 to 1, as measured with isotope-labeled (heavy) peptides and targeted proteomics. AltPIDD1 localizes to cytoskeletal structures labeled with phalloidin, including stress fibres, lamellipodia and filopodia, interacts with cytoskeletal proteins and is cleaved during apoptosis. Unlike most non-canonical proteins, altPIDD1 is not evolutionarily young but emerged in placental mammals. Overall, we identify PIDD1 as a dual-coding gene, with altPIDD1, not the annotated protein, being the primary product of translation. Summary blurbThis research uncovers PIDD1 as a dual-coding gene, revealing a previously unknown protein, altPIDD1, as the primary product with higher expression and cytoskeletal interactions.

biochemistry↗

bioRxiv2023

Motif selection enables efficient sequence-based classification of non-coding RNA

MotivationNon-coding RNAs (ncRNAs) classification is important for genome annotation and to perform functional analyses of these biological molecules. Efficient methods for large-scale RNA classification remain challenging. Existing methods often rely on structure similarity, and they require huge computing times because they use secondary structure information, which impedes any large-scale use. ResultsWe present a sequence-based method that relies on the computation and the selection of common sequence motifs to provide a set of features for effectively classifying ncRNAs families by a supervised learning approach. The results show that our method achieves an equal or higher accuracy than existing structure-based methods and drastically reduces required computing times. Results also demonstrate that, thanks to an appropriate selection of local sequence motifs, an efficient sequence-based ncRNA classification can be achieved through supervised learning. AvailabilityCode and datasets are available at https://github.com/chegrane/LSC-ncRNA ContactAida.Ouangraoua@usherbrooke.ca Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Choosing representative proteins based on splicing structure similarity improves the accuracy of gene tree reconstruction

Constructing accurate gene trees is important, as gene trees play a key role in several biological studies, such as species tree reconstruction, gene functional analysis and gene family evolution studies. The accuracy of these studies is dependent on the accuracy of the input gene trees. Although several methods have been developed for improving the construction and the correction of gene trees by making use of the relationship with a species tree in addition to multiple sequence alignment, there is still a large room for improvement on the accuracy of gene trees and the computing time. In particular, accounting for alternative splicing that allows eukaryote genes to produce multiple transcripts/proteins per gene is a way to improve the quality of multiple sequence alignments used by gene tree reconstruction methods. Current methods for gene tree reconstruction usually make use of a set of transcripts composed of one representative transcript per gene, to generate multiple sequence alignments which are then used to estimate gene trees. Thus, the accuracy of the estimated gene tree depends on the choice of the representative transcripts. In this work, we present an alternative-splicing-aware method called Splicing Homology Transcript (SHT) method to estimate gene trees based on wisely selecting an accurate set of homologous transcripts to represent the genes of a gene family. We introduce a new similarity measure between transcripts for quantifying the level of homology between transcripts by combining a splicing structure-based similarity score with a sequence-based similarity score. We present a new method to cluster transcripts into a set of splicing homology groups based on the new similarity measure. The method is applied to reconstruct gene trees of the Ensembl database gene families, and a comparison with current EnsemblCompara gene trees is performed. The results show that the new approach improves gene tree accuracy thanks to the use of the new similarity measure between transcripts. An implementation of the method as well as the data used and generated in this work are available at https://github.com/UdeS-CoBIUS/SplicingHomologGeneTree/.

bioinformatics↗

bioRxiv2018

SplicedFamAlign: CDS-to-gene spliced alignment and identification of transcript orthology groups

MotivationThe inference of splicing orthology relationships between gene transcripts is a basic step for the prediction of transcripts and the annotation of gene structures in genomes. Spliced alignment that consists in aligning a spliced cDNA sequence against an unspliced genomic sequence, constitutes a promising, yet unexplored approach for the identification of splicing orthology relationships. Existing spliced alignment algorithms do not exploit the information on the splicing structure of the input sequences, namely the exon structure of the cDNA sequence and the exon-intron structure of the genomic sequences. Yet, this information is often available for coding DNA sequences (CDS) and gene sequences annotated in databases, and it can help improve the accuracy of the computed spliced alignments. To address this issue, we introduce a new spliced alignment problem and a method called SplicedFamAlign (SFA) for computing the alignment of a spliced CDS against a gene sequence while accounting for the splicing structures of the input sequences, and then the inference of transcript splicing orthology groups in a gene family based on spliced alignments.\n\nResultsThe experimental results show that SFA outperforms existing spliced alignment methods in terms of accuracy and execution time for CDS-to-gene alignment. We also show that the performance of SFA remains high for various levels of sequence similarity between input sequences, thanks to accounting for the splicing structure of the input sequences. It is important to notice that unlike all current spliced alignment methods that are meant for cDNA-to-genome alignments and can be used for CDS-to-gene alignments, SFA is the first method specifically designed for CDS-to-gene alignments. We show its usefulness for the comparison of genes and transcripts within a gene family for the purpose of analyzing splicing orthologies. It can also be used for gene structure annotation and alternative splicing analyses.\n\nAvailabilitySplicedFamAlign was implemented in Python. Source code is freely available at https://github.com/UdeS-CoBIUS/SpliceFamAlign\n\nContactsafa.jammali@usherbrooke.ca

bioinformatics↗

bioRxiv2017

Deep transcriptome annotation suggests that small and large proteins encoded in the same genes often cooperate

Recent studies in eukaryotes have demonstrated the translation of alternative open reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and evolutionary patterns indicate that altORFs are particularly constrained in CDSs that evolve slowly. Thousands of predicted alternative proteins are detected in proteomic datasets by reanalysis with a database containing predicted alternative proteins. Protein domains and co-conservation analyses suggest potential functional cooperation or shared function between small and large proteins encoded in the same genes. This is illustrated with specific examples, including altMID51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many coding genes code for more than one protein that are often functionally related.

biochemistry↗