0000000000010202

AUTHOR

Lilia Ayadi

showing 7 related works from this author

High-Throughput Mapping of 2′-O-Me Residues in RNA Using Next-Generation Sequencing (Illumina RiboMethSeq Protocol)

2017

Detection of RNA modifications in native RNAs is a tedious and laborious task, since the global level of these residues is low and most of the suitable physico-chemical methods require purification of the RNA of interest almost to homogeneity. To overcome these limitations, methods based on RT-driven primer extension have been developed and successfully used, sometimes in combination with a specific chemical treatment. Nowadays, some of these approaches have been coupled to high-throughput sequencing technologies, allowing the access to transcriptome-wide data. RNA 2'-O-methylation is one of the ubiquitous nucleotide modifications found in many RNA types from bacteria, archaea, and eukarya.…

0301 basic medicinechemistry.chemical_classificationbiologyComputer science2'-O-methylationRNAComputational biology010402 general chemistrybiology.organism_classification01 natural sciencesPrimer extensionDNA sequencing0104 chemical sciences03 medical and health sciences030104 developmental biologychemistryRNA modificationDECIPHERNucleotideLigationProtocol (object-oriented programming)Throughput (business)Illumina dye sequencingBacteriaArchaea
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AlkAniline-Seq: A Highly Sensitive and Specific Method for Simultaneous Mapping of 7-Methyl-guanosine (m7G) and 3-Methyl-cytosine (m3C) in RNAs by Hi…

2021

Epitranscriptomics is an emerging field where the development of high-throughput analytical technologies is essential to profile the dynamics of RNA modifications under different conditions. Despite important advances during the last 10 years, the number of RNA modifications detectable by next-generation sequencing is restricted to a very limited subset. Here, we describe a highly efficient and fast method called AlkAniline-Seq to map simultaneously two different RNA modifications: 7-methyl-guanosine (m7G) and 3-methyl-cytosine (m3C) in RNA. Our protocol is based on three subsequent chemical/enzymatic steps allowing the enrichment of RNA fragments ending at position n + 1 to the modified nu…

chemistry.chemical_classification0303 health sciencesbiologyGuanosineRNA[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyComputational biologybiology.organism_classificationYeastDNA sequencing03 medical and health scienceschemistry.chemical_compound0302 clinical medicineEnzymechemistry[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]EpitranscriptomicsNucleotideComputingMilieux_MISCELLANEOUS030217 neurology & neurosurgeryBacteria030304 developmental biology
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Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

2019

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide re…

0301 basic medicinelcsh:QH426-470Downstream (software development)Computer scienceRT signatureMachine learningcomputer.software_genre[SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyField (computer science)m1A03 medical and health sciencesRNA modifications0302 clinical medicineEpitranscriptomics[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]GeneticsTechnology and CodeGalaxy platformGenetics (clinical)ComputingMilieux_MISCELLANEOUSbusiness.industryPrincipal (computer security)[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyAutomationWatson–Crick faceVisualizationlcsh:Geneticsmachine learningComputingMethodologies_PATTERNRECOGNITION030104 developmental biologyWorkflow030220 oncology & carcinogenesisMolecular Medicine[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]TrimmingArtificial intelligencebusinesscomputer
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Next‐Generation Sequencing‐Based RiboMethSeq Protocol for Analysis of tRNA 2′‐O‐Methylation

2017

Analysis of RNA modifications by traditional physico‐chemical approaches is labor  intensive,  requires  substantial  amounts  of  input  material  and  only  allows  site‐by‐site  measurements.  The  recent  development  of  qualitative  and  quantitative  approaches  based  on   next‐generation sequencing (NGS) opens new perspectives for the analysis of various cellular RNA  species.  The  Illumina  sequencing‐based  RiboMethSeq  protocol  was  initially  developed  and  successfully applied for mapping of ribosomal RNA (rRNA) 2′‐O‐methylations. This method also  gives excellent results in the quantitative analysis of rRNA modifications in different species and  under varying growth condi…

0301 basic medicine2 -O-methylationSaccharomyces cerevisiaelcsh:QR1-502Biochemistrylcsh:MicrobiologyDNA sequencingdeleted strain03 medical and health sciences[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN] deleted strainTrmH 2′‐O‐methylationMolecular BiologytRNAIllumina dye sequencingRiboMethSeq TRM3Genetics RiboMethSeq030102 biochemistry & molecular biologybiologytRNA; 2′‐O‐methylation; RiboMethSeq; high‐throughput sequencing; deleted strain;  TrmH; TRM32'-O-methylationRNAhigh-throughput sequencing[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyMethylation  TrmHRibosomal RNAbiology.organism_classification030104 developmental biology high‐throughput sequencingTRM3Transfer RNA
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AlkAniline-Seq: Profiling of m7 G and m3 C RNA Modifications at Single Nucleotide Resolution.

2018

RNA modifications play essential roles in gene expression regulation. Only seven out of >150 known RNA modifications are detectable transcriptome-wide by deep sequencing. Here we describe a new principle of RNAseq library preparation, which relies on a chemistry based positive enrichment of reads in the resulting libraries, and therefore leads to unprecedented signal-to-noise ratios. The proposed approach eschews conventional RNA sequencing chemistry and rather exploits the generation of abasic sites and subsequent aniline cleavage. The newly generated 5'-phosphates are used as unique entry for ligation of an adapter in library preparation. This positive selection, embodied in the AlkAnilin…

0301 basic medicineComputational biologyCatalysisDeep sequencing03 medical and health sciencesdeep sequencingAdapter (genetics)[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]Epitranscriptomicsabasic siteNucleotideAP siteComputingMilieux_MISCELLANEOUSchemistry.chemical_classificationRegulation of gene expressionChemistryRNA[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyGeneral ChemistryMethylationSciences bio-médicales et agricolesRNA modification3. Good health030104 developmental biologymethylationepitranscriptomics
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Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

2020

International audience; A major trend in the epitranscriptomics field over the last 5 years has been the high-throughput analysis of RNA modifications by a combination of specific chemical treatment(s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m5C), pseudouridine (ψ), 1-methyladenosine (m1A), and 2'-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in vari…

0301 basic medicinebioinformatic pipelinelcsh:QH426-470Computer scienceComputational biologyDeep sequencingPseudouridine03 medical and health scienceschemistry.chemical_compound0302 clinical medicine[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]ribose methylationEpitranscriptomicsGeneticsGenetics (clinical)receiver operating characteristic2'-O-methylation2′-O-methylationhigh-throughput sequencingRNA[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyBrief Research Reportlcsh:Genetics030104 developmental biologychemistry030220 oncology & carcinogenesisTransfer RNARNAMolecular MedicineSmall nuclear RNAReference genomeFrontiers in Genetics
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Mapping and Quantification of tRNA 2′-O-Methylation by RiboMethSeq

2018

Current development of epitranscriptomics field requires efficient experimental protocols for precise mapping and quantification of various modified nucleotides in RNA. Despite important advances in the field during the last 10 years, this task is still extremely laborious and time-consuming, even when high-throughput analytical approaches are employed. Moreover, only a very limited subset of RNA modifications can be detected and only rarely be quantified by these powerful techniques. In the past, we developed and successfully applied alkaline fragmentation-based RiboMethSeq approach for mapping and precise quantification of multiple 2'-O-methylation residues in ribosomal RNA. Here we descr…

chemistry.chemical_classification0303 health sciencesTRNA modificationChemistry2'-O-methylationRNA[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyComputational biologyRibosomal RNADNA sequencing03 medical and health sciences0302 clinical medicine030220 oncology & carcinogenesisEpitranscriptomics[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]Transfer RNANucleotideComputingMilieux_MISCELLANEOUS030304 developmental biology
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