Transcriptomic & Applied Genomic
The Transcriptomics & Applied Genomics (TAG) platform is a core facilities specialized in microbial genomics. Our team is composed for half in molecular biologists specialized in high throughput genomic solutions and for the other half in bio-analysts and bio-informaticians which are expert into the analysis of the generated data and in the development of specialized solutions of analysis.
Our platform belongs to the local network of genomics facilities IGL (Institut de Génomique de Lille) as well as to the local platform of bioinformatics https://wikis.univ-lille.fr/bilille/Bilille (Plateforme de Bioinformatique et Bioanalyses de Lille) which itself is member of the IFB (Institut Français de Bioinformatique) and of France Génomique.
Our fields of skills are:
- Microbial WGS: sequencing or re-sequencing of whole microbial genome. We developed efficient protocols to rapidly and efficiently sequence numbers of bacterial genomes in parallel. Together with these sequencing solutions, a full assessment and comparison of mapping algorithms was performed (Caboche et al., 2017b) and allowed the development of a new automatic analyzing pipeline MICRA which is able to rapidly extract important characteristics of a microbial genome starting just from the fastq file (Caboche et al., 2017a).
More recently a full protocol to sequence and analyze viral genome without reference genome required was also developed (manuscript in preparation).
- Targeted metagenomics: microbiote determination by 16S-sequencing. A full sequencing and analyzing protocol including ASV determination and counting and diversity analysis was settled with a specific effort to evaluate and encounter bias
(Siegwald et al., 2014).
- Differential Expression Analysis: transcriptomic study by RNA-seq. Protocol for bacterial RNA-sequencing and analyzing was settled to classically determine transcript differential expression. More touchy and specialized approaches such as differential RNA-seq and 5’RACE by HTS were also developed to study RNome architecture (Amman et al., 2018).