De novo transcriptome assembly is a powerful technique in the study of non-model organisms when the reference genome is not available. However, transcriptomics data analysis often presents significant challenges due to the vast amounts of data and the common difficulties faced when removing contaminants, identifying, and annotating transcripts, and using assembled transcripts in downstream analysis. Among different programs and packages suggested for de novo assembly (Oases, Trans-ABySS, SOAPdenovo-Trans, and IDBA-Tran), the most popular is the Trinity package.
In this presentation, I describe the use of the Trinity platform for de novo transcriptome assembly and the differential gene expression pipeline integrated into Trinity software.
PhD
Gayane Barseghyan received her PhD in Evolutionary and Environmental Biology from the Institute of Evolution, University of Haifa, Israel (2006-2010). In 2016, she was a postdoctoral fellow in the Department of Biological Sciences at the University of Montreal. She is a member of the editorial board of the International Journal of Medicinal Mushroom. Gayane Barseghyan has authored and co-autho…
Gayane Barseghyan received her PhD in Evolutionary and Environmental Biology from the Institute of Evolution, University of Haifa, Israel (2006-2010). In 2016, she was a postdoctoral fellow in the Department of Biological Sciences at the University of Montreal. She is a member of the editorial board of the International Journal of Medicinal Mushroom. Gayane Barseghyan has authored and co-authored more than 15 peer-reviewed scientific articles, including book chapters and a monograph. Her interests include evolution, diversity, community composition, medical biotechnology, species interactions, and fungal ecology. She is currently finishing the second year of the DESS program in Bioinformatics at UQAM (Montreal) to expand her knowledge and focus on bioinformatics and computational biology, which includes the analysis of biological data (molecular phylogenetics/omics, transcriptomics, and genomics).
Aleksandr is a PhD student at the University of Sherbrooke, Department of Information Technology. He has a Master of Science in Data Science degree (The University of Sheffield, UK) and conducts research in bioinformatics and machine learning. Aleksandr's professional interests include artificial intelligence, teaching, and No-code and Low-code technologies.
Université de Sherbrooke
Prof.
University of Sherbrooke
PhD (c)
Aleksandr is a PhD student at the University of Sherbrooke, Department of Information Technology. He has a Master of Science in Data Science degree (The University of Sheffield, UK) and conducts research in bioinformatics and machine learning. Aleksandr's professional interests include artificial intelligence, teaching, and No-code and Low-code technologies.
