Our work on metagenomic data to understand community functioning of microbes dates back to 2008, when we started to analyze the Global Ocean Sampling dataset for detoxification systems. Through the work with metagenomics projects, the lab has been involved in several software development efforts aiming to better extract and visualize information from the enormous datasets produced (Metaxa, Megraft, TriMetAss, PETKit, FARAO, Mumame). Currently, we use metagenomic sequencing data to analyze different environments for antibiotic resistance genes, and thereby try to uncover which risks for environmental resistance development and dissemination we should be most concerned about, as well as possible secondary effects of antibiotics on microbial communities.
Metagenomics and other types of large-scale microbial community analysis are intertwined into some aspect of nearly all projects the lab is pursuing, and we therefore do a considerable amount of methods development to enable cutting-edge research using these techniques.
Open questions of interest
- How can we better derive useful information and identify meaningful differences between sample types using shotgun metagenomics?
- How to improve metagenomic sequence assembly for highly conserved regions, found in multiple contexts (a common problem for resistance genes)?
- How can metagenomics be used to target specific research questions, moving from being a hypothesis-generating tool to a hypothesis-driven one?
- Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199 [Paper link]
- Abramova A, Osińska A, Kunche H, Burman E, Bengtsson-Palme J: CAFE: A software suite for analysis of paired-sample transposon insertion sequencing data. Bioinformatics, 37, 1, 121–122 (2021). doi: 10.1093/bioinformatics/btaa1086 [Paper link]
- Magesh S, Jonsson V, Bengtsson-Palme J: Mumame: A software tool for quantifying gene-specific point-mutations in shotgun metagenomic data. Metabarcoding and Metagenomics, 3: 59–67 (2019). doi: 10.3897/mbmg.3.36236 [Paper link]
- Hammarén R, Pal C, Bengtsson-Palme J: FARAO: The Flexible All-Round Annotation Organizer. Bioinformatics, 32, 23, 3664-3666 (2016). doi: 10.1093/bioinformatics/btw499 [Paper link]
- Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648 [Paper link]
- Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: The structure and diversity of human, animal and environmental resistomes. Microbiome, 4, 54 (2016). doi: 10.1186/s40168-016-0199-5 [Paper link]