Laboratory of Computational Metagenomics
A healthy adult human harbours some 100 trillion microbes outnumbering our own cells by a factor of ten and expanding our own gene repertoire by at least two orders of magnitude. Our laboratory employs multiple complementary approaches coupling computation and experimentation to study the diversity of the human microbiome and its role in human dysbiosis and infections. We work in a highly multidisciplinary and collaborative environment with effective interactions between computational scientists,
experimental biologists, statisticians, and clinical teams.
- Next generation computational metagenomic tools. The potential richness of shotgun metagenomic datasets (several GBs/sample) is currently only partially expressed due to several computational challenges currently not addressed by available methods. We are working on a principled framework to provide next-generation computational tools able to profile microbiome samples at multiple levels (taxonomic, phylogenetic, functional, metabolic). This involves the processing of several thousands of microbial genomes and is also resulting in new tools for comparative microbiology, phylogenomics, and pathogen identification.
- Integrative and machine learning meta’omic approaches. Given the complexity and variability of microbial communities, multiple complementary cultivation-free methods should ideally be employed simultaneously including metagenomics, metatranscriptomics, metametabolomics, metaproteomics, and single cell sequencing. As the cost of these meta’omic approaches are quickly decreasing the bottleneck becomes the lack of integrative computational approaches and statistically robust tools. The lab is developing such integrative methods and is applying them on the new data produced as well as on the compendium of publicly available meta’omic datasets.
- Microbiome-pathogen interaction in human infections. The study of microbiome-host interaction is now receiving a level of attention and funding almost comparable to the more classical pathogen-host interaction studies. Despite intriguing results from mouse models, the role of direct microbiome-pathogen interactions in the acquisition and development of human infections is instead currently under-investigated and unclear. By means of coupled pathogen isolate sequencing and shotgun metagenomics we aim to unravel the role of the microbiome in the development of antibiotic resistance and virulence modulation in human infections, with the medium-term goal of finding new potential therapeutic targets.
- Vertical microbiome transmission and infant probiotics. We study how members of a microbial community can be transmitted between different environments and become stable colonizers in the new environment. Specifically, we investigate the sources of variability for the early colonization of the infant gut, considering environmental factors as well as vertical mother-to-infant microbiota transfer. This may result in the identification of new probiotic strains that could potentially be used for preventing early dysbiotic microbiome configurations.
- Nicola Segata, PI
- Matthias Scholz, Postdoctoral fellow
- Adrian Tett, Postdoctoral fellow
- Duy Tin Truong, Postdoctoral fellow
- Pamela Ferretti, Bachelor student (DISI)
- Moreno Zolfo, Bachelor student (DISI)
We are currently looking for motivated candidates for post-doctoral positions. Candidates interested in metagenomics as well as candidates with strong computational background only, are invited to contact the PI (email@example.com) for informal inquiries. Students interested in small-to-medium computational projects are also welcome.
- Curtis Huttenhower, Harvard School of Public Health
- Olivier Jousson, CIBIO (Unitn)
- Flaminia Catteruccia, University of Perugia and Harvard School of Public Health
- Doyle Ward, Broad Institute
- Enrico Blanzieri, DISI (Unitn)
- Dirk Gevers, Broad Institute
- Ermanno Baldo (APSS, Rovereto)
- Anna Pedrotti and Valentina Gorfer (APSS, Trento)
- Marco Ventura, (University of Parma)
Please see http://scholar.google.com/citations?user=ZXjO-Q4AAAAJ for a complete and updated list of publications.
Katherine Huang, Arthur Brady, Anup Mahurkar, Owen White, Dirk Gevers, Curtis Huttenhower, Nicola Segata “MetaRef: a pan-genomic database for comparative and community microbial genomics.” Nucleic Acids Research 42 (D1), D617-D624
Nicola Segata, Daniela Börnigen, Xothitl Morgan, and Curtis Huttenhower. “PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes .”
Nature Communications, 4, 2013
Nicola Segata, Daniela Boernigen, Timothy L Tickle, Xochitl Morgan, Wendy S Garrett, and Curtis Huttenhower. “Computational meta’omics for microbial community studies.”
Molecular Systems Biology 9:666, 2013
Annalisa Ballarini*, Nicola Segata*, Curtis Huttenhower, and Olivier Jousson. “Simultaneous quantification of multiple bacteria by the BactoChip microarray designed to target species-specific marker genes.”
Plos One 8(2). 2013
Nicola Segata, Levi Waldron, Annalisa Ballarini, Vagheesh Narasimhan, Olivier Jousson, and Curtis Huttenhower. “Metagenomic microbial community profiling using unique clade-specific marker genes.”
Nature Methods 9: 811–814. 2012
The Human Microbiome Consortium (including Nicola Segata). “Structure, function and diversity of the healthy human microbiome.”
Nature 486(7402): 207–214. 2012
Nicola Segata, Susan Kinder Haake, Peter Mannon, Katherine P Lemon, Levi Waldron, Dirk Gevers, Curtis Huttenhower, and Jacques Izard. “Composition of the Adult Digestive Tract Microbiome Based on Seven Mouth Surfaces, Tonsils, Throat and Stool Samples.”
Genome Biology 13(6): R42. 2012
Nicola Segata, Jacques Izard, Levi Waldron, Dirk Gevers, Larisa Miropolsky, Wendy S Garrett, and Curtis Huttenhower. 2011. “Metagenomic Biomarker Discovery and Explanation.”
Genome Biology 12: R60. 2011.
Nicola Segata, and Curtis Huttenhower. 2011. “Toward an efficient method of identifying core genes for evolutionary and functional microbial phylogenies.”
PLoS ONE 6(9). 2011.
Nicola Segata, and Enrico Blanzieri. “Fast and Scalable Local Kernel Machines.”
Journal of Machine Learning Research 11: 1883–1926. 2010