High throughput sequencing of entire microbial communities is a revolutionary tool for medical, biological, pharmaceutical, environmental, and agricultural sciences. Data analysis tools are constantly improving; however, a lot of high quality empirical research data does not yet benefit from these latest methods. Trying to find and employ dedicated computational postdocs for data analysis can be difficult, and emerges as a structural challenge for modern biology.
We are a group of postdoctoral researchers who offer our expertise to help you analyze your data. We will work with you and produce publication-ready figures, write concise methods sections, and assist you all the way to publication - like project-based post docs. We have years of experience in analyzing and modeling microbiome and genomic data. Our goal is to maximize the impact of your research, and reduce the amount of data that sits on your drives without being properly analyzed.
kGenes is as much an experiment for us as it is for you! We love our research, and we would like to maximize your and our impact. If you think this has potential to solve a problem for you, please do contact us and we are happy to find a custom solution for your research project.
Starting from raw sequencing results, we provide you with clean data tables in your data format of choice (biom tables, phyloseq, MS Excel sheets), or can help you set up a SQL data base. For your 16s data, we offer all common classification pipelines (e.g. DADA2, QIIME, UPARSE, MOTHUR, etc.). We can also assist you in choosing which pipeline best fits your research questions. And if you have shotgun sequencing data, we can help you with assembly and community profiling.
We offer interactive visualization of your data for you to explore, and provide publication-ready summary figures of your entire sequencing results. We help you analyze what distinguishes communities in your samples using cutting edge methods from machine learning and compositional statistics - beyond UniFrac. Our own research focuses on developing new eco-evolutionarily inspired data analysis methods, and with these we can help you get one step closer towards understanding causation in microbiomes.
We are all scientists ourselves, and we understand how precious your data is. We keep your data safe and confidential, and any data you share will be encrypted at rest. We have years of experience working with proprietary and sensitive data (e.g. clinical data sets at Memorial Sloan Kettering Cancer Center), and professional expertise in computer security. We can offer secure means to upload your data, or can find custom solutions for you that are compliant with your and your institution’s data protection requirements.
We can help you choose from the most common metagenomic data processing pipelines to classify unique sequences in your samples. Currently, we recommend the DADA2 platform for 16s sequence data, and have extensive experience using UPARSE, QIIME, and MOTHUR. Please get in touch for shotgun sequence data processing, or custom solutions for your project.
We generate interactive graphs and provide you with a dashboard to quickly explore your complex microbiome samples. Our visualizations use established methods, and leverage cutting edge dimensionality reduction methods (PCoA, tSNE, topological data analysis) for an intuitive data exploration experience. In our experience, this is the fastest way towards meaningful hypothesis generation. Finally, we supply you with cutting-edge summary statistics on all of your samples using methods specifically designed for microbiome compositional data.
We help you analyze the correlation structures in your data using a combination of established methods as well as our inhouse methods that we are publishing in top journals. We will tailor our analyses to your specific research question - as scientists we know that there are no one-fits-all solutions for research projects. For example, we are experts in microbiome timeseries analysis and ecosystem theory, and can help you uncover dynamics and ecology in your microbiome communities
We can help you combine metabolomic data with microbiome data. Especially in longitudinal studies, this can elevate your microbiome study to new heights, and reveal true ecological relationships that can predict microbiome dynamics. In our experience, even easily measurable variables such as pH can help tremendously, and reveal hidden interactions between microbial taxa. Understanding interactions will be an essential step towards rational design of microbiota communities.
It is now clear that we need to move beyond descriptive studies of microbiome communities. Our research over the past years has yielded completely new ways to move towards identifying microbiome-caused changes in the environment or host phenotypes. Please get in touch to learn more about these methods, and how they can help you get one step closer to understanding how microbiome dynamics can causally affect their host, or the microbial habitat.
Jonas received his DPhil from the University of Oxford in 2014, and has since been a postdoc at Memorial Sloan Kettering Cancer Center and Sokendai. During his doctoral research, he developed computer simulations and ecosystems models of the human gut microbiome. He has applied evolutionary theory and theoretical ecology to understand microbiome dynamics. Currently, Jonas is developing new methods for microbiome timeseries data, and methods to identify causal drivers of host phenotypes within the microbiome.
Kat received her DPhil from the University of Oxford in 2016. She has been working on mathematical and experimental models of microbial ecology. Her expertise ranges from experimental evolution in microbial communities, to the effect of fluid dynamics on microbial ecosystems. Recently, she has been developing new methods and tools to predict how complex microbial communities may be reliably assembled to form stable, resilient communities.
Liam recently finished his PhD at the University of Cambridge, and holds degrees in Computer Science, and Physics from the University of Oxford, and St. Andrews. His doctoral research focused on developing novel computational methods to interpret large Hi-C data sets using high performance computing methods. Besides bioinformatics, Liam has significant experience in cryptology and data security from his MSc at the University of Oxford, and from having worked at Cisco Systems.
Sonya has a DPhil from the University of Oxford in Biochemistry, and has years of experience in computational modeling and simulation of proteins, and the small molecules that bind them. In the past, she developed kinetic models, and she has significant experience both in molecular modeling and experimental biochemistry to extract and answer relevant biological questions.