The explosion of microbiome data available in recent years has enabled rapid advancements in the scope of research, yet it has also introduced myriad challenges. This is especially true for metrics that relate to phylogenetic diversity and relatedness, as the computational cost of calculation is dependent on the size of the phylogenetic tree and underlying…
As with most nascent research fields, microbial metagenomics has grappled not just with the question of how to measure and contextualize data but how to develop and refine tools to perform those analyses. While many tools have been created and used effectively, benchmarking their performance has proven especially difficult. One of the critical issues in…
Microbiome research’s emergence as a burgeoning field has yielded an ever-increasing availability of data sets to study. While the massive amounts of data have presented the opportunity for a variety of novel studies, their intrinsic nature of being high-dimensional, undetermined, sparse, and compositional has also caused challenges. This explosion of data, both in volume and…
While the coronavirus may be able to survive on beds, floors, and other surfaces near COVID patients, it’s unlikely to be passed to another person, a new study finds.
The coronavirus that causes COVID-19 also tends to co-locate with one particular type of bacteria
The coronavirus that causes COVID-19 also tends to co-locate with one particular type of bacteria
The proliferation of sequencing technologies and an ever-increasing focus on ‘omic studies over the past several years have spawned massive quantities of data and many tools to process and analyze the results. As these tools have evolved, so has the need for even greater scale and the ability to visualize complex data sets in more…
The University of California San Diego and IBM are building on the existing AI for Healthy Living (AIHL) collaboration in order to help tackle the COVID-19 pandemic.
As we age, our skin changes, and so too do the bugs that live there
A new tool has been developed that can effectively predict a person’s chronological age based on a microbiome sample. The tool, developed in collaboration between UC San Diego and IBM researchers, is most accurate at predicting a person’s age when using a skin microbiome sample.
