There is increasing evidence of the human gut microbiome’s link to health and disease. Exploring the composition and function of these microbial communities can help us better understand the effects of microbiota perturbation on disease development and disease treatment.

Due to the high sequencing and computational expense of whole-genome shotgun sequencing (WGS), most microbiome research has employed 16S amplicon sequencing, which has been highly successful for broad taxonomic classification but is limited in its ability to distinguish microbes at the species level. Species-level identification is essential for understanding biological mechanisms underlying the link between the microbiome and human phenotypes, and for evaluating the therapeutic potential of specific gut microbes.

rexmap analysis NAR paper

This paper, contributed to by Sapient’s scientists and published in Nucleic Acids Research, details the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants and its application in analyzing the gut microbiome of more than 29,000 diverse individuals.

RExMap analysis of 16S data captures ∼75% of microbial species identified by WGS, despite hundreds-fold less sequencing depth. The method was applied to re-analyze existing 16S data from 29,349 individuals across diverse regions around the world and reveals a detailed landscape of gut microbial species across populations and geography.

To access the full paper, click here.

Sapient Featured as Collaborator in Cedars-Sinai Study on Immune Responses and COVID-19

Study infrastructure and biospecimen processing support is being provided by Sapient for the study, which is focused on advancing vital immune response research for COVID-19 and other autoimmune and inflammation-associated diseases. Cedars-Sinai’s press release highlights key study findings now published in JAMA Network Open on COVID-19 infection awareness rates, indicating… Read More

Deep Neural Networks for Classification of LC-MS Spectral Peaks

Nontargeted LC-MS can assay thousands of chemical entities in a single biospecimen, but in that crush of data, how do you isolate true spectral features from the noise? This paper, contributed to by Sapient’s scientists, describes machine learning-based approaches found to remove up to 90% of false peaks without reducing… Read More