Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads

Abstract

Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited in their ability to directly compare microbial metabolic function across samples and studies. We introduce Carnelian, an end-to-end pipeline for metabolic functional profiling uniquely suited to finding functional trends across diverse datasets. Carnelian is able to find shared metabolic pathways, concordant functional dysbioses, and distinguish Enzyme Commission (EC) terms missed by existing methodologies. We demonstrate Carnelian's effectiveness on type 2 diabetes, Crohn's disease, Parkinson's disease, and industrialized and non-industrialized gut microbiome cohorts.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

“…HUMAnN 3 functionally profiles genes, pathways, and modules from metagenomes, now using native UniRef90 annotations from ChocoPhlAn species pangenomes. We compared its performance against HUMAnN 2 (Franzosa et al, 2018) , and the recently published Carnelian (Nazeen et al, 2020) when profiling the 30 CAMI and 5 additional synthetic metagenomes introduced above (see Methods and Fig. 1 ).…”

“…The latter is especially supported by the corresponding growth of fragmentary, draft, and finished microbial isolate genomes, and their consistent annotation and clustering into genome groups and pan-genomes (Almeida et al, 2020, 2019; Pasolli et al, 2019). Most such methods focus on addressing a single profiling task within (most often) metagenomes, such as taxonomic profiling (Lu et al, 2017; Milanese et al, 2019; Truong et al, 2015; Wood et al, 2019), strain identification (Luo et al, 2015; Nayfach et al, 2016; Scholz et al, 2016; Truong et al, 2017), or functional profiling (Franzosa et al, 2018; Kaminski et al, 2015; Nayfach et al, 2015; Nazeen et al, 2020). In a few cases, platforms such as the bioBakery (McIver et al, 2018), QIIME 2 (Bolyen et al, 2019), or MEGAN (Mitra et al, 2011) integrate several such methods within an overarching environment.…”

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

“…More recently, advanced tools such as HUMAnN2 [167] allow the inference of the functional and metabolic potential of a microbial metagenome directly from short sequence reads. The recently developed Carnelian [168] tool is recommended to perform comparative functional metagenomics. Further, more flexible tools enabling customizable annotation, such as MetaStorm [169], a web server that supports read or assembly annotation based on a reference dataset uploaded by the users, have been developed.…”

RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing

“…ABUD is a kind of commonly used method to analyze microbial shotgun genome data, which can be applied without an existing reference database [ 8 ]. RAST [ 38 ], Megan4 [ 39 ], MOCat2 [ 40 ], Carnelian [ 41 ] and IMG4 [ 42 ] belong to ABUD method. The basic principal and the main workflows of these tools are similar.…”