TIMC provides high throughput next-generation sequencing (NGS) to enable analysis of the entire microbial community within a sample and the option to combine multiple samples in a sequencing run. By leveraging cutting-edge multi-omic technologies and sophisticated bioinformatics, TIMC is able to provide detailed microbiome analyses without sacrificing interpretability. Our abilities empower clinicians and researchers with precise data integration to explore new frontiers for novel discoveries and treatment options. TIMC maintains the highest standards of scientific rigor using Illumina sequencing platforms.
16S rRNA Amplicon Sequencing
16S gene sequencing is a culture-free and cost-effective technique used to genomically identify bacterial strains by examining ribosomal RNA (rRNA). The 16S region is highly conservative, and allows scientists to identify bacteria as far as the genus level. TIMC performs 16S rRNA amplicon sequencing using MiSeq and NextSeq 1000 sequencers.
Shotgun Metagenomics
Shallow shotgun metagenomics can identify microbial taxonomy down to the strain level. Building on this, deep shotgun metagenomics not only identifies microbial taxonomy to the strain level, but also elucidates the microbial functional gene pathways present in the sample. TIMC performs both shallow shotgun metagenomics and deep shotgun metagenomics amplicon sequencing using NextSeq 1000 and NovaSeq X Plus sequencers.
Standard Analysis
Our standard analysis includes:
- Taxonomic relative abundance analysis
- Alpha-diversity analysis
- Beta-diversity analysis
- Differentially abundant microbial feature identification
Any analyses beyond these standard services would be considered custom analyses.
Alpha Diversity
Alpha diversity analysis describes the richness and evenness of a microbial sample within a given community. It counts the number of taxa observed in the sample at a given taxonomic level using indices, such as the Shannon Diversity Index.
Beta Diversity
While alpha diversity measures richness and evenness within a sample, beta diversity describes the amount of differentiation between two samples. The exact interpretation and quantification of beta diversity varies substantially across studies1.
Differential abundant microbial feature identification
Differential abundant microbial feature identification illustrates microbial enrichment or depletion between samples. Common differential abundance (DA) analysis methods are complicated by the complexity of the data and the parameters. The Analysis of Composition of Microbiomes with Bias Correction (ANCOM-BC) model estimates the sampling fraction, the ratio of expected absolute abundance of a taxon within a random sample to its absolute abundance in a unit- volume of the ecosystem, utilizing a linear regression framework from observed data to offset bias. This allows for construction of confidence intervals and enables TIMC to conduct standard statistical tests for DA analysis. ANCOM-BC provides efficient computing capabilities, such as CPU time, compared to other popular DA analyses—meaning TIMC can return your results more quickly2.
REFERENCES
- Andermann T, Antonelli A, Barrett RL, Silvestro D. Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning. Front Plant Sci. 2022 Apr 19;13:839407. doi: 10.3389/fpls.2022.839407. PMID: 35519811; PMCID: PMC9062518.
- Lin, H., Peddada, S.D. Analysis of compositions of microbiomes with bias correction. Nat Commun 11, 3514 (2020). https://doi.org/10.1038/s41467-020-17041-7