Clinical Report Process
Last updated
Last updated
EzBioCloud’s clinical metagenomics service goes beyond quality control to provide healthcare professionals with a comprehensive analysis of microorganisms present in a patient’s sample. After performing the stringent QC process, we utilize a proprietary algorithm and state-of-the-art bioinformatics techniques to analyze the remaining microbial sequences. Our in-depth microorganisms report includes detection and identification of bacteria, viruses, and fungi, as well as insights into antibiotic resistance and virulence genes.
Our proprietary algorithm aligns the quality-processed sequencing reads to an extensive database of reference genomes for bacteria, viruses, and fungi. This alignment process allows for the accurate detection and identification of microorganisms present in the sample. By leveraging our cutting-edge algorithm, we ensure that healthcare professionals receive precise and reliable information about the microbial composition of the sample.
For bacterial identification, we perform multi-locus sequence typing (MLST) reconstruction, which enables us to determine the specific strains and lineages of detected bacteria. Additionally, we map the sequencing reads to known antibiotic resistance (AMR) and virulence gene databases. This information provides crucial insights into the potential antibiotic resistance profiles and virulence factors of the detected bacterial species, empowering healthcare professionals to make informed decisions about treatment strategies.
Our analysis pipeline also includes a specialized alignment process for detecting and identifying viral sequences. By aligning the sequencing reads to a comprehensive database of viral reference genomes, we can pinpoint the presence of viral pathogens in the sample, providing valuable information about possible viral infections.
Similarly, we employ a dedicated alignment process for detecting and identifying fungal sequences. Aligning the sequencing reads to a wide-ranging database of fungal reference genomes allows us to accurately detect and classify fungal species in the sample. This information can be vital for diagnosing and treating fungal infections.
After completing the analysis, we compile our findings into a detailed microorganisms report. This report provides a wealth of information, including the top three bacteria, viruses, and fungi detected in the sample, as well as comprehensive lists of all detected microorganisms. The report also includes information on antibiotic resistance genes associated with the top three bacteria, a full list of detected antibiotic resistance genes, and data on virulence factor genes associated with the top three bacteria.
