The 1928 Platform
1928 has developed a platform to support microbiologists and infection control professionals who want to take full advantage of sequencing for transmission analysis of bacteria and outbreak management.
1928 supports the most critical hospital associated infections, and enables transmission analysis of any bacteria. From pathogen-based pilot to a complete WGS workflow for all bacteria.
A WGS infection control workflow with 1928
WGS is the gold standard (https://www.cdc.gov/pulsenet/pathogens/wgs.html) for determining relatedness between two samples. Together with the discriminatory power of WGS and the context from metadata, the 1928 platform adds an increased resolution and understanding of the transmission pathways in the hospital.
Sample Source
- Environmental
- Screening
- Clinical
Culture
- Sample isolate from any bacteria
Sequence
- Independent of sequencing platform
1928
- Confirm transmission
- Determine severity
- Contextualize transmissions by adding metadata
- Report and communicate
- Build your own library of sequence references
All species bioinformatic analysis
service in the cloud for bacterial DNA sequences
Analysis includes:
- Quality control including sample quality
- Automatic species identification
- cgMLST for the most common HAI pathogens
- SNP for all species
- Resistance and virulence markers
- Epidemiological metadata
Reliability & Validity
All analysis pipelines are validated and benchmarked against public reference databases and in cooperation with regional and international collaborators.
Security & Privacy
ISO 27001 certification and compliance with GDPR and HIPAA ensure data and information security.
Best-in-class
1928’s analysis of WGS provides the highest discriminatory power for typing of pathogens.
Predictable Costs
Downstream analysis cost is fixed and easily predicted = improved control of cost of operations.
The 1928 Team is standing by
for the Bioinformatics Analysis
Bioinformatic and technical support include:
- Active monitoring by 1928 team to ensure a complete relevant analysis
- Hospital organization configuration for access to user accounts, units and sharing groups
- General training in the service and analysis for all users in the platform
- Assistance with technical evaluation of results
Our CTO Fredrik Dyrkell
about the 1928 platform
Cloud service platform for microbial analysis
1928 is a cloud-based service that analyzes resistance markers in bacteria from whole genome sequences (WGS). Our technology searches for resistance markers within the bacteria and performs relational analysis between samples.
From raw reads to result in minutes
You start by uploading the sample to our cloud-based platform and the analysis starts automatically. Your NGS raw data is transcribed to results, providing an understanding of both what is unique in the sample and the overall picture in between samples.
Start today
Several hospitals in Europe and the USA are already now getting value out of the 1928 service. So, if you are starting to do WGS, we will be happy to provide the analysis for you.
Methodology
Antibiotic resistance profiling and
virulence factor detection
The 1928 service contains well-validated and specially adapted algorithms that process the raw data file from the sequencing machine. By optimising data handling processes in the cloud, our calculations are always efficient and fast due to optimised workflows and use of distributed systems. The processed data is matched to our databases of genetic markers (genes and mutations) coding for antibiotic resistance or virulence factors. The database entries are collected from peer reviewed scientific journals and comprise clinically relevant markers that are carefully selected and manually curated. The result is delivered on the 1928 platform in an informative format and can also be exported.
Epidemiologic typing
The 1928 platform uses core genome multilocus sequence typing (cgMLST) for outbreak tracing. This method is robust and enables strong comparability between sample sets. We generate our own cgMLST schemes which consists of conserved genes that can be used to generate a "bacterial fingerprint". By visualising the number of identical genes found in phylogenetic trees, large sample sets can be compared and groups of closely related samples can be identified as outbreaks. The method also allows for new samples to be compared to historical data previously uploaded.