Cloud service platform for microbial analysis
1928 delivers a cloud service that analyze resistance markers in bacteria from whole genome sequences. The software looks for resistance markers within the bacteria and do relational analysis between samples.
From raw reads to result in minutes
You start by uploading the sample to our cloud platform and the analysis starts automatically. Your NGS raw data is transcribed to results, providing an understanding of both the unique in the sample and overall picture in between samples.
There are already several hospitals in Europe and the USA that are getting value out of the 1928 platform. So if you are starting to do WGS, we will be happy to provide the analysis for you.Read full article
|Species||cgMLST||MLST||Other typing schemes||Markers of special interest||Antibiotic classes||Usage|
|S. aureus||●||●||SCCmec, spa sequences||TSST-1, PVL, ETs||13||Research use only (CE-marked product available on request)|
|E. coli||●||●||phylogroup||ESBL, CRE||25+||Research use only|
|K. pneumoniae||●||●||ESBL, CRE||25+||Research use only|
|E. faecium||●||●||van||25+||Research use only|
|C. difficile||●||●||Multiple||25+||Research use only|
|N. gonorrhoeae||●||●||Multiple||25+||Research use only|
|S. enterica||●||●||Multiple||25+||Research use only|
|A. baumannii||●||●||Multiple||25+||Research use only|
|P. aeruginosa||●||●||Multiple||25+||Research use only|
1928 enables comprehensive and in-depth outbreak tracing from your sequencing data providing strong infection control which ultimately prevents outbreaks. By clustering on cgMLST differences, high resolution comparisons are made of different isolates. The results are visualised in an easy-to-interpret phylogenetic tree.
The source of hospital-acquired infections is really us, the people
The patients, the hospital staff, we all carry bacteria. And people need to know about the dangers and how to behave to prevent spreading infections. To be proactive in your infection control, irrespective of what healthcare system you’re working in, you have to make sure that the whole chain of events is carefully planned and organized.
Everyone can support the fight against antibiotic resistance
It’s one of those situations where we all have responsibility. It’s a bit like the climate catastrophe, everyone has to do their part and that part may seem small but unless we put all those small parts together, we will not succeed.Read full article
Methicillin-resistant Staphylococcus aureus (MRSA) is the most common cause of hospital-acquired infections (HAIs) causing thousands of deaths in hospitals worldwide. 1928DSA recieved its -marking in 2018, it's a high quality analysis product for Staphylococcus aureus WGS samples, that supplies a resistance profile for each uploaded sample by matching it to our manually curated database. The result has a very high accuracy in predicting resistance (see table below) and can be used to guide treatment.
|Antibiotic||Major error rate||Very major error rate|
|Isoxazolyl Penicillins (MRSA)||0,3%||0,0%|
The 1928 platform contains well-validated and specially adapted algorithms that processes the raw data file from the sequencing machine. By optimising data handling processes in the cloud, the 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 platform in an informative format and can also be exported.
1928 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.