Today, we have access to an enormous amount of data - and when it comes to diseases and the processes involving humans, plants, and animals, it is crucial to understand the complex translation process from DNA to RNA to proteins, and be able to effectively and efficiently analyse this data. When we consider this translation process, changes in the DNA code could have different consequences. For instance, sometimes changes in DNA do not lead to alterations in the translation process. Sometimes, there is a beneficial effect, such as developing resistance to a certain type of bacteria, or it has a detrimental impact and can cause several types of diseases. Because of this, it is vital to have a global overview of these three different levels of genomic data in all types of research across various industries.
The Limitations of current DNA, RNA and Protein analysis
Genetic research includes massive amounts of sequences to be analysed and is time consuming and overly fragmented. This causes a delay in discoveries. The fragmentation causes the data analysis to be organised in silos, having a complex series of procedures, hampering the accuracy and the way research could speed up novel discoveries. In addition, the current analysis methodology is based on heuristics, which results in an accumulation of errors during the process of analysis. This lack of accuracy causes errors in data interpretation.
An important challenge in current genetic research is the possibility to analyse DNA, RNA, and proteins altogether, at all levels. Until today, a tool able to analyse all levels, DNA, RNA, and proteins at once, and automatically give us the translations (for instance, searching for DNA and receiving results for all three levels) has not been available. As per current procedures, several separate stages are involved, and the whole process becomes long and unclear. After performing all the separate stages, the data has to be assembled in a single document, making it hard to have a comprehensive overview, causing difficulties in the interpretation of the data gathered.
The implications of new methods of analysis
The potential value of genetic information that can be unlocked by using an approach in which molecular analysis of DNA, RNA and proteins is truly integrated is immense. One tool could then improve the health and the quality of life of many individuals affected by different diseases.
For instance, in unraveling disease mechanisms, it is common practice to analyse whole genomes to discover which genes are involved. Once these genes are identified, their different forms of expression are examined, to discover differences in mechanisms of regulation and its impact on symptomatology. In these research processes, an integrated view from DNA down to proteins, and vice versa, is necessary to discover the impact of changes in each level of the transcription process on development of disease and ideally on prediction of its severity (severely or mildly affected).
Unraveling 'disease' is not only important in human medicine but also in veterinary medicine to protect animal health. An example here is the development of phage therapy, the use of bacteriophage viruses to treat bacterial disease, for tuberculosis in cows. Different virus variants have to be identified and tested for their efficacy.
If we look at agriculture, combating 'disease' is an important topic in which integrated analysis of the DNA, RNA and protein level is necessary. An example here is the use of bacteriophage viruses against bacterial infections of the tomato plant root.
Discovering new associations in the data is currently complex and requires highly skilled individuals. Therefore, it is crucial to step forward and avoid the silos in genetic analysis by providing integrated, comprehensive and relevant results to make sure that R&D departments can maximise their discoveries in the shortest amount of time. Biostrand revolutionises genetic analysis with the Lensai platform that allows us to check the data in a simple, intuitive, and yet highly accurate way by extracting the information of all levels (DNA, RNA and proteins) at once, providing researchers with an integrated view on their data, saving vast amounts of time and reducing costs.
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