Real-age predicted from transcriptome information: A new computational strategy that makes feel us inspired!
Among this week’s readings, a prestigious study has caught our attention. Once again, the importance of a strong computational strategy for data analysis in genomics, transcriptomics, proteomics, and metabolomics has been highlighted.
It seems that gene expression data analysis takes a huge step forward. In fact, extraordinary news comes from France, where researchers at Claude Bernard Lyon University have invented a method for studying the transcriptome more efficiently.
The new method is the solution to a very common problem in the analysis of gene expression, the existence of uncontrolled and unknown sources of variance that can mask or confound the effects of variables of interest.
The new computational strategy, called RAPToR and recently presented in the journal Nature Methods by Bulteau and Francesconi, has been successfully tested on models’ gene expression profiles and it works on whole organisms, dissected tissues, and single-cell samples.
The great innovation of RAPToR (real-age prediction from transcriptome staging on reference) consists of being able to study a transcriptome variant exhaustively, excluding factors related to the stage of development of the organism which could confuse or even mask the result.
RAPToR can determine the physiological age of a sample based on the analysis of its transcriptome and eliminate this factor to study the variable of interest properly and without disturbance. It will be especially useful in large-scale single-organism profiling because it eliminates the need for staging before profiling.
Furthermore, even though RAPToR has been programmed to be used for models (especially for fast life cycle species), such as fruit mosquitoes, zebrafish, and mice, the method can work with other species.
In fact, RAPToR performs well for non-model organisms. In close species, when data are available as a reference and even in distant species for stages with conserved developmental dynamics.
The results of this rigorous study are both fascinating and inspiring to us …
And you? What do you think about this amazing innovation?
DATAOMICS customers know that gene expression analysis is becoming increasingly affordable and are turning to molecular biology techniques for in-depth studies of their products.
At DATAOMICS we know that the barrier lies in transforming the enormous amount of data that the techniques provide into helpful knowledge for decision-making in companies.
Therefore, our clients turn to us, because they know that we can integrate all types of data to identify unique patterns and offer a holistic interpretation.
AI methods such as Machine Learning in DATAOMICS make predictive studies, very useful in the identification of biomarkers.
If you are interested in genomics, transcriptomics, proteomics, metagenomics, and metabolomics, DATAOMICS can guide you to maximise your experimental designs.
We will help you define the appropriate sampling, techniques and transform the results into useful information for decision-making in your organisation.