Genome decoding has gained significance in the recent times. It helps in understanding the human behaviour in terms of health. The biologists have come up with a the TeraStructure algorithm.
The TeraStructure algorithm can analyse genome sets much larger than current systems. It can efficiently handle, including those as big as 100,000 or 1 million genomes.
It was in 2003 that first complete mapping of the genome was done. Since then the biologists and scientists are putting efforts to come up with a system that can fasten the process and also make it efficient. They have now sequenced genomes of more than a million people. The biologists and scientists believe that it could rise to nearly 2 billion by 2025.
Machine learning helps in analysing the genome sequencing that has been completed till now. With the discovery of the Tera Structure algorithm, large genome data can be analysed quickly thereby paving way for personalised health care.
The algorithm currently being used is called Structure algorithm, invented in 2000. It examines each variant in each genome in a data set before updating its model to characterising ancestral populations. Thereby it studies how they affect an individual’s own genome. After which it moves on to the next genome.
The newly developed Tera Structure algorithm looks at one variant in all the genomes in a data set before it updates its model to produce a working estimate of population structure. Such a functioning system allows it to create ancestry models more accurately and quickly — two to three times faster, in fact, on a simulated data set of 10,000 genomes. It can even analyse sets as large as 100,000 or 1 million genomes.
Each mapped genome is several billion characters long and given that we could have as many as two billion mapped within the next decade. Machine learning in this context proves helpful and also lead to personalised health care.