In recent times businesses have succeed or failed based on the power of their algorithms. The field of Genetics’ has also harnessed the power of the algorithms. Algorithms are being used to decode, sequence and compare various genomes. Genome searching has become a staple research practise and it is even starting to emerge into more clinical areas. However its major draw back is that it takes a long time, around 2 weeks, to carry out.
Scientists at MIT and Harvard universities recently have developed a new algorithm. This will significantly reduced the time required to search through a genome. This new algorithm improves efficiency in two key areas:
First a major issue when sequencing a genome is that you need to store the masses of data that is produced. There is so much data that you need to compress it. The draw back occurs when you return to look at this data you have to decompress it and this takes a lot of time. The new system works by using compressed data, therefore removing the laborious decompressing step and consequently saving time while maintaining the accuracy of analysis.
The second step utilises the extensive genetic overlap between closely related species. The team developed a system to mathematically represent different species. This mathematical modelling allows researchers to only store overlapping genomic data once. Therefore searches can now focus on the differences between species rather than wasting time on the areas of similarities.
Currently the most common genome-searching tool is BLAST, the basic local alignment search tool, this finds regions of local similarities between sequences. The team compared their algorithm to BLAST, in the analysis of 36 yeast genomes. The results showed that the new algorithm was twice as fast in comparing 10 of the genomes and 4 times as fast in comparing all 36.
The most interesting development, with this algorithm, is that it utilises the work you have already done. It always takes a set amount of time to analyse a genome. Due to certain properties of the genome there is overlap between samples, therefore this property can be used to speed up sequencing time.
This new technique has many potential applications such as: identification of new microbes, determining the cause of infection and even study physical evidence from a crime scene. What do you think? Are scientific algorithms as important as people make out?