Drop us a line
If you are interested in the development of a custom solution — send us the message and we'll schedule a talk about it.
Azati Software
80-Fold Software Performance Improvement
Identifying and fixing the bottleneck for reducing the client’s program execution time. With this improvement, the client achieved a 1,000x reduction of an embedded algorithm execution time and 80x reduction for the whole client’s software.
Customer
On the basis of genetic engineering a whole branch of the pharmaceutical industry, called the “DNA industry”, has emerged and it is one of the modern branches of biotechnology. More than a quarter of all medicines currently used in the world contain ingredients from plants. Genetically modified plants are a cheap and safe source for obtaining fully functional medicinal proteins (antibodies, vaccines, enzymes, etc.) for both humans and animals.
A biotechnology customer company offers synthetic antibody products and services such as research reagents, diagnostic and biomarker discovery tools for use in drug discovery and targeted delivery for therapeutics, and bioindustrial applications.
Objective
Bioinformatics specialists are constantly dealing with huge amounts of data (i.g. the results of the genome sequencing just for one person occupy about 100 gigabytes). Therefore, the processing of such massive data requires Data Science approaches and tools.
That is why the customer turned to the Azati team with the main task of performance enhancement.
Challenge
In the process of DNA sequencing, the client receives lots of data. Previously, it took 48 hours for the client’s software to process the data. The company turned to Azati with a request for increasing their software performance within a short period.
Solution
Client’s software processes data in multiple steps; on one of them it uses an outside package FASTAptamer. FASTAptamer is a bioinformatic toolkit for high-throughput sequence analysis of combinatorial selections.
It was revealed that one of the FASTAptamer’s programs — clusterization — took most of the time of the whole software performance. The great deal of time of the clusterization program, in turn, was devoted to calculating edit time (the level of similarity between the biological sequences). This calculation is based on the Levenshtein algorithm.
Although the algorithm itself is appropriate, its implementation in Perl was rather poor and took a lot of time to process the submitted sequence data. Our team decided to rewrite the code into C++ language, as the difference in programming paradigms and execution model affected the time of execution.
