Design of a state-machine based genomic simulator and development of a system for prediction of Rheumatoid Arthritis (RA) using signal processing techniques.

Abstract

Rheumatic Arthritis (RA) is a chronic, autoimmune, inflammatory disease involving primarily the peripheral synovial joints. The diagnosis of RA in its pre-clinical phase is of at most importance as it can prevent progressive and irreversible joint damage if treated early. As RA is a genetic disorder, diagnosis through genomic sequence analysis has proven to be an appropriate solution to achieve the above goal [2]. Digital Signal Processing (DSP) applications in bio- informatics has received great attention in recent years, where computationally efficient methods for genome sequence analysis have been developed by utilizing existing signal processing algorithms. In the proposed work, a software module that uses signal processing techniques to predict probability of the future occurrence of RA has been developed. This is done by reviewing medical literature to identify the genes responsible for causing the disease and subsequently obtaining the nucleotide sequences of these genes through GenBank, a standard open-access gene database. The nucleotides are then mapped onto a unit circle in the complex plane so that complimentary base pairs are complex conjugates of each other and the magnitudes of the nucleotides are normalized at unity. Risk gene patterns are then searched in the chromosome sequence under test. Cross-correlation, which is a signal processing algorithm, was used for recognition of presence of risk genes in the chromosome sequence. The usage of cross- correlation not only allowed the identification of mutated sequences but also reduced the time complexity to O[Nlog2(N)].A relative genetic risk score and overall genetic risk score of probability of developing RA was then calculated using statistical methods. In order to test the system, a genome sequence simulator whose underlying architecture is that of a state machine, was created. Using this simulator multiple datasets containing several combinations of risk genes were generated. The system tested using the datasets thus obtained was found to be 95% accurate when the risk magnitudes obtained by the system was compared against the ground truth values given in RAVariome database for the same set of genes chosen. Hence by ensuring early diagnosis, the system will assist doctors to formulate effective treatment plans and thus prevent joint deterioration and permanent functional disability.