Earticle

Sequence similarity in biological databases is used to characterize a newly discovered protein and confirming the existence of its homologs. This is often computationally very expensive. We have implemented a new algorithm that performs sequence similarity search using a pre-search phase. The proposed algorithm works in three phases. As a pre-preparation for Pre-Search, we locate a sequence, similar to the query sequence to extract all common words between the former and the latter. In the second phase, the pre-search phase, we locate all sequenes containing any of the randomly chosen common words. The list is further scanned in the third phase and the results obtained from the second phase are refined using Similarity Search (SS) algorithm, described in the paper. We have preprocessed the Uniref100.FASTA protein database containing 9,757,328 records downloaded from uniprot.org, to suit our application of sequence similarity search. The algorithm is simple and can be applied in various perspectives. These include searching in DNA and protein sequence databases, motif finding, and gene identification search. Pre-Search reduces the search space using much faster simpler algorithm. In large database search, its effect could be phenomenal.

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.