Structural Repetitive subSequences are most important portion of biological Sequences, which play crucial roles on corresponding sequence’s fold and functionality.Biggest class of the Repetitive subSequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researchers have been performed to criticality determine the structure and function of Repetitive subSequences. The sequencing noises and the Sequences’ substitutions probability are obstacles of these researches. Some statistical and approximation algorithms have introduced to tackle these obstacles. By introducing conspicuous statistical machine learning methods upon Support Vector Machines, machine learning approaches act as potent methods to solve the pattern-finding problem. Support vector machines methods are time efficient approaches, which based on their parameters can be precise and accurate. In this Review, mathematical definition of structural Repetitive subSequences are introduced, thereafter proposed algorithm to tackle simple pattern finding problem, which can be applicable on structural patterns are reviewed. Theoretical aspects of Support Vector Machines on computational biology platform are considered. Finally, novel evolutionary Fuzzy SVM will be introduced, which is applicable on wide range of bioinformatics problems especially the problem of structural Repetitive subSequences.

Background and Purpose: Candida albicans is the most prevalent Candida species isolated from animals. Candidiasis can be systemic in animals or may affect a single organ, such as the mouth, urinary tract, and skin. The aim of the present study was to determine the genetic diversity of C. albicans isolated from different animals and investigate the presence of a relationship between host specificity and genetic typing of C. albicans. Materials and Methods: For the purpose of the study, DNA extraction was performed on 27 clinical isolates of C. albicans obtained from animals. Subsequently, they were subjected to 25S ribosomal DNA amplification and ALT repeats in Repetitive Sequences (RPSs). The minimum inhibitory concentrations of fluconazole, ketoconazole, clotrimazole, nystatin, amphotericin B, and caspofungin were determined using the microdilution method based on the Clinical and Laboratory Standards Institute M27-S4 standard. Results: Out of 27 C. albicans strains, 11, 6, 5, and 5 cases were recognized as genotypes A (40. 8%), E (22. 2%), B (18. 5%), and C (18. 5%), respectively, through amplification using AS-I, which revealed 17 different types of C. albicans. By combining the two typing methods, 27 C. albicans strains were finally divided into 22 genotypes. Conclusion: Different genotypes showed genetic diversity among the C. albicans strains isolated from animal sources. The results revealed no special genotype relationship according to the host, anatomical source of isolation, and antifungal susceptibility.

Objective About half of the human genome is covered by Repetitive Sequences. These Sequences have a large share in the other mammalian genomes, therefore studying this part of the genome can provide researchers valuable information on evolution. The aim of this study was to sequencing and assembly the whole genome of Iranian Bactrian camels to identify transposable elements and their distribution in the genome of this species. In addition, the results of Iranian Bactrian camels were compared with non-Iranian Bactrian camels and dromedary camels. Materials and methods In this study, the whole genome of six Iranian Bactrian camels was sequenced to transposable elements identification. Iranian Bactrian camel whole genome sequenced using Illumina HiSeq 2000 system in paired-end. FastQC and Trimmomatic software were used to quality control and quality filtering of raw sequencing reads, respectively. CLC Genomics Workbench (CLC Bio, Aarhus, Denmark) was used to de novo assembly of trimmed reads. Also, we used the RepeatMasker program to search for transposable elements using a homology-based method. Results Results of the assembling of sequenced genomes showed that the genome size in these samples ranged from 1. 9 to 1. 97 Gb. In the present study, the percentage of transposable elements for six Iranian Bactrian camels was 29. 89% on average of the whole genome. The percentage of LINE Sequences for the Iranian Bactrian camel was 17. 58% on average. So, these Sequences were considered as the largest group of transposable elements in the Bactrian camel in this study. SINE elements showed a lower number in comparison with LINEs. So that, only 3. 45% of the total Bactria camel genome length was dedicated to the SINEs. In accordance with the results of Iranian dromedary camels, no Alu element was identified in the genome of Iranian Bactrian camels. Conclusion Shortage of genomic and biological information about camels is one of the inhibiting factors in advancing the breeding goals and programs. Although this study is not enough alone, it can be a step towards starting the production of genomic data for camels. Continuing this kind of study and integrating biological and genomic information will provide the ground for the start of modern breeding in Iranian camels.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM network.