Computational Detection of piRNA in Human Using Support Vector Machine

Background: Piwi-interacting RNAs [piRNAs] are small non-coding RNAs [ncRNAs], with a length of about 24-32 nucleotides, which have been discovered recently. These ncRNAs play an important role in germline development, transposon silencing, epigenetic regulation, protecting the genome from invasive transposable elements, and the pathophysiology of diseases such as cancer. piRNA identification is challenging due to the lack of conserved piRNA sequences and structural elements

Methods: To detect piRNAs, an appropriate feature set, including 8 diverse feature groups to encode each RNA was applied. In addition, a Support Vector Machine [SVM] classifier was used with optimized parameters for RNA classification. According to the obtained results, the classification performance using the optimized feature subsets was much higher than the one in previously published studies

Results: Our results revealed 98% accuracy, Mathew' correlation coefficient of 98% and 99% specificity in discriminating piRNAs from the other RNAs. Also, the obtained results show that the proposed method outperforms its competitors

Conclusion: In this paper, a prediction method was proposed to identify piRNA in human. Also, 48 heterogeneous features [sequence and structural features] were used to encode RNAs. To assess the performance of the method, a benchmark dataset containing 515 piRNAs and 1206 types of other RNAs was constructed. Our method reached the accuracy of 99% on the benchmark dataset. Also, our analysis revealed that the structural features are the most contributing features in piRNA prediction

Cisplatin resistant patterns in ovarian cell line using FTIR and principle component analysis

Cisplatin is a common chemotherapeutic agent that used for treatment of many solid cancers. Rapid identification of chemotherapy resistance is very important and may lead to effective treatment plan. Spectroscopy techniques, such as infrared spectroscopy, which are sensitive to biochemical composition of samples, have shown potentials to discriminate tissues. Developing in Fourier transform infrared [FTIR] as a diagnostic tool support conventional technique in investigating cell phenotype. By this goal three different cell lines, two cisplatin resistant OV2008-DDP [C13] and A2780-CP ovarian cell lines and one cisplatin sensitive A2780 cell line were investigated by FTIR spectroscopy. Data were subjected to principle component analysis [PCA] to obtain FTIR pattern for cisplatin resistance. Using FTIR spectroscopy on these cells in the range of 400-4000 cm[-1] was shown dramatic change in cells. Results shows that Cisplatin resistance pattern is characterized in spectrum with the alteration of conformation in secondary structure of proteins and a shift toward the high wave numbers of CH2 stretching vibration. The FTIR data set between 1000 and 3000 cm[-1] could be consumed as biochemical typicality spectra among resistant and sensitive cell lines while correctly classified by PCA model. Our work supports the promise of PCA analysis of FTIR data as a powerful combined approach for the development of automated methods to recognize resistant to cisplatin in experimental cell lines. One of the advantages of this tool is to investigate the resistant percent of cancer cells .Such technique may bring new tool in cancer diagnosis and stage definition in cancerous tissues

Patterns prediction of chemotherapy sensitivity in cancer cell lines using FTIR spectrum, neural network and principal components analysis

Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a sensitive human ovarian cell line, A2780, and its cisplatin-resistant derivative, A2780-cp. In this study FTIR method have been evaluated via the use of principal components analysis [PCA], ANN [artificial neuronal network] and LDA [linear discriminate analysis]. FTIR spectroscopy on these cells in the range of 400-4000 cm[-1] showed alteration in the secondary structure of proteins and a CH stretching vibration. We have found that the ANN models correctly classified more than 95% of the cell lines, while the LDA models with the same data sets could classify 85% of cases. In the process of different ranges of spectra, the best classification of data set in the range of 1000-2000 cm[-1] was done using ANN model, while the data set between 2500-3000 cm[-1] was more correctly classified with the LDA model. PCA of the spectral data also provide a good separation for representing the variety of cell line spectra. Our work supports the promise of ANN analysis of FTIR spectrum as a supervised powerful approach and PCA as unsupervised modeling for the development of automated methods to determine the resistant phenotype of cancer classification

Proteomics databases and websites

Information avalanche [overload or expansion] in various scientific fields is a novel issue turned out by a number of factors considered necessary to facilitate their record and registration. Though, the biological science and its diverse fields like proteomics are not immune of this event and even may be as the event's herald. On the other hand, time as the most valued anxiety of human has encountered a huge mass of information. Therefore, in order to maintain access and ease the understanding of information in several fields some emprises have been prepared. Bioinformatics is an upshot of this anxiety and emprise. Interestingly, proteomics through studying proteins collection in alive things has covered a great portion of bioinformatics. Consequently, a noteworthy outlook on proteomics related databases [DBs] and websites not only can help investigators to face the upcoming archive of databases but also estimate the volume of the needed facilitates. Furthermore, enrichment of the DBs or related websites must be the priority of researchers. Herein, by covering the major proteomics related databases and websites, we have presented a comprehensive classification to simplify and clarify their understanding and applications