Analysis and risk assessment of risk technical systems to develop of methodology for teaching students using the method event tree (event tree analysis)

The aim of this paper is to develop a methodology for risk analysis, assessment and management using the event tree method. A sample sequence of risk analysis actions is shown with the use the event tree method in determining the probability of realizing a dangerous event including an exemplary event tree pattern according the example under consideration and with the possibility of calculations and for determining the risk at the accepted value of the damage. A methodology for risk analysis is proposed based on the event tree applicable to student training on risk analysis and management.

A new algorithm for DNA motif discovery using multiple sample sequence sets

DNA motif discovery plays an important role in understanding the mechanisms of gene regulation. Most existing motif discovery algorithms can identify motifs in an efficient and effective manner when dealing with small datasets. However, large datasets generated by high-throughput sequencing technologies pose a huge challenge: it is too time-consuming to process the entire dataset, but if only a small sample sequence set is processed, it is difficult to identify infrequent motifs. In this paper, we propose a new DNA motif discovery algorithm: first divide the input dataset into multiple sample sequence sets, then refine initial motifs of each sample sequence set with the expectation maximization method, and finally combine all the results from each sample sequence set. Besides, we design a new initial motif generation method with the utilization of the entire dataset, which helps to identify infrequent motifs. The experimental results on the simulated data show that the proposed algorithm has better time performance for large datasets and better accuracy of identifying infrequent motifs than the compared algorithms. Also, we have verified the validity of the proposed algorithm on the real data.

iAI-DSAE: A Computational Method for Adenosine to Inosine Editing Site Prediction

As an important post-transcriptional modification, adenosine-to-inosine RNA editing generally occurs in both coding and noncoding RNA transcripts in which adenosines are converted to inosines. Accordingly, the diversification of the transcriptome can be resulted in by this modification. It is significant to accurately identify adenosine-to-inosine editing sites for further understanding their biological functions. Currently, the adenosine-to-inosine editing sites would be determined by experimental methods, unfortunately, it may be costly and time consuming. Furthermore, there are only a few existing computational prediction models in this field. Therefore, the work in this study is starting to develop other computational methods to address these problems. Given an uncharacterized RNA sequence that contains many adenosine resides, can we identify which one of them can be converted to inosine, and which one cannot? To deal with this problem, a novel predictor called iAI-DSAE is proposed in the current study. In fact, there are two key issues to address: one is ‘what feature extraction methods should be adopted to formulate the given sample sequence?’ The other is ‘what classification algorithms should be used to construct the classification model?’ For the former, a 540-dimensional feature vector is extracted to formulate the sample sequence by dinucleotide-based auto-cross covariance, pseudo dinucleotide composition, and nucleotide density methods. For the latter, we use the present more popular method i.e. deep spare autoencoder to construct the classification model. Generally, ACC and MCC are considered as the two of the most important performance indicators of a predictor. In this study, in comparison with those of predictor PAI, they are up 2.46% and 4.14%, respectively. The two other indicators, Sn and Sp, rise at certain degree also. This indicates that our predictor can be as an important complementary tool to identify adenosine-toinosine RNA editing sites. For the convenience of most experimental scientists, an easy-to-use web-server for identifying adenosine-to-inosine editing sites has been established at: http://www.jci-bioinfo.cn/iAI-DSAE, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It is important to identify adenosine-to-inosine editing sites in RNA sequences for the intensive study on RNA function and the development of new medicine. In current study, a novel predictor, called iAI-DSAE, was proposed by using three feature extraction methods including dinucleotidebased auto-cross covariance, pseudo dinucleotide composition and nucleotide density. The jackknife test results of the iAI-DSAE predictor based on deep spare auto-encoder model show that our predictor is more stable and reliable. It has not escaped our notice that the methods proposed in the current paper can be used to solve many other problems in genome analysis.

Draft Genome Sequence of the Naturally Competent Bacillus simplex Strain WY10

ABSTRACT We sequenced a naturally competent bacterial isolate, WY10, cultured from a Wyoming soil sample. Sequence analysis revealed that WY10 is a novel strain of Bacillus simplex. To our knowledge, WY10 is the first B. simplex strain to be characterized as naturally competent for DNA uptake by transformation.

ℓ1-Norm support vector machine for ranking with exponentially strongly mixing sequence

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. Ranking from binary comparisons is a ubiquitous problem in modern machine learning applications. In this paper, we consider ℓ1-norm SVM for ranking. As well known, learning with ℓ1-norm restrictions usually leads to sparsity. Moreover, instead of independently draw sample sequence, we are given sample of exponentially strongly mixing sequence. Under some mild conditions, a learning rate is established.

Evaluating Bias of Illumina-Based Bacterial 16S rRNA Gene Profiles

ABSTRACTMassively parallel sequencing of 16S rRNA genes enables the comparison of terrestrial, aquatic, and host-associated microbial communities with sufficient sequencing depth for robust assessments of both alpha and beta diversity. Establishing standardized protocols for the analysis of microbial communities is dependent on increasing the reproducibility of PCR-based molecular surveys by minimizing sources of methodological bias. In this study, we tested the effects of template concentration, pooling of PCR amplicons, and sample preparation/interlane sequencing on the reproducibility associated with paired-end Illumina sequencing of bacterial 16S rRNA genes. Using DNA extracts from soil and fecal samples as templates, we sequenced pooled amplicons and individual reactions for both high (5- to 10-ng) and low (0.1-ng) template concentrations. In addition, all experimental manipulations were repeated on two separate days and sequenced on two different Illumina MiSeq lanes. Although within-sample sequence profiles were highly consistent, template concentration had a significant impact on sample profile variability for most samples. Pooling of multiple PCR amplicons, sample preparation, and interlane variability did not influence sample sequence data significantly. This systematic analysis underlines the importance of optimizing template concentration in order to minimize variability in microbial-community surveys and indicates that the practice of pooling multiple PCR amplicons prior to sequencing contributes proportionally less to reducing bias in 16S rRNA gene surveys with next-generation sequencing.