Integration Of Sequence Information Research Articles

In Silico Detection and FISH Analysis to Determine Location of miRNAs in Solea Senegalensis Chromosomes Using BACs

MicroRNAs (miRNAs) are small non-coding RNAs that play a very important role in gene expression through regulation of mRNA cleavage and translation. The Senegalese sole, Solea senegalensis (Kaup 1858), is a flatfish species that shows great potential for marine aquaculture. Nevertheless, the existence of sexual dysfunction of males reared in captivity, high larval mortality and diseases have hampered its production. The integration of sequence information with data on chromosomal physical location is useful for understanding genetic processes and is essential for comparative analysis, QTL mapping and positional cloning of genes. In this study we employed an in silico approach to identify potential miRNAs in a S. senegalensis BAC library. Mature sequences were obtained from miRBase and other sources and a BLAST analysis was carried out against sequences from a S. senegalensis BAC library. Sequences showing similarity were extracted and subjected to an analysis of their secondary structure. This information was combined with BAC-FISH data to assign miRNAs to chromosomes. In addition, a phylogenetic analysis of some miRNAs families was carried out and target genes were predicted against a reference transcriptome. After secondary structure analysis, 45 groups of sequences showed similarity to mature miRNAs. Of these, 19 were assigned to Senegalese Sole chromosomes using BAC-FISH data and thirty-four unique mature sequences targeted 12387 sequences from a S. senegalensis reference transcriptome. Phylogenetic trees showed different levels of support for clustering among types of miRNAs and S. senegalensis sequences usually were closed to other Pleuronectiformes. These results will be helpful to understand how miRNAs affect gene expression in this species and biological processes of interest for its aquaculture. Nevertheless, experimental validation through, for example, RT-PCR, northern blotting or microarrays is required.

Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods.

The identification of plant-pathogen protein-protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant-pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain-domain interaction and domain-motif interaction in predicting known plant-pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant-pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant-pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant-pathogen interactions.

A Meta-Path-Based Prediction Method for Human miRNA-Target Association.

MicroRNAs (miRNAs) are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.

Cancer survival classification using integrated data sets and intermediate information.

Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles. Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses.

The Cell Biology of Genomes: Bringing the Double Helix to Life

The recent ability to routinely probe genome function at a global scale has revolutionized our view of genomes. One of the most important realizations from these approaches is that the functional output of genomes is affected by the nuclear environment in which they exist. Integration of sequence information with molecular and cellular features of the genome promises a fuller understanding of genome function.