Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as microRNAs. Objective: Build genes regulation relationships modulated by causality inference-based microRNA-(transition factor)-(target gene) networks and analysis gene expression data to find gene expression regulators. Methods: Manually curate mouse gene expression regulation relationships from the literature using text mining method, and built microRNA-(transition factor)-(target gene) networks. Identifying gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships have been curated for 4,018 genes and 242 microRNAs. A software called GEREA were developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 was an important network regulator. Wet-lab experiments verified that miR-155 regulates the transition factors Sp1, Fgf2, and Ctla4. An in vitro analysis of target transcription levels in transition factor inhibitor-treated mouse CD4+ T-cells determined the regulatory effects of the two transition factors. Conclusion: The causality inference-based microRNA-(transition factor)-(target gene) networks is a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at http://www.thua45.cn/gerea.

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Understanding how genes are expressed and regulated in di®erent biological processes are fundamental and challenging issues. Considerable progress has been made in studying the relationship between the expression and regulation of human genes. However, it is di±cult to use these resources productively to analyze gene expression data. GEREDB (www.thua45.cn/geredb) has been developed to facilitate analyses that will provide insights into the regulation of genes that govern speci¯c biological responses. GEREDB is a publicly available, manually curated biological database that stores the data regarding relationships between expression and regulation of human genes. To date, more than 39,000 Links have been contextually annotated by reviewing more than 53,000 abstracts. GEREDB can be searched using the o±cial NCBI gene symbol as a query, and it can be downloaded along with the GEREA software package. GEREDB has the ability to analyze user-supplied gene expression data in a causal analysis oriented manner using the GEREA bioinformatics tool.

Read Full Text Journal of Bioinformatics and Computational Biology Paper

MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction speci¯city and sensitivity of the existing algorithms still need to be improved to generate useful hypotheses for subsequent experimental testing. A new microRNA binding-site representation method was developed, which uses four symbols \j", \:", \", and \ ^" (indicating paired, unpaired, insertion, and bulge, respectively) to represent the status of each nucleotide base pair in the microRNA binding site. New features were established with the information of every two adjacent symbols. There are 12 possible combinations and the frequency of each de¯nes a set of novel and useful features. A comprehensive training dataset is constructed for mammalian microRNAs with positive targets obtained from the microRNA target depository in the miRTarbase, while negative targets were derived from pseudo-microRNA bindings. An SVM model was established using the training dataset and a new software called Min3 was developed. Performance of Min3 was assessed with intensively studied examples of miR-155 and miR-92a. Prediction results showed that Min3 can discover 47% of experimental conformed targets on average. The overlapping is above 20% on average when compared with TargetScan and miRanda. Annotations of the public microRNA datasets showed that there is a negative e®ect (up-regulation) of the Min3 targets for the knock out/down of miR-155 and miR-92a. Six top ranked targets were selected for validation by wetlab experiments, and ¯ve of them showed a regulation e®ect. The Min3 can be a good alternative to current microRNA target discovery software. This tool is available at https://sourceforge. net/projects/mirt3.

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Gene-expression data obtained from the biological experiments always have thousands of dimensions, which can be very confusing and perplexing to biologists when viewed as a whole. Clustering analysis is an explorative data-mining technique for statistical data analysis that is widely used in gene-expression data analysis. Practical approaches employed for solving the clustering problem use iterative procedures such as K-means, which typically converge to one of many local minima. Here, we propose a simulated annealing approximation algorithm that is optimised using random walks to solve the K-means clustering problem. The algorithm is verified with synthetic and real-world data sets and compared with other well-known K-means variants. The new algorithm is less sensitive to initial cluster centres, and the primary strength of our algorithm is its ability to produce high-quality clustering results for thousands of high-dimensional data. However, the algorithm is computationally intensive.

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Background: MicroRNAs (miRNAs) are recognized as one of the most important families of noncoding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them. Results: We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current premiRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions. Conclusion: The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at http://www.bioinformatics.org/mirfinder/.

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Yes. We don't expect you to have such knowledge. However, for any new materials, you must be able to learn them by yourselves. BTW, we run many experiments, so programming ability is also very important.

Yes. Our members might not be familiar with such systems in the beginning, but they quickly catch up (by themselves).

Creativity to think about different directions and courage to try things.

Definitely not. We judge you by your research results but not by how hard you have worked.

Those who are enthusiastic about doing research. In general, if you would like to be a great researcher in the future, then this group is more suitable for you.

No. They are all treated as PhD students.

Yes and no. Good research problems come from active discussion. As I am more experienced than you, through discussion I may be able to identify some good topics for you. So if no discussion, no good topics.

Possible but a bit difficult. So usually we encourage undergraduate students to join the group as early as possible.

No. You need to talk to me first. Otherwise, we assume that you are leaving the group after your undergraduate study.

No. The authorship of a paper is related to the contribution rather than the group you are associated with. Here are a few situations that you may write a paper without my name on it. 1) Without my guidence, you finish some excellent work and write a paper. Then of course I shouldn't be a co-author. I always hope that this happens sometime, but so far our students need some my help on both the research work as well as writing. 2) You work with another group and write a paper with them. If I am not involved in this work, then of course I shouldn't be a co-author.

You are very welcome.

Yes at any time. We hope you feel like at your home.

The same as the previous question. But you may have found her sitting on my lap.