Shulou(Shulou.com)06/01 Report--

Today, I will talk to you about the principle of lncRNA function prediction, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

When we do genetic research, we need to predict in advance what the function of the gene is. Coding genes can predict what their main functions are through the protein structure of their genes. But for the function of long-chain non-coding RNA (lncRNA), because it does not encode a protein. In this way, the prediction of the function is not applicable. So how do you predict the function of an lncRNA?

Principle of lncRNA function prediction

With the emergence of the second generation sequencing and gene chip, we can get all the gene expression of a sample at one time. Although we have no way to know the functional prediction of lncRNA. But we can use the sequencing data to statistically analyze the coding genes associated with this lncRNA. Then these related coding genes were enriched, analyzed and predicted. You can indirectly understand what the function of lncRNA is.

Co-LncRNA

Co-LncRNA (http://bio-bigdata.hrbmu.edu.cn/Co-LncRNA/) is such a database based on the above ideas. This database collects a lot of sequencing data on related diseases. Through the target database, we can find out what the interacting genes of the target lncRNA are. In turn, major regulatory pathways can be obtained.

This database includes several functions:

CEGs: search for possible interacting genes of a gene

Through this function, we can find out which genes may interact with each other. All we need to do is select the relevant data. Then enter the target gene.

When the input is complete, we can get the genes that interact with this gene.

CEGsFuncs: enrichment analysis of coexpressed genes

? The function is to find co-expression genes, and this function is to enrich and analyze related co-expression genes. Here we can enter a lncRNA or several lncRNA. Then the enrichment analysis results of related genes can be obtained.

Another function: merge CEGsFuncs is a function that can fuse multiple data sets for co-expression, analysis and enrichment. But only three data sets can be selected for analysis.

CEGsNet: lncRNA Co-expression Analysis Network

For the target lncRNA, we can only see the relevant tables before. This can be presented in a web format. It's not pretty, though. But you can also take a look.

Database summary:

This database does not seem to provide the function of downloading results when searching. But in the end, it provides the ability to download all the results. If you want to predict the results, you can download all the results, and you can filter them according to the criteria. In this way, the co-expressed genes can be obtained. If you still want to enrich the results of the analysis, just put the genes in another database. For example, [database introduction] g:profiler multi-ID enrichment analysis can be used for enrichment analysis.

After reading the above, do you have any further understanding of the principle of lncRNA function prediction? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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