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A method for predicting indirect target genes of microRNAs using literature mining

A target gene prediction, target gene technology, applied in biochemical equipment and methods, biological testing, special data processing applications, etc., can solve the problems of false positives, inaccurate differential expression, and true negatives in prediction results.

Inactive Publication Date: 2011-12-07
SHANGHAI CLUSTER BIOTECH
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Problems solved by technology

[0004] However, the current target gene prediction algorithm is not very accurate for the definition of differential expression, so that the prediction results often have false positives and true negatives. For this reason, the present invention uses a method based on literature mining technology to perform microRNA Prediction of indirect target genes, increasing the accuracy of prediction

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  • A method for predicting indirect target genes of microRNAs using literature mining
  • A method for predicting indirect target genes of microRNAs using literature mining
  • A method for predicting indirect target genes of microRNAs using literature mining

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Embodiment Construction

[0014] The method of the present invention will take the prediction of the target gene of mir-122 as an example to introduce the specific implementation method of the present invention.

[0015] 1. First use microT 3.0 (http: / / diana.cslab.ece.ntua.gr / microT / ), miRanda v5 (http: / / microma.sanger.ac.uk / targets / ), TargetScan 5.1 (http: / / www.targetscan.org / ), PicTar vertebrate 2007 (http: / / pictar.mdc-berlin.de / ) and other 4 softwares were used for target gene prediction, and a result set of mir-122 direct target genes was obtained, with a total of 935 genes;

[0016] 2. Predict the expression protein of the above 935 target genes, and obtain the direct protein result set of mir-122.

[0017] 3. Use literature mining technology to build a protein-protein interaction database. The protein-protein interaction data were downloaded from the MIPS database (http: / / mips.helmholtz-muenchen.de / proj / ppi / ) and the pathway data in the kegg database to construct a database of protein-protein i...

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Abstract

The present invention designs a method for using literature mining technology to study the relationship between virus and human protein expression regulation, including the following main steps: step 1, predicting the direct target gene from microRNA; step 2, predicting the direct target protein from the above target gene; Step 3, use literature mining technology to construct a protein-protein interaction database; Step 4, use the constructed protein interaction database to screen out the indirect target protein and indirect target gene of microRNA from the direct target protein; Step 5, through the experiment Validation of indirect target genes of microRNAs. The feature of this method is that an "indirect" target gene model is proposed, and the literature mining technology is introduced to screen out the real microRNA target genes that are difficult to detect by conventional methods.

Description

technical field [0001] The invention belongs to the field of biotechnology, and relates to a method for predicting microRNA target genes by using literature mining technology, which is mainly applicable to the prediction of microRNA indirect target genes. Background technique [0002] MicroRNA (micro ribonucleic acid) is an endogenous non-coding microRNA with a length of 18-25 nucleotide single strands. It mainly inhibits its translation by completely or incompletely pairing with the 3' untranslated region of the target gene, thereby participating in the regulation of life activities such as individual development, cell apoptosis, proliferation and differentiation. Under pathological conditions, microRNAs can affect the occurrence and development of tumors by regulating their target genes and the signaling pathways they participate in, and play a role similar to oncogenes or tumor suppressor genes. Experiments have shown that in many tumors, some types of microRNAs are high...

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Application Information

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IPC IPC(8): C12Q1/68G01N33/53G06F19/24
Inventor 曾华宗
Owner SHANGHAI CLUSTER BIOTECH
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