RNA-binding proteins (RBPs) participate in all stages of RNA life cycle from transcription, splicing, to translation. Under the ENCODE project, a large number of RBPs were knocked down in human cancer cell lines, offering an excellent opportunity to infer targets of RBPs. Taking both RBP binding sites and RNA-seq profiles of RBP knockdown samples as input, we present a pipeline to identify causal RBP RNA interactions. The pipeline employs a recent functional chi-square test (FunChisq) that deciphers directional association, and utilizes a novel functional index that measures the effect size of functional dependency. We examined ∼45 million RBP RNA pairs in leukemia (K562) and liver cancer (HepG2) cell lines for functional patterns as causal interaction candidates. Here, we report a total of 936,707 RBP RNA pairs in the two cell lines that show statistically significant linear or nonlinear functional patterns. About 31% of these pairs have supportive biological evidence from other sources, suggesting the effectiveness of the pipeline. The interactions constitute RBP specific regulatory networks that may potentially represent core mechanisms in the two cancers. The pipeline is implemented through an R interface with pre-computed results and data libraries for users to query specific networks and visualize RBP RNA interactions. Such networks serve as a useful resource for studying RNA dysregulation in cancer.
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A fast exact functional test for directional association and cancer biology applications
Directional association measured by functional dependency can answer important questions on relationships between variables, for example, in discovery of molecular interactions in biological systems. However, when one has no prior information about the functional form of a directional association, there is not a widely established statistical procedure to detect such an association. To address this issue, here we introduce an exact functional test for directional association by examining the strength of functional dependency. It is effective in promoting functional patterns by reducing statistical power on dependent non-functional patterns. We designed an algorithm to carry out the test using a fast branch-and-bound strategy, which achieved a substantial speedup over brute-force enumeration. On data from an epidemiological study of liver cancer, the test identified the hepatitis status of a subject as the most influential risk factor among others for the cancer phenotype. On human lung cancer transcriptome data, the test selected 1068 transcription start sites of putative noncoding RNAs directionally associated with the presence or absence of lung cancer, stronger than 95 percent transcription start sites of 694 curated cancer genes. These predictions include non-monotonic interaction patterns, to which other routine tests were insensitive. Complementing symmetric (non-directional) association methods such as Fisher’s exact test, the exact functional test is a unique exact statistical test for evaluating evidence for causal relationships.
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- Award ID(s):
- 1661331
- NSF-PAR ID:
- 10055967
- Date Published:
- Journal Name:
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- ISSN:
- 1545-5963
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Telomerase reverse transcriptase (
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCBB.2018.2809743
