Dataset Information

MiRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.

ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNAs that are known for their role in the post-transcriptional regulation of target genes. Typically, their functions are predicted by first identifying their target genes and then finding biological processes enriched in these targets. Current tools for miRNA functional analysis use only genes with physical binding sites as their targets and exclude other genes that are indirectly targeted transcriptionally through transcription factors. Here, we introduce a method to predict gene ontology (GO) annotations indirectly targeted by microRNAs. The proposed method resulted in better performance in predicting known miRNA-GO term associations compared to the canonical approach. To facilitate miRNA GO enrichment analysis, we developed an R Shiny application, miRinGO, that is freely available online at GitHub.

SUBMITTER: Sayed M

PROVIDER: S-EPMC9962180 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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miRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.

Sayed Mohammed M Park Juw Won JW

Non-coding RNA 20230122 1

MicroRNAs (miRNAs) are small non-coding RNAs that are known for their role in the post-transcriptional regulation of target genes. Typically, their functions are predicted by first identifying their target genes and then finding biological processes enriched in these targets. Current tools for miRNA functional analysis use only genes with physical binding sites as their targets and exclude other genes that are indirectly targeted transcriptionally through transcription factors. Here, we introduc ...[more]

PMID: 36827544

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Project description:IntroductionAccurate early diagnosis of Parkinson's disease is hampered by its long prodromal period and the variable manifestations of its motor symptoms. While olfactory dysfunction can occur before motor-symptom onset and serve as a non-disease-specific diagnostic aid, its underlying causes are incompletely understood.MethodsCorrelation analyses, univariate density estimates, ANOVA and regression evaluated relationships between scores on the Montreal Cognitive Assessment and Hopkins Verbal Learning Test and those on the University of Pennsylvania Smell Identification Test in 1280 Parkinson's Progression Markers Initiative subjects placed into five diagnostic categories. Structural equation modeling identified cognitive measures having significant indirect effects on olfactory-function-test scores.ResultsGlobal cognition, verbal learning and memory, attention, delayed-recall, and visuospatial/executive function scores show weak-to-moderate, significant associations with olfactory-function-test scores. Associations are stronger in symptomatic than asymptomatic subjects having mutations in LRRK2, GBA or SNCA. Score distributions are nonuniform across diagnostic categories. Linear regression found that all cognitive measures except attention predicted olfactory-function-test scores. Three structural equation models assessing indirect effects of verbal learning/memory with either global cognition, visuospatial/executive function, or delayed-recall had a good statistical fit to the data. Only verbal learning/memory scores significantly help explain olfactory-function-test scores in all symptomatic diagnostic categories (-0.56 < b < -0.23, 0.001 < P < .005). Visuospatial/executive-function test scores help explain olfactory-function-test scores in both genetic Parkinson's disease diagnostic categories (-0.25 < b < -0.17, 0.032 < P < .033).ConclusionImpaired verbal learning/memory and visuospatial/executive function contributes to lower performance on olfactory function tests in Parkinson's disease. As both of these domains impact decision-making, decision-making in turn may impact olfactory assessment in Parkinson's disease.

Project description:A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.

Dataset Information

MiRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.

Publications

miRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.

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