Dataset Information

A computational pipeline for functional gene discovery.

ABSTRACT: Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets from two mouse retinal development studies, we identified 7 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., development of the outer segment and synapses of the photoreceptor cells in the mouse retina. This pipeline can also be useful to discover functional genes for other biological processes and in other organs and tissues.

SUBMITTER: Colon A

PROVIDER: S-EPMC8651667 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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A computational pipeline for functional gene discovery.

Colon Aolani A Hirday Rishabh R Patel Ami A Poddar Amrita A Tuberty-Vaughan Emma E Fu Tianyue T Ai Xin X Li Wei Vivian WV Cai Li L

Scientific reports 20211207 1

Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this p ...[more]

PMID: 34876638

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Project description:BackgroundGenome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity.ResultsCOMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer (Ostrinia nubilalis). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity.ConclusionsThe relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

Project description:Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

Dataset Information

A computational pipeline for functional gene discovery.

Publications

A computational pipeline for functional gene discovery.

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