Phenopolis: an open platform for harmonization and analysis of genetic and phenotypic data

Pontikos, Nikolas; Yu, Jingjing; Moghul, Ismail; Withington, Lucy; Blanco‐Kelly, Fiona; Vulliamy, Tom; Wong, Tsz Lun Ernest; Murphy, Cian; Cipriani, Valentina; Fiorentino, Alessia; Arno, Gavin; Greene, Daniel; Jacobsen, Julius O. B.; Clark, Tristan; Gregory, David S; Nemeth, Andrea M.; Halford, Stephanie; Inglehearn, Chris F.; Downes, Susan M.; Black, Graeme C M; Webster, Andrew R.; Hardcastle, Alison J.; Ukirdc,; Plagnol, Vincent

doi:10.1093/bioinformatics/btx147

Cited by 43 publications

(45 citation statements)

References 11 publications

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“…The allele frequency cutoffs per each mode of inheritance were set to match the filtering criteria of the bioinformatics pipeline that had been used to solve the cases. As an optional LOCAL in-house dataset for variant filtering, we used UCLex [74], an exome database of about 4500 whole-exomes collected at University College London (UK) from various research groups since 2012, to which the IRD patients included in this analysis belong to. The performance of Exomiser on the IRD patient dataset was assessed using the following eight analysis settings (Table 3): 1) DEFAULT: the Exomiser score was obtained from the variant score based on allele frequency (as defined in the filtering step) and the original pathogenicity algorithms PolyPhen-2 [14], MutationTaster [13], and SIFT [75] using pathogenicitySources: [POLYPHEN, MUTATION_TASTER, SIFT], plus the gene-specific phenotype score, using hiPhivePrioritiser: {}, and omimPrioritiser: {}.…”

Section: Software Analysis Settingsmentioning

confidence: 99%

“…LOCAL is University College London exome database (UCLex) [74]. Ensembl transcript annotation was used across all the analysis settings.…”

Section: Primate-aimentioning

confidence: 99%

“…WES was performed independently or as part of the National Institute for Health Research BioResource-Rare Disease study (NIHR BR-RD) as previously described [78]. Samples were analyzed as part of the UCLex WES dataset using the Phenopolis bioinformatics pipeline [74]. Reads were aligned to the hg19 human reference sequence (build GRCh37) with NovoAlign (version 3.02.08; Novocraft Technologies, Petaling Jaya, Malaysia).…”

Section: Whole-exome Sequencing Datamentioning

confidence: 99%

See 2 more Smart Citations

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Cipriani

Pontikos

Arno

et al. 2020

Genes

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Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

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Section: Software Analysis Settingsmentioning

confidence: 99%

“…LOCAL is University College London exome database (UCLex) [74]. Ensembl transcript annotation was used across all the analysis settings.…”

Section: Primate-aimentioning

confidence: 99%

Section: Whole-exome Sequencing Datamentioning

confidence: 99%

See 1 more Smart Citation

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Cipriani

Pontikos

Arno

et al. 2020

Genes

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“…The variant calling and annotation pipeline has been described previously 3 . A total of 973,426 variants were annotated with gnomAD frequencies 7 and CADD phred score 8 .…”

Section: Variant Call Filtration and Phasingmentioning

confidence: 99%

“…The UK Inherited Retinal Dystrophy Consortium (UKIRDC) adopted this initiative by collecting Supplementary Fig. 1) 3 .…”

Section: Introductionmentioning

confidence: 99%

Phenogenon: Gene to Phenotype Associations for Rare Genetic Diseases

Murphy

Moghul

Pontikos

et al. 2018

Preprint

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<=250 words)As genome sequencing is increasingly applied to molecular diagnosis of rare Mendelian disorders, large number of patients with diverse phenotypes have their genomic and phenotypic data pooled together to uncover new genotype -phenotype relations. We introduce Phenogenon, a method that combines: the power of Human Phenotype Ontology for describing patient phenotypes, gnomAD for estimating rare variant population frequency, and CADD for variant pathogenicity prediction. By using a divide and conquer approach, we demonstrate here that Phenogenon is able to uncover true gene to phenotype relations, such as " ABCA4 -Macular dystrophy" and " SCN1A -Seizures". Additionally, it accurately infers mode of inheritance, such as a recessive mode of inheritance in the case of the " ABCA4 -Macular dystrophy" relationship and a dominant mode of inheritance with the " SCN1A -Seizures" 1 relationship. We also found that CADD has more power to detect early-onset rare genetic diseases than late-onset diseases. In this study, we ran Phenogenon against a diverse cohort of 3288 patients. Among the top 13 gene-phenotype relations, seven were previously known. We also highlight four potentially novel gene -phenotype relations such as " SIPA1L3 -Abnormal electroretinogram ".

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ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting‐state and task‐based fMRI data

Waller

Erk

Pozzi

et al. 2022

Human Brain Mapping

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The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the individual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

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Phenopolis: an open platform for harmonization and analysis of genetic and phenotypic data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 43 publications

References 11 publications

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Phenogenon: Gene to Phenotype Associations for Rare Genetic Diseases

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting‐state and task‐based fMRI data

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