AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature

Birgmeier, Johannes; Haeussler, Maximilian; Deisseroth, Cole A.; Steinberg, Ethan; Jagadeesh, Karthik A.; Ratner, Alexander; Guturu, Harendra; Wenger, Aaron M.; Diekhans, Mark; Stenson, Peter D.; Cooper, David Neil; Ré, Christopher; Beggs, Alan H.; Bernstein, Jonathan A.; Bejerano, Gill

doi:10.1126/scitranslmed.aau9113

Cited by 71 publications

(93 citation statements)

References 58 publications

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“…We have launched a web portal containing a working implementation of AMELIE analysis 21 followed by automatic reanalysis at https://amelie.stanford.…”

Section: Web Portalmentioning

confidence: 99%

“…We have previously developed AMELIE [19][20][21] (Automatic MEndelian LIterature Evaluation), a natural language processing and machine learning framework that automatically analyzes literature about Mendelian diseases and matches it to patients with undiagnosed Mendelian diseases to prioritize candidate causative genes in the patients' genomes. Here, we adapted the use of AMELIE to perform continuous reanalysis of undiagnosed patients with suspected Mendelian disease.…”

Section: Introductionmentioning

confidence: 99%

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AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year

Birgmeier

Steinberg

Bodle

et al. 2021

Preprint

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BackgroundMany thousands of patients with a suspected Mendelian disease have their exomes/genomes sequenced every year, but only about 30% receive a definitive diagnosis. Since a novel Mendelian gene-disease association is published on average every business day, thousands of undiagnosed patient cases could receive a diagnosis each year if their genomes were regularly compared to the latest literature. With millions of genomes expected to be sequenced for rare disease analysis by 2025, and considering the current publication rate of 1.1 million new articles per annum in PubMed, manually reanalyzing the growing cases of undiagnosed patients is not sustainable.MethodsWe describe a fully automated reanalysis framework for patients with suspected, but undiagnosed, Mendelian disorders. The presented framework was tested by automatically parsing all ∼100,000 newly published peer reviewed papers every month and matching them on genotype and phenotype with all stored undiagnosed patients. If a new article contains a possible diagnosis for an undiagnosed patient, the system provides notification. We test the accuracy of the automatic reanalysis system on 110 patients, including 61 with available trio data.ResultsEven when trained only on older data, our system identifies 80% of reanalysis diagnoses, while sending only 0.5-1 alerts per patient per year, a 100-1,000-fold efficiency gain over manual literature surveillance of equivalent yield.ConclusionWe show that automatic reanalysis of patients with suspected Mendelian disease is feasible and has the potential to greatly streamline diagnosis. Our system is not intended to replace clinical judgment. Rather, clinical diagnostic services could greatly benefit from a modest re-allocation of time from manual literature exploration to review of automated reanalysis alerts. Our system additionally supports a new paradigm for medical IT systems: proactive, continuously learning and consequently able to autonomously identify valuable insights as they emerge in digital health records. We have launched automated patient reanalysis, trained on the latest data, with user accounts and daily literature updates at https://AMELIE.stanford.edu.

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“…We have launched a web portal containing a working implementation of AMELIE analysis 21 followed by automatic reanalysis at https://amelie.stanford.…”

Section: Web Portalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year

Birgmeier

Steinberg

Bodle

et al. 2021

Preprint

Self Cite

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show abstract

“…We compared rankings for the 29 subjects with a previous diagnosis between our pipeline and those achieved using Exomiser 18 and AMELIE 19 (Table 3). Compared to our pipeline, AMELIE achieved a better rank in 14%, the same rank in 34%, and a lower rank in 52% of cases.…”

Section: Summary Statistics For Variant Calling and Annotationmentioning

confidence: 99%

“…We compared rankings that were achieved in our pipeline for previously diagnosed cases with those obtained using Exomiser 18 and AMELIE 19 , performed according to author guidelines. Exomiser input consisted of patient VCF files, whereas rankings were obtained from the AMELIE gene list API using the same genes contained in each SeqNextGen html report as input.…”

Section: Other Pipelinesmentioning

confidence: 99%

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A flexible computational pipeline for research analyses of unsolved clinical exome cases

et al. 2020

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Exome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25−30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeline ranked 21/34 previously diagnosed variants as top, with 26 in total ranked ≤7th, 3 ranked ≥13th; 5 failed the pipeline filters. Pathogenic/likely pathogenic variants by ACMG criteria were identified for 22/145 unsolved cases, and a previously undefined candidate disease variant for 27/145. This open access pipeline supports the partnership between clinical and research laboratories to improve the diagnosis of unsolved exomes. It provides a flexible framework for iterative developments to further improve diagnosis.

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Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

2022

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Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)‐based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web‐based CDSS that provides ranked lists of genetic and rare diseases using HPO‐based phenotypic similarities, where top‐listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO‐based resources.

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AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature

Cited by 71 publications

References 58 publications

AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year

AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year

A flexible computational pipeline for research analyses of unsolved clinical exome cases

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

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