WHRI-Phenogenomics

We have a job opening for a Genomic Data Scientist in our group. This is an opportunity to work on a short-term project (12 months) with a large-scale human genomic resource. See the job description for more details: qmul.ac.uk/jobs/vacancies/ (Ref:3059, Closing date 12th Sept 2024)

@NEJM_AI We urge the community to present benchmarking results on standardized cohorts and with transparent settings for each tool to allow accurate and efficient comparisons and propose PhEval as a solution.

@NEJM_AI Evaluating variant prioritization software is challenging due to the lack of openly available cohorts and the complexity of installing and correctly configuring the tools and to address this we have developed the PhEval framework.

AI-MARRVEL is an #ArtificialIntelligence system for genetic diagnosis that improves diagnostic accuracy, surpassing state-of-the-art benchmarked methods. Read the Original Article by Dongxue Mao, PhD, et al.: nejm.ai/3WfeyQ5

Our most recent @WHRIPhenGeno work on computational identification of @impc mouse models of Mendelian disease through automated mouse-human phenotype comparison with PhenoDigm is now published in @DMM_Journal. journals.biologists.com/dmm/article/17…

Diana Baralle: Ongoing analyses include the integration with Exomiser results in collaboration with Damian Smedley and Letizia Vestito from @WHRIPhenGeno #eshg2024

Great to be helping Jenny Lord and Diana Baralle on their groundbreaking analysis of transcriptomic data for new diagnoses in the 100,000 Genomes Project #eshg2024

Join the geneBurdenRD network! Fantastic presentation by @Val3Cipriani on gene burden analysis of the 100,000 Genomes Project data! #eshg2024

Happy to be part of this great study by @Val3Cipriani and gene burden group who helped with identification of TUBA4A gene variants enrichment in a novel phenotype of Ataxia. Check our poster P11.005.A @IonSynapse. #eshg2024

Our very own @Val3Cipriani @WHRIPhenGeno on Rare disease gene association diacovery from burden analysis of the 100KGP data. #eshg2024

@dgmacarthur @SimonsCas Lovely talk from Zornitza. I would say "simple" panel-based approaches and phenotype ones are complementary though as we found for the 100,000 Genomes Project nejm.org/cms/10.1056/NE…. This is why both are used by Genomics England still for the UK NHS Genomic Medicine Service.

@SimonsCas This approach handily outcompetes other current open-source automated pipelines (eg LIRICAL) - major lesson for me has been that fancy phenotype ontology approaches have limited value vs simple focus on low-hanging fruit from genetic data. #eshg2024

Zornitza Stark (@ZornitzaS) presenting on a collaboration with my team to develop an automated pipeline to regularly reanalyse genomic data from undiagnosed patients. We know reanalysis is highly effective, but (shockingly) rarely done in clinical settings. #eshg2024

Look forward to the #genomes for #undiagnosed session at 10:30AM and delivering our talk on gene burden analysis of the #100KGP data! Also so exciting to be at the #ESHG2024 with the whole team @WHRIPhenGeno @QMULWHRI @QMUL_C4TB @QMULBartsTheLon

apps.m-anage.com/eshg2024/en-GB… Join us for Valentina's presentation at #ESHG2024 (Sunday, 2 June, 11:15 - 11:30 Room A6+7) on our gene burden analysis of the 100,000 Genomes Project.

Excited to be at #eshg2024 in Berlin and looking forward to catching-up with everyone! Also very excited for three incredible members of the team to share their awesome work: @quenchentin , @eloise96wells , and @Alextremophile . Details👇

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery medrxiv.org/content/10.110… . Many of the authors are here at #ESHG2024 if you want to get in touch.

The whole team will be at 2024.eshg.org. Hope to meet up with collaborators, both old and new.