Literature DB >> 31334884

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation.

Gaia Andreoletti1, Lipika R Pal2, John Moult2,3, Steven E Brenner1,4.   

Abstract

Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā-jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue (https://onlinelibrary.wiley.com/toc/10981004/2019/40/9) comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at https://genomeinterpretation.org.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  CAGI; Critical Assessment of Genome Interpretation; SNP; cancer genetics; genetic variation; genomics; variant impact predictors

Mesh:

Year:  2019        PMID: 31334884      PMCID: PMC7329230          DOI: 10.1002/humu.23876

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  2 in total

1.  Reports from CAGI: The Critical Assessment of Genome Interpretation.

Authors:  Roger A Hoskins; Susanna Repo; Daniel Barsky; Gaia Andreoletti; John Moult; Steven E Brenner
Journal:  Hum Mutat       Date:  2017-09       Impact factor: 4.878

2.  Predicting gene expression in massively parallel reporter assays: A comparative study.

Authors:  Anat Kreimer; Haoyang Zeng; Matthew D Edwards; Yuchun Guo; Kevin Tian; Sunyoung Shin; Rene Welch; Michael Wainberg; Rahul Mohan; Nicholas A Sinnott-Armstrong; Yue Li; Gökcen Eraslan; Talal Bin Amin; Ryan Tewhey; Pardis C Sabeti; Jonathan Goke; Nikola S Mueller; Manolis Kellis; Anshul Kundaje; Michael A Beer; Sunduz Keles; David K Gifford; Nir Yosef
Journal:  Hum Mutat       Date:  2017-03-09       Impact factor: 4.878

  2 in total
  20 in total

Review 1.  Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit.

Authors:  Fernando Meyer; Till-Robin Lesker; David Koslicki; Adrian Fritz; Alexey Gurevich; Aaron E Darling; Alexander Sczyrba; Andreas Bremges; Alice C McHardy
Journal:  Nat Protoc       Date:  2021-03-01       Impact factor: 13.491

2.  VIPdb, a genetic Variant Impact Predictor Database.

Authors:  Zhiqiang Hu; Changhua Yu; Mabel Furutsuki; Gaia Andreoletti; Melissa Ly; Roger Hoskins; Aashish N Adhikari; Steven E Brenner
Journal:  Hum Mutat       Date:  2019-08-17       Impact factor: 4.878

3.  Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.

Authors:  Rita Casadio; Castrense Savojardo; Piero Fariselli; Emidio Capriotti; Pier Luigi Martelli
Journal:  Methods Mol Biol       Date:  2022

Review 4.  High throughput and quantitative enzymology in the genomic era.

Authors:  D A Mokhtari; M J Appel; P M Fordyce; D Herschlag
Journal:  Curr Opin Struct Biol       Date:  2021-09-27       Impact factor: 6.809

5.  PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions.

Authors:  Nathan D Olson; Justin Wagner; Jennifer McDaniel; Sarah H Stephens; Samuel T Westreich; Anish G Prasanna; Elaine Johanson; Emily Boja; Ezekiel J Maier; Omar Serang; David Jáspez; José M Lorenzo-Salazar; Adrián Muñoz-Barrera; Luis A Rubio-Rodríguez; Carlos Flores; Konstantinos Kyriakidis; Andigoni Malousi; Kishwar Shafin; Trevor Pesout; Miten Jain; Benedict Paten; Pi-Chuan Chang; Alexey Kolesnikov; Maria Nattestad; Gunjan Baid; Sidharth Goel; Howard Yang; Andrew Carroll; Robert Eveleigh; Mathieu Bourgey; Guillaume Bourque; Gen Li; ChouXian Ma; LinQi Tang; YuanPing Du; ShaoWei Zhang; Jordi Morata; Raúl Tonda; Genís Parra; Jean-Rémi Trotta; Christian Brueffer; Sinem Demirkaya-Budak; Duygu Kabakci-Zorlu; Deniz Turgut; Özem Kalay; Gungor Budak; Kübra Narcı; Elif Arslan; Richard Brown; Ivan J Johnson; Alexey Dolgoborodov; Vladimir Semenyuk; Amit Jain; H Serhat Tetikol; Varun Jain; Mike Ruehle; Bryan Lajoie; Cooper Roddey; Severine Catreux; Rami Mehio; Mian Umair Ahsan; Qian Liu; Kai Wang; Sayed Mohammad Ebrahim Sahraeian; Li Tai Fang; Marghoob Mohiyuddin; Calvin Hung; Chirag Jain; Hanying Feng; Zhipan Li; Luoqi Chen; Fritz J Sedlazeck; Justin M Zook
Journal:  Cell Genom       Date:  2022-04-27

Review 6.  Interpreting protein variant effects with computational predictors and deep mutational scanning.

Authors:  Benjamin J Livesey; Joseph A Marsh
Journal:  Dis Model Mech       Date:  2022-06-23       Impact factor: 5.732

Review 7.  Open problems in human trait genetics.

Authors:  Nadav Brandes; Omer Weissbrod; Michal Linial
Journal:  Genome Biol       Date:  2022-06-20       Impact factor: 17.906

Review 8.  How Machine Learning Will Transform Biomedicine.

Authors:  Jeremy Goecks; Vahid Jalili; Laura M Heiser; Joe W Gray
Journal:  Cell       Date:  2020-04-02       Impact factor: 41.582

9.  Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations.

Authors:  Swarnendu Tripathi; Nikita R Dsouza; Raul Urrutia; Michael T Zimmermann
Journal:  Bioinformatics       Date:  2021-06-16       Impact factor: 6.937

10.  Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.

Authors:  Adi L Tarca; Bálint Ármin Pataki; Roberto Romero; Marina Sirota; Yuanfang Guan; Rintu Kutum; Nardhy Gomez-Lopez; Bogdan Done; Gaurav Bhatti; Thomas Yu; Gaia Andreoletti; Tinnakorn Chaiworapongsa; Sonia S Hassan; Chaur-Dong Hsu; Nima Aghaeepour; Gustavo Stolovitzky; Istvan Csabai; James C Costello
Journal:  Cell Rep Med       Date:  2021-06-15
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