Assessment of predicted enzymatic activity of α‐
            <i>N</i>
            ‐acetylglucosaminidase variants of unknown significance for CAGI 2016

Clark, Wyatt T.; Kasak, Laura; Bakolitsa, Constantina; Hu, Zhiqiang; Andreoletti, Gaia; Babbi, Giulia; Bromberg, Yana; Casadio, Rita; Dunbrack, Roland L.; Folkman, Lukas; Ford, Colby T.; Jones, David T.; Katsonis, Panagiotis; Kundu, Kunal; Lichtarge, Olivier; Martelli, Pier Luigi; Mooney, Sean D.; Nodzak, Conor; Pal, Lipika R.; Radivojac, Predrag; Savojardo, Castrense; Shi, Xinghua; Zhou, Yaoqi; Uppal, A.; Xu, Qifang; Yin, Yiyuan; Pejaver, Vikas; Wang, Meng; Wei, Liping; Moult, John; Yu, Guoying Karen; Brenner, Steven E.; LeBowitz, Jonathan H.

doi:10.1002/humu.23875

Cited by 15 publications

(21 citation statements)

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“…Many independent approaches to solve this challenge were implemented by the data science community. Coincidentally, the first and second best-performing teams were the same for both sub-challenges, which is indicative of the team’s skill, as opposed to chance, a fact that has been observed in several other crowdsourcing initiatives (e.g., sbv IMPROVER, 22 , 71 , 72 CAGI, 73 , 74 , 75 , 76 and DREAM 55 , 77 , 78 ). Another advantage of the DREAM Challenge framework is that the model development and the prediction assessment are separate; thus, the risk of overstating the prediction performance is reduced.…”

Section: Discussionsupporting

confidence: 58%

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

Tarca

Pataki

Romero

et al. 2021

Cell Reports Medicine

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Section: Discussionsupporting

confidence: 58%

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

Tarca

Pataki

Romero

et al. 2021

Cell Reports Medicine

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“…Coincidently, the first and second best-performing teams were the same for both sub-challenges, which is indicative of the team's skill as opposed to chance, a fact that has been observed in several other crowd-sourcing initiatives, e.g., sbv IMPROVER 23,59,60 , CAGI [61][62][63][64] and DREAM 50,65,66…”

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confidence: 58%

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

Tarca

Pataki

Romero

et al. 2020

Preprint

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53Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, 54 remains challenging given the syndromic nature of the disease. We report a longitudinal multi-55 omics study coupled with a DREAM challenge to develop predictive models of PTB. We found 56 that whole blood gene expression predicts ultrasound-based gestational ages in normal and 57 complicated pregnancies (r=0.83), as well as the delivery date in normal pregnancies (r=0.86), 58with an accuracy comparable to ultrasound. However, unlike the latter, transcriptomic data 59 collected at <37 weeks of gestation predicted the delivery date of one third of spontaneous (sPTB) 60 cases within 2 weeks of the actual date. Based on samples collected before 33 weeks in 61 asymptomatic women we found expression changes preceding preterm prelabor rupture of the 62 membranes that were consistent across time points and cohorts, involving, among others, 63 leukocyte-mediated immunity. Plasma proteomic random forests predicted sPTB with higher 64 accuracy and earlier in pregnancy than whole blood transcriptomic models (e.g. AUROC=0.76 vs. 65 AUROC=0.6 at 27-33 weeks of gestation). 66 67 68 69 Early identification of patients at risk for obstetrical disease is required to improve health outcomes 70 and develop new therapeutic interventions. One of the "great obstetrical syndromes" 1 , preterm 71 birth, defined as birth prior to the completion of 37 weeks of gestation, is the leading cause of 72 newborn deaths worldwide. In 2010, 14.9 million babies were born preterm, accounting for 11.1% 73 of all births across 184 countries-the highest preterm birth rates occurring in Africa and North 74 America 2 . In the United States, the rate of prematurity remained fundamentally unchanged in 75 recent years 3 and it has an annual societal economic burden of at least $26.2 billion 4 . The high 76 incidence of preterm birth is concerning: 29% of all neonatal deaths worldwide, approximately 1 77 million deaths in total, can be attributed to complications of prematurity 5 . Furthermore, children 78 born prematurely are at increased risk for several short-and long-term complications that may 79 include motor, cognitive, and behavioral impairments 6,7 . 80Approximately one-third of preterm births are medically indicated for maternal (e.g. preeclampsia) 81 or fetal conditions (e.g. growth restriction); the other two-thirds are categorized as spontaneous 82 preterm births, inclusive of spontaneous preterm labor and delivery with intact membranes (sPTD), 83and preterm prelabor rupture of the membranes (PPROM) 8 . Preterm birth is a syndrome with 84 multiple etiologies 9 , and its complexity makes accurate prediction by a single set of biomarkers 85 difficult. While genetic risk factors for preterm birth have been reported 10 , the two most powerful 86 predictors of spontaneous preterm birth are a sonographic short cervix in the midtrimester, and a 87 history of spontaneous preterm birth in a prior pregnancy. 11 As for prevention of the syndrome, 88vagi...

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“…Three participant papers describe results on all the missense challenges (Garg & Pal, ; Katsonis & Lichtarge, ; Savojardo, Babbi et al, ). The issue also contains assessment articles from two earlier missense challenges on monogenic disease related proteins: N‐acetyl‐glucosaminidase ( NAGLU; Clark et al, ), with total intracellular enzyme activity measured; and cystathionine beta‐synthase ( CBS ), using the metric of yeast growth in a complication assay (Kasak, Bakolitsa et al, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Assessment of predicted enzymatic activity of α‐ N ‐acetylglucosaminidase variants of unknown significance for CAGI 2016

Cited by 15 publications

References 37 publications

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

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

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

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

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