CAGI Special Issue Human Mutation 2019

The 2019 CAGI Special issue in Human Mutation has now been published. 36 papers are included in this issue, available through free access. We would like to thank all CAGI participants for your extraordinary contributions at all levels and look forward to your engagement in further advancing variant interpretation.

Two additional CAGI papers have been accepted for publication on the online collection of Human Mutation, and several more are in preparation or under review.

CAGI HuMu 2019

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation.
Andreoletti G, Pal LR, Moult J, Brenner SE.
PMCID: PMC7329230; DOI: 10.1002/humu.23876

VIPdb, a genetic Variant Impact Predictor Database.
Hu Z, Yu C, Furutsuki M, Andreoletti G, Ly M, Hoskins R, Adhikari AN, Brenner SE.
PMCID: PMC7288905; DOI: 10.1002/humu.23858

Assessing predictions of the impact of variants on splicing in CAGI5.
Mount SM, Avsec Ž, Carmel L, Casadio R, Çelik MH, Chen K, Cheng J, Cohen NE, Fairbrother WG7, Fenesh T, Gagneur J, Gotea V, Holzer T, Lin CF, Martelli PL, Naito T, Nguyen TYD, Savojardo C, Unger R, Wang R, Yang Y, Zhao H.
PMCID: PMC6744318; DOI: 10.1002/humu.23869

Future directions for high-throughput splicing assays in precision medicine.
Rhine CL, Neil C, Glidden DT, Cygan KJ, Fredericks AM, Wang J, Walton NA, Fairbrother WG.
PMCID: PMC6744296; DOI: 10.1002/humu.23866

Predicting the change of exon splicing caused by genetic variant using support vector regression.
Chen K, Lu Y, Zhao H, Yang Y.
PMCID: PMC6744342; DOI: 10.1002/humu.23785

CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice.
Cheng J, Çelik MH, Nguyen TYD, Avsec Ž, Gagneur J.
PMCID: PMC7241300; DOI: 10.1002/humu.23788

CAGI experiments: Modeling sequence variant impact on gene splicing using predictions from computational tools.
Gotea V, Margolin G, Elnitski L.
PMCID: PMC6744343; DOI: 10.1002/humu.23782

Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features.
Naito T.
PMCID: PMC7265986; DOI: 10.1002/humu.23794

Using secondary structure to predict the effects of genetic variants on alternative splicing.
Wang R, Wang Y, Hu Z.
PMCID: PMC7288985; DOI:10.1002/humu.23790

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay.
Shigaki D, Adato O, Adhikari AN, Dong S, Hawkins-Hooker A, Inoue F, Juven-Gershon T, Kenlay H, Martin B, Patra A, Penzar DD, Schubach M, Xiong C, Yan Z, Boyle AP, Kreimer A, Kulakovskiy IV, Reid J, Unger R, Yosef N, Shendure J, Ahituv N, Kircher M, Beer MA.
PMCID: PMC6879779; DOI: 10.1002/humu.23797

Predicting functional variants in enhancer and promoter elements using RegulomeDB.
Dong S, Boyle AP.
PMCID: PMC6744346; DOI: 10.1002/humu.23797

Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.
Kreimer A, Yan Z, Ahituv N, Yosef N.
PMCID: PMC6771677; DOI: 10.1002/humu.23820

Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome interpretation (CAGI) challenges.
McInnes G, Daneshjou R, Katsonis P, Lichtarge O, Srinivasan R, Rana S, Radivojac P, Mooney SD, Pagel KA, Stamboulian M, Jiang Y, Capriotti E, Wang Y, Bromberg Y, Bovo S, Savojardo C, Martelli PL, Casadio R, Pal LR, Moult J, Brenner SE, Altman R.
PMCID: PMC7047641; DOI: 10.1002/humu.23825

Identifying mutation‐driven changes in gene functionality that lead to venous thromboembolism.
Wang Y, Bromberg Y.
PMCID: PMC6745089; DOI: 10.1002/humu.23824

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge.
Carraro M, Monzon AM, Chiricosta L, Reggiani F, Aspromonte MC, Bellini M, Pagel K, Jiang Y, Radivojac P, Kundu K, Pal LR, Yin Y, Limongelli I, Andreoletti G, Moult J, Wilson SJ, Katsonis P, Lichtarge O, Chen J, Wang Y, Hu Z, Brenner SE, Ferrari C, Murgia A, Tosatto SCE, Leonardi E.
PMCID: PMC7341177; DOI:10.1002/humu.23823

Characterization of intellectual disability and autism comorbidity through gene panel sequencing.
Aspromonte MC, Bellini M, Gasparini A, Carraro M, Bettella E, Polli R, Cesca F, Bigoni S, Boni S, Carlet O, Negrin S, Mammi I, Milani D, Peron A, Sartori S, Toldo I, Soli F, Turolla L, Stanzial F, Benedicenti F, Marino-Buslje C, Tosatto SCE, Murgia A, Leonardi E.
PMCID: PMC7428836;
DOI: 10.1002/humu.23822

A fully‐automated event‐based variant prioritizing solution to the CAGI5 intellectual disability gene panel challenge.
Chen J.
PMCID: PMC6744322; DOI: 10.1002/humu.23781

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases.
Kasak L, Hunter JM, Udani R, Bakolitsa C, Hu Z, Adhikari AN, Babbi G, Casadio R, Gough J, Guerrero RF, Jiang Y, Joseph T, Katsonis P, Kotte S, Kundu K, Lichtarge O, Martelli PL, Mooney SD, Moult J, Pal LR, Poitras J, Radivojac P, Rao A, Sivadasan N, Sunderam U, Saipradeep VG, Yin Y, Zaucha J, Brenner SE, Meyn MS.
PMCID: PMC7318886; DOI: 10.1002/humu.23874

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge.
Savojardo C, Petrosino M, Babbi G, Bovo S, Corbi-Verge C, Casadio R, Fariselli P, Folkman L, Garg A, Karimi M, Katsonis P, Kim PM, Lichtarge O, Martelli PL, Pasquo A, Pal D, Shen Y, Strokach AV, Turina P, Zhou Y, Andreoletti G, Brenner SE, Chiaraluce R, Consalvi V, Capriotti E.
PMCID: PMC6744327; DOI: 10.1002/humu.23843

Characterization of human frataxin missense variants in cancer tissues.
Petrosino M, Pasquo A, Novak L, Toto A, Gianni S, Mantuano E, Veneziano L, Minicozzi V, Pastore A, Puglisi R, Capriotti E, Chiaraluce R, Consalvi V.
PMCID: PMC6744310; DOI: 10.1002/humu.23789

Predicting changes in protein stability caused by mutation using sequence‐and structure‐based methods in a CAGI5 blind challenge.
Strokach A, Corbi-Verge C, Kim PM.
PMCID: PMC6744338; DOI: 10.1002/humu.23852

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations.
Garg A, Pal D.
PMCID: PMC7318789; DOI: 10.1002/humu.23800

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.
Katsonis P, Lichtarge O.
PMCID: PMC6900054; DOI: 10.1002/humu.23873

Are machine learning based methods suited to address complex biological problems? Lessons from CAGI‐5 challenges.
Savojardo C, Babbi G, Bovo S, Capriotti E, Martelli PL, Casadio R.
PMCID: PMC7281835; DOI: 10.1002/humu.23784

Assessing predictions on fitness effects of missense variants in calmodulin.
Zhang J, Kinch LN, Cong Q, Katsonis P, Lichtarge O, Savojardo C, Babbi G, Martelli PL, Capriotti E, Casadio R, Garg A, Pal D, Weile J, Sun S, Verby M, Roth FP, Grishin NV.
PMCID: PMC6744288; DOI: 10.1002/humu.23857

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI‐5.
Monzon AM, Carraro M, Chiricosta L, Reggiani F, Han J, Ozturk K, Wang Y, Miller M, Bromberg Y, Capriotti E, Savojardo C, Babbi G, Martelli PL, Casadio R, Katsonis P, Lichtarge O, Carter H, Kousi M, Katsanis N, Andreoletti G, Moult J, Brenner SE, Ferrari C, Leonardi E, Tosatto SCE.
PMCID: PMC7354699; DOI: 10.1002/humu.23856

What went wrong with variant effect predictor performance for the PCM1 challenge.
Miller M, Wang Y, Bromberg Y.
PMCID: PMC6744297; DOI: 10.1002/humu.23832

Assessment of methods for predicting the effects of PTEN and TPMT protein variants.
Pejaver V, Babbi G, Casadio R, Folkman L, Katsonis P, Kundu K, Lichtarge O, Martelli PL, Miller M, Moult J, Pal LR, Savojardo C, Yin Y, Zhou Y, Radivojac P, Bromberg Y.
PMCID: PMC6744362; DOI: 10.1002/humu.23838

Gene‐specific features enhance interpretation of mutational impact on acid α‐glucosidase enzyme activity.
Adhikari AN.
PMCID: PMC7329270; DOI: 10.1002/humu.23846

Assessment of predicted enzymatic activity of α‐N‐acetylglucosaminidase variants of unknown significance for CAGI 2016.
Clark WT, Kasak L, Bakolitsa C, Hu Z, Andreoletti G, Babbi G, Bromberg Y, Casadio R, Dunbrack R, Folkman L, Ford CT, Jones D, Katsonis P, Kundu K, Lichtarge O, Martelli PL, Mooney SD, Nodzak C, Pal LR, Radivojac P, Savojardo C, Shi X, Zhou Y, Uppal A, Xu Q, Yin Y, Pejaver V, Wang M, Wei L, Moult J, Yu GK, Brenner SE, LeBowitz JH.
PMCID: PMC7156275; DOI: 10.1002/humu.23875

Assessing computational predictions of the phenotypic effect of cystathionine‐beta‐synthase variants.
Kasak L, Bakolitsa C, Hu Z, Yu C, Rine J, Dimster-Denk DF, Pandey G, De Baets G, Bromberg Y, Cao C, Capriotti E, Casadio R, Van Durme J, Giollo M, Karchin R, Katsonis P, Leonardi E, Lichtarge O, Martelli PL, Masica D, Mooney SD, Olatubosun A, Radivojac P, Rousseau F, Pal LR, Savojardo C, Schymkowitz J, Thusberg J, Tosatto SCE, Vihinen M, Väliaho J, Repo S, Moult J, Brenner SE, Friedberg I.
PMCID: PMC7325732; DOI: 10.1002/humu.23868

Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants.
Cline MS, Babbi G, Bonache S, Cao Y, Casadio R, de la Cruz X, Díez O, Gutiérrez-Enríquez S, Katsonis P, Lai C, Lichtarge O, Martelli PL, Mishne G, Moles-Fernández A, Montalban G, Mooney SD, O'Conner R, Ootes L, Özkan S, Padilla N, Pagel KA, Pejaver V, Radivojac P, Riera C, Savojardo C, Shen Y, Sun Y, Topper S, Parsons MT, Spurdle AB, Goldgar DE; ENIGMA Consortium.
PMCID: PMC6744348; DOI: 10.1002/humu.23861

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification.
Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadaló L, Aalfs CM, Agata S, Aittomäki K, Alducci E, Alonso-Cerezo MC, Arnold N, Auber B, Austin R, Azzollini J, Balmaña J, Barbieri E, Bartram CR, Blanco A, Blümcke B, Bonache S, Bonanni B, Borg Å, Bortesi B, Brunet J, Bruzzone C, Bucksch K, Cagnoli G, Caldés T, Caliebe A, Caligo MA, Calvello M, Capone GL, Caputo SM, Carnevali I, Carrasco E, Caux-Moncoutier V, Cavalli P, Cini G, Clarke EM, Concolino P, Cops EJ, Cortesi L, Couch FJ, Darder E, de la Hoya M, Dean M, Debatin I, Del Valle J, Delnatte C, Derive N, Diez O, Ditsch N, Domchek SM, Dutrannoy V, Eccles DM, Ehrencrona H, Enders U, Evans DG, Farra C, Faust U, Felbor U, Feroce I, Fine M, Foulkes WD, Galvao HCR, Gambino G, Gehrig A, Gensini F, Gerdes AM, Germani A, Giesecke J, Gismondi V, Gómez C, Gómez Garcia EB, González S, Grau E, Grill S, Gross E, Guerrieri-Gonzaga A, Guillaud-Bataille M, Gutiérrez-Enríquez S, Haaf T, Hackmann K, Hansen TVO, Harris M, Hauke J, Heinrich T, Hellebrand H, Herold KN, Honisch E, Horvath J, Houdayer C, Hübbel V, Iglesias S, Izquierdo A, James PA, Janssen LAM, Jeschke U, Kaulfuß S, Keupp K, Kiechle M, Kölbl A, Krieger S, Kruse TA, Kvist A, Lalloo F, Larsen M, Lattimore VL, Lautrup C, Ledig S, Leinert E, Lewis AL, Lim J, Loeffler M, López-Fernández A, Lucci-Cordisco E, Maass N, Manoukian S, Marabelli M, Matricardi L, Meindl A, Michelli RD, Moghadasi S, Moles-Fernández A, Montagna M, Montalban G, Monteiro AN, Montes E, Mori L, Moserle L, Müller CR, Mundhenke C, Naldi N, Nathanson KL, Navarro M, Nevanlinna H, Nichols CB, Niederacher D, Nielsen HR, Ong KR, Pachter N, Palmero EI, Papi L, Pedersen IS, Peissel B, Perez-Segura P, Pfeifer K, Pineda M, Pohl-Rescigno E, Poplawski NK, Porfirio B, Quante AS, Ramser J, Reis RM, Revillion F, Rhiem K, Riboli B, Ritter J, Rivera D, Rofes P, Rump A, Salinas M, Sánchez de Abajo AM, Schmidt G, Schoenwiese U, Seggewiß J, Solanes A, Steinemann D, Stiller M, Stoppa-Lyonnet D, Sullivan KJ, Susman R, Sutter C, Tavtigian SV, Teo SH, Teulé A, Thomassen M, Tibiletti MG, Tischkowitz M, Tognazzo S, Toland AE, Tornero E, Törngren T, Torres-Esquius S, Toss A, Trainer AH, Tucker KM, van Asperen CJ, van Mackelenbergh MT, Varesco L, Vargas-Parra G, Varon R, Vega A, Velasco Á, Vesper AS, Viel A, Vreeswijk MPG, Wagner SA, Waha A, Walker LC, Walters RJ, Wang-Gohrke S, Weber BHF, Weichert W, Wieland K, Wiesmüller L, Witzel I, Wöckel A, Woodward ER, Zachariae S, Zampiga V, Zeder-Göß C, Investigators K, Lázaro C, De Nicolo A, Radice P, Engel C, Schmutzler RK, Goldgar DE, Spurdle AB.
PMCID: PMC6772163; DOI: 10.1002/humu.23818

Predicting pathogenicity of missense variants with weakly supervised regression.
Cao Y, Sun Y, Karimi M, Chen H, Moronfoye O, Shen Y.
PMCID: PMC6744350; DOI: 10.1002/humu.23826

BRCA1‐ and BRCA2‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge.
Padilla N, Moles-Fernández A, Riera C, Montalban G, Özkan S, Ootes L, Bonache S, Díez O, Gutiérrez-Enríquez S, de la Cruz X.
PMCID: PMC6744361; DOI: 10.1002/humu.23802

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.
Voskanian A, Katsonis P, Lichtarge O, Pejaver V, Radivojac P, Mooney SD, Capriotti E, Bromberg Y, Wang Y, Miller M, Martelli PL, Savojardo C, Babbi G, Casadio R, Cao Y, Sun Y, Shen Y, Garg A, Pal D, Yu Y, Huff CD, Tavtigian SV, Young E, Neuhausen SL, Ziv E, Pal LR, Andreoletti G, Brenner SE, Kann MG.
PMCID: PMC6744287; DOI: 10.1002/humu.23849

Additional publications (online):

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge.
Pal LR, Kundu K, Yin Y, Moult J.
PMCID: PMC7182498; DOI: 10.1002/humu.23933

LEAP: Using Machine Learning to Support Variant Classification in a Clinical Setting.
Lai C, Zimmer AD, O'Connor R, Kim S, Chan R, van den Akker J, Zhou AY, Topper S, Mishne G.
PMCID: 32176384; DOI: 10.1002/humu.24011