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For: Tang H, Thomas PD. Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation. Genetics 2016;203:635-47. [PMID: 27270698 DOI: 10.1534/genetics.116.190033] [Cited by in Crossref: 60] [Cited by in F6Publishing: 49] [Article Influence: 12.0] [Reference Citation Analysis]
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2 Cafarelli TM, Desbuleux A, Wang Y, Choi SG, De Ridder D, Vidal M. Mapping, modeling, and characterization of protein-protein interactions on a proteomic scale. Curr Opin Struct Biol 2017;44:201-10. [PMID: 28575754 DOI: 10.1016/j.sbi.2017.05.003] [Cited by in Crossref: 36] [Cited by in F6Publishing: 25] [Article Influence: 7.2] [Reference Citation Analysis]
3 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. Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants. Hum Mutat 2019;40:1530-45. [PMID: 31301157 DOI: 10.1002/humu.23868] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
4 R Hamre J 3rd, Klimov DK, McCoy MD, Jafri MS. Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics. Comput Biol Med 2021;140:105060. [PMID: 34920365 DOI: 10.1016/j.compbiomed.2021.105060] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, Beaty KA, Dehan E, Parikh B. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet. 2019;138:109-124. [PMID: 30671672 DOI: 10.1007/s00439-019-01970-5] [Cited by in Crossref: 59] [Cited by in F6Publishing: 35] [Article Influence: 19.7] [Reference Citation Analysis]
6 Else T, Fishbein L. Discovery of new susceptibility genes: proceed cautiously. Genet Med 2018;20:1512-4. [DOI: 10.1038/s41436-018-0139-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
7 Agrahari AK, Muskan M, George Priya Doss C, Siva R, Zayed H. Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach. Metab Brain Dis 2018;33:1443-57. [PMID: 29804243 DOI: 10.1007/s11011-018-0251-1] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 5.0] [Reference Citation Analysis]
8 Paananen J. Bioinformatics in the Identification of Novel Targets and Pathways in Neurodegenerative Diseases. Curr Genet Med Rep 2017;5:15-21. [DOI: 10.1007/s40142-017-0115-8] [Reference Citation Analysis]
9 Niroula A, Vihinen M. How good are pathogenicity predictors in detecting benign variants? PLoS Comput Biol 2019;15:e1006481. [PMID: 30742610 DOI: 10.1371/journal.pcbi.1006481] [Cited by in Crossref: 26] [Cited by in F6Publishing: 21] [Article Influence: 8.7] [Reference Citation Analysis]
10 Marian AJ. Clinical Interpretation and Management of Genetic Variants. JACC Basic Transl Sci 2020;5:1029-42. [PMID: 33145465 DOI: 10.1016/j.jacbts.2020.05.013] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
11 Vaattovaara A, Leppälä J, Salojärvi J, Wrzaczek M. High-throughput sequencing data and the impact of plant gene annotation quality. J Exp Bot 2019;70:1069-76. [PMID: 30590678 DOI: 10.1093/jxb/ery434] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Sánchez-gracia A, Guirao-rico S, Hinojosa-alvarez S, Rozas J. Computational prediction of the phenotypic effects of genetic variants: basic concepts and some application examples in Drosophila nervous system genes. Journal of Neurogenetics 2017;31:307-19. [DOI: 10.1080/01677063.2017.1398241] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.4] [Reference Citation Analysis]
13 Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022. [PMID: 35488922 DOI: 10.1007/s00439-022-02457-6] [Reference Citation Analysis]
14 Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11:70. [PMID: 31744524 DOI: 10.1186/s13073-019-0689-8] [Cited by in Crossref: 45] [Cited by in F6Publishing: 34] [Article Influence: 15.0] [Reference Citation Analysis]
15 Schneider K, White TJ, Mitchell S, Adams CE, Reeve R, Elmer KR. The pitfalls and virtues of population genetic summary statistics: Detecting selective sweeps in recent divergences. J Evol Biol 2021;34:893-909. [DOI: 10.1111/jeb.13738] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Zhou Y, Fujikura K, Mkrtchian S, Lauschke VM. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front Pharmacol 2018;9:1437. [PMID: 30564131 DOI: 10.3389/fphar.2018.01437] [Cited by in Crossref: 32] [Cited by in F6Publishing: 28] [Article Influence: 8.0] [Reference Citation Analysis]
17 Kulandaisamy A, Zaucha J, Sakthivel R, Frishman D, Michael Gromiha M. Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins. Human Mutation 2019;41:581-90. [DOI: 10.1002/humu.23961] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
18 Caswell RC, Owens MM, Gunning AC, Ellard S, Wright CF. Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1. J Endocr Soc 2019;3:2258-75. [PMID: 31737856 DOI: 10.1210/js.2019-00260] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
19 Abolhassani H, Marcotte H, Fang M, Hammarström L. Clinical implications of experimental analyses of AID function on predictive computational tools: Challenge of missense variants. Clin Genet 2020;97:844-56. [PMID: 32162335 DOI: 10.1111/cge.13737] [Reference Citation Analysis]
20 Nykamp K, Anderson M, Powers M, Garcia J, Herrera B, Ho YY, Kobayashi Y, Patil N, Thusberg J, Westbrook M, Topper S; Invitae Clinical Genomics Group. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet Med 2017;19:1105-17. [PMID: 28492532 DOI: 10.1038/gim.2017.37] [Cited by in Crossref: 242] [Cited by in F6Publishing: 213] [Article Influence: 48.4] [Reference Citation Analysis]
21 Miller JE, Veturi Y, Ritchie MD. Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes. BioData Min 2019;12:10. [PMID: 31114635 DOI: 10.1186/s13040-019-0197-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
22 Borooah S, Stanton CM, Marsh J, Carss KJ, Waseem N, Biswas P, Agorogiannis G, Raymond L, Arno G, Webster AR. Whole genome sequencing reveals novel mutations causing autosomal dominant inherited macular degeneration. Ophthalmic Genet 2018;39:763-70. [PMID: 30451557 DOI: 10.1080/13816810.2018.1546406] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
23 Meireles MR, Stelmach LH, Bandinelli E, Vieira GF. Unveiling the influence of Factor VIII physicochemical properties on Hemophilia A phenotype through an in silico methodology. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106768] [Reference Citation Analysis]
24 Wang F, Zhang S, Kim TB, Lin YY, Iqbal R, Wang Z, Mohanty V, Sircar K, Karam JA, Wendl MC, Meric-Bernstam F, Weinstein JN, Ding L, Mills GB, Chen K. Integrated transcriptomic-genomic tool Texomer profiles cancer tissues. Nat Methods 2019;16:401-4. [PMID: 30988467 DOI: 10.1038/s41592-019-0388-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
25 Li X, Yung G, Zhou H, Sun R, Li Z, Hou K, Zhang MJ, Liu Y, Arapoglou T, Wang C, Ionita-Laza I, Lin X. A multi-dimensional integrative scoring framework for predicting functional variants in the human genome. Am J Hum Genet 2022;109:446-56. [PMID: 35216679 DOI: 10.1016/j.ajhg.2022.01.017] [Reference Citation Analysis]
26 Tang B, Li B, Gao L, He N, Liu X, Long Y, Zeng Y, Yi Y, Su T, Liao W. Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification. Briefings in Bioinformatics 2020;21:1776-86. [DOI: 10.1093/bib/bbz115] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
27 Ernst C, Hahnen E, Engel C, Nothnagel M, Weber J, Schmutzler RK, Hauke J. Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics. BMC Med Genomics 2018;11:35. [PMID: 29580235 DOI: 10.1186/s12920-018-0353-y] [Cited by in Crossref: 39] [Cited by in F6Publishing: 31] [Article Influence: 9.8] [Reference Citation Analysis]
28 Khoruddin NA, Noorizhab MN, Teh LK, Mohd Yusof FZ, Salleh MZ. Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays. Sci Rep 2021;11:16158. [PMID: 34373545 DOI: 10.1038/s41598-021-95618-y] [Reference Citation Analysis]
29 S Niranjana Murthy A, V Suresh R, Nallur B R. Comprehensive in silico mutational-sensitivity analysis of PTEN establishes signature regions implicated in pathogenesis of Autism Spectrum Disorders. Genomics 2021;113:999-1017. [PMID: 33152507 DOI: 10.1016/j.ygeno.2020.10.035] [Reference Citation Analysis]
30 Monroe JG, McKay JK, Weigel D, Flood PJ. The population genomics of adaptive loss of function. Heredity (Edinb) 2021;126:383-95. [PMID: 33574599 DOI: 10.1038/s41437-021-00403-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Niroula A, Vihinen M. Predicting Severity of Disease-Causing Variants. Hum Mutat 2017;38:357-64. [PMID: 28070986 DOI: 10.1002/humu.23173] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 4.8] [Reference Citation Analysis]
32 Raraigh KS, Han ST, Davis E, Evans TA, Pellicore MJ, McCague AF, Joynt AT, Lu Z, Atalar M, Sharma N, Sheridan MB, Sosnay PR, Cutting GR. Functional Assays Are Essential for Interpretation of Missense Variants Associated with Variable Expressivity. Am J Hum Genet 2018;102:1062-77. [PMID: 29805046 DOI: 10.1016/j.ajhg.2018.04.003] [Cited by in Crossref: 43] [Cited by in F6Publishing: 37] [Article Influence: 10.8] [Reference Citation Analysis]
33 Suay-Corredera C, Alegre-Cebollada J. The mechanics of the heart: zooming in on hypertrophic cardiomyopathy and cMyBP-C. FEBS Lett 2022. [PMID: 35224729 DOI: 10.1002/1873-3468.14301] [Reference Citation Analysis]
34 Yazar M, Ozbek P. Assessment of 13 in silico pathogenicity methods on cancer-related variants. Computers in Biology and Medicine 2022;145:105434. [DOI: 10.1016/j.compbiomed.2022.105434] [Reference Citation Analysis]
35 Gray VE, Hause RJ, Luebeck J, Shendure J, Fowler DM. Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data. Cell Syst 2018;6:116-124.e3. [PMID: 29226803 DOI: 10.1016/j.cels.2017.11.003] [Cited by in Crossref: 90] [Cited by in F6Publishing: 60] [Article Influence: 18.0] [Reference Citation Analysis]
36 Li X, Li Z, Zhou H, Gaynor SM, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Aslibekyan S, Ballantyne CM, Bielak LF, Blangero J, Boerwinkle E, Bowden DW, Broome JG, Conomos MP, Correa A, Cupples LA, Curran JE, Freedman BI, Guo X, Hindy G, Irvin MR, Kardia SLR, Kathiresan S, Khan AT, Kooperberg CL, Laurie CC, Liu XS, Mahaney MC, Manichaikul AW, Martin LW, Mathias RA, McGarvey ST, Mitchell BD, Montasser ME, Moore JE, Morrison AC, O'Connell JR, Palmer ND, Pampana A, Peralta JM, Peyser PA, Psaty BM, Redline S, Rice KM, Rich SS, Smith JA, Tiwari HK, Tsai MY, Vasan RS, Wang FF, Weeks DE, Weng Z, Wilson JG, Yanek LR, Neale BM, Sunyaev SR, Abecasis GR, Rotter JI, Willer CJ, Peloso GM, Natarajan P, Lin X; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium., TOPMed Lipids Working Group. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 2020;52:969-83. [PMID: 32839606 DOI: 10.1038/s41588-020-0676-4] [Cited by in Crossref: 23] [Cited by in F6Publishing: 10] [Article Influence: 11.5] [Reference Citation Analysis]
37 Feng X, Cozma C, Pantoom S, Hund C, Iwanov K, Petters J, Völkner C, Bauer C, Vogel F, Bauer P, Weiss FU, Lerch MM, Knospe AM, Hermann A, Frech MJ, Luo J, Rolfs A, Lukas J. Determination of the Pathological Features of NPC1 Variants in a Cellular Complementation Test. Int J Mol Sci 2019;20:E5185. [PMID: 31635081 DOI: 10.3390/ijms20205185] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
38 Landry KK, Seward DJ, Dragon JA, Slavik M, Xu K, McKinnon WC, Colello L, Sweasy J, Wallace SS, Cuke M, Wood ME. Investigation of discordant sibling pairs from hereditary breast cancer families and analysis of a rare PMS1 variant. Cancer Genet 2022;260-261:30-6. [PMID: 34852986 DOI: 10.1016/j.cancergen.2021.11.004] [Reference Citation Analysis]
39 Fanale D, Fiorino A, Incorvaia L, Dimino A, Filorizzo C, Bono M, Cancelliere D, Calò V, Brando C, Corsini LR, Sciacchitano R, Magrin L, Pivetti A, Pedone E, Madonia G, Cucinella A, Badalamenti G, Russo A, Bazan V. Prevalence and Spectrum of Germline BRCA1 and BRCA2 Variants of Uncertain Significance in Breast/Ovarian Cancer: Mysterious Signals From the Genome. Front Oncol 2021;11:682445. [PMID: 34178674 DOI: 10.3389/fonc.2021.682445] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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41 Calarco L, Barratt J, Ellis J. Genome Wide Identification of Mutational Hotspots in the Apicomplexan Parasite Neospora caninum and the Implications for Virulence. Genome Biol Evol 2018;10:2417-31. [PMID: 30165699 DOI: 10.1093/gbe/evy188] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
42 Khalighi S, Singh S, Varadan V. Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms. J Genet Genomics 2020;47:595-609. [PMID: 33423960 DOI: 10.1016/j.jgg.2020.11.001] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
43 Dlamini Z, Skepu A, Kim N, Mkhabele M, Khanyile R, Molefi T, Mbatha S, Setlai B, Mulaudzi T, Mabongo M, Bida M, Kgoebane-maseko M, Mathabe K, Lockhat Z, Kgokolo M, Chauke-malinga N, Ramagaga S, Hull R. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100965] [Reference Citation Analysis]
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45 Plona KL, Eastman JF, Drumm ML. Classifying molecular phenotypes of G6PC variants for pathogenic properties and to guide therapeutic development. JIMD Rep 2021;60:56-66. [PMID: 34258141 DOI: 10.1002/jmd2.12215] [Reference Citation Analysis]
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47 Al Amri WS, Baxter DE, Hanby AM, Stead LF, Verghese ET, Thorne JL, Hughes TA. Identification of candidate mediators of chemoresponse in breast cancer through therapy-driven selection of somatic variants. Breast Cancer Res Treat 2020;183:607-16. [PMID: 32734521 DOI: 10.1007/s10549-020-05836-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
48 Sebate B, Cuttler K, Cloete R, Britz M, Christoffels A, Williams M, Carr J, Bardien S. Prioritization of candidate genes for a South African family with Parkinson's disease using in-silico tools. PLoS One 2021;16:e0249324. [PMID: 33770142 DOI: 10.1371/journal.pone.0249324] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Bonjoch L, Mur P, Arnau-Collell C, Vargas-Parra G, Shamloo B, Franch-Expósito S, Pineda M, Capellà G, Erman B, Castellví-Bel S. Approaches to functionally validate candidate genetic variants involved in colorectal cancer predisposition. Mol Aspects Med 2019;69:27-40. [PMID: 30935834 DOI: 10.1016/j.mam.2019.03.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
50 Maharaj A, Buonocore F, Meimaridou E, Ruiz-Babot G, Guasti L, Peng HM, Capper CP, Burgos-Tirado N, Prasad R, Hughes CR, Maudhoo A, Crowne E, Cheetham TD, Brain CE, Suntharalingham JP, Striglioni N, Yuksel B, Gurbuz F, Gupta S, Lindsay R, Couch R, Spoudeas HA, Guran T, Johnson S, Fowler DJ, Conwell LS, McInerney-Leo AM, Drui D, Cariou B, Lopez-Siguero JP, Harris M, Duncan EL, Hindmarsh PC, Auchus RJ, Donaldson MD, Achermann JC, Metherell LA. Predicted Benign and Synonymous Variants in CYP11A1 Cause Primary Adrenal Insufficiency Through Missplicing. J Endocr Soc 2019;3:201-21. [PMID: 30620006 DOI: 10.1210/js.2018-00130] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
51 Chi YI, Stodola TJ, De Assuncao TM, Leverence EN, Tripathi S, Dsouza NR, Mathison AJ, Basel DG, Volkman BF, Smith BC, Lomberk G, Zimmermann MT, Urrutia R. Molecular mechanics and dynamic simulations of well-known Kabuki syndrome-associated KDM6A variants reveal putative mechanisms of dysfunction. Orphanet J Rare Dis 2021;16:66. [PMID: 33546721 DOI: 10.1186/s13023-021-01692-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
52 Kim YE, Ki CS, Jang MA. Challenges and Considerations in Sequence Variant Interpretation for Mendelian Disorders. Ann Lab Med 2019;39:421-9. [PMID: 31037860 DOI: 10.3343/alm.2019.39.5.421] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
53 Onodera S, Saito A, Hasegawa D, Morita N, Watanabe K, Nomura T, Shibahara T, Ohba S, Yamaguchi A, Azuma T. Multi-layered mutation in hedgehog-related genes in Gorlin syndrome may affect the phenotype. PLoS One 2017;12:e0184702. [PMID: 28915250 DOI: 10.1371/journal.pone.0184702] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 3.4] [Reference Citation Analysis]
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56 Mossotto E, Ashton JJ, O'Gorman L, Pengelly RJ, Beattie RM, MacArthur BD, Ennis S. GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data. BMC Bioinformatics 2019;20:254. [PMID: 31096927 DOI: 10.1186/s12859-019-2877-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
57 Monroe J, Arciniegas J, Moreno J, Sánchez F, Sierra S, Valdes S, Torkamaneh D, Chavarriaga P. The lowest hanging fruit: Beneficial gene knockouts in past, present, and future crop evolution. Current Plant Biology 2020;24:100185. [DOI: 10.1016/j.cpb.2020.100185] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
58 Zhou JB, Xiong Y, An K, Ye ZQ, Wu YD. IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions. Bioinformatics 2020;36:4977-83. [PMID: 32756939 DOI: 10.1093/bioinformatics/btaa618] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]