MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Abstract

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

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     Mishra P, Flynn JM, Starr TN, Bolon DNA. Systematic mutant analyses elucidate general and client-specific aspects of Hsp90 function. Cell Rep. 2016;15:588-598.

     Not in our library10.1016/j.celrep.2016.03.046

103. 103.

     Puchta O, Cseke B, Czaja H, Tollervey D, Sanguinetti G, Kudla G. Network of epistatic interactions within a yeast snoRNA. Science. 2016;352:840-844.

     Not in our library10.1126/science.aaf0965

104. 104.

     Li C, Qian W, Maclean CJ, Zhang J. The fitness landscape of a tRNA gene. Science. 2016;352:837-840.

     Not in our library10.1126/science.aae0568

105. 105.
     Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting · 2016

     Not checked
106. 106.
     The complete local genotype–phenotype landscape for the alternative splicing of a human exon · 2016

     Not checked
107. 107.

     Vvedenskaya IO, Vahedian-Movahed H, Zhang Y, Taylor DM, Ebright RH, Nickels BE. Interactions between RNA polymerase and the core recognition element are a determinant of transcription start site selection. Proc Natl Acad Sci. 2016;113:E2899-E2905.

     Not in our library10.1073/pnas.1603271113

108. 108.

     Steinberg B, Ostermeier M. Shifting fitness and epistatic landscapes reflect trade-offs along an evolutionary pathway. J Mol Biol. 2016;428:2730-2743.

     Not in our library10.1016/j.jmb.2016.04.033

109. 109.

     Sarkisyan KS, Bolotin DA, Meer MV, Usmanova DR, Mishin AS, Sharonov GV, et al. Local fitness landscape of the green fluorescent protein. Nature. 2016;533:397-401.

     Not in our library10.1038/nature17995

110. 110.

     Tewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell. 2016;165:1519-1529.

     Not in our library10.1016/j.cell.2016.04.027

111. 111.

     Ulirsch JC, Nandakumar SK, Wang L, Giani FC, Zhang X, Rogov P, et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell. 2016;165:1530-1545.

     Not in our library10.1016/j.cell.2016.04.048

112. 112.
     Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin · 2016

     No notice
113. 113.

     Gamble CE, Brule CE, Dean KM, Fields S, Grayhack EJ. Adjacent codons act in concert to modulate translation efficiency in yeast. Cell. 2016;166:679-690.

     Not in our library10.1016/j.cell.2016.05.070

114. 114.
     Adaptation in protein fitness landscapes is facilitated by indirect paths · 2016

     No notice
115. 115.

     Tripathi A, Gupta K, Khare S, Jain PC, Patel S, Kumar P, et al. Molecular determinants of mutant phenotypes, inferred from saturation mutagenesis data. Mol Biol Evol. 2016;33:2960-2975.

     Not in our library10.1093/molbev/msw182

116. 116.

     Harris DT, Wang N, Riley TP, Anderson SD, Singh NK, Procko E, et al. Deep mutational scans as a guide to engineering high affinity T cell receptor interactions with peptide-bound major histocompatibility complex. J Biol Chem. 2016;291:24566-24578.

     Not in our library10.1074/jbc.m116.748681

117. 117.

     Kowalsky CA, Whitehead TA. Determination of binding affinity upon mutation for type I dockerin–cohesin complexes from Clostridium thermocellum and Clostridium cellulolyticum using deep sequencing. Proteins Struct Funct Bioinforma. 2016;84:1914-1928.

     Not in our library10.1002/prot.25175

118. 118.

     Ernst J, Melnikov A, Zhang X, Wang L, Rogov P, Mikkelsen TS, et al. Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nat Biotechnol. 2016;34:1180-1190.

     Not in our library10.1038/nbt.3678

119. 119.

     Brenan L, Andreev A, Cohen O, Pantel S, Kamburov A, Cacchiarelli D, et al. Phenotypic characterization of a comprehensive set of MAPK1/ERK2 missense mutants. Cell Rep. 2016;17:1171-1183.

     Not in our library10.1016/j.celrep.2016.09.061

120. 120.

     Qiu C, Erinne OC, Dave JM, Cui P, Jin H, Muthukrishnan N, et al. High-resolution phenotypic landscape of the RNA polymerase II trigger loop. PLOS Genet. 2016;12:e1006321.

     Not in our library10.1371/journal.pgen.1006321

121. 121.

     Haddox HK, Dingens AS, Bloom JD. Experimental estimation of the effects of all amino-acid mutations to HIV’s envelope protein on viral replication in cell culture. PLOS Pathog. 2016;12:e1006114.

     Not in our library10.1371/journal.ppat.1006114

122. 122.

     van Rosmalen M, Janssen BMG, Hendrikse NM, van der Linden AJ, Pieters PA, Wanders D, et al. Affinity maturation of a cyclic peptide handle for therapeutic antibodies using deep mutational scanning. J Biol Chem. 2017;292:1477-1489.

     Not in our library10.1074/jbc.m116.764225

123. 123.

     Fernandes JD, Faust TB, Strauli NB, Smith C, Crosby DC, Nakamura RL, et al. Functional segregation of overlapping genes in HIV. Cell. 2016;167:1762-1773.

     Not in our library10.1016/j.cell.2016.11.031

124. 124.

     Keskin A, Akdoğan E, Dunn CD. Evidence for amino acid snorkeling from a high-resolution, in vivo analysis of Fis1 tail-anchor insertion at the mitochondrial outer membrane. Genetics. 2017;205:691-705.

     Not in our library10.1534/genetics.116.196428

125. 125.

     Kelsic ED, Chung H, Cohen N, Park J, Wang HH, Kishony R. RNA structural determinants of optimal codons revealed by MAGE-Seq. Cell Syst. 2016;3:563-571.

     Not in our library10.1016/j.cels.2016.11.004

126. 126.

     Yartseva V, Takacs CM, Vejnar CE, Lee MT, Giraldez AJ. RESA identifies mRNA-regulatory sequences at high resolution. Nat Methods. 2017;14:201-207.

     Not in our library10.1038/nmeth.4121

127. 127.

     Koenig P, Lee CV, Walters BT, Janakiraman V, Stinson J, Patapoff TW, et al. Mutational landscape of antibody variable domains reveals a switch modulating the interdomain conformational dynamics and antigen binding. Proc Natl Acad Sci. 2017;114:E486-E495.

     Not in our library10.1073/pnas.1613231114

128. 128.

     Grossman SR, Zhang X, Wang L, Engreitz J, Melnikov A, Rogov P, et al. Systematic dissection of genomic features determining transcription factor binding and enhancer function. Proc Natl Acad Sci. 2017;114:E1291-E1300.

     Not in our library10.1073/pnas.1621150114

129. 129.

     Klesmith JR, Bacik J-P, Wrenbeck EE, Michalczyk R, Whitehead TA. Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning. Proc Natl Acad Sci. 2017;114:2265-2270.

     Not in our library10.1073/pnas.1614437114

130. 130.

     Maricque BB, Dougherty JD, Cohen BA. A genome-integrated massively parallel reporter assay reveals DNA sequence determinants of cis-regulatory activity in neural cells. Nucleic Acids Res. 2017;45:e16.

     Not in our library10.1093/nar/gkw942

131. 131.

     Levo M, Avnit-Sagi T, Lotan-Pompan M, Kalma Y, Weinberger A, Yakhini Z, et al. Systematic investigation of transcription factor activity in the context of chromatin using massively parallel binding and expression assays. Mol Cell. 2017;65:604-617.

     Not in our library10.1016/j.molcel.2017.01.007

132. 132.

     Chan YH, Venev SV, Zeldovich KB, Matthews CR. Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints. Nat Commun. 2017;8:14614.

     Not in our library10.1038/ncomms14614

133. 133.

     Matreyek KA, Stephany JJ, Fowler DM. A platform for functional assessment of large variant libraries in mammalian cells. Nucleic Acids Res. 2017;45:e102.

     Not in our library10.1093/nar/gkx183

134. 134.

     Ashenberg O, Padmakumar J, Doud MB, Bloom JD. Deep mutational scanning identifies sites in influenza nucleoprotein that affect viral inhibition by MxA. PLoS Pathog. 2017;13:e1006288.

     Not in our library10.1371/journal.ppat.1006288

135. 135.
     Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded · 2017

     No notice
136. 136.

     Wu NC, Xie J, Zheng T, Nycholat CM, Grande G, Paulson JC, et al. Diversity of functionally permissive sequences in the receptor-binding site of influenza hemagglutinin. Cell Host Microbe. 2017;21:742-753.

     Not in our library10.1016/j.chom.2017.05.011

137. 137.

     Bhagavatula G, Rich MS, Young DL, Marin M, Fields S. A massively parallel fluorescence assay to characterize the effects of synonymous mutations on TP53 expression. Mol Cancer Res. 2017;15:1301-1307.

     Not in our library10.1158/1541-7786.mcr-17-0245

138. 138.
     Deconstruction of the Ras switching cycle through saturation mutagenesis · 2017

     No notice
139. 139.

     Starr TN, Picton LK, Thornton JW. Alternative evolutionary histories in the sequence space of an ancient protein. Nature. 2017;549:409-413.

     Not in our library10.1038/nature23902

140. 140.

     Woodsmith J, Apelt L, Casado-Medrano V, Özkan Z, Timmermann B, Stelzl U. Protein interaction perturbation profiling at amino-acid resolution. Nat Methods. 2017;14:1213-1221.

     Not in our library10.1038/nmeth.4464

141. 141.

     Ma L, Boucher JI, Paulsen J, Matuszewski S, Eide CA, Ou J, et al. CRISPR-Cas9–mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy. Proc Natl Acad Sci. 2017;114:11751-11756.

     Not in our library10.1073/pnas.1708268114

142. 142.

     Cuperus JT, Groves B, Kuchina A, Rosenberg AB, Jojic N, Fields S, et al. Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences. Genome Res. 2017;27:2015-2024.

     Not in our library10.1101/gr.224964.117

143. 143.

     Spencer JM, Zhang X. Deep mutational scanning of S. pyogenes Cas9 reveals important functional domains. Sci Rep. 2017;7:16836.

     Not in our library10.1038/s41598-017-17081-y

144. 144.

     Rabani M, Pieper L, Chew G-L, Schier AF. A massively parallel reporter assay of 3′ UTR sequences identifies in vivo rules for mRNA degradation. Mol Cell. 2017;68:1083-1094.

     Not in our library10.1016/j.molcel.2017.11.014

145. 145.

     Ke S, Anquetil V, Zamalloa JR, Maity A, Yang A, Arias MA, et al. Saturation mutagenesis reveals manifold determinants of exon definition. Genome Res. 2018;28:11-24.

     Not in our library10.1101/gr.219683.116

146. 146.
     A framework for exhaustively mapping functional missense variants · 2017

     No notice
147. 147.

     Plesa C, Sidore AM, Lubock NB, Zhang D, Kosuri S. Multiplexed gene synthesis in emulsions for exploring protein functional landscapes. Science. 2018;359:343-347.

     Not in our library10.1126/science.aao5167

148. 148.

     Vainberg Slutskin I, Weingarten-Gabbay S, Nir R, Weinberger A, Segal E. Unraveling the determinants of microRNA mediated regulation using a massively parallel reporter assay. Nat Commun. 2018;9:529.

     Not in our library10.1038/s41467-018-02980-z

149. 149.

     Staller MV, Holehouse AS, Swain-Lenz D, Das RK, Pappu RV, Cohen BA. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 2018;6:444-455.

     Not in our library10.1016/j.cels.2018.01.015

150. 150.
     The genetic landscape of a physical interaction · 2018

     No notice
151. 151.

     Hartman EC, Jakobson CM, Favor AH, Lobba MJ, Álvarez-Benedicto E, Francis MB, et al. Quantitative characterization of all single amino acid variants of a viral capsid-based drug delivery vehicle. Nat Commun. 2018;9:1385.

     Not in our library10.1038/s41467-018-03783-y

152. 152.
     How single mutations affect viral escape from broad and narrow antibodies to H1 influenza hemagglutinin · 2018

     No notice
153. 153.

     Mason DM, Weber CR, Parola C, Meng SM, Greiff V, Kelton WJ, et al. High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis. Nucleic Acids Res. 2018;46:7436-7449.

     Not in our library10.1093/nar/gky550

154. 154.

     Kotler E, Shani O, Goldfeld G, Lotan-Pompan M, Tarcic O, Gershoni A, et al. A systematic p53 mutation library links differential functional impact to cancer mutation pattern and evolutionary conservation. Mol Cell. 2018;71:178-190.

     Not in our library10.1016/j.molcel.2018.06.012

155. 155.
     Coevolution-based inference of amino acid interactions underlying protein function · 2018

     No notice
156. 156.

     Mavor D, Barlow KA, Asarnow D, Birman Y, Britain D, Chen W, et al. Extending chemical perturbations of the ubiquitin fitness landscape in a classroom setting reveals new constraints on sequence tolerance. Biol Open. 2018;7:bio036103.

     Not in our library10.1242/bio.036103

157. 157.

     Dorrity MW, Cuperus JT, Carlisle JA, Fields S, Queitsch C. Preferences in a trait decision determined by transcription factor variants. Proc Natl Acad Sci. 2018;115:E7997-E8006.

     Not in our library10.1073/pnas.1805882115

158. 158.

     Castaldi PJ, Guo F, Qiao D, Du F, Naing ZZC, Li Y, et al. Identification of functional variants in the FAM13A chronic obstructive pulmonary disease genome-wide association study locus by massively parallel reporter assays. Am J Respir Crit Care Med. 2018;199:52-61.

     Not in our library10.1164/rccm.201802-0337oc

159. 159.

     Wong MS, Kinney JB, Krainer AR. Quantitative activity profile and context dependence of all human 5′ splice sites. Mol Cell. 2018;71:1012-1026.

     Not in our library10.1016/j.molcel.2018.07.033

160. 160.

     Phillips AM, Doud MB, Gonzalez LO, Butty VL, Lin Y-S, Bloom JD, et al. Enhanced ER proteostasis and temperature differentially impact the mutational tolerance of influenza hemagglutinin. eLife. 2018;7:e38795.

     Not in our library10.7554/elife.38795

161. 161.

     Blaesi EJ, Palowitch GM, Hu K, Kim AJ, Rose HR, Alapati R, et al. Metal-free class Ie ribonucleotide reductase from pathogens initiates catalysis with a tyrosine-derived dihydroxyphenylalanine radical. Proc Natl Acad Sci. 2018;115:10022-10027.

     Not in our library10.1073/pnas.1811993115

162. 162.

     Giacomelli Andrew O., Yang Xiaoping, Lintner Robert E., McFarland James M., Duby Marc, Kim Jaegil, Howard Thomas P., Takeda David Y., Ly Seav Huong, Kim Eejung, Gannon Hugh S., Hurhula Brian, Sharpe Ted, Goodale Amy, Fritchman Briana, Steelman Scott, Vazquez Francisca, Tsherniak Aviad, Aguirre Andrew J., Doench John G., Piccioni Federica, Roberts Charles W. M., Meyerson Matthew, Getz Gad, Johannessen Cory M., Root David E., Hahn William C. Mutational processes shape the landscape of TP53 mutations in human cancer. Nature Genetics. 2018;50(10):1381-1387.

     Not in our library10.1038/s41588-018-0204-y

163. 163.

     Naftaly S, Cohen I, Shahar A, Hockla A, Radisky ES, Papo N. Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries. Nat Commun. 2018;9:3935.

     Not in our library10.1038/s41467-018-06403-x

164. 164.

     Bassalo MC, Garst AD, Choudhury A, Grau WC, Oh EJ, Spindler E, et al. Deep scanning lysine metabolism in Escherichia coli. Mol Syst Biol. 2018;14:e8371.

     Not in our library10.15252/msb.20188371

165. 165.

     Cheung R, Insigne KD, Yao D, Burghard CP, Wang J, Hsiao Y-HE, et al. A multiplexed assay for exon recognition reveals that an unappreciated fraction of rare genetic variants cause large-effect splicing disruptions. Mol Cell. 2019;73:183-194.

     Not in our library10.1016/j.molcel.2018.10.037

166. 166.

     Michaels YS, Barnkob MB, Barbosa H, Baeumler TA, Thompson MK, Andre V, et al. Precise tuning of gene expression levels in mammalian cells. Nat Commun. 2019;10:818.

     Not in our library10.1038/s41467-019-08777-y

167. 167.

     Noda-García L, Davidi D, Korenblum E, Elazar A, Putintseva E, Aharoni A, et al. Chance and pleiotropy dominate genetic diversity in complex bacterial environments. Nat Microbiol. 2019;4:1221-1230.

     Not in our library10.1038/s41564-019-0412-y

168. 168.

     Ahler Ethan, Register Ames C., Chakraborty Sujata, Fang Linglan, Dieter Emily M., Sitko Katherine A., Vidadala Rama Subba Rao, Trevillian Bridget M., Golkowski Martin, Gelman Hannah, Stephany Jason J., Rubin Alan F., Merritt Ethan A., Fowler Douglas M., Maly Dustin J. A Combined Approach Reveals a Regulatory Mechanism Coupling Src’s Kinase Activity, Localization, and Phosphotransferase-Independent Functions. Molecular Cell. 2019;74(2):393-408.e20.

     Not in our library10.1016/j.molcel.2019.02.003

169. 169.

     Sample PJ, Wang B, Reid DW, Presnyak V, McFadyen IJ, Morris DR, et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat Biotechnol. 2019;37:803.

     Not in our library10.1038/s41587-019-0164-5

170. 170.

     Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J. shiny: web application framework for R. 2018. https://CRAN.R-project.org/package=shiny.

     Not in our library

171. 171.

     Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41:D991-D995.

     Not in our library10.1093/nar/gks1193

172. 172.

     Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2011;39:D19-D21.

     Not in our library10.1093/nar/gkq1019

173. 173.

     Wang CY, Chang PM, Ary ML, Allen BD, Chica RA, Mayo SL, et al. ProtaBank: a repository for protein design and engineering data. Protein Sci. 2018;27:1113-1124.

     Not in our library10.1002/pro.3406

174. 174.

     Pires DEV, Blundell TL, Ascher DB. Platinum: a database of experimentally measured effects of mutations on structurally defined protein–ligand complexes. Nucleic Acids Res. 2015;43:D387-D391.

     Not in our library10.1093/nar/gku966

175. 175.

     Bava KA, Gromiha MM, Uedaira H, Kitajima K, Sarai A. ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic Acids Res. 2004;32:D120-D121.

     Not in our library10.1093/nar/gkh082

176. 176.

     Bouaoun L, Sonkin D, Ardin M, Hollstein M, Byrnes G, Zavadil J, et al. TP53 variations in human cancers: new lessons from the IARC TP53 Database and Genomics Data. Hum Mutat. 2016;37:865-876.

     Not in our library10.1002/humu.23035

177. 177.

     Gaedigk A, Ingelman-Sundberg M, Miller NA, Leeder JS, Whirl-Carrillo M, Klein TE. The Pharmacogene Variation (PharmVar) Consortium: incorporation of the Human Cytochrome P450 (CYP) Allele Nomenclature Database. Clin Pharmacol Ther. 2018;103:399-401.

     Not in our library10.1002/cpt.910

178. 178.

     Oscarson M, Ingelman-Sundberg M. CYPalleles: a web page for nomenclature of human cytochrome P450 alleles. Drug Metab Pharmacokinet. 2002;17:491-495.

     Not in our library10.2133/dmpk.17.491

179. 179.

     Fokkema IFAC, Taschner PEM, Schaafsma GCP, Celli J, Laros JFJ, den Dunnen JT. LOVD v.2.0: the next generation in gene variant databases. Hum Mutat. 2011;32:557-563.

     Not in our library10.1002/humu.21438

180. 180.
     UniProt: the universal protein knowledgebase · 2016

     No notice
181. 181.

     Matuszewski S, Hildebrandt ME, Ghenu A-H, Jensen JD, Bank C. A statistical guide to the design of deep mutational scanning experiments. Genetics. 2016;204:77-87.

     Not in our library10.1534/genetics.116.190462

182. 182.
     A statistical framework for analyzing deep mutational scanning data · 2017

     Corrected
183. 183.

     Fowler DM, Araya CL, Gerard W, Fields S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics. 2011;27:3430-3431.

     Not in our library10.1093/bioinformatics/btr577

184. 184.

     Hietpas R, Roscoe B, Jiang L, Bolon DNA. Fitness analyses of all possible point mutations for regions of genes in yeast. Nat Protoc. 2012;7:1382-1396.

     Not in our library10.1038/nprot.2012.069

185. 185.

     Python. https://www.python.org/.

     Not in our library

186. 186.

     Django. https://www.djangoproject.com/.

     Not in our library

187. 187.

     PostgreSQL. https://www.postgresql.org/.

     Not in our library

188. 188.

     RabbitMQ. https://www.rabbitmq.com/.

     Not in our library

189. 189.

     Celery. http://www.celeryproject.org/.

     Not in our library

190. 190.

     Saint-Andre P, Klensin J. Uniform Resource Names (URNs). 2017.

     Not in our library

191. 191.

     den Dunnen JT, Dalgleish R, Maglott DR, Hart RK, Greenblatt MS, McGowan-Jordan J, et al. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat. 2016;37:564-569.

     Not in our library10.1002/humu.22981

192. 192.

     ORCID. https://orcid.org/.

     Not in our library

193. 193.

     D. Hardt E. The OAuth 2.0 Authorization Framework. 2012. http://www.rfc-editor.org/info/rfc6749.

     Not in our library

194. 194.

     Markdown. https://daringfireball.net/projects/markdown/.

     Not in our library

195. 195.

     Pandoc. https://pandoc.org/.

     Not in our library

196. 196.

     R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. https://www.R-project.org.

     Not in our library

197. 197.

     Docker. https://www.docker.com/index.html.

     Not in our library

198. 198.
     FreeSASA: An open source C library for solvent accessible surface area calculations · 2016

     No notice
199. 199.

     Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22:2577-2637.

     Not in our library10.1002/bip.360221211

200. 200.

     Livingstone CD, Barton GJ. Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation. Bioinformatics. 1993;9:745-756.

     Not in our library10.1093/bioinformatics/9.6.745

201. 201.

     Sievers F, Higgins DG. Clustal Omega, accurate alignment of very large numbers of sequences. Methods Mol Biol Clifton NJ. 2014;1079:105-116.

     Not in our library10.1007/978-1-62703-646-7_6

202. 202.

     Creative Commons — Attribution-NonCommercial-ShareAlike 4.0 International — CC BY-NC-SA 4.0. https://creativecommons.org/licenses/by-nc-sa/4.0/.

     Not in our library

203. 203.

     Creative Commons — Attribution 4.0 International — CC BY 4.0. https://creativecommons.org/licenses/by/4.0/.

     Not in our library

204. 204.

     Creative Commons — CC0 1.0 Universal. https://creativecommons.org/publicdomain/zero/1.0/.

     Not in our library

205. 205.

     Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28:235-242.

     Not in our library10.1093/nar/28.1.235

206. 206.
     Analysis of protein-coding genetic variation in 60,706 humans · 2016

     No notice
207. 207.

     Wu Y, Weile J, Cote AG, Sun S, Knapp J, Verby M, et al. A web application and service for imputing and visualizing missense variant effect maps. Bioinforma Oxf Engl. 2019;35:3191-3193.

     Not in our library10.1093/bioinformatics/btz012

208. 208.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
209. 209.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
210. 210.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
211. 211.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
212. 212.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
213. 213.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
214. 214.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
215. 215.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
216. 216.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
217. 217.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
218. 218.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice
219. 219.
     MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect · 2019

     No notice

Cited by papers we hold 12

• Site-saturation mutagenesis of 500 human protein domains · 2025
  No notice
• CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions · 2024
  No notice
• Whole genome sequencing in clinical practice · 2024
  No notice
• An Atlas of Variant Effects to understand the genome at nucleotide resolution · 2023
  No notice
• Ensembl 2024 · 2023
  No notice
• Genome-wide prediction of disease variant effects with a deep protein language model · 2023
  No notice
• Updated benchmarking of variant effect predictors using deep mutational scanning · 2023
  No notice
• ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines · 2022
  Not checked
• A guide for the diagnosis of rare and undiagnosed disease: beyond the exome · 2022
  No notice
• Embeddings from protein language models predict conservation and variant effects · 2021
  No notice
• Neural networks to learn protein sequence–function relationships from deep mutational scanning data · 2021
  No notice
• Improved pathogenicity prediction for rare human missense variants · 2021
  Corrected

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.