{"doi":"10.1126/science.1192001","title":"Rapid Construction of Empirical RNA Fitness Landscapes","abstract":"<jats:title>Nonrandom Walks</jats:title>\n          <jats:p>\n            Fitness landscapes of RNA sequences can help us to see the connection between all possible phenotypes and all possible genotypes. In molecular evolution, the fitness landscape is formalized as the distribution of fitness in sequence space, a hyperdimensional object of staggering complexity. To better understand these complex processes,\n            <jats:bold>Pitt and Ferré-D'Amaré</jats:bold>\n            (p.\n            <jats:related-article xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"doi\" page=\"376\" related-article-type=\"in-this-issue\" vol=\"330\" xlink:href=\"10.1126/science.1192001\">376</jats:related-article>\n            ; see the Perspective by\n            <jats:bold>\n              <jats:related-article xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"doi\" issue=\"6002\" page=\"330\" related-article-type=\"in-this-issue\" vol=\"330\" xlink:href=\"10.1126/science.1197667\">Kluwe and Ellington</jats:related-article>\n            </jats:bold>\n            ) used deep sequencing to analyze the composition of a population of variants of an RNA ligase ribozyme, both before and after one round of in vitro selection. Relating the sequences of individuals in the population to a measure of their corresponding fitness provides a detailed picture of an evolutionary fitness landscape from empirical data.\n          </jats:p>","journal":"Science","year":2010,"id":589316,"datarank":8.394804552330218,"base_score":5.170483995038151,"endowment":5.170483995038151,"self_citation_contribution":0.7755725992557229,"citation_network_contribution":7.619231953074495,"self_endowment_contribution":0.7755725992557229,"citer_contribution":7.619231953074495,"corpus_percentile":null,"corpus_rank":null,"citation_count":175,"citer_count":166,"citers_with_citation_signal":149,"citers_with_endowment":149,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1507758,"name":"Adrian R. Ferré-D’Amaré","orcid":null,"position":1,"is_corresponding":false},{"id":937639,"name":"Jason N. Pitt","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Rapid Construction of Empirical RNA Fitness Landscapes","abstract":"<jats:title>Nonrandom Walks</jats:title>\n          <jats:p>\n            Fitness landscapes of RNA sequences can help us to see the connection between all possible phenotypes and all possible genotypes. In molecular evolution, the fitness landscape is formalized as the distribution of fitness in sequence space, a hyperdimensional object of staggering complexity. To better understand these complex processes,\n            <jats:bold>Pitt and Ferré-D'Amaré</jats:bold>\n            (p.\n            <jats:related-article xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"doi\" page=\"376\" related-article-type=\"in-this-issue\" vol=\"330\" xlink:href=\"10.1126/science.1192001\">376</jats:related-article>\n            ; see the Perspective by\n            <jats:bold>\n              <jats:related-article xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"doi\" issue=\"6002\" page=\"330\" related-article-type=\"in-this-issue\" vol=\"330\" xlink:href=\"10.1126/science.1197667\">Kluwe and Ellington</jats:related-article>\n            </jats:bold>\n            ) used deep sequencing to analyze the composition of a population of variants of an RNA ligase ribozyme, both before and after one round of in vitro selection. Relating the sequences of individuals in the population to a measure of their corresponding fitness provides a detailed picture of an evolutionary fitness landscape from empirical data.\n          </jats:p>","is_dataset_classified":null,"base_score":5.170483995038151,"endowment":5.170483995038151,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"20947767","pmcid":"PMC3392653","openalex_id":"https://openalex.org/W2032433599","authors":[],"funders":[{"funder_name":"Intramural NIH HHS","grant_id":"ZIA HL006102","title":null},{"funder_name":"Intramural NIH HHS","grant_id":"Z99 HL999999","title":null},{"funder_name":"Howard Hughes Medical Institute","grant_id":"","title":null},{"funder_name":"Howard Hughes Medical Institute","grant_id":"","title":null}],"total_grants":4,"fwci":5.3698,"citation_percentile":0.9650419,"influential_citations":0,"citation_trend":[{"year":2012,"count":12},{"year":2013,"count":18},{"year":2014,"count":11},{"year":2015,"count":16},{"year":2016,"count":17},{"year":2017,"count":12},{"year":2018,"count":9},{"year":2019,"count":15},{"year":2020,"count":10},{"year":2021,"count":13},{"year":2022,"count":13},{"year":2023,"count":6},{"year":2024,"count":8},{"year":2025,"count":5}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://www.science.org/doi/pdf/10.1126/science.1192001","host_type":"publisher"},{"url":"https://doi.org/10.1126/science.1192001","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/20947767","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3392653","host_type":"repository"}],"fields_of_study":["RNA and protein synthesis mechanisms","RNA Research and Splicing","CRISPR and Genetic Engineering","Algorithms","Base Sequence","Biocatalysis","Evolution, Molecular","Genotype","Nucleic Acid Conformation","Phenotype","Point Mutation","RNA","RNA, Catalytic","Selection, Genetic","Sequence Analysis, RNA"],"mesh_terms":["Algorithms","Base Sequence","Genotype","Nucleic Acid Conformation","Phenotype","RNA","Selection, Genetic","RNA, Catalytic","Point Mutation","Sequence Analysis, RNA","Evolution, Molecular","Biocatalysis"],"keywords":["RNA","Computational biology","Biology","Genetics","Gene"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"gen"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-23T16:18:07.528966Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}