A computational algorithm to predict shRNA potency
The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular...
Gespeichert in:
| Veröffentlicht in: | Molecular cell Jg. 56; H. 6; S. 796 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
18.12.2014
|
| Schlagworte: | |
| ISSN: | 1097-4164, 1097-4164 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and used this method to generate ∼250,000 shRNA efficacy data points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that specifically target the majority of each gene's multiple transcripts. We validated the performance of our shRNA designs using several orthogonal strategies and constructed genome-wide collections of shRNAs for humans and mice based on our approach. |
|---|---|
| AbstractList | The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and used this method to generate ∼250,000 shRNA efficacy data points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that specifically target the majority of each gene's multiple transcripts. We validated the performance of our shRNA designs using several orthogonal strategies and constructed genome-wide collections of shRNAs for humans and mice based on our approach. The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and used this method to generate ∼250,000 shRNA efficacy data points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that specifically target the majority of each gene's multiple transcripts. We validated the performance of our shRNA designs using several orthogonal strategies and constructed genome-wide collections of shRNAs for humans and mice based on our approach.The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and used this method to generate ∼250,000 shRNA efficacy data points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that specifically target the majority of each gene's multiple transcripts. We validated the performance of our shRNA designs using several orthogonal strategies and constructed genome-wide collections of shRNAs for humans and mice based on our approach. |
| Author | Maceli, Ashley Gordon, Assaf Fellmann, Christof Erard, Nicolas Chang, Kenneth Demerdash, Osama El Wagenblast, Elvin Zhou, Xin Marran, Krista Kim, Sun Hannon, Gregory J Knott, Simon R V |
| Author_xml | – sequence: 1 givenname: Simon R V surname: Knott fullname: Knott, Simon R V organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 2 givenname: Ashley surname: Maceli fullname: Maceli, Ashley organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 3 givenname: Nicolas surname: Erard fullname: Erard, Nicolas organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 4 givenname: Kenneth surname: Chang fullname: Chang, Kenneth organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 5 givenname: Krista surname: Marran fullname: Marran, Krista organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 6 givenname: Xin surname: Zhou fullname: Zhou, Xin organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 7 givenname: Assaf surname: Gordon fullname: Gordon, Assaf organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 8 givenname: Osama El surname: Demerdash fullname: Demerdash, Osama El organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 9 givenname: Elvin surname: Wagenblast fullname: Wagenblast, Elvin organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 10 givenname: Sun surname: Kim fullname: Kim, Sun organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 11 givenname: Christof surname: Fellmann fullname: Fellmann, Christof organization: Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA – sequence: 12 givenname: Gregory J surname: Hannon fullname: Hannon, Gregory J organization: Cancer Research UK Cambridge Insitute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25435137$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNj01LxDAYhIOsuOvqPxDp0Utr3qRJk-Oy-AWLgui5pMlbt0vb1CY97L-34AqeZhieGZhLsuh9j4TcAM2Agrw_ZJ1vLbYZo5DPUUaZOCMroLpIc5D54p9fkssQDnQGhdIXZMlEzgXwYkXYJrG-G6ZoYuN70yam_fJjE_ddEn0yjOgaG5Owf3_dJIOP2NvjFTmvTRvw-qRr8vn48LF9TndvTy_bzS61QrKYMmSUu6oWWjnFnJJFpbnklhoF1BZCqUobqBCoQYcKDC9kgbSmMFecqNma3P3uDqP_njDEsmvCfLg1PfoplCC5zrWQQGf09oROVYeuHMamM-Ox_PvJfgAgmVc8 |
| CitedBy_id | crossref_primary_10_1038_nature25465 crossref_primary_10_1038_s41586_021_03421_6 crossref_primary_10_1038_s43018_022_00353_6 crossref_primary_10_7554_eLife_49796 crossref_primary_10_1016_j_molcel_2015_08_015 crossref_primary_10_1016_j_stem_2019_09_008 crossref_primary_10_1016_j_biomaterials_2017_05_032 crossref_primary_10_1016_j_ccell_2020_04_006 crossref_primary_10_1016_j_jim_2019_02_002 crossref_primary_10_1016_j_molmet_2019_04_007 crossref_primary_10_1016_j_stem_2021_07_003 crossref_primary_10_1016_j_cels_2016_01_012 crossref_primary_10_1073_pnas_1508821112 crossref_primary_10_1016_j_jprot_2021_104424 crossref_primary_10_3389_fbioe_2022_913728 crossref_primary_10_3390_pharmaceutics15020685 crossref_primary_10_1016_j_omtn_2023_102057 crossref_primary_10_7554_eLife_80447 crossref_primary_10_1038_nm_4475 crossref_primary_10_1186_s12859_017_1697_6 crossref_primary_10_1158_1078_0432_CCR_14_2180 crossref_primary_10_1038_s41576_020_0247_7 crossref_primary_10_7554_eLife_46793 crossref_primary_10_1186_s13059_023_03020_w crossref_primary_10_1186_s12935_019_0726_0 crossref_primary_10_1038_s41598_022_13783_0 crossref_primary_10_1083_jcb_201809123 crossref_primary_10_1146_annurev_genet_120215_034902 crossref_primary_10_1182_blood_2018_01_828418 crossref_primary_10_1016_j_devcel_2021_10_008 crossref_primary_10_1007_s11240_022_02426_x crossref_primary_10_1016_j_molmet_2025_102175 crossref_primary_10_1016_j_virs_2024_05_001 crossref_primary_10_15252_embr_202153691 crossref_primary_10_1007_s12015_024_10836_x crossref_primary_10_1016_j_celrep_2024_114747 crossref_primary_10_1038_nature24993 crossref_primary_10_1016_j_ymeth_2016_04_003 crossref_primary_10_1016_j_molcel_2017_06_030 crossref_primary_10_1083_jcb_201802144 crossref_primary_10_1093_nar_gky546 crossref_primary_10_1111_jdi_13646 crossref_primary_10_7554_eLife_25607 crossref_primary_10_1016_j_molmet_2019_09_009 crossref_primary_10_1111_febs_13248 crossref_primary_10_3389_fnmol_2018_00178 crossref_primary_10_1158_2159_8290_CD_20_1202 crossref_primary_10_1172_JCI172436 crossref_primary_10_1016_j_omtn_2023_102038 crossref_primary_10_1038_mt_2015_113 crossref_primary_10_1093_narmme_ugaf030 crossref_primary_10_1523_JNEUROSCI_0254_18_2018 crossref_primary_10_1016_j_it_2015_03_007 crossref_primary_10_1371_journal_pbio_2003213 crossref_primary_10_26508_lsa_201900623 crossref_primary_10_3390_cells10030523 crossref_primary_10_1038_s41467_022_29822_3 crossref_primary_10_1016_j_nlm_2015_07_005 crossref_primary_10_1038_s41467_017_00772_5 crossref_primary_10_1038_nbt_3807 crossref_primary_10_1172_JCI94840 crossref_primary_10_1038_s41467_020_15188_x crossref_primary_10_1016_j_neuron_2025_03_035 crossref_primary_10_1126_science_aao4227 crossref_primary_10_1093_nar_gkaa1260 crossref_primary_10_1016_j_celrep_2023_112791 crossref_primary_10_1016_j_neuron_2019_08_035 |
| ContentType | Journal Article |
| Copyright | Copyright © 2014 Elsevier Inc. All rights reserved. |
| Copyright_xml | – notice: Copyright © 2014 Elsevier Inc. All rights reserved. |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.molcel.2014.10.025 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1097-4164 |
| ExternalDocumentID | 25435137 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Cancer Research UK grantid: 21143 – fundername: NIGMS NIH HHS grantid: R01 GM062534 – fundername: NCI NIH HHS grantid: P01 CA013106 – fundername: NIGMS NIH HHS grantid: R37 GM062534 – fundername: NCI NIH HHS grantid: P30 CA045508 |
| GroupedDBID | --- --K -DZ -~X 0R~ 123 1~5 2WC 4.4 457 4G. 5RE 5VS 62- 7-5 AAEDT AAEDW AAHBH AAKRW AAKUH AALRI AAMRU AAQFI AAVLU AAXUO AAYWO ABDGV ABJNI ABMAC ACGFO ACGFS ACNCT ACVFH ADBBV ADCNI ADEZE ADVLN AEFWE AENEX AEUPX AEXQZ AFFNX AFPUW AFTJW AGCQF AGHFR AGKMS AIGII AITUG AKAPO AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ APXCP ASPBG AVWKF AZFZN BAWUL CGR CS3 CUY CVF DIK DU5 E3Z EBS ECM EFKBS EIF EJD F5P FCP FDB FEDTE FIRID HH5 HVGLF IH2 IHE IXB J1W JIG KQ8 L7B M3Z M41 N9A NPM O-L O9- OK1 P2P RIG ROL RPZ SDG SES SSZ TR2 7X8 |
| ID | FETCH-LOGICAL-c562t-2e203dbf598d82d867b9363c0a810c7588b9a1be10aede81a3767e0f01f59d5f2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 79 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000346653500009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1097-4164 |
| IngestDate | Sun Sep 28 11:43:41 EDT 2025 Mon Jul 21 06:03:03 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| License | Copyright © 2014 Elsevier Inc. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c562t-2e203dbf598d82d867b9363c0a810c7588b9a1be10aede81a3767e0f01f59d5f2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.molcel.2014.10.025 |
| PMID | 25435137 |
| PQID | 1639495610 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_1639495610 pubmed_primary_25435137 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-12-18 |
| PublicationDateYYYYMMDD | 2014-12-18 |
| PublicationDate_xml | – month: 12 year: 2014 text: 2014-12-18 day: 18 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Molecular cell |
| PublicationTitleAlternate | Mol Cell |
| PublicationYear | 2014 |
| SSID | ssj0014589 |
| Score | 2.44399 |
| Snippet | The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 796 |
| SubjectTerms | Algorithms Base Sequence Cell Line, Tumor Computer Simulation Consensus Sequence Gene Knockdown Techniques Humans MicroRNAs - genetics Models, Genetic Molecular Sequence Data RNA, Small Interfering - genetics Software |
| Title | A computational algorithm to predict shRNA potency |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/25435137 https://www.proquest.com/docview/1639495610 |
| Volume | 56 |
| WOSCitedRecordID | wos000346653500009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA7qKnjx_VhfRPBaTdqkTU6yiIsXyyIKeytJmrrCblu3Vdh_b6btsidB8NJTE8okmX4z32Q-hG5IJN05cEGO5j73mNTMkym3nnHYVjMZMqpasYkojsV4LEddwq3qyiqXPrFx1GlhIEd-53CDBDBPyX356YFqFLCrnYTGOuoFDsrAro7GKxaB8UYCD0hWzwEPtrw619R3zYqpsUA-UHYL9V0-_x1kNj-b4e5_P3MP7XQwEw_afbGP1mx-gLZa4cnFIfIH2DRyDl0qEKvpu5ulnsxwXeByDuxNjavJSzzAZQGwenGE3oaPrw9PXqee4Owc-rXnW58Eqc64FKnwUxFGWgZhYIgSlBgXJggtFdWWEmVTK6iCvi6WZIS6ISnP_GO0kRe5PUXYveYiaGkZpZZJxQTT0LfdhEHjEngfXS-NkbjdCZSDym3xVSUrc_TRSWvRpGzbaCRwDZ_TIDr7w-hztA0LBXUkVFygXubOpr1Em-a7_qjmV82yu2c8ev4BhWG0SQ |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+computational+algorithm+to+predict+shRNA+potency&rft.jtitle=Molecular+cell&rft.au=Knott%2C+Simon+R+V&rft.au=Maceli%2C+Ashley&rft.au=Erard%2C+Nicolas&rft.au=Chang%2C+Kenneth&rft.date=2014-12-18&rft.eissn=1097-4164&rft.volume=56&rft.issue=6&rft.spage=796&rft_id=info:doi/10.1016%2Fj.molcel.2014.10.025&rft_id=info%3Apmid%2F25435137&rft_id=info%3Apmid%2F25435137&rft.externalDocID=25435137 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1097-4164&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1097-4164&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1097-4164&client=summon |