HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation
Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases—so-called batch effects. D...
Uložené v:
| Vydané v: | BMC bioinformatics Ročník 26; číslo 1; s. 47 - 16 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
11.02.2025
BioMed Central Ltd BMC |
| Predmet: | |
| ISSN: | 1471-2105, 1471-2105 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Background
Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases—so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment.
Results
In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a “unique removal” strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets.
Conclusion
The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. |
|---|---|
| AbstractList | Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases--so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment. Results In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a "unique removal" strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets. Conclusion The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. Keywords: Batch effects, Proteomics, Computational efficiency, Big data, Dataset integration Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases-so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment.BACKGROUNDData adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases-so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment.In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a "unique removal" strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets.RESULTSIn this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a "unique removal" strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets.The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction.CONCLUSIONThe proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases--so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment. In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a "unique removal" strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets. The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases—so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment. Results In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a “unique removal” strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets. Conclusion The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. Abstract Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases—so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment. Results In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a “unique removal” strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets. Conclusion The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases-so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment. In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severely reduce runtime, while still supporting parallel architectures. Additionally, a "unique removal" strategy has been integrated into HarmonizR to maintain even more features for adjustment in datasets, showing a feature rescue of up to 103.9% for our tested datasets. In this work, we show (1) severely improved runtime for both small and large, real datasets and (2) the ability retain more features from the integrated dataset during adjustment, showing a feature rescue of up to 103.9% for our tested datasets. The proposed improvements tackle the previous shortcomings of the published HarmonizR version. Since HarmonizR was mainly developed for dataset integration on rare tumor entities, it did not include runtime improvements beyond parallelization, which has been addressed in this update. An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction. |
| Audience | Academic |
| Author | Neumann, Philipp Neumann, Julia E. Schlumbohm, Simon |
| Author_xml | – sequence: 1 givenname: Simon surname: Schlumbohm fullname: Schlumbohm, Simon email: schlumbohm@hsu-hh.de organization: Chair for High Performance Computing, Helmut-Schmidt-University, University of the Federal Armed Forces Hamburg – sequence: 2 givenname: Julia E. surname: Neumann fullname: Neumann, Julia E. organization: Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Institute of Neuropathology, University Medical Center Hamburg-Eppendorf (UKE) – sequence: 3 givenname: Philipp surname: Neumann fullname: Neumann, Philipp organization: IT-Department, German Electron Synchrotron (DESY), High Performance Computing and Data Science, University of Hamburg |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39934730$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkktv1DAUhSNURB_wB1igSGxgkeJHbCdsqqoCWqkSUoG1deNH8JDYg-2MKL8ez0xBHQl5Yev6u0c-vue0OvLBm6p6idE5xh1_lzDpWN8gwhrEkaBN_6Q6wa3ADcGIHT06H1enKa0QwqJD7Fl1TPuetoKik8pdQ5yDd7_v3tfDFNQP58cavK5TOSwTxNoayEs0tYYMNejVkvJsfK7dvI5hY-q4-OxmUxtrnXLGq_td_w5fR5NM3EB2wT-vnlqYknnxsJ9V3z5--Hp13dx-_nRzdXnbaEZIbnAvhqGnbCCCo0ENA7WqH3oFCoAwZnhHtABB-QAtgY4RpE3XWwRUKN6hjp5VN3tdHWAl19HNEO9lACd3hRBHCTE7NRlplUYtIlRjS1ulDSjbgwZONOeYgC1aF3ut9TLMRqviO8J0IHp44913OYaNLPPBAiNaFN48KMTwczEpy9klZaYJvAlLkhRz1rG2DKqgr_foCOVtzttQJNUWl5cdEaT8DN_aO_8PVZY2s1MlINaV-kHD24OGwmTzK4-wpCRvvtwdsq8e-_1n9G9eCkD3QCpXfjRRrsISfZmnxGjrmst9KmVJpdylUvb0D1sH1Yg |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: The Author(s) 2025 2025 |
| DBID | C6C CGR CUY CVF ECM EIF NPM ISR 7X8 5PM DOA |
| DOI | 10.1186/s12859-025-06073-9 |
| DatabaseName | Springer Nature OA Free Journals Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals (DOAJ) |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 16 |
| ExternalDocumentID | oai_doaj_org_article_fcd04023d1f34cdeacf9ada62d6612af PMC11817103 A827219768 39934730 10_1186_s12859_025_06073_9 |
| Genre | Journal Article |
| GeographicLocations | Germany |
| GeographicLocations_xml | – name: Germany |
| GrantInformation_xml | – fundername: Deutsche Forschungsgemeinschaft funderid: http://dx.doi.org/10.13039/501100001659 – fundername: Helmut-Schmidt-Universität Universität der Bundeswehr Hamburg (3103) |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB ALIPV CGR CUY CVF ECM EIF NPM AFFHD 7X8 5PM |
| ID | FETCH-LOGICAL-d522t-197bb935b2760bcbb3fc9b9cacaa255e682d7a736ba42a8520de89f0a37c68083 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001418256300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Tue Oct 14 18:51:22 EDT 2025 Tue Nov 04 02:06:11 EST 2025 Thu Oct 02 07:15:23 EDT 2025 Sat Nov 29 13:51:55 EST 2025 Sat Nov 29 10:30:33 EST 2025 Thu Nov 13 15:59:11 EST 2025 Thu May 08 05:30:03 EDT 2025 Sat Sep 06 07:27:29 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Big data Computational efficiency Dataset integration Batch effects Proteomics |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-d522t-197bb935b2760bcbb3fc9b9cacaa255e682d7a736ba42a8520de89f0a37c68083 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/fcd04023d1f34cdeacf9ada62d6612af |
| PMID | 39934730 |
| PQID | 3165854471 |
| PQPubID | 23479 |
| PageCount | 16 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_fcd04023d1f34cdeacf9ada62d6612af pubmedcentral_primary_oai_pubmedcentral_nih_gov_11817103 proquest_miscellaneous_3165854471 gale_infotracmisc_A827219768 gale_infotracacademiconefile_A827219768 gale_incontextgauss_ISR_A827219768 pubmed_primary_39934730 springer_journals_10_1186_s12859_025_06073_9 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-11 |
| PublicationDateYYYYMMDD | 2025-02-11 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2025 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| SSID | ssj0017805 |
| Score | 2.4700263 |
| Snippet | Background
Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from... Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell)... Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from... Abstract Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data... |
| SourceID | doaj pubmedcentral proquest gale pubmed springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 47 |
| SubjectTerms | Algorithms Analysis Batch effects Big data Bioinformatics Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computational efficiency Computer Appl. in Life Sciences Dataset integration Electronic data processing Genomics Life Sciences Methods Microarrays Proteomics RNA sequencing Software |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bi9UwEA66Kvji_VJdJYrgi8WTpG0S31ZxWUEWOavLvoVc1wr2yOk5gv56Z9J2sasv-tpMoCHfXJLMfEPIM1UngG7jwPrxAAcU7UsNoqUPkfmKs8ByfcXxe3l4qE5O9IexKKyfst2nJ8lsqbNaq-Zlz5BrrcT2q4sGgFnqi-QSuDuFDRuWR8dnbwfI0j-Vx_x13kjP_6cR_s0Lnc-QPPdMmr3P_vX_--8b5NoYbdK9AR43yYXY3SJXhv6TP26T9sCuAYbtz-Ur6sCr4bU5tV2geIGA-ak0xUz8STGRlNrwZdvnrHTa5ruISJG3oP0aacxMFFjGmedncUyxna5875BP-28_vjkox94LZYCIbFMyLZ3TonZcNgvnnRPJa6e99dbCKSQ2igdppWicrbhVNV-EqHRaWCE9dvMQd8lOt-rifUJrl4LUKXLvmso6ZsEBcguRlpZCVsEV5DVuh_k20GsYJLzOH1brUzPqj0k-gLnhIrAkKgCS9UnbYBseIMDgNhXkKW6mQUqLDnNmTu227827o6XZUxyOuRB2qYI8H4XSCrYVF5NLEOA_kQVrJrk7kwSd87PhJxNmDA5holoXV9veCAYhXV2Byy_IvQFDZwvDWLACi1oQNUPXbOXzka79nCm_sTwYYkFRkBcTyMxobHqTz3GqMQPSDCDNZKQZ_eDfxB-SqzzjlJeM7ZKdzXobH5HL_vum7dePs5b9AoKzJ3E priority: 102 providerName: Springer Nature |
| Title | HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation |
| URI | https://link.springer.com/article/10.1186/s12859-025-06073-9 https://www.ncbi.nlm.nih.gov/pubmed/39934730 https://www.proquest.com/docview/3165854471 https://pubmed.ncbi.nlm.nih.gov/PMC11817103 https://doaj.org/article/fcd04023d1f34cdeacf9ada62d6612af |
| Volume | 26 |
| WOSCitedRecordID | wos001418256300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: Open Access: BioMedCentral Open Access Titles customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Biological Science customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LbxMxELaggMQF8SwLJTIIiQurZr0P29xa1KoVEK1SqAIXy09YJDYomyDBr2fGu0HdcuDCxVLiSbT2jOfhnfmGkOeiDCC6lQHtxxwEKNKmEkhT63xmC5a5LNZXnL_ls5lYLGR9odUX5oT18MD9xu0H60DOWO6ykBfwD9oGqZ2umAPLwnRA7TvlchtMDe8PEKl_WyIjqv0uQ5y2FFu3TisQ6lQOEP1_K-ILluhyluSlV6XRAh3fJrcG15Ee9I98h1zx7V1yo28m-fMeaU70CmSq-TV_RQ2YKLwDp7p1FG8DMNmUBh9RPClmhVLtvm66mGJOm3ix4CmCEDTfPPURVgJrMuPvIznmy27vb--TD8dH71-fpEMjhdSBe7VOM8mNkXlpGK-mxhqTByuNtNpqDSGFrwRzXPO8MrpgWpRs6ryQYapzbrE1R_6A7LTL1j8ktDTBcRk8s6YqtMk0WDOmwW2SPOeFMwk5xH1V33usDIXo1fEL4KkaeKr-xdOEPEOuKMSnaDEB5rPedJ06PZurA8EgZgUfSiTkxUAUlsAfXEysJ4DnREirEeXeiBIOkB1NP90yX-EUZp21frnpVJ6Bf1YWYL8TstsLw5-FoWNXgHpMiBiJyWjl45m2-RLxu7HWFxy7PCEvtxKlBs3RqRiUiUr1IqtAZFUUWSUf_Y-tfUxusngMWJple2Rnvdr4J-S6_bFuutWEXOULHkcxIdcOj2b1fBIPFoxveDrBzNgaxrr8BPP16bv6I3yan53_BgAwLfU |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELaggODC-xEoYBASl0as7bzMrSCqrVhWaFuq3iw_21Qiiza7SPDrmXGSihQucI3HkS1_nhnbM98Q8qrKA0C3MKD9uIMDirSpBNHUOs9sxpljMb_iaFbO59XxsfzcJ4W1Q7T78CQZNXXc1lXxpmXItZZi-dVJAcBM5WVyBf6WI2P-4uDo_O0AWfqH9Ji_9uvp-f9Uwr9ZoYsRkheeSaP12bv1f-O-TW723ibd7eBxh1zyzV1yras_-eMeqad6BTCsfy7eUgNWDa_NqW4cxQsEjE-lwUfiT4qBpFS7s00bo9JpHe8iPEXegvqrpz4yUWAaZ-wfxTHEdrjyvU--7H04fD9N-9oLqQOPbJ0yWRojRW54WUyMNUYEK4202moNpxBfVNyVuhSF0RnXVc4nzlcyTLQoLVbzEA_IVrNs_CNCcxNcKYPn1hSZNkyDAeQaPC1ZijJzJiHvcDnUt45eQyHhdfywXJ2ofv-oYB2oGy4cCyIDIGkbpHa64A4cDK5DQl7iYiqktGgwZuZEb9pW7R8s1G7F4ZgLbleVkNe9UFjCsuJkYgoCjBNZsEaS2yNJ2HN21PxiwIzCJgxUa_xy0yrBwKXLMzD5CXnYYeh8YugLZqBRE1KN0DWa-bilqU8j5TemB4MvKBKyM4BM9cqmVfEcVxWqQ5oCpKmINCUf_5v4c3J9evhppmb7849PyA0eMctTxrbJ1nq18U_JVft9XberZ3HH_QJfkipV |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwELagHOKF-wgUMAiJF6Ku7Rw2b-VYtaJaVS1UfbN8liCRrTa7SPDrmXGyVVN4QbzGYymWP4-_sWc-E_JKlhGgW1nwftxDgKJcrsA0dz4wV3DmWaqvONqrZzN5fKz2z1Xxp2z39ZVkX9OAKk3tcuvUx36Jy2qrY6i7luNTrJMKQJqry-RKgYn0GK8fHp3dI6Bi_7pU5q_9Bqn-Px3yuR3pYrbkhSvTtBNNb_3_GG6TmwMLpds9bO6QS6G9S67171L-vEeaHbMAeDa_Dt5SC7sdHqdT03qKBwuYt0pjSIKgFBNMqfHfVl3KVqdNOqMIFPUMmu-BhqRQgeWdqX8yx9Tb9VHwffJl-vHz-518eJMh98DUljlTtbVKlJbX1cQ6a0V0yipnnDEQnYRKcl-bWlTWFNzIkk98kCpOjKgdvvIhHpCNdt6GR4SWNvpaxcCdrQpjmYGNkRtgYKoWdeFtRt7h1OjTXnZDoxB2-jBfnOhhXenoPLghLjyLogCAGReV8abiHogHNzEjL3FiNUpdtJhLc2JWXad3Dw_0tuQQ_gIdkxl5PRjFOUwxDiaVJsB_ojrWyHJzZAlr0Y2aX6zxo7EJE9jaMF91WjCgemUBVCAjD3s8nQ0MOWIBnjYjcoS00cjHLW3zNUmBY9kwcESRkTdrwOnBCXU6xXey0j3SNCBNJ6Rp9fjfzJ-T6_sfpnpvd_bpCbnBE2R5ztgm2VguVuEpuep-LJtu8Swtvt_WdTM5 |
| 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=HarmonizR%3A+blocking+and+singular+feature+data+adjustment+improve+runtime+efficiency+and+data+preservation&rft.jtitle=BMC+bioinformatics&rft.au=Schlumbohm%2C+Simon&rft.au=Neumann%2C+Julia+E&rft.au=Neumann%2C+Philipp&rft.date=2025-02-11&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=26&rft.issue=1&rft.spage=47&rft_id=info:doi/10.1186%2Fs12859-025-06073-9&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |