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Research Article
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Open Access

Identification of human gene research articles with wrongly identified nucleotide sequences

View ORCID ProfileYasunori Park, View ORCID ProfileRachael A West, View ORCID ProfilePranujan Pathmendra, Bertrand Favier, Thomas Stoeger, View ORCID ProfileAmanda Capes-Davis, View ORCID ProfileGuillaume Cabanac, Cyril Labbé, View ORCID ProfileJennifer A Byrne  Correspondence email
Yasunori Park
1Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
Roles: Data curation, Formal analysis, Methodology, Writing—original draft, review, and editing
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  • ORCID record for Yasunori Park
Rachael A West
1Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
2Children’s Cancer Research Unit, Kids Research, The Children’s Hospital at Westmead, Westmead, Australia
Roles: Data curation, Formal analysis, Writing—original draft, review, and editing
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Pranujan Pathmendra
1Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
Roles: Data curation, Formal analysis, Writing—review and editing
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  • ORCID record for Pranujan Pathmendra
Bertrand Favier
3Université Grenoble Alpes, Translationnelle et Innovation en Médecine et Complexité, Grenoble, France
Roles: Data curation, Formal analysis, Writing—review and editing
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Thomas Stoeger
4Successful Clinical Response in Pneumonia Therapy Systems Biology Center, Northwestern University, Evanston, IL, USA
5Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
6Center for Genetic Medicine, Northwestern University School of Medicine, Chicago, IL, USA
Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing—original draft, review, and editing
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Amanda Capes-Davis
1Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
7CellBank Australia, Children’s Medical Research Institute, Westmead, Australia
Roles: Conceptualization, Funding acquisition, Writing—review and editing
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Guillaume Cabanac
8Computer Science Department, Institut de Recherche en Informatique de Toulouse, Unité Mixte de Recherche 5505 Centre National de la Recherche Scientifique (CNRS), University of Toulouse, Toulouse, France
Roles: Conceptualization, Data curation, Formal analysis, Methodology, Writing—review and editing
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  • ORCID record for Guillaume Cabanac
Cyril Labbé
9Université Grenoble Alpes, CNRS, Grenoble INP, Laboratoire d'Informatique de Grenoble, Grenoble, France
Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing—review and editing
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Jennifer A Byrne
1Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
10New South Wales Health Statewide Biobank, New South Wales Health Pathology, Camperdown, Australia
Roles: Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing—original draft, review, and editing
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  • ORCID record for Jennifer A Byrne
  • For correspondence: jennifer.byrne@health.nsw.gov.au
Published 12 January 2022. DOI: 10.26508/lsa.202101203
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Figures

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  • Figure 1.
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    Figure 1. Diagram describing the five literature corpora screened by S&B.

    For each corpus (top row), the diagram shows the numbers of articles that were (i) screened by S&B (white), (ii) flagged by S&B with sequences manually verified (grey), and (iii) found to be problematic by describing at least one wrongly identified nucleotide sequence (dark grey). Total numbers of problematic articles and wrongly identified sequences are indicated below the diagram, corrected for duplicate articles between the corpora.

  • Figure 2.
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    Figure 2. Percentages of sequence identity error types in each corpus.

    Percentages of wrongly identified nucleotide sequence reagents that correspond to the three identity error types (y-axis) in each corpus (x-axis). Percentages corresponding to each error type are indicated, rounded to the nearest single digit. The numbers of incorrect sequences in each corpus are shown below the x-axis.

  • Figure 3.
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    Figure 3. Percentages of wrongly identified nucleotide sequences that were either unique or repeated within each corpus.

    Percentages of wrongly identified sequences that were identified at least twice in any single corpus (black) are shown above each image, rounded to the nearest single digit. All other wrongly identified sequences were unique in the indicated corpus (grey). Numbers of wrongly identified sequences identified in each corpus are shown below each image.

  • Figure S1.
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    Figure S1. Problematic Gene articles per year, according to country of origin and institutional affiliation type.

    Total numbers of problematic articles for each country/group of countries are shown in the upper left corner of each panel. (A) Numbers of problematic Gene articles (y-axes) per publication year (x-axes) according to country of origin, shown above each graph. Countries are shown in alphabetical order, from top left. (B) Numbers of problematic Gene articles (y-axis) per publication year (x-axis) from China (right panel) or all other countries (left panel). Articles affiliated with hospitals or other institution types are shown in blue or grey, respectively. Numbers of problematic articles per year are shown below each stacked bar graph.

  • Figure 4.
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    Figure 4. Percentages of problematic Gene and Oncology Reports articles according to hospital affiliation status and country of origin.

    Percentages of problematic Gene and Oncology Reports articles according to hospital affiliation status (y-axis) from either China or all other countries (x-axis). The journal and relevant date ranges of problematic articles are shown above each panel. Problematic articles that were (not) affiliated with hospitals are shown in blue (grey), respectively. Percentages shown have been rounded to the nearest single digit. Numbers of problematic articles from China or all other countries are indicated below the x-axis. For the comparisons shown in each panel, significantly higher proportions of problematic articles from China were affiliated with hospitals versus problematic articles from other countries (Fisher’s Exact test, P < 0.001).

  • Figure S2.
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    Figure S2. Problematic Oncology Reports articles per year, according to country of origin and institutional affiliation type.

    Total numbers of problematic articles for each country/group of countries are shown in the upper left corner of each panel. (A) Numbers of problematic Oncology Reports articles (y-axes) per publication year (x-axes) according to country of origin, shown above each graph. Countries are shown in alphabetical order, from top left. (B) Numbers of problematic Oncology Reports articles (y-axis) per publication year (x-axis) from China (right panel) or all other countries (left panel). Articles affiliated with hospitals or other institution types are shown in blue or grey, respectively. Numbers of problematic articles per year are shown below each stacked bar graph.

  • Figure 5.
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    Figure 5. Percentages and numbers of problematic Gene and Oncology Reports articles per year.

    Percentages of all Gene or Oncology Reports articles that were found to be problematic (y-axis) per publication year (x-axis). The journal and relevant publication year ranges are shown above each panel. Problematic articles from China or all other countries are shown in orange or grey, respectively. Percentages shown are rounded to one decimal place. Total numbers of problematic articles per year are shown below each graph.

  • Figure 6.
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    Figure 6. Summary of (RT-)PCR primer pairings that involved at least one wrongly identified primer.

    For n = 851 primer pairs that were claimed to target particular genes/sequences (gene X) (left panel), one or both primers were predicted to be incorrect (right panel), either by targeting unrelated genes or sequences (gene Y or gene Z), or by having no predicted human target (no target). Numbers of primer pairs and affected articles are indicated below each incorrect primer pair category. Some problematic articles described more than one (category of) incorrect primer pairing. Left- or right-hand primers are not intended to indicate forward or reverse primer orientations.

  • Figure 7.
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    Figure 7. Numbers of past research articles that have studied human protein-coding genes in problematic articles.

    (A, B) Numbers (log base 10) of problematic articles (y-axis) versus past research articles (x-axis) for (A) primary protein-coding genes in problematic articles and (B) claimed protein-coding gene targets of wrongly identified reagents. Vertical dashed lines indicate the median number of research articles for protein-coding genes, with the associated interquartile range shown in grey. Subsets of protein-coding genes are highlighted in each panel.

  • Figure S3.
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    Figure S3. Human protein-coding genes in problematic articles appear frequently in PubMed.

    (A, B) Numbers (log base 10) of PubMed articles (y-axis) for primary protein-coding genes in problematic articles (A) (green) or claimed protein-coding gene targets of wrongly identified reagents (B) (green), versus all other human protein-coding genes (A, B) (grey). Horizontal lines within box plots indicate median values, with box plots showing percentiles according to letter proportions, that is, ±25% percentile, ±(25 + 25/2)% percentile, ±(25 + 25/2 + 25/4)% percentile.

  • Figure 8.
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    Figure 8. Clinical trial citations and approximate potential to translate (APT) for problematic articles.

    (A) Percentages of problematic articles that are cited at least once according to the NIH Open Citation Collection (y-axis), according to publication corpus (x-axis). Error bars indicate 95% confidence intervals of bootstrapped estimates of percentages. Numbers of problematic articles with at least one clinical citation are shown below the x-axis for each corpus. (B) Average APT for problematic articles (y-axis) according to publication corpus (x-axis). Error bars indicate bootstrapped 95% confidence intervals. Numbers of problematic articles for which the APT computed by iCite (88) are shown below the x-axis for each corpus.

Tables

  • Figures
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    Table 1.

    Descriptions of the targeted corpora screened by Seek & Blastn with manual verification of nucleotide sequence reagent identities.

    Single gene knockdown (SGK)miR-145Cisplatin + Gemcitabine (C + G)
    CorpusProblematicCorpusProblematicCorpusProblematic
    Number of articles (% of corpus)174 (100%)75 (43%)50 (100%)31 (62%)100 (100%)51 (50%)
    Number of journals834235254831
    Publication year median (range)2015 (2006–2019)a2015 (2010–2019)2017 (2009–2019)2017 (2009–2019)2017 (2008–2019)2017 (2009–2019)
    Journal impact factor at publication year median (range)2.204 (0.098–8.459)1.778 (0.098–5.712)3.34 (0.700–9.050)3.23 (0.700–8.278)3.571 (1.099–10.391)3.041 (1.099–8.579)
    Number of sequences/article median (range)6 (0–24)6 (1–24)11 (4–46)10 (4–46)11 (2–71)12 (2–70)
    Number of incorrect sequences/article median (range)ND1 (1–8)ND1 (1–5)ND2 (1–8)
    Articles from China proportion (%)159/174 (91%)73/75 (97%)44/50 (88%)31/31 (100%)90/100 (90%)50/51 (98%)
    Articles from China affiliated with hospitals proportion (%)147/159 (92%)68/73 (93%)40/44 (91%)28/31 (90%)82/90 (91%)48/50 (96%)
    Articles from all other countries affiliated with hospitals proportion (%)6/15 (40%)0/2 (0%)0/6 (0%)0/3 (0%)1/10 (10%)0/1 (0%)
    Articles with post-publication noticesb proportion (%)20/174 (12%)13/75 (17%)1/50 (2%)1/31 (3%)1/100 (1%)1/51 (2%)
    • ↵a SGK articles were published until June 2019.

    • ↵b Post-publication notices include retractions, expressions of concern and corrections.

    • View popup
    Table 2.

    Cancer types studied in the Single Gene Knockdown (SGK) corpus, where each cancer type corresponds to a single article.

    GenePreviously reported SGK articlesNew SGK articles
    ADAM8LiveraBreast, Breast, Colorectal, Gastric, Liver, Lung, Pancreatic
    ANXA1N/ABreast, Breast, Breast, Esophageal, Leukemia, Liver, Lung, Prostate
    EAG1Liposarcoma, OsteosarcomaBrain, Osteosarcoma, Osteosarcoma, Ovarian, Sarcoma
    GPR137Bladder, Brain, Colorectalb, PancreaticBrain, Gastric, Leukemia, Liver, Osteosarcoma, Ovarian, Prostate
    ICT1BrainBreast, Gastric, Leukemia, Lung, Lymphoma, Prostate
    KLF8OsteosarcomaBladder, Brain, Brain, Brain, Breast, Colorectal, Colorectal, Gastric, Gastric, Gastric, Gastric, Liver, Liver, Nasopharyngeal, Oral, Ovarian, Pancreatic, Renal
    MACC1OvarianBladder, Brain, Cervical, Cervical, Colorectal, Colorectal, Esophageal, Esophageal, Gallbladder, Gastric, Liver, Liver, Lung, Oral, Oral, Nasopharyngeal, Ovarian, Ovarian, Skin
    MYO6Brain, Colorectal, Liver, LungBreast, Gastric, Oral, Prostate
    NOB1Brain, Breast, Colorectal, Liver, Osteosarcoma, Ovarian, ProstateLaryngeal, Lung, Lung, Oral, Osteosarcoma, Renal, Thyroid, Thyroid
    PP4R1Breast, LiverLung
    PP5Colorectal, OvarianBladder, Brain, Leukemia, Liver, Osteosarcoma, Pancreatic, Prostate
    PPM1DBladder, LungBrain, Brain, Breast, Breast, Liver, Pancreatic
    RPS15ABrain, LungBrain, Gastric, Leukemia, Liver, Lung, Osteosarcoma, Renal, Thyroid
    TCTN1Brain, Brain, PancreaticBrain, Colorectal, Gastric, Thyroid
    TPD52L2Brain, Breast, Gastric, Liver, OralBrain
    USP39Liver, ThyroidBreast, Colorectal, Colorectal, Gastric, Liver, Liver, Lung, Oral, Osteosarcoma, Renal, Skin
    ZFXBrain, BreastBrain, Brain, Gallbladder, Laryngeal, Leukemia, Lung, Lung, Oral, Oral, Osteosarcoma, Pancreatic, Prostate, Renal
    • ↵a Cancer types shown in bold correspond to problematic articles with wrongly identified nucleotide sequence(s).

    • ↵b Underlined cancer types correspond to articles that have been retracted or assigned an expression of concern.

    • View popup
    Table 3.

    Wrongly identified nucleotide sequences summarized according to experimental technique and identity error type.

    CorpusTechnique“Non-targeting” yet targeting proportion (%)“Targeting” yet non-targeting proportion (%)Targeting wrong gene/sequence proportion (%)Total per corpus proportion (%)
    SGK (n = 115 reagents in n = 75 articles)PCRa0/45 (0)7/14 (50)45/57 (79)52/115 (45)
    Gene knockdownb44/44 (100)7/14 (50)12/57 (21)63/115 (55)
    Otherc0/45 (0)0/14 (0)0/57 (0)0/115 (0)
    Total (Error type)44/44 (100)14/14 (100)57/57 (100)115/115 (100)
    miR-145 (n = 49 reagents in n = 31 articles)PCR0/2 (0)8/9 (89)33/38 (87)41/49 (84)
    Gene knockdown2/2 (100)1/9 (11)5/38 (13)8/49 (16)
    Other0/2 (0)0/9 (0)0/38 (0)0/49 (0)
    Total (Error type)2/2 (100)9/9 (100)38/38 (100)49/49 (100)
    C + G (n = 109 reagents in n = 51 articles)PCR0/6 (0)23/24 (96)73/79 (93)96/109 (88)
    Gene knockdown4/6 (67)1/24 (4)5/79 (6)10/109 (9)
    Other2/6 (33)0/24 (0)1/79 (1)3/109 (3)
    Total (Error type)6/6 (100)24/24 (100)79/79 (100)109/109 (100)
    Gene (n = 284 reagents in n = 128 articles)PCR0/9 (0)35/42 (83)218/233 (94)253/284 (88)
    Gene knockdown9/9 (100)7/42 (17)15/233 (6)31/284 (11)
    Other0/9 (0)0/42 (0)0/233 (0)0/284 (0)
    Total (Error type)9/9 (100)42/42 (100)233/233 (100)284/284 (100)
    Oncology Reports (n = 995 reagents in n = 436 articles)PCR0/36 (0)296/335 (88)573/630 (91)869/995 (87)
    Gene knockdown30/30 (100)37/335 (11)54/630 (8)121/995 (12)
    Other0/36 (0)2/335 (1)3/630 (1)5/995 (1)
    Total (Error type)30/30 (100)335/335 (100)630/630 (100)995/995 (100)
    Total (n = 1,535 reagents in n = 712 articles)PCR0/89 (0)364/416 (87)937/1,030 (90)1,301/1,535 (84)
    Gene knockdown87/89 (98)50/416 (12)89/1,030 (9)226/1,535 (15)
    Other2/89 (2)2/416 (1)4/1,030 (1)8/1,535 (1)
    Total (Error type)89/89 (100)416/416 (100)1,030/1,030 (100)1,535/1,535 (100)
    • ↵a PCR = Human gene or genomic targeting primers for PCR, RT–PCR or methylation-specific PCR.

    • ↵b Gene knockdown = siRNA or shRNA.

    • ↵c Other = Claimed Ribozyme, TALEN, mimic sequences, and other oligonucleotide sequences.

    • Bold text indicates the most frequent error types per corpus.

    • View popup
    Table 4.

    Summary of features of Gene and Oncology Reports journals and problematic articles.

    FeatureGeneOncology Reports
    Publication years screened by Seek & Blastn2007–20182014–2018
    Journal impact factor (range during years screened)2.082–2.8712.301–3.041
    Flagged/screened articles proportion (%)742/7,399 (10%)1,709/3,778 (45%)
    Problematic/flagged articles proportion (%)128/742 (17%)436/1,709 (26%)
    Incorrect sequences/problematic article median (range)2 (1–36)2 (1–15)
    Problematic articles from China proportion (%)69/128 (54%)393/436 (90%)
    Problematic articles from all other countries proportion (%)59/128 (46%)43/436 (10%)
    Problematic articles from China affiliated with hospitals proportion (%)54/69 (78%)342/393 (87%)
    Problematic articles from all other countries affiliated with hospitals proportion (%)5/59 (9%)5/43 (12%)
    Retracted or corrected problematic articles proportion (%)2/128 (2%)2/436 (0.5%)

Supplementary Materials

  • Figures
  • Tables
  • Supplemental Data 1.

    Single gene knockdown (SGK) corpus. All SGK articles screened by S&B and problematic SGK articles are shown in separate tabs. Primary human genes refer to the first-mentioned gene in the publication title or abstract.[LSA-2021-01203_Supplemental_Data_1.xlsx]

  • Table S1 Wrongly identified nucleotide sequence reagents.

  • Supplemental Data 2.

    miR-145 corpus. All miR-145 articles flagged by S&B screening and problematic miR-145 articles are shown in separate tabs. Primary human genes refer to the first-mentioned gene(s) in the publication title or abstract.[LSA-2021-01203_Supplemental_Data_2.xlsx]

  • Supplemental Data 3.

    Cisplatin + gemcitabine (C + G) corpus. All C + G articles flagged by S&B screening and problematic C + G articles are shown in separate tabs. Primary human genes refer to the first-mentioned gene(s) in the publication title or abstract.[LSA-2021-01203_Supplemental_Data_3.xlsx]

  • Supplemental Data 4.

    Problematic Gene articles. Primary human genes refer to the first-mentioned gene(s) in the publication title or abstract.[LSA-2021-01203_Supplemental_Data_4.xlsx]

  • Supplemental Data 5.

    Problematic Oncology Reports corpus. Primary human genes refer to the first-mentioned gene(s) in the publication title or abstract.[LSA-2021-01203_Supplemental_Data_5.xlsx]

  • Supplemental Data 6.

    Journals and publishers that have published problematic articles. Journals and publishers of problematic single gene knockdown, miR-145, and C + G articles and all problematic articles are shown in separate tabs.[LSA-2021-01203_Supplemental_Data_6.xlsx]

  • Table S2 Author corrections, expressions of concern, and retractions associated with problematic articles.

  • Table S3 Problematic articles curated within gene knowledge bases.

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Incorrect nucleotide sequence reagents
Yasunori Park, Rachael A West, Pranujan Pathmendra, Bertrand Favier, Thomas Stoeger, Amanda Capes-Davis, Guillaume Cabanac, Cyril Labbé, Jennifer A Byrne
Life Science Alliance Jan 2022, 5 (4) e202101203; DOI: 10.26508/lsa.202101203

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Incorrect nucleotide sequence reagents
Yasunori Park, Rachael A West, Pranujan Pathmendra, Bertrand Favier, Thomas Stoeger, Amanda Capes-Davis, Guillaume Cabanac, Cyril Labbé, Jennifer A Byrne
Life Science Alliance Jan 2022, 5 (4) e202101203; DOI: 10.26508/lsa.202101203
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