Human gene function publications that describe wrongly identified nucleotide sequence reagents are unacceptably frequent within the genetics literature

Abstract

Nucleotide sequence reagents underpin a range of molecular genetics techniques that have been applied across hundreds of thousands of research publications. We have previously reported wrongly identified nucleotide sequence reagents in human gene function publications and described a semi-automated screening tool Seek & Blastn to fact-check the targeting or non-targeting status of nucleotide sequence reagents. We applied Seek & Blastn to screen 11,799 publications across 5 literature corpora, which included all original publications in Gene from 2007-2018 and all original open-access publications in Oncology Reports from 2014-2018. After manually checking the Seek & Blastn screening outputs for over 3,400 human research papers, we identified 712 papers across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of over 13,700 nucleotide sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs, respectively. The 712 problematic papers have received over 17,000 citations, which include citations by human clinical trials. Given our estimate that approximately one quarter of problematic papers are likely to misinform or distract the future development of therapies against human disease, urgent measures are required to address the problem of unreliable gene function papers within the literature.

The Piroplasmida Babesia, Cytauxzoon, and Theileria in farm and companion animals: species compilation, molecular phylogeny, and evolutionary insights

“…XXB/HangZhou likely originates from the fungi Cladosporidium; Malandrin 2021, the 18S RNA gene of B. bennetti represents highly likely an artificial chimeric sequence). This observation is not limited to piroplasmid research, but the growing report of artifactual research results due to sequence errors is already becoming increasingly evident in human genetics research (Else 2021;Park et al 2021). It needs to be considered that databases are only as good as the data they contain and only members of the scientific community can prevent their deterioration by taking responsibility to keep them highly accurate.…”

The ground truth of the Data-Iceberg: Correct Meta-data

“…We found irregularities in RNA-Seq data 1 (Fig. 1, Han et al): two of their human lung tissue samples (GSM4697983 and GSM4697984 from GSE155241) appear to be exactly the same as two other unrelated samples of human lung tissue that were generated during research for the Cell publication 4 . Sample GSM4462413, from a healthy 77-year-old-man in Blanco-Melo's study 2 (SRA metadata), appears to be the same as GSM4697983 (Han et al) 1 labelled as healthy 56-year-old-woman.…”

“…However, Park and colleagues also analysed the papers further. They found a high probability of 15-35% for the problematic publications in each of the five corpora, respectively, to be cited in clinical research in future as they resemble papers that have been cited in clinical research 4 . Hence, there is serious concern that about a quarter of these publications are likely to impair clinical research by misinforming or distracting the development of potential cures.…”

“…Hence, there is serious concern that about a quarter of these publications are likely to impair clinical research by misinforming or distracting the development of potential cures. 4 Transcriptome meta-data errors: Mishandling of meta-data is always possible for transcriptome data sets, as well as for all other omics data-sets (proteome, phosphor-proteome, metabolome, genomics) and, the problem is, wrong metadata, wrong labels, wrong conditions, mislabelled controls are very difficult to spot and correct in retrospect. The control best is done using all info present at submission.…”

Metadata integrity in bioinformatics: Bridging the gap between data and knowledge

“…The notion of abounding mistakes in data and metadata is correct, as one can already see with basic mistakes regarding nucleotide errors [63] , [64] :…”