Next Article in Journal
Isolation, Identification, and Biological Activities of a New Chlorin e6 Derivative
Previous Article in Journal
A CAM-Related NF-YB Transcription Factor Enhances Multiple Abiotic Stress Tolerance in Arabidopsis
Previous Article in Special Issue
Antimicrobial Ionic Liquids: Ante-Mortem Mechanisms of Pathogenic EPEC and MRSA Examined by FTIR Spectroscopy
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425

by
Venkat R. Pannala
1,2,*,
Michele R. Balik-Meisner
3,
Deepak Mav
3,
Dhiral P. Phadke
3,
Elizabeth H. Scholl
3,
Ruchir R. Shah
3,
Scott S. Auerbach
4 and
Anders Wallqvist
1,*
1
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
2
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
3
Sciome LLC, Research Triangle Park, Durham, NC 27709, USA
4
Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC 27709, USA
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(13), 7108; https://doi.org/10.3390/ijms25137108
Submission received: 12 May 2024 / Accepted: 15 May 2024 / Published: 28 June 2024
In the original publication [1], the following authors were not included. The newly included authors (Michele R. Balik-Meisner, Deepak Mav, Dhiral P. Phadke, Elizabeth H. Scholl, Ruchir R. Shah, and Scott S. Auerbach) were involved in data curation, processing, and quality control along with writing—review and editing of the published article. The affiliations, author contributions statement, data availability statement, and conflicts of interest have been corrected accordingly.
There were also errors in the second and third paragraphs of Section 3.2, including an incorrect description of some of the parameters used in the RNA-sequencing data analysis and in the S1500+ data extrapolation to the whole transcriptome using GeniE software. In Section 3.2, on Page 12, the second paragraph, Lines 7 to 9, the sentence should be changed to the following: sequencing depth < 500 K, total alignment rate < 40%, unique alignment rate < 30%, number of aligned reads < 500 K, and percentage of probes with at least five reads < 50%. In Section 3.2, on Page 12, the third paragraph, Lines 1 to 5, the sentence should be changed to the following: Finally, the normalized log-transformed values from the S1500+ dataset were then used for extrapolation to the whole transcriptome (~19 K genes) using a commercial platform (GeniE, version 3.0.4) [12]. This approach incorporated PC regression [43] and was updated to use roughly 20 K samples of publicly available rat transcriptomics data from the Gene Expression Omnibus (GEO) and Short Read Archive (SRA) to train the rat model and a large collection of publicly available RNA-seq data [44] to train the human model. In Section 3.2, on Page 12, the third paragraph, Line 7, the percentage (35%) should be changed to 25%. In Section 3.2, on Page 12, the third paragraph, Line 9, the number of genes (25,599) should be changed to 18,699.
The corrected author contributions statement, data availability statement, conflicts of interest, appear here.
  • Author Contributions: Conceptualization, V.R.P. and A.W.; methodology and analysis, V.R.P.; data curation, processing, and quality control, M.R.B.-M., D.M., D.P.P., E.H.S., R.R.S. and S.S.A.; writing—original draft preparation, V.R.P.; writing—review and editing, V.R.P., M.R.B.-M., D.M., D.P.P., E.H.S., R.R.S., S.S.A. and A.W.; supervision, A.W.; funding acquisition, A.W. All authors have read and agreed to the published version of the manuscript.
  • Data Availability Statement: The datasets presented in this study are derived from the original study which is openly available in the NCBI’s GEO database gene repository for rats under accession number GSM4415261. The derived datasets supporting the conclusions of this article will be made available by the authors on request.
  • Conflicts of Interest: V.R.P. is employed by The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and M.R.B.-M., D.M., D.P.P., E.H.S., and R.R.S. are employed by Sciome LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Pannala, V.R.; Wallqvist, A. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pannala, V.R.; Balik-Meisner, M.R.; Mav, D.; Phadke, D.P.; Scholl, E.H.; Shah, R.R.; Auerbach, S.S.; Wallqvist, A. Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425. Int. J. Mol. Sci. 2024, 25, 7108. https://doi.org/10.3390/ijms25137108

AMA Style

Pannala VR, Balik-Meisner MR, Mav D, Phadke DP, Scholl EH, Shah RR, Auerbach SS, Wallqvist A. Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425. International Journal of Molecular Sciences. 2024; 25(13):7108. https://doi.org/10.3390/ijms25137108

Chicago/Turabian Style

Pannala, Venkat R., Michele R. Balik-Meisner, Deepak Mav, Dhiral P. Phadke, Elizabeth H. Scholl, Ruchir R. Shah, Scott S. Auerbach, and Anders Wallqvist. 2024. "Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425" International Journal of Molecular Sciences 25, no. 13: 7108. https://doi.org/10.3390/ijms25137108

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop