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Here, we demonstrate two intrinsic biases (input sequence length and nucleotide position) affecting SegmentNT results, a model included with the Nucleotide Transformer that provides nucleotide-level predictions of biological features. We demonstrate that nucleotide position within the input sequence (beginning, middle, or end) alters the nature of SegmentNT\u2019s raw prediction probabilities, which can be standardized to improve prediction consistency. While longer input sequence length improves model performance, diminishing returns suggest a surprisingly small input length of \u223c3072 nucleotides might be sufficient for many applications. We further identify a 24-nucleotide periodic oscillation in SegmentNT\u2019s prediction probabilities, revealing an intrinsic bias potentially linked to the model\u2019s training tokenization (6-mers) and architecture. We identify potential approaches to account for these biases and provide generalizable insights for utilizing nucleotide-resolution functional prediction models.<\/jats:p>","DOI":"10.1093\/nar\/gkag704","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T18:43:52Z","timestamp":1784573032000},"source":"Crossref","is-referenced-by-count":0,"title":["Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9158-4440","authenticated-orcid":false,"given":"Mark T W","family":"Ebbert","sequence":"first","affiliation":[{"name":"Sanders-Brown Center on Aging, University of Kentucky , Lexington, KY 40506 ,","place":["United States"]},{"name":"Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky , Lexington, KY 40506 ,","place":["United States"]},{"name":"Department of Neuroscience, University 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