iM-Seeker: a webserver for DNA i-motifs prediction and scoring via automated machine learning.
Nucleic Acids Res
; 52(W1): W19-W28, 2024 Jul 05.
Article
in En
| MEDLINE
| ID: mdl-38676949
ABSTRACT
DNA, beyond its canonical B-form double helix, adopts various alternative conformations, among which the i-motif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and telomere biology. Despite recognizing the crucial role of i-motifs, predictive software for i-motif forming sequences has been limited. Addressing this gap, we introduce 'iM-Seeker', an innovative computational platform designed for the prediction and evaluation of i-motifs. iM-Seeker exhibits the capability to identify potential i-motifs within DNA segments or entire genomes, calculating stability scores for each predicted i-motif based on parameters such as the cytosine tracts number, loop lengths, and sequence composition. Furthermore, the webserver leverages automated machine learning (AutoML) to effortlessly fine-tune the optimal i-motif scoring model, incorporating user-supplied experimental data and customised features. As an advanced, versatile approach, 'iM-Seeker' promises to advance genomic research, highlighting the potential of i-motifs in cell biology and therapeutic applications. The webserver is freely available at https//im-seeker.org.
Full text:
1
Database:
MEDLINE
Main subject:
Software
/
DNA
/
Internet
/
Nucleotide Motifs
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Machine Learning
Limits:
Humans
Language:
En
Journal:
Nucleic Acids Res
Year:
2024
Type:
Article