ArticleBMC plant biology2019
A k-mer grammar analysis to uncover maize regulatory architecture.
Article in BMC plant biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 61 citations in OpenAlex.
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- HKDE-LACM: a hybrid model for lactic acid bacteria classification via k-mer and DNABERT-2 embedding fusion with cyclic DE-BO optimization.BMC genomics · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Recent advances in designing synthetic plant regulatory modules.Frontiers in plant science · 2025Review
- kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species.Computational and structural biotechnology journal · 2024Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- Identification of lineage-specific cis-trans regulatory networks related to kiwifruit ripening initiation.The Plant journal : for cell and molecular biology · 2024Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Omics-Driven Strategies for Developing Saline-Smart Lentils: A Comprehensive Review.International journal of molecular sciences · 2024Review
- When less is more: sketching with minimizers in genomics.Genome biology · 2024Review
- PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of cheminformatics · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
- Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence.Nature communications · 2022Article
- Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.The Plant cell · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information.The Plant cell · 2022Article
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
- Smart breeding approaches in post-genomics era for developing climate-resilient food crops.Frontiers in plant science · 2022Review
- Advances in Cereal Crop Genomics for Resilience under Climate Change.Life (Basel, Switzerland) · 2021Review
- Dissecting cis-regulatory control of quantitative trait variation in a plant stem cell circuit.Nature plants · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundOnly a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.
resultsWe developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - k-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) "bag-of-words" which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-k-mers), that captures semantic and linguistic relationships between words. We built "bag-of-k-mers" and "vector-k-mers" models that distinguish between regulatory and non-regulatory regions with an average accuracy above 90%. Our "bag-of-k-mers" achieved higher overall accuracy, while the "vector-k-mers" models were more useful in highlighting key groups of sequences within the regulatory regions.
conclusionsThese models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.
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