PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article TEffectBayes: A Nextflow Pipeline for Exploring the Potential Effect of Transposable Elements in Gene Regulatory Network with Multi-Omic Bayesian Network Model(Springer Heidelberg, 2026-03-10) Karakülah, Gökhan; Güner, Hüseyin; Kutlu, Necati KaanTransposable elements (TEs) are critical contributors to gene regulatory networks, yet their repetitive and abundant nature complicates efforts to elucidate their precise regulatory roles. While existing computational tools facilitate systematic identification of associations between TEs and gene expression, these methods typically cannot account for confounding variables or capture causal and directional interactions. To address these limitations, we developed TEffectBayes, a Nextflow-based pipeline leveraging a multi-omic Bayesian network (BN) framework designed to systematically infer directional, probabilistic regulatory dependencies involving TEs. TEffectBayes integrates diverse omics datasets, including RNA-seq-derived gene and locus-specific TE expression, along with ChIP-seq-based histone modification data processed via custom R and Python scripts. Integrated multi-omic datasets are subsequently employed to build gene-centric Bayesian models, enabling robust inference of context-dependent, probabilistic relationships between TEs, chromatin modifications, and gene expression. TEffectBayes thus provides a reproducible and scalable computational framework for unraveling the complex regulatory landscape shaped by TEs. In summary, TEffectBayes supports systematic prioritization of TE-chromatin-gene regulatory candidates for downstream benchmarking and experimental validation, enabling hypothesis-driven follow-up studies in diverse biological contexts. The pipeline, along with comprehensive user tutorials and example datasets, is publicly accessible at https://github.com/nkaan-kutlu/TEffectBayes.Article Citation - WoS: 19Citation - Scopus: 28A Toolbox of Machine Learning Software to Support Microbiome Analysis(Frontiers Media S.A., 2023-11-22) Marcos-Zambrano, Laura Judith; Lopez-Molina, Victor Manuel; Bakir-Gungor, Burcu; Frohme, Marcus; Karaduzovic-Hadziabdic, Kanita; Klammsteiner, Thomas; Carrillo de Santa Pau, EnriqueThe human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.