NAR Genomics and Bioinformatics最新文献

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iModEst: disentangling -omic impacts on gene expression variation across genes and tissues. 解缠组学对基因和组织间基因表达变异的影响。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-03-04 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf011
Dustin J Sokolowski, Mingjie Mai, Arnav Verma, Gabriela Morgenshtern, Vallijah Subasri, Hareem Naveed, Maria Yampolsky, Michael D Wilson, Anna Goldenberg, Lauren Erdman
{"title":"iModEst: disentangling -omic impacts on gene expression variation across genes and tissues.","authors":"Dustin J Sokolowski, Mingjie Mai, Arnav Verma, Gabriela Morgenshtern, Vallijah Subasri, Hareem Naveed, Maria Yampolsky, Michael D Wilson, Anna Goldenberg, Lauren Erdman","doi":"10.1093/nargab/lqaf011","DOIUrl":"10.1093/nargab/lqaf011","url":null,"abstract":"<p><p>Many regulatory factors impact the expression of individual genes including, but not limited, to microRNA, long non-coding RNA (lncRNA), transcription factors (TFs), <i>cis-</i>methylation, copy number variation (CNV), and single-nucleotide polymorphisms (SNPs). While each mechanism can influence gene expression substantially, the relative importance of each mechanism at the level of individual genes and tissues is poorly understood. Here, we present the integrative Models of Estimated gene expression (iModEst), which details the relative contribution of different regulators to the gene expression of 16,000 genes and 21 tissues within The Cancer Genome Atlas (TCGA). Specifically, we derive predictive models of gene expression using tumour data and test their predictive accuracy in cancerous and tumour-adjacent tissues. Our models can explain up to 70% of the variance in gene expression across 43% of the genes within both tumour and tumour-adjacent tissues. We confirm that TF expression best predicts gene expression in both tumour and tumour-adjacent tissue whereas methylation predictive models in tumour tissues does not transfer well to tumour adjacent tissues. We find new patterns and recapitulate previously reported relationships between regulator and gene-expression, such as CNV-predicted <i>FGFR2</i> expression and SNP-predicted <i>TP63</i> expression. Together, iModEst offers an interactive, comprehensive atlas of individual regulator-gene-tissue expression relationships as well as relationships between regulators.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf011"},"PeriodicalIF":4.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ribosomal DNA arrays are the most H-DNA rich element in the human genome. 核糖体DNA阵列是人类基因组中H-DNA最丰富的元素。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-03-04 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf012
Nikol Chantzi, Candace S Y Chan, Michail Patsakis, Akshatha Nayak, Austin Montgomery, Ioannis Mouratidis, Ilias Georgakopoulos-Soares
{"title":"Ribosomal DNA arrays are the most H-DNA rich element in the human genome.","authors":"Nikol Chantzi, Candace S Y Chan, Michail Patsakis, Akshatha Nayak, Austin Montgomery, Ioannis Mouratidis, Ilias Georgakopoulos-Soares","doi":"10.1093/nargab/lqaf012","DOIUrl":"10.1093/nargab/lqaf012","url":null,"abstract":"<p><p>Repetitive DNA sequences can form noncanonical structures such as H-DNA. The new telomere-to-telomere genome assembly for the human genome has eliminated gaps, enabling examination of highly repetitive regions including centromeric and pericentromeric repeats and ribosomal DNA arrays. We find that H-DNA appears once every 25 000 base pairs in the human genome. Its distribution is highly inhomogeneous with H-DNA motif hotspots being detectable in acrocentric chromosomes. Ribosomal DNA arrays are the genomic element with a 40.94-fold H-DNA enrichment. Across acrocentric chromosomes, we report that 54.82% of H-DNA motifs found in these chromosomes are in rDNA array loci. We discover that binding sites for the PRDM9-B allele, a variant of the PRDM9 protein, are enriched for H-DNA motifs. We further investigate these findings through an analysis of PRDM-9 ChIP-seq data across various PRDM-9 alleles, observing an enrichment of H-DNA motifs in the binding sites of A-like alleles (including A, B, and N alleles), but not C-like alleles (including C and L4 alleles). The enrichment of H-DNA motifs at ribosomal DNA arrays is consistent in nonhuman great ape genomes. We conclude that ribosomal DNA arrays are the most enriched genomic loci for H-DNA sequences in human and other great ape genomes.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf012"},"PeriodicalIF":4.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Low-complexity regions in fungi display functional groups and are depleted in positively charged amino acids. 真菌中的低复杂性区域显示官能团,并且在带正电的氨基酸中耗尽。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-27 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf014
Kamil Steczkiewicz, Aleksander Kossakowski, Stanisław Janik, Anna Muszewska
{"title":"Low-complexity regions in fungi display functional groups and are depleted in positively charged amino acids.","authors":"Kamil Steczkiewicz, Aleksander Kossakowski, Stanisław Janik, Anna Muszewska","doi":"10.1093/nargab/lqaf014","DOIUrl":"10.1093/nargab/lqaf014","url":null,"abstract":"<p><p>Reports on the diversity and occurrence of low-complexity regions (LCR) in Eukaryota are limited. Some studies have provided a more extensive characterization of LCR proteins in prokaryotes. There is a growing body of knowledge about a plethora of biological functions attributable to LCRs. However, it is hard to determine to what extent observed phenomena apply to fungi since most studies of fungal LCRs were limited to model yeasts. To fill this gap, we performed a survey of LCRs in proteins across all fungal tree of life branches. We show that the abundance of LCRs and the abundance of proteins with LCRs are positively correlated with proteome size. We observed that most LCRs are present in proteins with protein domains but do not overlap with the domain regions. LCRs are associated with many duplicated protein domains. The quantity of particular amino acids in LCRs deviates from the background frequency with a clear over-representation of amino acids with functional groups and a negative charge. Moreover, we discovered that each lineage of fungi favors distinct LCRs expansions. Early diverging fungal lineages differ in LCR abundance and composition pointing at a different evolutionary trajectory of each fungal group.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf014"},"PeriodicalIF":4.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DeleteomeTools: utilizing a compendium of yeast deletion strain transcriptomes to identify co-functional genes. DeleteomeTools:利用酵母缺失菌株转录组简编来鉴定协同功能基因。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-27 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf008
Maxwell L Neal, Sanjeev K Choudhry, John D Aitchison
{"title":"DeleteomeTools: utilizing a compendium of yeast deletion strain transcriptomes to identify co-functional genes.","authors":"Maxwell L Neal, Sanjeev K Choudhry, John D Aitchison","doi":"10.1093/nargab/lqaf008","DOIUrl":"10.1093/nargab/lqaf008","url":null,"abstract":"<p><p>We introduce DeleteomeTools, an R package that leverages the Deleteome compendium of yeast single-gene deletion transcriptomes to predict gene function. Primarily, the package provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our software predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. To assess the package's broader predictive capabilities, we performed a systematic evaluation that tested how well it predicted Gene Ontology (GO) annotations already applied to the subset of genes deleted in Deleteome strains. We show that our package predicted a majority of reported GO:<i>biological process</i> annotations with semantic similarities ranging from moderate to identical. We also discuss how our strategy for quantifying similarity between deletion strains, which relies on differential expression signatures, differs from other approaches that use global expression profiles, and why it has the potential to identify functional relationships that might otherwise go undetected.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf008"},"PeriodicalIF":4.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comprehensive analysis of RNA-chromatin, RNA-, and DNA-protein interactions. RNA-染色质,RNA-和dna -蛋白质相互作用的综合分析。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-24 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf010
Daniil A Khlebnikov, Arina I Nikolskaya, Anastasia A Zharikova, Andrey A Mironov
{"title":"Comprehensive analysis of RNA-chromatin, RNA-, and DNA-protein interactions.","authors":"Daniil A Khlebnikov, Arina I Nikolskaya, Anastasia A Zharikova, Andrey A Mironov","doi":"10.1093/nargab/lqaf010","DOIUrl":"10.1093/nargab/lqaf010","url":null,"abstract":"<p><p>RNA-chromatin interactome data are considered to be one of the noisiest types of data in biology. This is due to protein-coding RNA contacts and nonspecific interactions between RNA and chromatin caused by protocol specifics. Therefore, finding regulatory interactions between certain transcripts and genome loci requires a wide range of filtering techniques to obtain significant results. Using data on pairwise interactions between these molecules, we propose a concept of triad interaction involving RNA, protein, and a DNA locus. The constructed triads show significantly less noise contacts and are more significant when compared to a background model for generating pairwise interactions. RNA-chromatin contacts data can be used to validate the proposed triad object as positive (Red-ChIP experiment) or negative (RADICL-seq NPM) controls. Our approach also filters RNA-chromatin contacts in chromatin regions associated with protein functions based on ChromHMM annotation.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf010"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11850300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LoVis4u: a locus visualization tool for comparative genomics and coverage profiles. LoVis4u:用于比较基因组学和覆盖概况的基因座可视化工具。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-24 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf009
Artyom A Egorov, Gemma C Atkinson
{"title":"LoVis4u: a locus visualization tool for comparative genomics and coverage profiles.","authors":"Artyom A Egorov, Gemma C Atkinson","doi":"10.1093/nargab/lqaf009","DOIUrl":"10.1093/nargab/lqaf009","url":null,"abstract":"<p><p>Comparative genomic analysis often involves visualization of alignments of genomic loci. While several software tools are available for this task, ranging from Python and R libraries to stand-alone graphical user interfaces, a tool is lacking that offers fast, automated usage and the production of publication-ready vector images. Here we present LoVis4u, a command-line tool and Python API designed for highly customizable and fast visualization of multiple genomic loci. LoVis4u generates vector images in PDF format based on annotation data from GenBank or GFF files. It is capable of visualizing entire genomes of bacteriophages as well as plasmids and user-defined regions of longer prokaryotic genomes. Additionally, LoVis4u offers optional data processing steps to identify and highlight accessory and core genes in input sequences. Finally, LoVis4u supports the visualization of genomic signal track profiles from sequencing experiments. LoVis4u is implemented in Python3 and runs on Linux and MacOS. The command-line interface covers most practical use cases, while the provided Python API allows usage within a Python program, integration into external tools, and additional customization. The source code is available at the GitHub page: github.com/art-egorov/lovis4u. Detailed documentation that includes an example-driven guide is available from the software home page: art-egorov.github.io/lovis4u.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf009"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11850299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives. Halfpipe:用于分析代谢标记RNA-seq数据以量化RNA半衰期的工具。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-18 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf006
Jason M Müller, Elisabeth Altendorfer, Susanne Freier, Katharina Moos, Andreas Mayer, Achim Tresch
{"title":"Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives.","authors":"Jason M Müller, Elisabeth Altendorfer, Susanne Freier, Katharina Moos, Andreas Mayer, Achim Tresch","doi":"10.1093/nargab/lqaf006","DOIUrl":"10.1093/nargab/lqaf006","url":null,"abstract":"<p><p>We introduce Halfpipe, a tool for analyzing RNA-seq data from metabolic RNA labeling experiments. Its main features are the absolute quantification of 4-thiouridine-labeling-induced T>C conversions in the data as generated by SLAM-seq, calculating the proportion of newly synthesized transcripts, and estimating subcellular RNA half-lives. Halfpipe excels at correcting critical biases caused by typically low labeling efficiency. We measure and compare the RNA metabolism in the G1 phase and during the mitosis of synchronized human cells. We find that RNA half-lives of constantly expressed RNAs are similar in mitosis and G1 phase, suggesting that RNA stability of those genes is constant throughout the cell cycle. Our estimates correlate well with literature values and with known RNA sequence features. Halfpipe is freely available at https://github.com/IMSBCompBio/Halfpipe.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf006"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Current state and future prospects of Horizontal Gene Transfer detection. 水平基因转移检测的现状与展望。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-02-11 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf005
Andre Jatmiko Wijaya, Aleksandar Anžel, Hugues Richard, Georges Hattab
{"title":"Current state and future prospects of Horizontal Gene Transfer detection.","authors":"Andre Jatmiko Wijaya, Aleksandar Anžel, Hugues Richard, Georges Hattab","doi":"10.1093/nargab/lqaf005","DOIUrl":"10.1093/nargab/lqaf005","url":null,"abstract":"<p><p>Artificial intelligence (AI) has been shown to be beneficial in a wide range of bioinformatics applications. Horizontal Gene Transfer (HGT) is a driving force of evolutionary changes in prokaryotes. It is widely recognized that it contributes to the emergence of antimicrobial resistance (AMR), which poses a particularly serious threat to public health. Many computational approaches have been developed to study and detect HGT. However, the application of AI in this field has not been investigated. In this work, we conducted a review to provide information on the current trend of existing computational approaches for detecting HGT and to decipher the use of AI in this field. Here, we show a growing interest in HGT detection, characterized by a surge in the number of computational approaches, including AI-based approaches, in recent years. We organize existing computational approaches into a hierarchical structure of computational groups based on their computational methods and show how each computational group evolved. We make recommendations and discuss the challenges of HGT detection in general and the adoption of AI in particular. Moreover, we provide future directions for the field of HGT detection.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf005"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143399361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancing personalized cancer therapy: Onko_DrugCombScreen-a novel Shiny app for precision drug combination screening. 推进个性化癌症治疗:onko_drugcombscreen——一款用于精确药物组合筛选的新型闪亮应用程序。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-01-31 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf004
Jingyu Yang, Meng Wang, Jürgen Dönitz, Björn Chapuy, Tim Beißbarth
{"title":"Advancing personalized cancer therapy: Onko_DrugCombScreen-a novel Shiny app for precision drug combination screening.","authors":"Jingyu Yang, Meng Wang, Jürgen Dönitz, Björn Chapuy, Tim Beißbarth","doi":"10.1093/nargab/lqaf004","DOIUrl":"10.1093/nargab/lqaf004","url":null,"abstract":"<p><p>Identifying and validating genotype-guided drug combinations for a specific molecular subtype in cancer therapy represents an unmet medical need and is important in enhancing efficacy and reducing toxicity. However, the exponential increase in combinatorial possibilities constrains the ability to identify and validate effective drug combinations. In this context, we have developed Onko_DrugCombScreen, an innovative tool aiming at advancing precision medicine based on identifying significant drug combination candidates in a target cancer cohort compared to a comparison cohort. Onko_DrugCombScreen, inspired by the molecular tumor board process, synergizes drug knowledgebase analysis with various statistical methodologies and data visualization techniques to pinpoint drug combination candidates. Validated through a TCGA-BRCA case study, Onko_DrugCombScreen has demonstrated its proficiency in discerning established drug combinations in a specific cancer type and in revealing potential novel drug combinations. By enhancing the capability of drug combination discovery through drug knowledgebases, Onko_DrugCombScreen represents a significant advancement in personalized cancer treatment by identifying promising drug combinations, setting the stage for the development of more precise and potent combination treatments in cancer care. The Onko_DrugCombScreen Shiny app is available at https://rshiny.gwdg.de/apps/onko_drugcombscreen/. The Git repository can be accessed at https://gitlab.gwdg.de/MedBioinf/mtb/onko_drugcombscreen.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf004"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
GEMCAT-a new algorithm for gene expression-based prediction of metabolic alterations. 基于基因表达预测代谢改变的新算法。
IF 4
NAR Genomics and Bioinformatics Pub Date : 2025-01-31 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf003
Suraj Sharma, Roland Sauter, Madlen Hotze, Aaron Marcellus Paul Prowatke, Marc Niere, Tobias Kipura, Anna-Sophia Egger, Kathrin Thedieck, Marcel Kwiatkowski, Mathias Ziegler, Ines Heiland
{"title":"GEMCAT-a new algorithm for gene expression-based prediction of metabolic alterations.","authors":"Suraj Sharma, Roland Sauter, Madlen Hotze, Aaron Marcellus Paul Prowatke, Marc Niere, Tobias Kipura, Anna-Sophia Egger, Kathrin Thedieck, Marcel Kwiatkowski, Mathias Ziegler, Ines Heiland","doi":"10.1093/nargab/lqaf003","DOIUrl":"10.1093/nargab/lqaf003","url":null,"abstract":"<p><p>The interpretation of multi-omics datasets obtained from high-throughput approaches is important to understand disease-related physiological changes and to predict biomarkers in body fluids. We present a new metabolite-centred genome-scale metabolic modelling algorithm, the Gene Expression-based Metabolite Centrality Analysis Tool (GEMCAT). GEMCAT enables integration of transcriptomics or proteomics data to predict changes in metabolite concentrations, which can be verified by targeted metabolomics. In addition, GEMCAT allows to trace measured and predicted metabolic changes back to the underlying alterations in gene expression or proteomics and thus enables functional interpretation and integration of multi-omics data. We demonstrate the predictive capacity of GEMCAT on three datasets and genome-scale metabolic networks from two different organisms: (i) we integrated transcriptomics and metabolomics data from an engineered human cell line with a functional deletion of the mitochondrial NAD transporter; (ii) we used a large multi-tissue multi-omics dataset from rats for transcriptome- and proteome-based prediction and verification of training-induced metabolic changes and achieved an average prediction accuracy of 70%; and (iii) we used proteomics measurements from patients with inflammatory bowel disease and verified the predicted changes using metabolomics data from the same patients. For this dataset, the prediction accuracy achieved by GEMCAT was 79%.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf003"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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