Interpreting omics data with pathway enrichment analysis

Trends in Genetics, Год журнала: 2023, Номер 39(4), С. 308 - 319

Опубликована: Фев. 6, 2023

Язык: Английский

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PTMNavigator: interactive visualization of differentially regulated post-translational modifications in cellular signaling pathways

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 8, 2025

Abstract Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how modulate pathways on molecular level. Here, we present PTMNavigator, tool integrated into ProteomicsDB platform that offers an interactive interface researchers overlay experimental PTM data with pathway diagrams. PTMNavigator provides ~3000 canonical manually curated databases, enabling users modify create custom diagrams tailored data. Additionally, automatically runs kinase enrichment algorithms whose results are directly visualization. This view intricate relationship between signaling pathways. We demonstrate utility by applying it two phosphoproteomics datasets, showing can enhance analysis, visualize drug treatments result discernable flow PTM-driven aid proposing extensions existing By enhancing understanding dynamics facilitating discovery PTM-pathway interactions, advances knowledge biology its implications health disease.

Язык: Английский

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Eight quick tips for biologically and medically informed machine learning

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012711 - e1012711

Опубликована: Янв. 9, 2025

Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration give rise to informed machine learning, contrast studies that lack domain knowledge and treat all variables equally (uninformed learning). While application of bioinformatics health informatics datasets more seamless, likelihood errors also increased. To address this drawback, we present eight guidelines outlining best practices employing methods sciences. These quick tips offer recommendations on various aspects analysis, aiming assist researchers generating robust, explainable, dependable results. Even if originally crafted these simple suggestions novices, believe they are deemed relevant expert as well.

Язык: Английский

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Potential Adaptive Introgression From Dogs in Iberian Grey Wolves (Canis lupus)

Molecular Ecology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

ABSTRACT Invading species along with increased anthropogenization may lead to hybridization events between wild and closely related domesticates. As a consequence, carry introgressed alleles from domestic species, which is generally assumed yield adverse effects in populations. The opposite evolutionary adaptive introgression, where genes are positively selected the possible but has rarely been documented. Grey wolves ( Canis lupus ) widely distributed across Holarctic frequently coexist their close relative, dog C. familiaris ). Despite ample opportunity, occurs most Here we studied geographically isolated grey of Iberian Peninsula, who have coexisted large population loosely controlled dogs for thousands years human‐modified landscape. We assessed extent impact introgression on current wolf by analysing 150 whole genomes other Eurasian as well originating Europe western Siberia. identified almost no recent small (< 5%) overall ancient ancestry. Using combination single scan statistics ancestry enrichment estimates, positive selection six DAPP1 , NSMCE4A MPPED2 PCDH9 MBTPS1 CDH13 dogs. include functions immune response brain functions, explain some unique behavioural phenotypes such reduced dispersal compared

Язык: Английский

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Eleven quick tips for data cleaning and feature engineering

PLoS Computational Biology, Год журнала: 2022, Номер 18(12), С. e1010718 - e1010718

Опубликована: Дек. 15, 2022

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient generate robust and reliable outcomes results. Before applying discovery method, preprocessing steps are necessary prepare the analysis. In this framework, cleaning feature engineering pillars study involving analysis that should adequately designed performed since first phases project. We call "feature" variable describing particular trait person an observation, recorded usually as column dataset. Even if pivotal, these sometimes done poorly inefficiently, especially by beginners unexperienced researchers. For reason, we propose here our quick tips for on how carry out important correctly avoiding common mistakes pitfalls. Although guidelines with bioinformatics health informatics scenarios mind, believe they can more general applied area. therefore target researcher practitioners wanting perform engineering. simple recommendations help researchers scholars better analyses lead, turn, solid discoveries.

Язык: Английский

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Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

PLoS Computational Biology, Год журнала: 2023, Номер 19(7), С. e1011224 - e1011224

Опубликована: Июль 6, 2023

Data are the most important elements of bioinformatics: Computational analysis bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments therapies for patients. Bioinformatics high-throughput biological data coming from different sources be more helpful, because each these chunks provide alternative, complementary information a specific phenomenon, similar to multiple photos same subject taken angles. In this context, integration gets pivotal role running successful study. last decades, originating proteomics, metabolomics, metagenomics, phenomics, transcriptomics, epigenomics have been labelled -omics as unique name refer them, omics has gained importance all areas. Even if is useful relevant, due its heterogeneity, it not uncommon make mistakes during phases. We therefore decided present ten quick tips perform an correctly, avoiding common we experienced or noticed published studies past. designed our guidelines beginners, by using simple language that (we hope) understood anyone, believe recommendations should into account bioinformaticians performing integration, including experts.

Язык: Английский

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Ten quick tips for computational analysis of medical images

PLoS Computational Biology, Год журнала: 2023, Номер 19(1), С. e1010778 - e1010778

Опубликована: Янв. 5, 2023

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate disease site, resulting in precise intervention diagnosis and treatment, observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis medical images, moreover, can allow the discovery patterns correlations among cohorts patients with same disease, thus suggesting common causes or providing useful information better therapies cures. Machine learning deep applied particular, have produced new, unprecedented results pave way advanced frontiers discoveries. While computational images has become easier, however, possibility make mistakes generate inflated misleading too, hindering reproducibility deployment. In this article, we provide ten quick tips perform avoiding pitfalls noticed multiple studies past. We believe our guidelines, if taken into practice, help computational-medical community scientific research eventually positive impact on lives worldwide.

Язык: Английский

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A brief survey of tools for genomic regions enrichment analysis

Frontiers in Bioinformatics, Год журнала: 2022, Номер 2

Опубликована: Окт. 26, 2022

Functional enrichment analysis or pathway (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in list of genes compared to those that would be associated with them by chance. These functions are found on annotated databases such as The Gene Ontology KEGG; more abundant identified through statistical techniques Fisher's exact test. All PEA tools require input. A few tools, however, read lists genomic regions input rather than genes, and first associate these chromosome their corresponding genes. perform procedure called analysis, can useful for detecting related set regions. In this brief survey, we analyze six (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, ReactomePA), outlining comparing main features. Our comparison results indicate inclusion data regulatory elements, ChIP-seq, common among could therefore improve results.

Язык: Английский

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Genes adaptability and NOL6 protein inhibition studies of fabricated flavan-3-ols lead skeleton intended to treat breast carcinoma

International Journal of Biological Macromolecules, Год журнала: 2023, Номер 258, С. 127661 - 127661

Опубликована: Окт. 26, 2023

Язык: Английский

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Combined inhibition of EZH2 and CDK4/6 perturbs endoplasmic reticulum-mitochondrial homeostasis and increases antitumor activity against glioblastoma

npj Precision Oncology, Год журнала: 2024, Номер 8(1)

Опубликована: Июль 25, 2024

Abstract Here, we show that combined use of the EZH2 inhibitor GSK126 and CDK4/6 abemaciclib synergistically enhances antitumoral effects in preclinical GBM models. Dual blockade led to HIF1α upregulation CalR translocation, accompanied by massive impairment mitochondrial function. Basal oxygen consumption rate, ATP synthesis, maximal respiration decreased, confirming disrupted endoplasmic reticulum-mitochondrial homeostasis. This was paralleled depolarization UPR sensors PERK, ATF6α, IRE1α. Notably, dual EZH2/CDK4/6 also reduced 3D-spheroid invasion, partially inhibited tumor growth ovo , impaired viability patient-derived organoids. Mechanistically, this due transcriptional changes genes involved mitotic aberrations/spindle assembly ( Rb, PLK1, RRM2, PRC1, CENPF, TPX2 ), histone modification HIST1H1B, HIST1H3G DNA damage/replication stress events TOP2A, ATF4 immuno-oncology DEPDC1 EMT-counterregulation PCDH1 ) a shift stemness profile towards more differentiated state. We propose for further investigation.

Язык: Английский

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