PANTHER : Making genome‐scale phylogenetics accessible to all

Protein Science, Год журнала: 2021, Номер 31(1), С. 8 - 22

Опубликована: Окт. 30, 2021

Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must performed carefully in order avoid systematic errors artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org) publicly available, user-focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline includes computational manual processes quality control steps. First, fully reconciled trees (including ancestral sequences) reconstructed set "reference" sequences obtained from sequenced genomes organisms across tree life. Second, resulting manually reviewed annotated with function evolution events: inferred gains losses along branches tree. Here, we describe detail current contents PANTHER, how those generated, used variety applications. The PANTHER downloaded or accessed via API. In addition, provides software tools facilitate application common sequence analysis tasks: exploring genome gene function; performing "enrichment analysis" lists genes; annotating single large batch homology; assessing likelihood genetic variant at particular site will have deleterious effects.

Язык: Английский

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A Guide to Conquer the Biological Network Era Using Graph Theory

Frontiers in Bioengineering and Biotechnology, Год журнала: 2020, Номер 8

Опубликована: Янв. 31, 2020

Networks are one of the most common ways to represent biological systems as complex sets binary interactions or relations between different bioentities. In this article, we discuss basic graph theory concepts and various types, well available data structures for storing reading graphs. addition, describe several network properties highlight some widely used topological features. We briefly mention patterns, motifs models, further comment on types biomedical networks along with their corresponding computer- human-readable file formats. Finally, a variety algorithms metrics analyses regarding drawing, clustering, visualization, link prediction, perturbation, alignment current state-of-the-art tools. expect review reach very broad spectrum readers varying from experts beginners while encouraging them enhance field further.

Язык: Английский

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Multi-omics integration in biomedical research – A metabolomics-centric review

Analytica Chimica Acta, Год журнала: 2020, Номер 1141, С. 144 - 162

Опубликована: Окт. 22, 2020

Язык: Английский

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Applications of multi‐omics analysis in human diseases

MedComm, Год журнала: 2023, Номер 4(4)

Опубликована: Июль 31, 2023

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell proteomics and metabolomics, spatial so on, which play a great role in promoting study human diseases. Most current reviews focus on describing development multi-omics technologies, data integration, particular disease; however, few them provide comprehensive systematic introduction multi-omics. This review outlines existing technical categories multi-omics, cautions for experimental design, focuses integrated analysis methods especially approach machine learning deep integration corresponding tools, medical researches (e.g., cancer, neurodegenerative diseases, aging, drug target discovery) as well open-source tools databases, finally, discusses challenges future directions precision medicine. With algorithms, important disease research, also provided detailed introduction. will guidance researchers, who are just entering into research.

Язык: Английский

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CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics

Nucleic Acids Research, Год журнала: 2020, Номер 49(D1), С. D1083 - D1093

Опубликована: Окт. 19, 2020

Abstract CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular pharmacological data within across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH MD Anderson Center (MDACC). We present CellMinerCDB 1.2 with updates to NCI-60, Cell Line Encyclopedia Sanger/MGH, addition new datasets, including NCI-ALMANAC drug combination, MDACC Project proteomic, NCI-SCLC DNA copy number methylation data, methylation, genetic dependency metabolomic datasets. (v1.2) includes several improvements over previously published version: (i) updated datasets; (ii) support for pattern comparisons multivariate analyses sources; (iii) annotations mechanism action information biologically relevant multigene signatures; (iv) speedups via caching; (v) a dataset download feature; (vi) improved visualization subsets multiple tissue types; (vii) breakdown univariate associations by type; (viii) enhanced help information. The curation common (e.g. tissues origin identifiers) provided here pharmacogenomic increase utility individual address researcher question types, reproducibility, biomarker discovery activity.

Язык: Английский

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Artificial Intelligence and Early Detection of Pancreatic Cancer

Pancreas, Год журнала: 2021, Номер 50(3), С. 251 - 279

Опубликована: Март 1, 2021

Abstract Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and 5-year survival rate of only 10%. Early symptoms the disease are mostly nonspecific. The premise improved through early detection that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as successful tool for risk stratification identification in general health care. In response to maturity AI, Kenner Family Research Fund conducted 2020 AI Detection Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) conjunction American Association, focus on potential advance efforts this disease. This comprehensive presummit article was prepared based information provided by each interdisciplinary participants one 5 following topics: Progress, Problems, Prospects Detection; Machine Learning; Cancer—Current Efforts; Collaborative Opportunities; Moving Forward—Reflections Government, Industry, Advocacy. outcome robust conversations, be presented future white paper, indicate significant progress must result strategic collaboration among investigators institutions multidisciplinary backgrounds, supported committed funders.

Язык: Английский

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Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Nucleic Acids Research, Год журнала: 2023, Номер 51(20), С. 10934 - 10949

Опубликована: Окт. 16, 2023

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, their target genes, so called TF regulons, can be coupled with computational algorithms to estimate activity TFs. However, interpret these findings accurately, regulons high reliability coverage are needed. In this study, we present evaluate collection created using CollecTRI meta-resource containing signed TF-gene interactions for 1186 context, introduce workflow integrate information from multiple resources assign sign could applied other comprehensive knowledge bases. We find CollecTRI-derived outperform public collections accurately inferring changes activities perturbation experiments. Furthermore, showcase value by examining profiles three different cancer types exploring at level single-cells. Overall, enable accurate estimation thereby help transcriptomics data.

Язык: Английский

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Machine learning approaches to predict drug efficacy and toxicity in oncology

Cell Reports Methods, Год журнала: 2023, Номер 3(2), С. 100413 - 100413

Опубликована: Фев. 1, 2023

In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) oncology, particularly for biomedical applications such as drug discovery, repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights predictions these areas by representing both disease state therapeutic agents used treat it. To fully utilize capabilities it is important understand fundamental concepts underlying how they can be applied assess efficacy toxicity therapeutics. this perspective, we lay out approaches represent derive novel make relevant predictions.

Язык: Английский

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Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets

Cancer Cell, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels new cancer specimens from other studies or clinical trials. Here, we address this barrier applying five different machine learning multi-omic data 8,791 TCGA tumor samples comprising 106 26 cohorts build models based upon small numbers features that can classify into previously molecular subtypes-a step toward application clinic. We validate select classifiers using external datasets. Predictive performance classifier-selected yield insight machine-learning genomic platforms. For each type provide containerized versions top-performing public resource.

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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