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Bioinformatics

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bioRxiv2026

SPIN: A Scalable Bioinformatics Pipeline for Screening Pathogenicity Related Host-Pathogen Protein INteractions Using AlphaFold3

Traditional experimental approaches have greatly advanced our understanding of protein-protein interactions (PPIs) that govern host susceptibility or resistance. Nonetheless, the molecular characterization of microbial effectors and their cognate host targets remains challenging in many economically important plant pathosystems. AlphaFold3 (AF3) has transformed structural biology by achieving near-experimental accuracy in protein structure and complex prediction. A bioinformatics pipeline SPIN (Screening Pathogenicity-Related Host/Pathogen Protein INteractions) was developed for large-scale prediction of interactions between the host and pathogen-secreted proteins. SPIN integrates three core modules: pathogen and host protein preprocessing using bioinformatics tools (SignalP6.0, OrthoFinder, CD-HIT), AF3-based interaction modeling, supported by automated input generation and output filtering. However, AF3 training bias toward mammalian proteins necessitated careful evaluation in plant systems. Benchmarking against experimentally validated plant PPIs revealed that derivative metrics emphasizing interfacial geometry and residue-level contact (ipSAE and pDockQ) provide superior discrimination compared to native global confidence measures (pTM and ipTM), particularly for proteins with intrinsically disordered regions. A multi-metric confidence scoring framework combining pLDDT, PAE, ipSAE, and pDockQ, improved prediction reliability by enhancing recall and reduced false positives through robust assessment of structural confidence and interface quality. For proof-of-concept, SPIN was applied to examine the molecular landscape underlying two economically important diseases of citrus (Citrus L.) caused by Candidatus Liberibacter asiaticus and Ca. Phytoplasma citri. Both pathogens are phloem-limited and cause distinct symptoms, citrus greening and witches broom, respectively. AF3-predicted interactome data revealed conserved host colonization strategies alongside disease-specific molecular mechanisms, demonstrating the utility of SPIN for dissecting and supporting mechanistic studies in plant-pathogen interactions.

genomics↗

bioRxiv2026

Democratizing Agentic Access to Bioinformatics and Biopharmaceutical Databases and Analyses with BioMCP-TS

Practitioners want to give AI agents direct access to biomedical databases and analyses, but today that means choosing between dependency-heavy skill packages, data-lookup-focused server tools, and closed vendor platforms. BioMCP-TS removes the choice: one open-source server that installs with a single pinned command and verifies its own setup. An agent working through BioMCP-TS can search and cross-reference 50+ bioinformatics, pharmaceutical, and patent databases (genes, variants, drugs, diseases, literature, clinical trials, patents, functional genomics, and structures) and, uniquely among open bioinformatics servers, run heavyweight analyses in-process as WebAssembly: Bioconductor differential expression and htslib genomics operations with no R installation, C toolchain, or containers on the host, plus read-only SQL over curated local databases. We demonstrate these strengths with seven practical cases spanning drug-target due diligence, translational intelligence, GWAS follow-up, dependency analysis, cohort genomics, and differential expression, ranging from a first federated lookup through target-disease landscapes to published-structure shortlists and reproducible RNA-seq results, each answered end-to-end within minutes. The server, recorded transcripts, and the full problem set are available at https://github.com/yeyuan98/biomcp-ts (npm: biomcp).

bioengineering↗

bioRxiv2014

bíogo: a simple high-performance bioinformatics toolkit for the Go language

biogo is a framework designed to ease development and maintenance of computationally intensive bioinformatics applications. The library is written in the Go programming language, a garbage-collected, strictly typed compiled language with built in support for concurrent processing, and performance comparable to C and Java. It provides a variety of data types and utility functions to facilitate manipulation and analysis of large scale genomic and other biological data. biogo uses a concise and expressive syntax, lowering the barriers to entry for researchers needing to process large data sets with custom analyses while retaining computational safety and ease of code review. We believe biogo provides an excellent environment for training and research in computational biology because of its combination of strict typing, simple and expressive syntax, and high performance.

Bioinformatics↗

bioRxiv2017

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness, and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records.\n\nSpecifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record-literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using Principal Component Analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that 1 record out of 4 is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records.

bioinformatics↗

bioRxiv2017

Cysteine Proteases Of Hookworm Necator Americanus As Virulence Factors And Implications For Future Drug Design: A Bioinformatics-Based Study

Human hookworm Necator Americanus (NA) causes iron deficiency anemia, as the parasite ingests blood from the gastrointestinal tract of its human host. This bioinformatics-based study focuses on eight of the cathepsin B-like cysteine proteases (CPs) of the worm to explore their pathogenic potential. CP1 - CP6, which harbored the active site cysteine residue for enzymatic activity, were relevantly observed to have N-terminal signal peptide for extracellular localization. The secretory CPs could be releasing indigenous worm heparin at the host-pathogen interface for anticoagulation purposes. CP2 and CP3 showed a novel hemoglobinase motif that could be a prerequisite for hemoglobin degradation. CP1 and CP6 shared similar enzymatic-pocket features with cathepsin B and cruzain that cleave high molecular weight kininogen for blood-thinning activity. CP1, CP2, CP3, CP5 and CP6 were predicted to bind heparin, at their C terminal domain, like human cathepsin B and cruzain non-covalently bind heparin to enhance their activity. NA CPs action in concert with heparin, have implications for anti-heparin and heparin analog design against hookworm infection.

bioinformatics↗

bioRxiv2017

Experimenting with reproducibility in bioinformatics

Reproducibility has been shown to be limited in many scientific fields. This question is a fundamental tenet of the scientific activity, but the related issues of reusability of scientific data are poorly documented. Here, we present a case study of our attempt to reproduce a promising bioinformatics method [1] and illustrate the challenges to use a published method for which code and data were available. First, we tried to re-run the analysis with the code and data provided by the authors. Second, we reimplemented the method in Python to avoid dependency on a MATLAB licence and ease the execution of the code on HPCC (High-Performance Computing Cluster). Third, we assessed reusability of our reimplementation and the quality of our documentation. Then, we experimented with our own software and tested how easy it would be to start from our implementation to reproduce the results, hence attempting to estimate the robustness of the reproducibility. Finally, in a second part, we propose solutions from this case study and other observations to improve reproducibility and research efficiency at the individual and collective level.\n\nAvailabilitylast version of StratiPy (Python) with two examples of reproducibility are available at GitHub [2].\n\nContactyang-min.kim@pasteur.fr

bioinformatics↗

bioRxiv2017

A Bioinformatics Pipeline for Whole Exome Sequencing: Overview of the Processing and Steps from Raw Data to Downstream Analysis

Recent advances in next generation sequencing (NGS) technologies have given an impetus to find causality for rare genetic disorders. Since 2005 and aftermath of the human genome project, efforts have been made to understand the rare variants of genetic disorders. Benchmarking the bioinformatics pipeline for whole exome sequencing (WES) has always been a challenge. In this protocol, we discuss detailed steps from quality check to analysis of the variants using a WES pipeline comparing them with reposited public NGS data and survey different techniques, algorithms and software tools used during each step. We observed that variant calling performed on exome and whole genome datasets have different metrics generated when compared to variant callers, GATK and VarScan with different parameters. Furthermore, we found that VarScan with strict parameters could recover 80-85% of high quality GATK SNPs with decreased sensitivity from NGS data. We believe our protocol in the form of pipeline can be used by researchers interested in performing WES analysis for genetic diseases and by large any clinical phenotypes.

bioinformatics↗

bioRxiv2019

Identification of Six novel missense single nucleotide polymorphisms in MAOA gene predisposing to aggressive behavior. Bioinformatics study.

BackgroundAn astonishing observation is that aggressive behavior is actually a highly heritable. Recent experimental work and behavior research has linked individual variation in a functional polymorphism of the monoamine oxidase-A gene (MAOA) to the occurrence of anger-driven aggression. Aggressive antisocial and violent behavior has become a regularly debated topic in the scientific community; the impending question is what is the source of aggressive behavior, is it genetic or environmental or is it just an individual choice. This study aims to analyses the SNPs found in MAOA gene and it is possible association to aggressive behavior. MethodVarious bioinformatics software (SIFT, PolyPhen-2, PROVEAN, SNAP22, SNP&GO and PMut)is used to analyses the SNPs within the MAOA gene to study the structural and functional implication on the associated protein, which is further clarified using chimera software. Then gene-gene interaction is studied with geneMANIA software. Furthermore, conservation and annotation studies were done through the ConSurf server and Variant Effect Predictor (VEP) respectively. ResultSix missense SNPs were found to affect the structural and functional prospect of MAOA protein. ConclusionGenetic mutation within MAOA is likely to be associated with aggressive behavior; this will enrich future management and screening possibilities for this behavior.

bioinformatics↗

bioRxiv2020

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

The World Health Organization declared the COVID-19 epidemic a public health emergency of international concern on March 11th, 2020, and the pandemic is rapidly spreading worldwide. COVID-19 is caused by a novel coronavirus SARS-CoV-2, which enters human target cells via angiotensin converting enzyme 2 (ACE2). We used a number of bioinformatics tools to computationally characterize ACE2 by determining its cell-specific expression in trachea, lung, and small intestine, derive its putative functions, and predict transcriptional regulation. The small intestine expressed higher levels of ACE2 than any other organ. The large intestine, kidney and testis showed moderate signals, whereas the signal was weak in the lung. Single cell RNA-Seq data from trachea indicated positive signals along the respiratory tract in key protective cell types including club, goblet, proliferating, and ciliary epithelial cells; while in lung the ratio of ACE2-expressing cells was low in all cell types (<2.6%), but was highest in vascular endothelial and goblet cells. Gene ontology analysis suggested that, besides its classical role in renin-angiotensin system, ACE2 may be functionally associated with angiogenesis/blood vessel morphogenesis. Using a novel tool for the prediction of transcription factor binding sites we identified several putative binding sites within two tissue-specific promoters of the ACE2 gene. Our results also confirmed that age and gender play no significant role in the regulation of ACE2 mRNA expression in the lung. IMPORTANCEVaccines and new medicines are urgently needed to prevent spread of COVID-19 pandemic, reduce the symptoms, shorten the duration of disease, prevent virus spread in the body, and most importantly to save lives. One of the key drug targets could be angiotensin-converting enzyme 2 (ACE2), which is a crucial receptor for the corona virus (SARS-CoV-2). It is known that SARS coronavirus infections lead to worse outcome in the elderly and in males. Therefore, one aim of the present study was to investigate whether age or sex could contribute to the regulation of ACE2 expression. We also decided to explore the transcriptional regulation of ACE2 gene expression. Since data on ACE2 distribution is still conflicting, we aimed to get a more comprehensive view of the cell types expressing the receptor of SARS-CoV-2. Finally, we studied the coexpression of ACE2 with other genes and explored its putative functions using gene ontology enrichment analysis.

bioinformatics↗

bioRxiv2020

Identification of molecular specificity between reprogrammed cardiomyocytes and normal cardiomyocytes by bioinformatics

The adult human heart lacks an effective endogenous repair mechanism and cannot fully restore heart function after injury. We obtained two microarray data sets GSE99814 and GSE22292 from the NCBI GEO database, which include 5 groups of directly reprogrammed cardiomyocyte-like cells (iCMs) and 5 groups of normal mouse cardiomyocytes (CM). And through the GEO2R tool and Venn diagram software to screen the differentially expressed genes (DEG) between iCM and CM. Next, we performed functional enrichment analysis on these DEGs. The protein-protein interaction network (PPI) was constructed by STRING and Cytoscape for module analysis. We have selected a total of 243 DEGs consistently expressed genes in the two data sets, of which 127 up-regulated genes function and pathway enrichment mainly concentrated on biological processes such as innate immune response, inflammatory response, immune system process, positive regulation of apoptosis process and complement and coagulation cascade, while the 116 down-regulated genes are mainly enriched in cell cycle, cell division, cardiac development, myocardial contraction and myocardial cell development and signaling pathways such as cell cycle and adrenergic signaling in cardiomyocytes. Then in the PPI network, we found 27 central genes when analyzing these 243 DEGs by the Molecular Complex Detection (MCODE) plug-in. Finally, we reanalyzed these 27 central genes through DAVID and found that 6 genes (CCNB1, CCNB2, BUB1, TTK, CDC25C, CCNA2) significantly enriched the cell cycle signaling pathway (p <1.20E-07). Therefore, through integrated bioinformatics methods, we found that compary with iCMs and CMs, iCMs were induced by direct reprogramming of neonatal mouse cardiomyocytes, the DEGs were mainly enriched in immune-related processes and myocardial contraction and cell cycle signaling pathways (p < 0.05).

bioinformatics↗

bioRxiv2020

Kaleidoscope: A New Bioinformatics Pipeline Web Application for In Silico Hypothesis Exploration of Omics Signatures

BackgroundIn silico data exploration is a key first step of exploring a research question. There are many publicly available databases and tools that offer appealing features to help with such a task. However, many applications lack exposure or are constrained with unfriendly or outdated user interfaces. Thus, it follows that there are many resources that are relevant to investigation of medical disorders that are underutilized. ResultsWe developed an R Shiny web application, called Kaleidoscope, to address this challenge. The application offers access to several omics databases and tools to let users explore research questions in silico. The application is designed to be user- friendly with a unified user interface, while also scalable by offering the option of uploading user-defined datasets. We demonstrate the application features with a starting query of a single gene (Disrupted in schizophrenia 1, DISC1) to assess its protein-protein interactions network. We then explore expression levels of the gene network across tissues and cell types in the brain, as well as across 34 schizophrenia versus control differential gene expression datasets. ConclusionKaleidoscope provides easy access to several databases and tools under a unified user interface to explore research questions in silico. The web application is open-source and freely available at https://kalganem.shinyapps.io/Kaleidoscope/. This application streamlines the process of in silico data exploration for users and expands the efficient use of these tools to stakeholders without specific bioinformatics expertise.

bioinformatics↗

bioRxiv2020

Bioinformatic analysis for the identification of potential gene interactions and therapeutic targets in atrial fibrillation

BackgroundAtrial fibrillation (AF) is the most prevalent tachycardia. The major injuries caused by AF are systemic embolism and heart failure. Although AF therapies have evolved substantially in recent years, the success rate of sinus rhythm maintenance is relatively low. The reason is the incomplete understanding of the AF mechanisms. Material and methodIn this study, profiles were downloaded from the GEO database (GSE79762). Bioinformatic analysis was used to identify differentially expressed genes (DEGs). GO analysis and KEGG analysis were performed to identify the most enriched terms and pathways. A protein-protein interaction network was constructed to determine regulatory genes. Key modules and hub genes were identified by MOCDE and cytoHubba. Transcription factors (TFs) were predicted by PASTAA. ResultsSeventy-seven up-regulated DEGs and 236 downregulated DEGs were identified. In the GO biological process, cellular components, and molecular function analysis, positive regulation of cell migration, anchoring junction and cell adhesion molecule binding were the most significant enrichment terms. The Hippo signaling pathway was the most significantly enriched pathway. In the PPI network analysis, we found that Class A/1 (rhodopsin-like receptors) may be the critical module in AF. Ten hub genes were extracted, including 4 upregulated genes and 6 downregulated genes. CXCR2, TLR4 and CXCR4 may play critical roles in AF. In TF prediction, we found that Irf-1 may be implicated in AF. ConclusionOur study found that the CXCR4, TLR4, CXCR2; Hippo signaling pathway; and class A/1 (rhodopsin-like receptors) modules may play critical roles in AF occurrence and maintenance. This may provide novel targets for AF treatment.

bioinformatics↗

bioRxiv2020

Identification of potential biomarkers or therapeutic targets of mesenchymal stem cells in multiple myeloma by bioinformatics analysis.

ObjectivesMesenchymal stem cells (MSCs) play important roles in multiple myeloma (MM) pathogenesis. Previous studies have discovered a group of MM-associated potential biomarkers in MSCs derived from bone marrow (BM-MSCs). However, no study of the bioinformatics analysis was conducted to explore the key genes and pathways of MSCs derived from adipose (AD-MSCs) in MM. The aim of this study was to screen potential biomarkers or therapeutic targets of AD-MSCs and BM-MSCs in MM. MethodsThe gene expression profiles of AD-MSCs (GSE133346) and BM-MSCs (GSE36474) were downloaded from Gene Expression Omnibus (GEO) database. Gene Oncology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network of differentially expressed genes (DEGs) were performed. ResultsA total of 456 common downregulated DEGs in two datasets were identified and the remaining DEGs in GSE133346 were further identified as specific DEGs of AD-MSCs. Furthermore, a PPI network of common downregulated DEGs was constructed and seven hub genes were identified. Importantly, cell cycle was the most significantly enrichment pathway both in AD-MSCs and BM-MSCs from MM patients. ConclusionWe identified key genes and pathways closely related with MM progression, which may act as potential biomarkers or therapeutic targets of MM.

bioinformatics↗

bioRxiv2020

METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments

Multi-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as for molecular ecology and biomonitoring applications in different sectors from academic research to regulatory agencies and industry. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are often used to process animal metabarcode markers from the same study. MetaWorks provides a harmonized processing environment, pipeline, and taxonomic assignment approach for demultiplexed Illumina reads for all biota using a wide range of metabarcoding markers such as 16S, ITS, and COI. A Conda environment is provided to quickly gather most of the programs and dependencies for the pipeline. Several workflows are provided such as: taxonomically assigning exact sequence variants, provides an option to generate operational taxonomic units, and facilitates single-read processing. Pipelines are automated using Snakemake to minimize user intervention and facilitate scalability. All pipelines use the RDP classifier to provide taxonomic assignments with confidence measures. We extend the functionality of the RDP classifier for taxonomically assigning 16S (bacteria), ITS (fungi), and 28S (fungi), to also support COI (animals), rbcL (eukaryotes, land plants, diatoms), 12S (fish), 18S (eukaryotes, diatoms) and ITS (fungi, plants). MetaWorks properly handles ITS by trimming flanking conserved rRNA gene regions as well as protein coding genes by providing two options for removing obvious pseudogenes. MetaWorks is available at https://github.com/terrimporter/MetaWorks along with quick-start instructions using test data, detailed workflow descriptions, and a tutorial for new users.

bioinformatics↗

bioRxiv2020

SARS-CoV2 spike protein displays biologically significant similarities with paramyxovirus surface proteins; a bioinformatics study

Recent emergence of SARS-CoV2 and associated COVID-19 pandemic has posed a great challenge for the scientific community. Understanding various aspects of SARS-CoV2 biology, virulence and pathogenesis as well as determinants of immune response have become a global research priority. In this study, we performed bioinformatic analyses on SAR-CoV2 protein sequences, trying to unravel biologically important similarities between this newly emerged virus with other RNA viruses. Comparing the proteome of SARS-CoV2 with major positive and negative strand ssRNA viruses showed significant homologies between SARS-CoV2 spike protein with pathogenic paramyxovirus fusion proteins. This spike-fusion homology was not limited to SARS-CoV2 and it existed for some other pathogenic coronaviruses; nonetheless, SARS-CoV2 spike-fusion homology was orders of magnitude stronger than homologies observed for other known coronaviruses. Moreover, this homology did not seem to be a consequence of general ssRNA virus phylogenetic relations. We also explored potential immunological significance of this spike-fusion homology. Spike protein epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that the majority of spikes T cell epitopes as well as many B cell and MHC binding epitopes map within the spike-fusion homology region. Overall, our data indicate that there might be a relation between SARS-CoV2 and paramyxoviruses at the level of their surface proteins and this relation could be of crucial immunological importance.

bioinformatics↗

bioRxiv2020

Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis

The novel betacoronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after initially emerging in Wuhan, China. Here we applied a novel, comprehensive bioinformatic strategy to public RNA sequencing and viral genome sequencing data, to better understand how SARS-CoV-2 interacts with human cells. To our knowledge, this is the first meta-analysis to predict host factors that play a specific role in SARS-CoV-2 pathogenesis, distinct from other respiratory viruses. We identified differentially expressed genes, isoforms and transposable element families specifically altered in SARS-CoV-2 infected cells. Well-known immunoregulators including CSF2, IL-32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were overexpressed. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as hnRNPA1, PABPC1 and eIF4b, which may play important roles in the viral life cycle. We also detected four viral sequence variants in the spike, polymerase, and nonstructural proteins that correlate with severity of COVID-19. The host factors we identified likely represent important mechanisms in the disease profile of this pathogen, and could be targeted by prophylactics and/or therapeutics against SARS-CoV-2. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/225581v3_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15ca5a6org.highwire.dtl.DTLVardef@17f455forg.highwire.dtl.DTLVardef@a39f50org.highwire.dtl.DTLVardef@306ec1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Identification of key genes in SARS-CoV-2 patients on bioinformatics analysis

The COVID-19 pandemic has infected millions of people and overwhelmed many health systems globally. Our study is to identify differentially expressed genes (DEGs) and associated biological processes of COVID-19 using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE152075 datasets were originally produced by using the high-throughput Illumina NextSeq 500. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and biochemical pathways. GO and KEGG results suggested that several biological pathways such as "Fatty acid metabolism" and "Cilium morphogenesis" are mostly involved in the development of COVID-19. Moreover, several genes are critical for virus invasion and adhesion including FLOC, DYNLL1, FBXL3, and FBXW11 and show significant differences in COVID-19 patients. Thus, our study provides further insights into the underlying pathogenesis of COVID-19.

bioinformatics↗

bioRxiv2020

Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis

There is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

bioinformatics↗