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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBAs versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.

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bioRxiv2024

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequancing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analysed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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bioRxiv2024

Discovery of RdRp Thumb-1 as a novel broad-spectrum antiviral family of targets and MDL-001 as a potent broad-spectrum inhibitor thereof - Part I: A Bioinformatics and Deep Learning Approach

RNA viruses cause human diseases ranging from mild colds to deadly pandemics. Direct-acting, broad-spectrum, non-nucleoside antivirals have been characterized as impossible to develop because allosteric binding sites are poorly conserved. The HCV NS5B RNA-dependent RNA polymerase (RdRp) Thumb-1 allosteric site and its interaction with the HCV NS5B {Lambda}1-loop governs an essential conformational change required for polymerase initiation. The only approved NS5B Thumb-1 inhibitor, beclabuvir, has been shown to be inactive against a broad panel of non-HCV viruses, including poliovirus, rhinovirus, coronavirus, coxsackievirus, influenzavirus, and HIV. A conserved, homologous allosteric site on RdRp that spans multiple viral families has not been reported. Here, we report GALILEO's discovery that the Thumb-1 pocket, its associated {Lambda}1-loop and their interaction are conserved across RNA viral families for the first time. The discovery is validated through comparative structural analysis of Protein Data Bank (PDB) deposited viral polymerases utilizing a method that allows independent, public validation by any researcher. We further demonstrate that beclabuvir's dependence on its indole C6 carbonyl to interact with the HCV-specific residue R503 restricts its activity to HCV. We validate the target discovery with MDL-001, which does not contain a C6 carbonyl substituent. MDL-001 directly blocks viral RNA synthesis in isolated replication complexes and selects for the canonical Thumb-1 resistance mutation P495S in HCV NS5B. MDL-001 demonstrates broad-spectrum in vitro inhibition of both HCV and SARS-CoV-2. Preclinical proof of concept and development of MDL-001 across HCV, HBV, HDV, influenza, SARS-CoV-2, and RSV have been previously reported. These findings establish RdRp Thumb-1 as a conserved allosteric pocket and a druggable target for broad-spectrum direct-acting antiviral development.

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bioRxiv2024

Benchmarking of bioinformatics tools for the hybrid de novo assembly of human whole-genome sequencing data

Accurate and complete de novo assembled genomes sustain variant identification and catalyze the discovery of new genomic features and biological functions. However, accurate and precise de novo assemblies of large and complex genomes remains a challenging task. Long-read sequencing data alone or in hybrid mode combined with more accurate short-read sequences facilitate the de novo assembly of genomes. A number of software exists for de novo genome assembly from long-read data although specific performance comparisons to assembly human genomes are lacking. Here we benchmarked 11 different pipelines including four long-read only assemblers and three hybrid assemblers, combined with four polishing schemes for de novo genome assembly of a human reference material sequenced with Oxford Nanopore Technologies and Illumina. In addition, the best performing choice was validated in a non-reference routine laboratory sample. Software performance was evaluated by assessing the quality of the assemblies with QUAST, BUSCO, and Merqury metrics, and the computational costs associated with each of the pipelines were also assessed. We found that Flye was superior to all other assemblers, especially when relying on Ratatosk error-corrected long-reads. Polishing improved the accuracy and continuity of the assemblies and the combination of two rounds of Racon and Pilon achieved the best results. The assembly of the non-reference sample showed comparable assembly metrics as those of the reference material. Based on the results, a complete optimal analysis pipeline for the assembly, polishing, and contig curation developed on Nextflow is provided to enable efficient parallelization and built-in dependency management to further advance in the generation of high-quality and chromosome-level human assemblies.

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bioRxiv2024

Bioinformatics analysis of the potential biomarkers of Multiple Sclerosis and Guillain-Barre syndrome

Recent research emphasizes the intricate interplay of genetics and epigenetics in neurological disorders, notably Multiple Sclerosis (MS) and Guillain-Barre Syndrome (GBS), both of which exhibit cardiovascular dysregulation, with GBS often featuring serious bradyarrhythmias requiring prompt recognition and treatment. While cardiovascular autonomic dysfunction in MS is typically less severe, orthostatic intolerance affects around half of MS patients. Their distinction lies in their autoimmune responses, MS is an autoimmune disease affecting the central nervous system, causes demyelination and axon damage, leading to cognitive, ocular, and musculoskeletal dysfunction. In contrast, GBS primarily affects the peripheral nervous system, resulting in paralysis and respiratory complications. Despite their differences, both diseases share environmental risk factors such as viral infections and Vitamin D deficiency. This study aims to explore shared gene expression pathways, functional annotations, and molecular pathways between MS and GBS to enhance diagnostics, pathogenesis understanding, and treatment strategies through molecular analysis techniques. Through the gene expression analysis, five significant genes were found UTS2, TNFSF10, GBP1, VCAN, FOS. Results shows that Common DEGs are linked to apoptosis, bacterial infections, and atherosclerosis. Molecular docking analysis suggests Aflatoxin B1 as a potential therapeutic compound due to its high binding affinity with common differentially expressed proteins.

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bioRxiv2024

A bioinformatic approach for the prediction andfunctional classification of Toxoplasma gondii longnon-coding RNAs

Long non-coding RNAs (lncRNAs) have emerged as significant players in diverse cellular processes, including cell differentiation. Advancements in computational methodologies have facilitated the prediction of lncRNA functions, enabling insights even in non-model organisms like pathogenic parasites, in roles such as parasite development, antigenic variation, and epigenetics. In this work, we focus on the apicomplexan Toxoplasma gondii differentiation process, where the infective stage, tachyzoite, can develop into the cysted stage, bradyzoite, under stress conditions. Using a publicly available transcriptome dataset, we predicted lncRNA sequences associated with this differentiation process. Notably, a substantial proportion of these predicted lncRNAs exhibited stage-specific expression, particularly at the bradyzoite stage. Furthermore, co-expression patterns between coding transcripts and lncRNAs suggest their involvement in shared processes, such as bradyzoite development. TglncRNA loci analysis revealed their potential influence on the expression of nearby coding genes, including subtelomeric genes unique to the T. gondii genome. Finally, with a k-mer analysis approach, we identified functional relationships between characterized lncRNAs from model organisms like Homo sapiens and predicted T. gondii lncRNAs. Our perspective led to the identification of a T. gondii lncRNA potentially mediating DNA damage repair pathways, shedding light on the adaptive mechanisms of T. gondii in response to stress conditions.

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bioRxiv2024

Integrating Bioinformatics and Machine Learning to Investigate the Mechanisms by Which Three Major Respiratory Infectious Diseases Exacerbate Heart Failure

BackgroundHeart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumonia (CAP). This study aims to uncover the molecular commonalities among these respiratory diseases and their impact on HF, identifying key mediating genes. MethodsDatasets from the GEO database were analyzed for differential expression to find common molecular features of the three respiratory diseases. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify gene modules associated with HF. GO and KEGG enrichment analyses determined the biological processes and pathways involved in HF exacerbation by respiratory diseases. Key genes were screened using LASSO, RF, and SVM-RFE machine learning algorithms, with accuracy validated by ROC curves. Single-sample GSEA (ssGSEA) was performed, and the Drug Signature Database (DSigDB) was used for drug prediction. Immune infiltration analysis was conducted using CIBERSORT. ResultsWe identified 51 characteristic genes of respiratory diseases and 10 potential genes exacerbating HF, primarily involved in innate immune response, inflammation, and coagulation pathways. Machine learning algorithms identified RSAD2 and IFI44L as key genes with high accuracy (AUC > 0.7). ssGSEA indicated RSAD2s involvement in complement and coagulation cascades, while IFI44L is associated with myocardial contraction in HF progression. DSigDB predicted six potential therapeutic drugs. Immune infiltration analysis revealed significant differences in eight immune cell types between HF patients and healthy controls. ConclusionOur findings enhance the understanding of molecular interactions between respiratory diseases and heart failure, paving the way for future research and therapeutic strategies.

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bioRxiv2024

Exploring Harmala Alkaloids as Novel Antimalarial Agents against Plasmodium falciparum through Bioinformatics Approaches

Malaria, caused by the Plasmodium falciparum, remains a significant global health challenge, with Plasmodium falciparum accounting for approximately 50% of cases and posing a considerable threat. Despite advances in control measures, malaria continues to cause an estimated one million deaths annually. The complex lifecycle of P. falciparum, involving both vertebrate hosts and Anopheles mosquitoes, complicates eradication efforts. The parasites resistance to existing antimalarial drugs, along with medication toxicity, necessitates innovative therapeutic approaches. Recent research has revealed that harmine, an alkaloid produced by an endophytic gut bacterium of Anopheles mosquitoes, can impede the transmission of the malarial parasite to humans by inhibiting a crucial life stage. This study investigates harmala alkaloids, sourced from plants and bacteria such as Peganum harmala, as potential alternatives to conventional antimalarial drugs. Notably, harmine and harmaline have shown promising antimalarial activity by inhibiting the essential enzyme protein kinase 4 (PK4), which is vital for the parasites survival. These compounds exhibit lower toxicity, effectively inhibiting both the blood stage growth and transmission of the parasite. Using in silico methodologies, including ADME analysis, molecular docking, MD simulation, and toxicity analysis, this study identifies harmala alkaloids as potential inhibitors against crucial P. falciparum proteins. Targeting proteins essential for the parasites survival, similar to established drugs like pfCRT protein, lays the foundation for developing effective antimalarial treatments. The comprehensive screening of harmala alkaloid molecules opens avenues for the pharmaceutical industry to tackle challenges related to drug resistance and toxicity, offering a promising route for the biorational management of malaria.

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bioRxiv2024

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research

Global changes in Cannabis legislation after decades of stringent regulation, and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host Cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, QTL and genetic maps, gene and protein expression, metabolic profile and their sample attributes. SNPs were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service API, developed on top of existing Tripal modules, returns generic tables of sample, property, and values. Use-cases demonstrate the APIs utility for various -omics analyses, enabling researchers to perform multi- omics analyses efficiently.

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bioRxiv2024

Identification of novel key genes and signaling pathways in hypertrophic cardiomyopathy: evidence from bioinformatics and next generation sequencing data analysis

Hypertrophic cardiomyopathy (HCM) is a global health problem characterized by left ventricle become thick and stiff with effect of indication including chest pain, fluttering, fainting and shortness of breath. In this investigation, we aimed to identify diagnostic markers and analyzed the therapeutic potential of essential genes. Next generation sequencing (NGS) dataset GSE180313 was obtained from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs) in HCM. DEGs were screened using the DESeq2 Rbioconductor tool. Then, Gene Ontology (GO) and REACTOME pathway enrichment analyses were performed. Moreover, a protein-protein interaction (PPI) network of the DEGs was constructed, and module analysis was performed. Next, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were constructed and analyzed. Next, diagnostic values of hub genes were assessed by receiver operating characteristic (ROC) curve analysis. Finely, molecular docking and ADMET studies were performed. By performing DEGs analysis, total 958 DEGs ( 479 up regulated genes and 479 down regulated genes) were successfully identified from GSE180313, respectively. GO and REACTOME pathway enrichment analyses revealed that GO functions and signaling pathways were significantly enriched including response to stimulus, multicellular organismal process, metabolism and extracellular matrix organization. The hub genes of FN1, SOX2, TUBA4A, RPS2, TUBA1C, ESR1, SNCA, LCK, PAK1 and APLNR might be associated with HCM. The hub gens of FN1 and TPM3, together with corresponding predicted miRNAs (e.g., hsa-mir-374a-5p and hsa-miR-8052), SH3KBP1 and ESR1 together with corresponding predicted TFs (e.g PRRX2 and STAT3) and CHRNA4 and CHRM3 together with corresponding predicted drug molecules (e.g. Succinylcholine and Umeclidinium) were found to be significantly correlated with HCM. Molecular Docking study revealed that all tested phytoconstituents effectively occupied LGALS3 and ESR1. ADMET analysis found that all screened compounds have adequate solubility and high projected intestine absorption, indicating their appropriateness for oral administration, This investigation could serve as a basis for further understanding the molecular pathogenesis and potential therapeutic targets of HCM.

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bioRxiv2024

Identification of candidate biomarkers and signaling pathways associated with Alzheimer's disease using bioinformatics analysis of next generation sequencing data

Alzheimers disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the specific molecular mechanisms of AD have not been fully investigated. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. These DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein-protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify key miRNAs and TFs. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that microRNA (miRNA) hsa-mir-545-3p and hsa-miR-548f-5p, and transcription factor (TF) PLAG1 and MEF2A might be involved in the development of AD. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.

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bioRxiv2025

Off-target prediction of SGLT2 inhibitors: an integrative bioinformatics approach to uncover structural mechanisms

Gliflozin is known to inhibit sodium glucose transporter 2 (SGLT2) inhibitors (SGLT2i). They have been approved for the treatments of diabetes mellitus, cardiovascular diseases, and chronic kidney disease in recent years. However, the mechanisms for the multifunction remain unclear. We here propose a hypothesis of other protein targets for gliflozins. Additionally, the multistage of the protein, the specificity of the drugs, along with the structural data shortage have posed a hindrance to disclose gliflozin and its binding environment (BE). Considering the difficulties on the issue, this thesis provides an approach to uncover protein off-targets and to find the underlying pathways. We predicted pyranose was a critical substructure from > 1,500 SGLT2i. Hierarchical clustering on atom-based interaction, combined with propensity, revealed key interaction among 1,572 pyranose BEs. The other criterium presented was the concept of compound spanning space via protein pocket size. Binding sites (BSs) from proteins like SGLT2 were able to provide similar pockets, approximating the occupied space of the ligands. Finally, gliflozin binding pockets prefer (1) aromatic residues for van der Waals force, (2) aspartate or asparagine as hydrogen bond providers, and (3) protein pocket size [greater double equals]442 [A] 2. These criteria were integrated into a scoring function ST and predicted several possible proteins. This research presents a pipeline for target identification of emerging drug agents and proteins with crystallographic difficulties. The study provides an innovative computational methodology on extraction of binding features on transmembrane proteins and further pharmaceutical development.

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bioRxiv2025

Bioinformatics analysis of next generation sequencing data reveals novel biomarkers and signaling pathways associated with recurrent implantation failure

Recurrent implantation failure (RIF) is a cases in which women have had three fruitless in vitro fertilization (IVF) bid with positive quality embryos. The RIF originates from uterine endometrium microbiota has been implicated in reproductive failure, and poor prognosis and lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of RIF. To identify key genes and signaling pathways in RIF, the next genetation sequencing data GSE243550 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between RIF and normal controls samples were identified using t-tests in the limma R bioconductor package. Using the DEGs, we further performed a series of gene ontology (GO) and REACTOME pathway enrichment analyses. Protein-protein interaction network was derived using the IMex interactome database and visualized using Cytoscape software. The most significant modules from the PPI network were selected for GO and pathway enrichment analysis. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed depending on key hub genes and visualized using Cytoscape software. A receiver operating characteristic curve (ROC) analysis was plotted to diagnose RIF. In total, 958 DEGs were identified, of which 479 were up regulated genes and 479 were down regulated genes. GO and REACTOME pathway enrichment analysis results revealed that the upregulated genes were mainly enriched in multicellular organismal process, membrane, small molecule binding and extracellular matrix organization, whereas downregulated genes were mainly enriched in organonitrogen compound metabolic process, intracellular anatomical structure, catalytic activity and translation. Through analyzing the PPI network, we screened hub genes APP, HSP90AA1, CAND1, CUL1, HSP90AB1, SIRT7, SRC, CDKN1A, ISG15 and RPS16 by the Cytoscape software. The regulatory network analysis revealed that microRNAs (miRNAs) include hsa-miR-574-3p and hsa-mir-208a-3p, and transcription factors (TFs) include SREBF1 and RELA might be involved in the development of RIF. Receiver operating characteristic curve analysis demonstrated that the hub genes screened for RIF were of good diagnostic significance. Overall, these results thus highlight a range of novel signaling pathways and genes that are linked to the incidence and progression of RIF, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of RIF.

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bioRxiv2025

EpiMolBio: A Novel User-Friendly Bioinformatic Program for Genetic Variability Analysis

MotivationGenetic sequence analysis has become essential in many medicine, biology, and epidemiology fields. However, the currently available tools can pose a challenge for users without advanced computational skills. ResultsWe present EpiMolBio, a free-to-use software designed with an intuitive, user-friendly interface that enables a broad spectrum of users to explore genetic variability. Its diverse toolkit encompasses sequence processing, conservation and variability analysis, consensus sequence generation, and genome mutation or amino acid changes identification, including specialized tools for HIV and SARS-CoV-2 analysis. AvailabilityFreely available on the web at https://www.epimolbio.com and https://github.com/EpiMolBio/EpiMolBio. Contactafrica.holguin@salud.madrid.org; roberto117343@gmail.com Supplementary informationSupplementary Table 1, 2, and Supplementary Text 1.

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bioRxiv2025

Bioinformatics Study of Genetic Variability in Obelisks

MotivationThe importance of in silico genetic variability analyses of new biological entities, such as obelisks, lies in their potential to serve as a starting point for experimental studies. The study of obelisks could open new horizons in the field of biological sciences. ResultsSeveral findings have been determined, including high conservation among the sequences within each cluster, despite the presence of significant nucleotide mutations; high variability among the Oblin-1 proteins found in the consensus sequences of each cluster, despite all sharing a known conserved region; structural consistency in the 3D model of the Oblin-2 consensus with previous studies; a series of nucleotide and protein- level consensus sequences for each cluster, where nucleotide-level patterns show color- coded conservation percentages that could be used in further research, such as primer or probe design, and protein-level sequences are presented in FASTA format for Oblin- 1, Oblin-2, and unknown Open Reading Frames (ORFs).

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bioRxiv2025

Bioinformatics Approach on Hypothetical Protein from Clostridioides difficile: Structural, Functional and Molecular Docking Analysis

Clostridioides difficile (C. difficile) has become a globally important pathogen as epidemic strains spread through hospitals in many countries. Despite the advancements in infection therapy, there is a need for more efficacious medicines against C. difficile that simultaneously minimize injury to the resident gut microbiota. The study aims to investigate various aspects of the protein, including its physicochemical properties, subcellular location, functional elucidation, protein-protein interactions, structure prediction, validation, determination of active sites for potential ligands, and MD study. The protein is partially basic and hydrophobic, according to the physicochemical properties analysis. The protein has activities in the inner membrane and the cytoplasm with two transmembrane helices. Furthermore, the protein is involved in secondary transporters in the MFS system, resulting in the movement of various substances through cytoplasmic or internal membranes. We targeted the active sites of the protein as potential binding sites for ligand molecules to discover novel therapeutic agents. The MD study documented the interaction of the selected ligands (PA{beta}N and CCCP) with the protein. PA{beta}N demonstrated the most suitable ligand compared to CCCP, as it required the lowest energy (- 6.7 kcal/mol) to interact with the protein. This functional protein can be targeted for further study on potential therapeutic candidates against the protein of C. difficile.

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bioRxiv2025

QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets

Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remains challenging due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.

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bioRxiv2025

Investigating the prognostic value of KCNN4 gene expression in human pancreatic adenocarcinoma by bioinformatic analysis

Finding accurate biomarkers for early detection and prognosis is critical because pancreatic adenocarcinoma (PAAD) is a deadly cancer with a poor prognosis. Though its function in PAAD is still unknown, the gene KCNN4, which codes for a calcium-activated potassium channel, has been linked to a number of malignancies. In this work, the cancer studies of the CPTAC, TCGA, and GTEx projects were targeted using a thorough database mining technique. Several analytical methods were used to examine full-scale molecular, expression, and prognostic profiles of the KCNN4 gene in pancreatic adenocarcinoma tissues. It was discovered that the majority of the cancerous tissue had different levels of mRNA and protein expression for this gene. The results of comparative immunohistochemistry also revealed that normal pancreatic tissues had reduced expression of the KCNN4 protein. An analysis overview of copy-number alterations and KCNN4 mutations in PAAD samples was also conducted. Furthermore, there was a correlation found between the expression of KCNN4 and both overall and disease-free survival in pancreatic adenocarcinoma. In pancreatic adenocarcinoma tissue, co-expressed genes of KCNN4 were found to be involved in cell maintenance-related functions, according to gene coexpression, additional ontology, and pathway analysis. When it comes to cancer diagnosis and treatment, the experimental findings of this study should aid in integrating KCNN4 into clinical applications.

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