Analysis Of High-throughput Data Research Articles

BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data

BackgroundInterpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still requires bioinformatics expertise, limiting accessibility for the broader scientific community.ResultsBiomiX tackles the bottleneck in high-throughput omics data analysis, enabling efficient and integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data, using DESeq2/Limma packages for transcriptomics, and quantifying metabolomics peak differences, evaluated via the Wilcoxon test with the False Discovery Rate correction. The metabolomics annotation for Liquid Chromatography-Mass Spectrometry untargeted metabolomics is additionally supported using the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package. Methylomics analysis is performed using the ChAMP R package. Finally, Multi-Omics Factor Analysis (MOFA) integration identifies shared sources of variation across omics data. BiomiX also generates statistics, report figures and integrates EnrichR and GSEA for biological process exploration and subgroup analysis based on user-defined gene panels enhancing condition subtyping. BiomiX fine-tunes MOFA models, to optimize factors number selection, distinguishing between cohorts and providing tools to interpret discriminative MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. Furthermore, discriminant MOFA factors are correlated with clinical data, and the top contributing pathways are explored, all with the aim of guiding the user in factor interpretation.ConclusionsThe analysis of single-omics and multi-omics integration in a standalone tool, along with MOFA implementation and its interpretability via literature, represents significant progress in the multi-omics field in line with the “Findable, Accessible, Interoperable, and Reusable” data principles. BiomiX offers a wide range of parameters and interactive data visualization, allowing for personalized analysis tailored to user needs. This R-based, user-friendly tool is compatible with multiple operating systems and aims to make multi-omics analysis accessible to non-experts in bioinformatics.

Utilizing deep learning for swift analysis of high-throughput spectroscopic ellipsometry data on anodized oxides of valve metals

Spectroscopic ellipsometry is a powerful high-throughput method for mapping the optical properties of combinatorial anodic oxides on alloys. However, the traditional ellipsometry data fitting using non-linear regression highly depends on correct assumptions and is tedious. The determination of the transition concentration of parent alloys that influences its properties to change, based on existing experimental data, without requiring further experimental measurements is also crucial for alloy engineering. Herein, anodic oxides grown on Nb-Ta and Nb-Ti combinatorial thin film binary libraries using a co-sputtering process are prepared (Nb concentration range is 10 ∼ 90at.%, the oxidation voltage range is 1 ∼ 10 V). A deep learning method is developed to predict the refractive index (n) and extinction coefficient (k) of the oxide film from the ellipsometry data (606 groups). Four algorithms Convolutional Neural Networks (CNN), Convolutional Sequence-to-Sequence (ConvSeq2Seq), Temporal Convolutional Network (TCN), Gated Recurrent Unit Sequence-to-Sequence (GRUSeq2Seq) are compared. CNN achieves superior performance, yielding root mean square error values (RMSE) as low as 0.094 for predicting n and 0.037 for k. Additionally, a predictive model to reveal band-gap trends as a function of parent alloy composition and oxidation voltage at unmeasured locations along the libraries is developed. This approach helps identify the transition concentration that lead to optical property changes, thereby avoiding high experimental costs and potential experimental errors.

What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files.

A Python script for the systematic, high-throughput analysis of accurate mass data was developed and tested on more than 3000 Supporting Information (SI) PDFs from Organic Letters. For each SI file, quadruplets of molecular formula, measured ion, e.g., [M + Na]+, and reported calculated and found masses were extracted and analyzed. Interestingly, only 40% of the files containing readable accurate mass data were both internally consistent and in compliance with The ACS Guide to Scholarly Communication. The analysis revealed unexpected errors and provided actionable advice on how to improve data quality.

Exploring RPA1-ETAA1 axis via high-throughput data analysis: implications for PD-L1 nuclear translocation and tumor-immune dynamics in liver cancer.

ETAA1 is recruited to DNA damage sites via its RPA -binding and ATR -activating domain (AAD) motifs, where RPA binding is crucial for ETAA1's regulation of ATR activity. Our findings associate Programmed Death- Ligand1 (PD-L1) with the RPA1-ETAA1 axis, suggesting that upregulated RPA1 -dependent ETAA1 may facilitate PD-L1 nuclear accumulation. We observed strong correlations between ETAA1 and RPA1 with the components involved in HDAC2-mediated deacetylation, clathrin -dependent endocytosis, and PD-L1 nucleocytoplasmic shuttling, aligning with the established regulatory pathway of PD-L1 nuclear translocation. Moreover, nuclear PD-L1 transactivates a panel of pro-inflammatory and immune response transcription factors, potentially reshaping the tumor immune microenvironment. We identified a landscape of infiltrating lymphocytes influenced by ETAA1, finding that levels of ETAA1 were negatively correlated with CD8+ T and Natural Killer T (NKT) cells, but positively correlated with CD4+ T helper 2 (Th2) cells, cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs), neutrophils and regulatory T cells (Tregs), suggesting a potential role in immune evasion. Further analysis shows that the RPA1-ETAA1 axis is significantly associated with multiple metastasis mediators and unfavorable liver cancer progression, with higher expression observed in advanced stages and poorly differentiated subgroups. These findings expand the role of the RPA1-ETAA1 axis beyond DNA repair, highlighting its potential as a target for cancer therapy.

Antibody Data Analysis from Diverse Immune Libraries.

Antibody functional screening studies and next-generation sequencing require careful processing and interpretation of sequence data for optimal results. Here, we provide a detailed protocol for the functional analysis of antibody gene data, including antibody repertoire quantification and functional mapping of high-throughput screening data based on enrichment ratio values, which are a simple way to determine the enrichment of each sequenced antibody after sorting a display library against desired antigens. This protocol enables a user to apply a set of simple yet powerful bioinformatic tools for high-throughput analysis and interpretation of antibody data that is especially well suited for display library screening and for antibody discovery applications.

Metagenomic Detection of Multiple Viruses in Monk Parakeet (Myiopsitta monachus) in Australia.

Birds are known to harbour many pathogens, including circovirus, herpesviruses, adenoviruses and Chlamydia psittaci. Some of these pose zoonotic risks, while others, such as beak and feather disease virus (BFDV), have a significant impact on the conservation of endangered bird species. This study was aimed to determine the faecal virome of a group of apparently healthy Monk parakeet using high-throughput sequencing. Fresh faecal samples were collected from four Monk parakeets at a pet shop in Melbourne, Australia. Virus enrichment and nucleic acid extraction were performed on the faecal samples, followed by high-throughput sequencing at the Australian Genome Research Facility (AGRF). Utilising an established pipeline for high-throughput sequencing data analysis, this study revealed the presence of three viruses of the families Circoviridae, Parvoviridae and Adenoviridae. Subsequent sequence comparison and phylogenetic analyses further confirmed that the detected viruses belong to the genera Chaphamaparvovirus (unassigned species), Circovirus (species Circovirus parrot) and Siadenovirus (species Siadenovirus viridis). Despite non-pathogenicity, the existence of multiple viruses within a bird species underscores the risk of these viruses spreading into the pet trade. Detection and a better understanding of avian viruses are crucial for the establishment of appropriate management and biosecurity measures in the domestic and international bird trade, which ultimately supports the conservation of vulnerable bird species.

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Rapid genome-wide profiling of DNA methylation and genetic variation using guide positioning sequencing (GPS).

Whole-genome bisulfite sequencing (WGBS) has been extensively utilized for DNA methylation profiling over the past decade. However, it has shown limitations in terms of high costs and inefficiencies. The productivity and accuracy of DNA methylation detection rely critically on the optimization of methodologies and the continuous refinements of related sequencing platforms. Here, we describe a detailed protocol of guide positioning sequencing (GPS), a bisulfite-based, location-specific sequencing technology designed for comprehensive DNA methylation characterization across the genome. The fundamental principle of GPS lies in the substitution of dCTP with 5-methyl-dCTP (5mC) at the 3'-end of DNA fragments by T4 DNA polymerase, which protects cytosines from bisulfite conversion to preserve the integrity of the base composition. This alteration allows the 3'-end to independently facilitate genetic variation profiling and guides the 5'-end, enriched with methylation information, to align more rapidly to the reference genome. Hence, GPS enables the concurrent detection of both genetic and epigenetic variations. Additionally, we provide an accessible description of the data processing, specifically involving certain software and scripts. Overall, the entire GPS procedure can be completed within a maximum of 15days, starting with a low initial DNA input of 100-500ng, followed by 4-5days for library construction, 8-10days for high-throughput sequencing (HTS) and data analysis, which can greatly facilitate the promotion and application of DNA methylation detection, especially for the rapid clinical diagnosis of diverse disease pathologies associated with concurrent genetic and epigenetic variations.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Decoding recurrent pregnancy loss: insights from comparative proteomics studies.

Recurrent pregnancy loss (RPL) represents a common disorder that affects up to 2% of the women aiming at childbirth with long-term consequences on family and society. Factors contributing to it in more than half of the cases are still unknown. Comparative proteomic analysis can provide new insights into the biological pathways underlining the pathogenesis of RPL. Until now, chorionic villi, decidua, placenta, endometrium, and maternal blood from women with RPL have been analyzed by comparative proteomics studies. In this review, we aimed to provide a critical evaluation of the published comparative studies of RPL on human samples, gathered by systematic literature search using PubMed and Google Scholar databases. We provide a detailed overview of the analyzed materials, proteomics platforms, proposed candidate biomarkers and altered pathways and processes linked with RPL. The top, most identified and validated biomarker candidates from all studies are discussed, followed by bioinformatics analysis of the available high-throughput data and presentation of common altered processes and pathways in RPL. Finally, future directions aimed at developing new and efficient therapeutic strategies are discussed as well.

Using Large Language Models for Microbiome Findings Reports in Laboratory Diagnostics

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called “MicroFlow”. Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model’s accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model’s effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data.

Impact of wheat processing on growth, serum biochemistry, and ruminal microbiota in sheep (Ovis aries)

This study investigated the impact of wheat processing methods (wheat flour vs wheat pellets) on the growth performance, serum biochemical parameters, and rumen microbiome composition in sheep. Results indicated that feeding of wheat flour resulted in significantly higher terminal weight and average daily gain (P < 0.05) and lower cholesterol and ALP04 levels (P < 0.05) in sheep compared to those fed wheat pellets. Analysis of 16s rDNA high-throughput sequencing data revealed significantly higher microbial richness (Chao1 index) in the rumen of sheep fed wheat flour (P < 0.05), even though the phylum-level composition dominated by Firmicutes, Bacteroidetes, and Proteobacteria was similar in both groups of sheep. Notably, sheep fed wheat flour were found to have a significantly higher relative abundance of Bacteroidetes (P < 0.05). At the genus level, Succinivibrionaceae_UCG-001 and Prevotella_1 were significantly more abundant in the rumen of sheep fed wheat flour (P < 0.05). Correlation analysis identified that both terminal weight and average daily gain were positively correlated with ruminal abundance of Bacteroidetes and Prevotella_1, while ALP04 was negatively correlated with the abundance of these taxa. Functional prediction using PICRUSt2 indicated enrichment of pathways related to the ABC-type glycerol-3-phosphate transport system, and periplasmic components in both wheat flour and pellet fed sheep. Overall, these findings suggest that dietary wheat flour modulates rumen microbiota composition, and improves growth performance in sheep.

Molecular mechanism of Rolupram reducing neuroinflammation in noise induced tinnitus mice through TLR4/NF kB/NLRP3 protein/caspase-1/IL-1 β signaling pathway

Noisy tinnitus is a common auditory system disease characterized by persistent tinnitus symptoms. The TLR4/NF - κ B/NLRP3 signaling pathway plays an important role in neuroinflammatory response. Select 6 control and 6 noise exposed mice for transcriptome sequencing analysis in the hippocampus, conduct high-throughput data analysis, identify differentially expressed genes, and screen for pathways. Auditory brainstem response (ABR) detection was performed to understand the hearing changes, and the modeling effect was evaluated using the GPIAS% inhibition experiment of auditory startle reflex. Morphological observation of the basement membrane was performed to determine whether the inner hair cells were damaged. Immunohistochemistry and immunofluorescence were used to determine the activation of microglia in the hippocampus of noise induced tinnitus mice. Finally, qPCR and Western Blot were used to detect the expression of TLR4, NF kB, NLRP3, caspase-1, and IL-1 β in the hippocampus of each group of mice. Through high-throughput data analysis, it was found that there was no significant difference in the auditory threshold of the three groups of mice; After 2 h of exposure to 100 dB SPL noise, the GPIAS% of mice decreased significantly compared to before exposure, and membrane construction was successful. After 7 days, the GPIAS% of the drug intervention group increased. After noise exposure, mice developed tinnitus, and hippocampus neuroinflammation. Roflupram can inhibit neuroinflammation and improve tinnitus through the TLR4/NF kB/NLRP3/caspase-1/IL-1 β signaling pathway.

Mapping the composite nature of clay matrix in mudstones: integrated micromechanics profiling by high-throughput nanoindentation and data analysis

AbstractMudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based k-means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus (Er) and hardness (H) values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks.

Comparison on the mechanism and potency of hepatotoxicity among hemp extract and its four major constituent cannabinoids

Cannabidiol (CBD) has been reported to induce hepatotoxicity in clinical trials and research studies; however, little is known about the safety of other nonintoxicating cannabinoids. New approach methodologies (NAMs) based on bioinformatic analysis of high-throughput transcriptomic data are gaining increasing importance in risk assessment and regulatory decision-making of data-poor chemicals. In the current study, we conducted a concentration response transcriptomic analysis of hemp extract and its four major constituent cannabinoids [CBD, cannabichromene (CBC), cannabigerol (CBG), and cannabinol (CBN)] in hepatocytes derived from human induced pluripotent stem cells (iPSCs). Each compound impacted a distinctive combination of biological functions and pathways. However, all the cannabinoids impaired liver metabolism and caused oxidative stress in the cells. Benchmark concentration (BMC) analysis showed potencies in transcriptional activity of the cannabinoids were in the order of CBN > CBD > CBC > CBG, consistent with the order of their cytotoxicity IC50 values. Patterns of transcriptomic changes induced by hemp extract and its median overall BMC were very similar to CBD but differed significantly from other cannabinoids, suggesting that potential adverse effects of hemp extract were largely due to its major constituent CBD. Lastly, transcriptomic point-of-departure (tPoD) values were determined for each of the compounds, with the value for CBD (0.106 µM) being concordant with a previously reported one derived from apical endpoints of clinical and animal studies. Taken together, the current study demonstrates the potential utility of transcriptomic BMC analysis as a NAM for hazard assessment of data-poor chemicals, improves our understanding of the possible health effects of hemp extract and its constituent cannabinoids, and provides important tPoD data that could contribute to inform human safety assessment of these cannabinoid compounds.

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

Accumulation and ecological risk assessment of diazinon in surface sediments of Baiyangdian lake and its potential impact on probiotics and pathogens

Diazinon is an organophosphorus pesticide widely used in agriculture and household pest control, and its use also poses several environmental and health hazards. In this study, we investigated the spatial and temporal distribution of diazinon in Baiyangdian, evaluated its potential ecological risk and toxicity to aquatic organisms based on RQ (Risk quotient) and TU (Toxic unit) analysis, and assessed the potential effects of diazinon accumulation on probiotics and pathogens based on statistical analysis of high-throughput sequencing data. The results showed that diazinon in Baiyangdian posed a low to moderate chronic risk to sediment-dwelling organisms and a low toxicity effect on aquatic invertebrates, which was mainly concentrated in October and human-intensive areas. Meanwhile, increases in sediment electrical conductivity (EC), amorphous iron oxides content and phenol oxidase activity favored diazinon accumulation in sediments, whereas the opposite was the case for sediment organic carbon, β-1,4-glucosidase, phosphatase, catalase and pH, suggesting that environmental indicators play a key role in the behavior and distribution of diazinon. In addition, diazinon in heavily contaminated areas seem to inhibit the rare probiotics (Bifidobacterium adolescentis and Serratia sp.), while promoted dominant pathogens (e.g., Burkholderia cenocepacia), which can lead to increased disease risk to humans and ecosystems, disruption of ecological balance and potential health problems. However, probiotic Streptomyces xiamenensis resist to diazinon would be a potential degrader for diazinon remove. In conclusion, this study unveiled the effects of diazinon pollution on wetland ecosystems, emphasizing ecological impacts and potential health concerns. In addition, the discovery of diazinon resistant probiotics provided new insights into wetland ecological restoration.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies.

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.