Bioinformatics Applications Research Articles

Concordant B and T Cell Heterogeneity Inferred from the Multiomic Landscape of Peripheral Blood Mononuclear Cells in a Crohn's Disease Cohort.

Crohn's disease is characterized by inflammation in the gastrointestinal tract due to a combination of genetic, immune, and environmental factors. Transcriptomic and epigenomic profiling of intestinal tissue of Crohn's disease patients have revealed valuable insights into pathology, however have not been conducted jointly on less invasive peripheral blood mononuclear cells (PBMCs). Furthermore, the heterogeneous responses to treatments among individuals with Crohn's disease imply hidden diversity of pathological mechanisms. We employed single nucleus multiomic analysis, integrating both snRNA-seq and snATAC-seq of PBMCs with a variety of open source bioinformatics applications. Our findings reveal a diverse range of transcriptional signatures among individuals, highlighting the heterogeneity in PBMC profiles. Nevertheless, striking concordance between three heterogeneous groups was observed across B cells and T cells. Differential gene regulatory mechanisms partially explain these profiles, notably including a signature involving TGFß signaling in two individuals with Crohn's disease. A mutation mapped to a transcription factor binding site within a differentially accessible peak associated with the expression of this pathway, with implications for a personalized approach to understanding disease pathology. This study highlights how multiomic analysis can reveal common regulatory mechanisms that underlie heterogeneity of PBMC profiles, one of which may be specific to inflammatory disease.

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis.

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU. In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses. In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways. PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024.

The EMBL-EBI Job Dispatcher sequence analysis tools framework (https://www.ebi.ac.uk/jdispatcher) enables the scientific community to perform a diverse range of sequence analyses using popular bioinformatics applications. Free access to the tools and required sequence datasets is provided through user-friendly web applications, as well as via RESTful and SOAP-based APIs. These are integrated into popular EMBL-EBI resources such as UniProt, InterPro, ENA and Ensembl Genomes. This paper overviews recent improvements to Job Dispatcher, including its brand new website and documentation, enhanced visualisations, improved job management, and a rising trend of user reliance on the service from low- and middle-income regions.

Designing efficient randstrobes for sequence similarity analyses.

Substrings of length k, commonly referred to as k-mers, play a vital role in sequence analysis. However, k-mers are limited to exact matches between sequences leading to alternative constructs. We recently introduced a class of new constructs, strobemers, that can match across substitutions and smaller insertions and deletions. Randstrobes, the most sensitive strobemer proposed in [Sahlin, 2021a], has been used in several bioinformatics applications such as read classification, short read mapping, and read overlap detection. Recently, we showed that the more pseudo-random the behavior of the construction (measured in entropy), the more efficient the seeds for sequence similarity analysis. The level of pseudo-randomness depends on the construction operators, but no study has investigated the efficacy. In this study, we introduce novel construction methods, including a Binary Search Tree (BST)-based approach that improves time complexity over previous methods. To our knowledge, we are also the first to address biases in construction and design three metrics for measuring bias. Our evaluation shows that our methods have favorable speed and sampling uniformity compared to existing approaches. Lastly, guided by our results, we change the seed construction in strobealign, a short-read mapper, and find that the results change substantially. We suggest combining the two results to improve strobealign's accuracy for the shortest reads in our evaluated datasets. Our evaluation highlights sampling biases that can occur and provides guidance on which operators to use when implementing randstrobes. All methods and evaluation benchmarks are available in a public Github repository at https://github.com/Moein-Karami/RandStrobes. The scripts for running the strobealign analysis are found at https://github.com/NBISweden/strobealign-evaluation.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: a bioinformatics approach

BackgroundIn the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. MethodsDownload glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. ResultsThe expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. ConclusionsOur study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Comparing RT-PCR of individual samples with high throughput sequencing of pooled plant samples for field-level surveillance of viruses in blackberry and wild Rubus.

Blackberry production is increasing in the southeastern U.S. with the availability of new cultivars. In addition to high production costs, growers are challenged by virus diseases. Blackberry yellow vein disease (BVYD) significantly limits blackberry production. BYVD is associated with the crinivirus blackberry yellow vein-associated virus (BYVaV) in mixed infections with other viruses. The specific disease etiology and ecological factors underlying BYVD are not well understood and rely on the effective diagnosis of several viruses involved in the complex. In 2021, we collected samples from blackberry plants showing BYVD symptoms, asymptomatic blackberry plants, and wild Rosaceae spp. from nine farms across South Carolina, for a total of 372 individual plant samples. RNA from individual samples was isolated and pooled into sample groups (i.e., symptomatic, asymptomatic, and wild) from each farm for a total of 24 pooled samples. We sequenced the pooled RNA using Illumina and analyzed sequence profiles using the Virtool bioinformatics application. We also tested each plant for six viruses by RT-PCR or RT-qPCR and compared plant (PCR)-level and field (high throughput sequencing (HTS))-level data. Virtool detected 17 known viruses in the pooled samples, including 11 blackberry viruses. PCR testing was mostly consistent with HTS, with some notable disagreements for specific viruses. Our study demonstrates that HTS could be used as an efficient tool to detect viruses in bulked samples in blackberry fields, though limitations to using HTS for field-level surveillance are also discussed here.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

DNA Profiling, Bioinformatics and Databases in Forensics: Human Identification Purposes

Individual identification is an essential element in establishing truth the forensic investigation process, be it in criminal, medico-legal, or mass disasters case. When ante-mortem data are not available, the only thing that can be used is identification trough DNA. Alec Jeffrey, a geneticist (1984), found that certain regions of DNA contain repetitive sequences and the number of repetitions in individuals differs from one another. This identification method known as DNA profiling. DNA profiling is described as an important and significant discovery in forensics and has been considered as the standard in modern human identification. Until now, the recommended DNA profiling method must be based on the PCR to analyze degraded DNA and  short-sized DNA (Short Tandem Repeats) through PCR amplification. In profiling, the result DNA amplification are analyzed through genetic markers (DNA Markers) and then matched into the database or compared with the collected reference samples. In the human genome, the genetic markers most frequently used in forensics are autosomal STR, Y-STR, mtDNA, X-STR, SNPs, and Amelogenin. Accomplishment of proving the suitability of DNA profiles, an application of bioinformatics in forensics is carried out. Bioinformatics is a combination of molecular biology and computer informatics that aims to manage and analyze data and store biological (genetic) information. DNA database is an application of bioinformatics in the form of special software which has become an important tool for biologists and forensics. This genetic database will predict the similarities between one DNA profile and another.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications

BackgroundThe analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.ResultsCREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

The Research Progress of the Application of Bioinformatics in the Diagnosis and Treatment of Alzheimer's Disease

AbstractAlzheimer's disease (AD) is characterized by a complex pathogenesis, limited diagnostic methods, and a lack of effective therapeutic drugs in clinical settings, posing significant challenges in modern medical research. Bioinformatics offers new perspectives for identifying key pathological biomarkers of AD, analyzing differentially expressed genes in AD, screening for effective drug targets against AD, studying the mechanisms of AD pathogenesis, and discovering novel anti-AD drugs. However, data preprocessing and statistical analysis methods in bioinformatics research can significantly impact results, and there is a lack of consistency and coordination in analysis methods across platforms and laboratories in practical studies, making it difficult to compare data between studies. Therefore, it is crucial to establish standardized operating procedures and quality control protocols, improve the reproducibility of methods across platforms, and promote data comparison between studies.

Drug target prediction through deep learning functional representation of gene signatures

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute’s L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states and edges capture modifications (i.e., insertions, deletions, and substitutions) by assigning probabilities to them. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. Accurate computation of these probabilities is essential for the correct identification of sequence similarities. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. When we analyze state-of-the-art works, we identify an urgent need for a flexible, high-performance, and energy-efficient hardware-software co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM , the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM employs hardware-software co-design to tackle the major inefficiencies in the Baum-Welch algorithm by (1) designing flexible hardware to accommodate various pHMM designs, (2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, (3) rapidly filtering out unnecessary computations using a hardware-based filter, and (4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55×–260.03×, 1.83×–5.34×, and 27.97× when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: (1) error correction, (2) protein family search, and (3) multiple sequence alignment, by 1.29×–59.94×, 1.03×–1.75×, and 1.03×–1.95×, respectively, while improving their energy efficiency by 64.24×–115.46×, 1.75×, and 1.96×.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BackgroundBiological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank.ResultsWe first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics.ConclusionOverall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Generic COVID-19 epidemic forecast for Estonia by Gaidai multivariate reliability method

This study aims at accurate spatiotemporal assessment of future epidemic outbreak risks, occurring within any given return period, and within any relevant administrative region of Estonia. Authors have recently developed novel statistical spatiotemporal methodology, that can be applied directly to multivariate raw clinical datasets. Novel Gaidai multivariate reliability methodology, described in this study, being particularly appropriate for multi-regional environmental, biological and public health systems. Advocated methodology has been sufficiently validated in the recent studies, yielding reliable long-term risk forecasts of the future epidemic outbreaks. COVID-19 (SARS-COV-2) daily recorded patient numbers were selected in all impacted Estonia national regions. This study results conclude that recommended spatiotemporal approach may effectively utilize even limited raw clinical datasets, the latter can be useful in a wide range of bioinformatics and public health applications. Confidence intervals have been estimated for predicted epidemiological levels.

Hardware acceleration of DNA pattern matching using analog resistive CAMs

DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient pipelined hardware accelerator that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and accelerates the DNA pattern matching task by more than 100× compared to the approaches described in the literature.

Horizontal gene transfer after faecal microbiota transplantation in adolescents with obesity

BackgroundHorizontal gene transfer (HGT) describes the transmission of DNA outside of direct ancestral lineages. The process is best characterised within the bacterial kingdom and can enable the acquisition of genetic traits that support bacterial adaptation to novel niches. The adaptation of bacteria to novel niches has particular relevance for faecal microbiota transplantation (FMT), a therapeutic procedure which aims to resolve gut-related health conditions of individuals, through transplanted gut microbiota from healthy donors.ResultsThree hundred eighty-one stool metagenomic samples from a placebo-controlled FMT trial for obese adolescents (the Gut Bugs Trial) were analysed for HGT, using two complementary methodologies. First, all putative HGT events, including historical HGT signatures, were quantified using the bioinformatics application WAAFLE. Second, metagenomic assembly and gene clustering were used to assess and quantify donor-specific genes transferred to recipients following the intervention. Both methodologies found no difference between the level of putative HGT events in the gut microbiomes of FMT and placebo recipients, post-intervention. HGT events facilitated by engrafted donor species in the FMT recipient gut at 6 weeks post-intervention were identified and characterised. Bacterial strains contributing to this subset of HGT events predominantly belonged to the phylum Bacteroidetes. Engraftment-dependent horizontally transferred genes were retained within recipient microbiomes at 12 and 26 weeks post-intervention.ConclusionOur study suggests that novel microorganisms introduced into the recipient gut following FMT have no impact on the basal rate of HGT within the human gut microbiome. Analyses of further FMT studies are required to assess the generalisability of this conclusion across different FMT study designs and for the treatment of different gut-related conditions.2UrCUamaK1-EwS1Lh8ra1uVideo

Tools and Applications of Bioinformatics in Sericulture: A Review

Rapid advances in genomic and other molecular research tools, as well as advances in information technology, have resulted in a massive volume of data relevant to molecular biology during the last several decades. The term "bioinformatics" refers to the mathematical and computational methods used to get a better understanding of biological processes. Bioinformatics commonly uses genetic sequences, which are easier to manage with computers than by hand. Although genomic and proteomic techniques have advanced our understanding of how genes and proteins contribute to physiology, biochemistry, genetics, and evolution in insects, a complete picture of genomics and proteomics and their interactions remains difficult. Meanwhile, bioinformatics developments have discovered hidden omics features in other species, leaving insects with a substantial information gap. Bombyx mori has sparked research interest in countries all over the world due to its adaptability and status as the first Lepidopteran species to have its genome sequenced. The entire genome sequence of the silkworm was reported in 2004 and updated in 2008 based on its biological and industrial value. The genome of B. mori was the first in a lepidopteran to be sequenced, and it has served as a foundation for Lepidoptera research. Bioinformatics tools and techniques not only aided in the detection of proteomic and genomic variation among species/strains, but also led to the discovery of a gap in a silkworm genome sequence that diverged during domestication. Databases of Seri-bioinformatics are an important Seri-bioresource. The accessible internet resources on silkworms and associated organisms, such as databases and informational websites like SilkDB, MorusDB, WildSilkbase, KAIKObase, SilkPathDB etc., have aid in the improvement of silkworm health, disease resistance, and productivity. These databases contain information on gene, protein, disease sequences, and have evolved as a highly beneficial platform via which the scientific community may communicate molecular data across the globe with a single mouse click, not only minimizing the gap and time but also allowing information to be accessed. This review paper will cover all of these bioinformatics tools and methodologies in order to better understand their application in silkworms and host plants.

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

This manuscript provides a detailed exploration of the pivotal role that bioinformatics plays in elucidating the intricate molecular landscape associated with chronic diseases. Emphasizing the significance and prevalence of these enduring health issues, the introduction establishes the broader context of bioinformatics in chronic disease research. This review systematically covers the application of bioinformatics tools and techniques in comprehending, identifying, and managing chronic illnesses. The first section highlights the importance of genetics and genomics, detailing the utilization of genomic data and advancements in genetic biomarker discovery. Subsequently, the discussion extends to transcriptomics and gene expression, encompassing profiling methods, the identification of dysregulated genes, and the regulatory functions of non-coding RNA in long-term illnesses.Moving forward, the manuscript delves into proteomics, elucidating protein-protein interaction networks, associated tools and techniques, and post-translational modifications. This comprehensive coverage aims to provide readers with a nuanced understanding of the molecular complexities underlying chronic diseases. The subsequent section focuses on metabolomics and metabolic pathways, with an emphasis on the clinical utility of metabolite biomarkers, changes in metabolic pathways, and techniques for characterizing diseases. Following this, the manuscript explores machine learning applications in bioinformatics, providing insights into their role in enhancing our understanding of chronic diseases. The later part of the manuscript addresses practical applications and case studies, showcasing disease-specific bioinformatics tools, databases, and the broader utility of research findings. Additionally, the penultimate section examines privacy, ethical considerations, and data quality concerns, addressing challenges and potential paths for the field of bioinformatics. In conclusion, the manuscript discusses forthcoming trends and prospective research directions, contributing to the advancement of bioinformatics research in chronic illnesses. Overall, this review provides a comprehensive overview of the multifaceted applications of bioinformatics in chronic disease research.

The Role of Genome-Wide Association Study in Pulmonary Disease Diagnostics: A Review in Medical Bioinformatics

Biomedical science, which initially required only conventional research in the laboratory, currently involves information technology and has created bioinformatics in its development. Bioinformatics, a branch of biology, quantitatively analyzes information within biological macromolecules using software. Contemporary applications of bioinformatics have advanced biotechnological, medical, and pharmaceutical practices. Among the established applications of bioinformatics is diagnosing lung diseases using the genome-wide association study (GWAS) technique. Owing to sequencing technology and rapid computational methods, this technique is applied to analyze the link between genes with essential traits in the population, thus mapping the target genes to diagnose and treat diseases. The lung diseases diagnosed using GWAS include the responsible locus in asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Moreover, it can identify the treatment for COPD and suggest a new locus in lung cancer. Advancing the current gene-mapping technology demands genotype and phenotype data to study disease-linked genomes. Currently, bioinformatics is barely known and receives little attention in Indonesia. However, it can grow rapidly through open-source basis data and cross-disciplinary collaboration.