Strengthening the Diagnosis of Drug-Resistant Tuberculosis Using NGS-Based Approaches and Bioinformatics Pipelines for Data Analysis in India.

In India, drug-resistant tuberculosis (DR-TB) is a major public health issue and a significant challenge to stop TB program. An estimated 27% of new TB cases and 44% of previously treated TB cases are resistant to at least one anti-TB drug. The conventional methods for DR-TB diagnosis are time-consuming and have limitations, leading to delays in treatment initiation and the spread of the disease. Next-generation sequencing (NGS) based approaches have emerged as a promising tool for diagnosing DR-TB, simultaneously offering rapid and accurate detection of resistance mutations in multiple genes. NGS-based approaches generate a large amount of data, which requires efficient and reliable bioinformatics pipelines for data analysis. TBProfiler and Mykrobe are the bioinformatics pipelines that have been created to analyze NGS data for the diagnosis of DR-TB. These pipelines use reference-based and machine-learning approaches to detect resistance mutations and predict drug susceptibility, enabling clinicians to make informed treatment decisions. Implementing NGS-based approaches and bioinformatics pipelines for DR-TB diagnosis can potentially improve patient outcomes by facilitating early detection of drug resistance and guiding personalized treatment regimens. However, the widespread adoption of these approaches in India faces several challenges, including high costs, limited infrastructure, and a lack of trained personnel. Addressing these challenges requires concerted effort to ensure equitable access to and effective implementation of these innovative technologies.

Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome.

BACKGROUND: Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. RESULTS: While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). CONCLUSIONS: NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Genome-wide screening and characterization of phospholipase A (PLA)-like genes in sorghum (Sorghum bicolor L.).

MAIN CONCLUSION: The characterisation of PLA genes in the sorghum genome using in-silico methods revealed their essential roles in cellular processes, providing a foundation for further detailed studies. Sorghum bicolor (L.) Moench is the fifth most cultivated crop worldwide, and it is used in many ways, but it has always gained less popularity due to the yield, pest, and environmental constraints. Improving genetic background and developing better varieties is crucial for better sorghum production in semi-arid tropical regions. This study focuses on the phospholipase A (PLA) family within sorghum, comprehensively characterising PLA genes and their expression across different tissues. The investigation identified 32 PLA genes in the sorghum genome, offering insights into their chromosomal localization, molecular weight, isoelectric point, and subcellular distribution through bioinformatics tools. PLA-like family genes are classified into three groups, namely patatin-related phospholipase A (pPLA), phospholipase A1 (PLA1), and phospholipase A2 (PLA2). In-silico chromosome localization studies revealed that these genes are unevenly distributed in the sorghum genome. Cis-motif analysis revealed the presence of several developmental, tissue and hormone-specific elements in the promoter regions of the PLA genes. Expression studies in different tissues such as leaf, root, seedling, mature seed, immature seed, anther, and pollen showed differential expression patterns. Taken together, genome-wide analysis studies of PLA genes provide a better understanding and critical role of this gene family considering the metabolic processes involved in plant growth, defence and stress response.

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services.

The European Bioinformatics Institute (EMBL-EBI)'s Job Dispatcher framework provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web services clients provided in Perl, Python, and Java or who would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Retrieving data from EMBL-EBI using Dbfetch via the web interface Alternate Protocol 1: Retrieving data from EMBL-EBI using WSDbfetch via the REST interface Alternate Protocol 2: Retrieving data from EMBL-EBI using Dbfetch via RESTful web services with Python client Support Protocol 1: Installing Python REST web services clients Basic Protocol 2: Sequence similarity search using FASTA search via the web interface Alternate Protocol 3: Sequence similarity search using FASTA via RESTful web services with Perl client Support Protocol 2: Installing Perl REST web services clients Basic Protocol 3: Sequence similarity search using NCBI BLAST+ RESTful web services with Python client Basic Protocol 4: Sequence similarity search using HMMER3 phmmer REST web services with Perl client and Docker Support Protocol 3: Installing Docker and running the EMBL-EBI client container Basic Protocol 5: Protein functional analysis using InterProScan 5 RESTful web services with the Python client and Docker Alternate Protocol 4: Protein functional analysis using InterProScan 5 RESTful web services with the Java client Support Protocol 4: Installing Java web services clients Basic Protocol 6: Multiple sequence alignment using Clustal Omega via web interface Alternate Protocol 5: Multiple sequence alignment using Clustal Omega with Perl client and Docker Support Protocol 5: Exploring the RESTful API with OpenAPI User Inferface.

Vaccine Designing Technology against Leishmaniasis: Current Challenges and Implication.

Leishmaniasis, a debilitating disease caused by protozoan parasites of the genus Leishmania and transmitted by the bite of a female sandfly, continues to present significant challenges despite ongoing research and collaboration in vaccine development. The intricate interaction between the parasite's life cycle stages and the host's immunological response, namely the promastigote and amastigote forms, adds complexity to vaccine design. The quest for a potent vaccine against Leishmaniasis demands a comprehensive understanding of the immune mechanisms that confer long-lasting protection, which necessitates extensive research efforts. In this pursuit, innovative approaches such as reverse vaccinology and computer-aided design offer promising avenues for unraveling the intricacies of host-pathogen interactions and identifying effective vaccine candidates. However, numerous obstacles, including limited treatment options, the need for sustained antigenic presence, and the prevalence of co-infections, particularly with HIV, impede progress. Nevertheless, through persistent research endeavours and collaborative initiatives, the goal of developing a highly efficacious vaccine against Leishmaniasis can be achieved, offering hope through the latest Omics data development with immunoinformatics approaches for effective vaccine design for the prevention of this disease.

WormBase 2024: status and transitioning to Alliance infrastructure.

WormBase has been the major repository and knowledgebase of information about the genome and genetics of Caenorhabditis elegans and other nematodes of experimental interest for over 2 decades. We have 3 goals: to keep current with the fast-paced C. elegans research, to provide better integration with other resources, and to be sustainable. Here, we discuss the current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance). As an Alliance member, WormBase will continue to interact with the C. elegans community, develop new features as needed, and curate key information from the literature and large-scale projects.

MarkerScan: Separation and assembly of cobionts sequenced alongside target species in biodiversity genomics projects.

Contamination of public databases by mislabelled sequences has been highlighted for many years and the avalanche of novel sequencing data now being deposited has the potential to make databases difficult to use effectively. It is therefore crucial that sequencing projects and database curators perform pre-submission checks to remove obvious contamination and avoid propagating erroneous taxonomic relationships. However, it is important also to recognise that biological contamination of a target sample with unexpected species' DNA can also lead to the discovery of fascinating biological phenomena through the identification of environmental organisms or endosymbionts. Here, we present a novel, integrated method for detection and generation of high-quality genomes of all non-target genomes co-sequenced in eukaryotic genome sequencing projects. After performing taxonomic profiling of an assembly from the raw data, and leveraging the identity of small rRNA sequences discovered therein as markers, a targeted classification approach retrieves and assembles high-quality genomes. The genomes of these cobionts are then not only removed from the target species' genome but also available for further interrogation. Source code is available from https://github.com/CobiontID/MarkerScan. MarkerScan is written in Python and is deployed as a Docker container.

This article addresses a common issue in genetic research: the accidental mixing of genetic information from different species in public databases, often due to mislabelling or contamination. Interestingly, this 'contamination' can sometimes lead to exciting discoveries, like identifying DNA from unexpected species in a sample, revealing insights about organisms that live in the environment of the target organism. In our study, we developed a tool called MarkerScan for identifying these additional species found alongside the target species in eukaryotic genome sequencing projects. The method includes a way to sequence the whole genomes of the additional species. Our method involves sorting through the genetic data to identify certain small RNA sequences, which we then use as markers. These markers help to classify and assemble high-quality genomes from these additional species. This not only cleans up the main target species' genome data but also provides new, valuable genomes for further exploration.

Periodontal ligament stem cell-derived exosome-loaded Emodin mediated antimicrobial photodynamic therapy against cariogenic bacteria.

BACKGROUND: This study was conducted to investigate the efficiency of periodontal ligament (PDL) stem cell-derived exosome-loaded Emodin (Emo@PDL-Exo) in antimicrobial photodynamic therapy (aPDT) on Streptococcus mutans and Lactobacillus acidophilus as the cariogenic bacteria. MATERIALS AND METHODS: After isolating and characterizing PDL-Exo, the study proceeded to prepare and verify the presence of Emo@PDL-Exo. The antimicrobial effect, anti-biofilm activity, and anti-metabolic potency of Emo, PDL-Exo, and Emo@PDL-Exo were then evaluated with and without irradiation of blue laser at a wavelength of 405 ± 10 nm with an output intensity of 150 mW/cm2 for a duration of 60 s. In addition, the study assessed the binding affinity of Emodin with GtfB and SlpA proteins using in silico molecular docking. Eventually, the study examined the generation of endogenous reactive oxygen species (ROS) and changes in the gene expression levels of gelE and sprE. RESULTS: The study found that using Emo@PDL-Exo-mediated aPDT resulted in a significant decrease in L. acidophilus and S. mutans by 4.90 ± 0.36 and 5.07 log10 CFU/mL, respectively (P < 0.05). The study found that using Emo@PDL-Exo for aPDT significantly reduced L. acidophilus and S. mutans biofilms by 44.7% and 50.4%, respectively, compared to untreated biofilms in the control group (P < 0.05). Additionally, the metabolic activity of L. acidophilus and S. mutans decreased by 58.3% and 71.2%, respectively (P < 0.05). The molecular docking analysis showed strong binding affinities of Emodin with SlpA and GtfB proteins, with docking scores of -7.4 and -8.2 kcal/mol, respectively. The study also found that the aPDT using Emo@PDL-Exo group resulted in the most significant reduction in gene expression of slpA and gtfB, with a decrease of 4.2- and 5.6-folds, respectively, compared to the control group (P < 0.05), likely due to the increased generation of endogenous ROS. DISCUSSION: The study showed that aPDT using Emo@PDL-Exo can effectively reduce the cell viability, biofilm activity, and metabolic potency of S. mutans and L. acidophilus. aPDT also significantly reduced the expression levels of gtfB and slpA mRNA due to the increased endogenous ROS generation. The findings suggest that Emo@PDL-Exo-mediated aPDT could be a promising antimicrobial approach against cariogenic microorganisms.

An integrative database and its application for plant synthetic biology research.

Plant synthetic biology research requires diverse bioparts that facilitate the redesign and construction of new-to-nature biological devices or systems in plants. Limited by few well-characterized bioparts for plant chassis, the development of plant synthetic biology lags behind that of its microbial counterpart. Here, we constructed a web-based Plant Synthetic BioDatabase (PSBD), which currently categorizes 1677 catalytic bioparts and 384 regulatory elements and provides information on 309 species and 850 chemicals. Online bioinformatics tools including local BLAST, chem similarity, phylogenetic analysis, and visual strength are provided to assist with the rational design of genetic circuits for manipulation of gene expression in planta. We demonstrated the utility of the PSBD by functionally characterizing taxadiene synthase 2 and its quantitative regulation in tobacco leaves. More powerful synthetic devices were then assembled to amplify the transcriptional signals, enabling enhanced expression of flavivirus non-structure 1 proteins in plants. The PSBD is expected to be an integrative and user-centered platform that provides a one-stop service for diverse applications in plant synthetic biology research.

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.

BACKGROUND: Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are time-- consuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. METHODS: In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. RESULTS: We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. CONCLUSION: Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

Analysis of PROS1 mutations and clinical characteristics in three Chinese families with hereditary protein S deficiency.

We report three heterozygous PROS1 mutations that caused type I protein S deficiency in three unrelated Chinese families. We measured protein S activity and antigen levels for all participants, screened them for mutations in the PROS1 gene. And we employed the calibrated automated thrombin generation (CAT) method to investigate thrombin generation. Numerous bioinformatics tools were utilized to analyze the conservation, pathogenicity of mutation, and spatial structure of the protein S. Phenotyping analysis indicated that all three probands exhibited simultaneous reduced levels of PS:A, TPS:Ag, and FPS:Ag. Genetic testing revealed that proband A harbored a heterozygous c.458_458delA (p.Lys153Serfs*6) mutation in exon 5, proband B carried a heterozygous c.1687C>T (p.Gln563stop) mutation in exon 14, and proband C exhibited a heterozygous c.200A>C (p.Glu67Ala) mutation in exon 2. Bioinformatic analysis predicted that the p.Lys153Serfs*6 frameshift mutation and the p.Gln563stop nonsense mutation in the protein S were classified as "disease-causing." The identification of the novel mutation p.Lys153Serfs*6 in PROS1 enriches the Human Genome Database. Our research suggests that these three mutations (p.Lys153Serfs*6, p.Gln563stop, and p.Glu67Ala) are possibly responsible for the decreased level of protein S in the three families. Furthermore, the evidence also supports the notion that individuals who are asymptomatic but have a family history of PSD can benefit from genetic analysis of the PROS1 gene.

BioTreasury: a community-based repository enabling indexing and rating of bioinformatics tools.

The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments. Therefore, to aid scientists in making informed choices, a community-based platform that indexes and rates bioinformatics tools is urgently needed. In this study, we introduce BioTreasury ( http://biotreasury.rjmart.cn ), an integrated community-based repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools. BioTreasury offers a comprehensive collection of well-indexed bioinformatics software, tools, and databases, totaling over 10,000 entries. In the past two years, we have continuously improved and maintained BioTreasury, adding several exciting features, including creating structured homepages for every tool and user, a hierarchical category of bioinformatics tools and classifying tools using large language model (LLM). BioTreasury streamlines the tool submission process with intelligent auto-completion. Additionally, BioTreasury provides a wide range of social features, for example, enabling users to participate in interactive discussions, rate tools, build and share tool collections for the public. We believe BioTreasury can be a valuable resource and knowledge-sharing platform for the biomedical community. It empowers researchers to effectively discover and evaluate bioinformatics tools, fostering collaboration and advancing bioinformatics research.

Bioinformatics Analysis of Global Diversity in Meningococcal Vaccine Antigens over the Past 10 Years: Vaccine Efficacy Prognosis.

Neisseria meningitidis (N. meningitidis) serogroup B (MenB) is the leading cause of invasive meningococcal disease worldwide. The pathogen has a wide range of virulence factors, which are potential vaccine components. Studying the genetic variability of antigens within a population, especially their long-term persistence, is necessary to develop new vaccines and predict the effectiveness of existing ones. The multicomponent 4CMenB vaccine (Bexsero), used since 2014, contains three major genome-derived recombinant proteins: factor H-binding protein (fHbp), Neisserial Heparin-Binding Antigen (NHBA) and Neisserial adhesin A (NadA). Here, we assessed the prevalence and sequence variations of these vaccine antigens in a panel of 5667 meningococcal isolates collected worldwide over the past 10 years and deposited in the PubMLST database. Using multiple amino acid sequence alignments and Random Forest Classifier machine learning methods, we estimated the potential strain coverage of fHbp and NHBA vaccine variants (51 and about 25%, respectively); the NadA antigen sequence was found in only 18% of MenB genomes analyzed, but cross-reactive variants were present in less than 1% of isolates. Based on our findings, we proposed various strategies to improve the 4CMenB vaccine and broaden the coverage of N. meningitidis strains.

Hepatitis B Virus and microRNAs: A Bioinformatics Approach.

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations.

Background: Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations. Method: In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations. Results: We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.

PURPOSE OF REVIEW: Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. RECENT FINDINGS: In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Reconnoitering the sequence and structural analysis of Staphylococcus aureus "A" protein.

Background: The Staphylococcus aureus "A" protein plays an essential role in the pathogenicity and virulence of this bacterial species. To gain deeper insights into the protein's characteristics, we conducted an in-depth analysis of its sequence and structure. Objective: This study aimed to unravel the underlying genetic and structural components that contribute to the protein's functional properties. Results: Utilizing various bioinformatics tools and techniques, we first examined the protein's primary sequence, identifying key amino acid residues and potential functional domains. Additionally, we employed computational modeling and simulation approaches to determine the tertiary structure of the "A" protein. Through this comprehensive analysis, we discovered novel features and interactions within the protein's structure, shedding light on its potential mechanisms of action. Furthermore, we investigated the protein's evolutionary conservation and compared it with related proteins from other bacterial species. Conclusions: Overall, our findings provide valuable insights into the sequence and structure of the Staphylococcus aureus "A" protein, which may have implications for understanding its role in pathogenicity and guiding the development of novel therapeutic strategies.