In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.

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.

CONCLUSIONS: 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.

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.
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).
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.

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.

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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.

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.

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.
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.
CONCLUSIONS: 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.

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.