Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

The growing interest in chemogenomics approaches over the last years has led to an increasing amount of data regarding chemical and the corresponding biological activity space. The resulting data, collected in either in-house or public databases, need to be analyzed efficiently to speed-up the increasingly difficult task of drug discovery. Unfortunately, the discovery of new chemical entities or new targets for known drugs ('drug repurposing') is not suitable to a fully automated analysis or a simple drill down process. Visual interactive interfaces that allow to explore chemical space in a systematic manner and facilitate analytical reasoning can help to overcome these problems. Scaffold Hunter is a tool for the visual analysis of chemical compound databases that provides integrated visualization and analysis of biological activity data and fosters the interactive exploration of data imported from a variety of sources. We describe the features and illustrate the use by means of an exemplary analysis workflow.

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Bioinformaticians should not concern themselves with problems related to computing technologies, and only focus on analysis of biological data. In reality, however, they need to know quite a bit about computer hardware, networking, robotics, image processing, and many others that affect the collection, storage, analysis, and distribution of biological information. Computing technologies enable bioinformtics effectively only if proper management of data and computational resources accompanies their deployments. Bioinformatics organizations - pharmaceutical companies, biotechnology firms, research institutions and the like - need an IT infrastructure that can handle vast, heterogeneous data produced internally or acquired from third parties, and manage diverse computational resources. The goal is to provide scientists with the data and computational resources they need in the right forms on-demand, relieving them from attending to IT issues. This paper describes an architecture that leverages current technologies for bioinformatics data management and analysis to minimize scientists' involvements in the IT minutia, in order to achieve a more efficient and productive bioinformatics environment.

Bioinformatics is one of the fastest growing scientific areas over the last decade. It focuses on the use of informatics tools for the organization and analysis of biological data. An example of their importance is the availability nowadays of dozens of software programs for genomic and proteomic studies. Thus, there is a growing field (private and academic) with a need for bachelor of science students with bioinformatics skills. In consideration of this need, described here is a problem-based class in which students are asked to design a set of intrageneric primers for PCR. The exercise is divided into five classes of 1 h each, in which students use freeware bioinformatics tools and data bases available through the Internet. Besides designing the set of primers, the students will consequently learn the significance and use of the major bioinformatics procedures, such as searching a data base, conducting and analyzing sequence multialignment, comparing sequences with a data base, and selecting primers.

Next-generation sequencing (NGS) techniques produce high quantities of DNA and RNA sequencing data, enabling variation analysis at a whole genome level. Due to the massive availability of NGS data, the processing and analysis now constitute a serious challenge for research in life sciences. This necessitates the development of novel, user-friendly and scalable software tools, which are able to jointly handle large NGS data sets in order to break the bottleneck that has already moved from data generation to data analysis in high-throughput biology. Here we introduce a highly efficient NGS analysis and visualization software tool (Rikurator), which is designed for researchers who are struggling with NGS data analysis and may not have access to a dedicated bioinformatics team. Rikurator can be applied to a multitude of genomic analysis tasks, including discovery of pathogenic mutations in familial or sporadic sample sets, analysis of structural variants and somatic characterization of cancer genomes. To these ends the software implements interactive variant filtering and annotation methods based on sample comparison, control data (e.g. ExAC), amino acid changes and quality scores, which provide an intuitive view at the studied samples. Shared candidate genes in a set of dozens of individuals can be discovered in a matter of minutes with an ordinary laptop computer. Importantly, the software is able to analyze hundreds of whole-genome sequenced samples using only a modest amount of RAM. The tool also visualizes and integrates genes, variants (VCF file format), sequence reads (BAM) and user defined features (BED/GFF). In addition, Rikurator is not limited to human studies, supporting reference genomes and genome annotations in standard formats. The software supports targeted (e.g. exome), whole-genome, RNA, Oxford Nanopore and PacBio sequencing data. Rikurator can be run on Windows, Linux and Mac, and it will be freely available after publication. Citation Format: Riku Katainen, Veli Mäkinen, Lauri A. Aaltonen, Esa Pitkänen. Fast and scalable software for comparative variant analysis and visualization of massive next-generation sequencing data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5281.

BackgroundNew technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity.ResultsWe illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.ConclusionsThis work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.

This paper aims to analyze the current context of Application Programming Interface (API) technology, with focus in External API (Public API or Open API) by news organizations. The adoption of API has been considered a technological strategy that introduces innovation processes in media groups. This technological approach allows the public access to data, increase the distribution of journalistic information and transparency which improves the credibility of organizations. However, there is not a pattern strategic in the use of API adopted by news organizations and the external developers have used various technologies, named toolkit, to store, process, analyze and visualize data. The study cases were The New York Times, The Guardian, NPR and, ESPN.

The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

Increasingly, the analysis of biological data involves collection of data over time, especially when the phenomena of interest concern identification of patterns of differential growth or response to long-term environmental stressors. When different patterns over time can be identified, they may indicate the presence of different survival strategies in a population of interest, species-specific responses to stressors, or may be markers for as yet unidentified subspecies in the population. In this chapter, two techniques for the analysis of biological data over time are described. The first technique is a two-stage analysis of data over time in which polynomial regressions are conducted to identify distinct growth patterns in the data. These growth curve patterns are then used in subsequent structural models to identify the correlates of growth curves. The second technique, the method of “Tuckerized” growth curves, proposes identification of growth curves by an eigenvalue decomposition. This solution is then rotated to make the solution conceptually interpretable within the context of overall growth over time. Factor scores from this rotated solution are then used to investigate whether such growth patterns differ as a function of environmental or species classification. Both techniques are compared and contrasted using longitudinal data on tree circumference taken from three species of trees located in the wetlands of two North American states, South Carolina and Louisiana. Rather than being competing models of growth over time, these two techniques represent complementary analyses, which differ in terms of their assumptions of categorization and growth curve processes over time.

Background The analysis of large-scale public health data is crucial for evidence-based policymaking, but conventional workflows involving manual data handling and static reporting are inefficient and lack reproducibility. There is a need for automated tools that bridge the gap from live data sources to sophisticated, dynamically generated insights. Objective To design, implement, and validate a fully automated Python (Python Software Foundation,Wilmington,DE, USA) pipeline for real-time application programming interface (API)-based ingestion of existing datasets, analysis, and dynamic report generation in public health informatics. Methods We developed alightweight, Python-only, Word-report-oriented pipeline using packages including Pandas, scikit-learn,statsmodels, and python-docx. The pipeline ingests data from public APIs with automated retries, performs preprocessing, calculates composite health scores, applies K-means clustering (k = 3) for state stratification, and performs correlation analysis. A custom rule-based engine generates dynamic textual interpretations based on statistical results. The final output is a programmatically constructed Microsoft Word (Microsoft Corporation, Redmond, WA, USA) document containing narrative, tables, and embedded figures. The pipeline was tested using India's Health and Family Welfare Statistics 2015 dataset via the data.gov.in API. Results The pipeline executed successfully in approximately 95 seconds, ingesting 37 records. It generated a composite health score, identifying Meghalaya as the top performer (score: 100.0). K-means clustering stratified states into three distinct performance tiers. Correlation analysis revealed a significant negative association between sub-health centre (SHC) infrastructure and specialist availability (r = -0.446, p = 0.01), as well as between 24×7 service availability and auxiliary nurse midwife (ANM) staffing (r = -0.358, p = 0.05), highlighting a systemic disconnect between capital investment in facilities and human resource allocation. A complete Word report including these findings, figures, and tables was automatically generated. Conclusion This automated framework provides a robust, efficient, and reproducible solution for transforming raw public health data into actionable insights and can significantly accelerate data-driven discovery and reporting in public health and bioinformatics. This study validates a computational framework for automated public health data analysis and reporting.

A Resource for Manipulating Gene Expression and Analyzing cis-Regulatory Modules in the Drosophila CNS

Medication recommendation in cancer treatment based on cell line similarity

BackgroundDeep learning has emerged as a powerful tool in the analysis of biological data, including the analysis of large metagenome data. However, its application remains limited due to high computational costs, model complexity, and difficulty extracting biological insights from these artificial neural networks (ANNs). In this study, we applied a transfer learning approach using the ESM-2 protein structure prediction model and our own smaller ANN to classify proteins containing the domain of unknown function 3494 (DUF3494) by their source environments. DUF3494 is found in a diverse group of putative ice-binding and substrate-binding proteins across a range of environments in prokaryotic and eukaryotic microorganisms. They present a compelling test case for exploring the balance between prediction accuracy and interpretability in sequence classification.ResultsOur ANN analysed 50,669 DUF3494 sequences from publicly available metagenomes, and successfully classified a large proportion of sequences by source environment (polar marine, glacier ice, frozen sediment, rock, subsurface). We identified environment-specific features that appear to drive classification. Our best-performing ANN was able to classify between 75.9 and 97.8% of sequences correctly. To enhance biological interpretability of these predictions, we compared this model with a genetic algorithm (GA), which, although it had lower predictive ability, provided transparent classification rules and predictors. Further in silico mutagenesis of key residues uncovered a vertically aligned column of amino acids on the b-face of the protein which was important for environmental differentiation, suggesting that both methods captured distinct evolutionary and ecological aspects of the sequences. Feature importance analysis identified that steric and electronic properties of the protein were associated with predictive ability.ConclusionsOur findings highlight the utility of deep learning for classification of diverse biological sequences and provide a framework for combining methods to improve model interpretability and ecological insights.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12915-025-02361-1.

The analysis of biological data is a key topic in bioinformatics, computational genomics, molecular modeling, and systems biology. The methods covered in this article can reduce the cost of experiments aimed at obtaining biological data. The problem of the identifiability of mathematical models in physiology, pharmacokinetics, and epidemiology is considered. The processes considered are modeled using nonlinear systems of ordinary differential equations. The mathematical modeling of dynamic processes is based on the use of the mass conservation law. The problem of estimating the parameters characterizing the process under study raises the question of nonuniqueness. When the input and output data are known, it is useful to perform an a priori analysis of the relevance of these data. The definition of the identifiability of mathematical models is considered. Methods for the analysis of the identifiability of dynamic models are reviewed. In this review article, the following approaches are considered: the transfer function method applied to linear models (useful for the analysis of pharmacokinetic data, since a large class of drugs is characterized by linear kinetics); the Taylor series expansion method applied to nonlinear models; the differential algebra methods (the structure of this algorithm allows it to be run on a computer); and a method based on graph theory (this method allows for the analysis of the identifiability of the model and finding a proper reparametrization reducing the initial model to an identifiable one). The need to perform a priori identifiability analysis before estimating the parameters characterizing any process is demonstrated with several examples. The examples of identifiability analysis of mathematical models in medical biology are presented.