MOTIVATION: Unique molecular identifiers (UMIs) enable efficient error correction in amplicon sequencing but UMI-aware analysis workflows for long-read sequencing and particularly for nanopore data are still sparse. Existing approaches lack portability, real-time sequencing support, GPU acceleration, and efficient use of resources. RESULTS: We present umi-pipeline-nf, a portable, fully containerized, modular and scalable workflow to create single-molecule consensus sequences from UMI-tagged long-read nanopore amplicon data. Umi-pipeline-nf supports flexible UMI-designs and is built in Nextflow DSL2 for seamless deployment across computing platforms and a high degree of parallelization, allowing analysis of several targets at once. It scales linearly from single samples to large cohorts, outperforming existing tools in efficiency and flexibility. Additionally, we integrated real-time read processing, robust UMI clustering, and GPU-accelerated consensus polishing. Umi-pipeline-nf supports two different polishing strategies [reference sequence-based and partial order alignment (POA)-based]. Implementation of GPU-accelerated, reference sequence-based polishing results in up to 100-fold runtime improvements and reduced usage of computational resources, compared to other UMI analysis pipelines and POA-based polishing. AVAILABILITY AND IMPLEMENTATION: The umi-pipeline-nf analysis pipeline and test data are available at https://github.com/genepi/umi-pipeline-nf, and a frozen snapshot is available at DOI: 10.5281/zenodo.18607956. Scripts and configuration files for the analyses in the present manuscript can be found at https://github.com/AmstlerStephan/umi-pipeline-nf_Paper.

SUMMARY: To enable direct comparison of ancestry background in different studies, we developed LASER to estimate individual ancestry by placing either sezquenced or genotyped samples in a common ancestry space, regardless of the sequencing strategy or genotyping array used to characterize each sample. Here we describe the LASER server to facilitate application of the method to a wide range of genetic studies. The server provides genetic ancestry estimation for different geographic regions and user-friendly interactive visualization of the results. AVAILABILITY AND IMPLEMENTATION: The LASER server is freely accessible at http://laser.sph.umich.edu/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster. In Bioinformatics, MapReduce has already been adopted to various case scenarios such as mapping next generation sequencing data to a reference genome, finding SNPs from short read data or matching strings in genotype files. Nevertheless, tasks like installing and maintaining MapReduce on a cluster system, importing data into its distributed file system or executing MapReduce programs require advanced knowledge in computer science and could thus prevent scientists from usage of currently available and useful software solutions. RESULTS: Here we present Cloudgene, a freely available platform to improve the usability of MapReduce programs in Bioinformatics by providing a graphical user interface for the execution, the import and export of data and the reproducibility of workflows on in-house (private clouds) and rented clusters (public clouds). The aim of Cloudgene is to build a standardized graphical execution environment for currently available and future MapReduce programs, which can all be integrated by using its plug-in interface. Since Cloudgene can be executed on private clusters, sensitive datasets can be kept in house at all time and data transfer times are therefore minimized. CONCLUSIONS: Our results show that MapReduce programs can be integrated into Cloudgene with little effort and without adding any computational overhead to existing programs. This platform gives developers the opportunity to focus on the actual implementation task and provides scientists a platform with the aim to hide the complexity of MapReduce. In addition to MapReduce programs, Cloudgene can also be used to launch predefined systems (e.g. Cloud BioLinux, RStudio) in public clouds. Currently, five different bioinformatic programs using MapReduce and two systems are integrated and have been successfully deployed. Cloudgene is freely available at http://cloudgene.uibk.ac.at.

Today filesystems of big companies are both huge and distributed amongst the world. They contain huge sets of metadata, but are not optimized to analyze them. In contrast, if metadata is stored in a database system and updated synchronously, it could be analyzed and processed in a much easier and straightforward way. Then even adding new attributes, not natively supported by the underlying filesystem, is easily possible. Thus, synchronous metadata storage in a database system can help managing and administrating huge filesystems efficiently but must not slow down the filesystem significantly. The aim of this paper is to describe possible solutions for synchronous metadata storage, inspect how such an integration of filesystem and database system might look like and evaluate the performance.

BACKGROUND: Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs. RESULTS: CONAN is a freely available client-server software solution which provides an intuitive graphical user interface for categorizing, analyzing and associating CNVs with phenotypes. Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions. Various file formats including the information on CNVs in population samples are supported as input data. CONCLUSIONS: CONAN facilitates the performance of GWAS based on CNVs and the visual analysis of calculated results. CONAN provides a rapid, valid and straightforward software solution to identify genetic variation underlying the 'missing' heritability for complex traits that remains unexplained by recent GWAS. The freely available software can be downloaded at http://genepi-conan.i-med.ac.at.

BACKGROUND: Mitochondrial DNA (mtDNA) is widely being used for population genetics, forensic DNA fingerprinting and clinical disease association studies. The recent past has uncovered severe problems with mtDNA genotyping, not only due to the genotyping method itself, but mainly to the post-lab transcription, storage and report of mtDNA genotypes. DESCRIPTION: eCOMPAGT, a system to store, administer and connect phenotype data to all kinds of genotype data is now enhanced by the possibility of storing mtDNA profiles and allowing their validation, linking to phenotypes and export as numerous formats. mtDNA profiles can be imported from different sequence evaluation programs, compared between evaluations and their haplogroup affiliations stored. Furthermore, eCOMPAGT has been improved in its sophisticated transparency (support of MySQL and Oracle), security aspects (by using database technology) and the option to import, manage and store genotypes derived from various genotyping methods (SNPlex, TaqMan, and STRs). It is a software solution designed for project management, laboratory work and the evaluation process all-in-one. CONCLUSIONS: The extended mtDNA version of eCOMPAGT was designed to enable error-free post-laboratory data handling of human mtDNA profiles. This software is suited for small to medium-sized human genetic, forensic and clinical genetic laboratories. The direct support of MySQL and the improved database security options render eCOMPAGT a powerful tool to build an automated workflow architecture for several genotyping methods. eCOMPAGT is freely available at http://dbis-informatik.uibk.ac.at/ecompagt.

BACKGROUND: High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects. DESCRIPTION: We developed a database system for an efficient combination and management of phenotypes and genotypes (eCOMPAGT) deriving from genetic epidemiological studies. eCOMPAGT securely stores and manages genotype and phenotype data and enables different user modes with different rights. Special attention was drawn on the import of data deriving from TaqMan and SNPlex genotyping assays. However, the database solution is adjustable to other genotyping systems by programming additional interfaces. Further important features are the scalability of the database and an export interface to statistical software. CONCLUSION: eCOMPAGT can store, administer and connect phenotype data with all kinds of genotype data and is available as a downloadable version at (http://dbis-informatik.uibk.ac.at/ecompagt).