Both pre-designed and custom-built SNP arrays allow scientists and experts to cost efficiently genotype hundreds or thousands of individuals for large numbers of bi-allelic SNP markers. The number of examined markers can range from thousands to millions. This allows geneticists to implement a wide range of genetic studies, for example, to investigate the mechanics of speciation, examine the differences between breeds of a given species at the genetic level, or animal breeders to implement genomic selection in agriculturally important species.

SNP and indels (short insertions/deletions) can be detected from a range of second generation sequencing data, including whole-genome, whole-exome and ddRAD sequence data. SNP variants are the most numerous on the genome and received considerable attention in the past from both scientists and experts from many fields.

Copy number variations are defined as „a variation that increases or decreases the copy number of a given region” in the [American] National Center for Biotechnology Information. Usually, CNVs are considered to have a minimum length of 5000 basepairs, but their length can reach millions of basepairs. Although less numerous than SNP mutations, they usually cover a larger proportion of the genome. CNVs can be predicted, for example from whole genome sequence data, based on genome coverage information.

Our pipelines address specific cases, aiming not only for problem-solving but also for standardization. We integrate advanced algorithms and quality control measures to enhance the accuracy and reproducibility of SNP and indel predictions. Beyond solving individual challenges, our commitment extends to establishing standardized approaches. Meticulous attention to data preprocessing, variant calling, and downstream up to dates ensures our pipelines adhere to high standards of quality and consistency.

Collaborative research support features facilitate teamwork by providing tools for data sharing, collaborative analysis, writing the method section for publications and communication among research teams. The platform may include features like shared workspaces, version control, and real-time collaboration tools, enabling researchers to collectively contribute to and enhance projects.

Interactive data visualization creates user-friendly interfaces for exploring and interpreting complex genomic datasets. Users can visualize genomic information through interactive graphs, charts, and plots, enhancing their ability to comprehensively analyze large-scale genomic data effectively.

Integrative omics analysis combines multiple omics data types, such as genomics, transcript omics, and epigenomics, to provide a more comprehensive understanding of biological processes. Data integration involves merging information from different omics layers to identify correlations and relationships, allowing researchers to unravel complex biological mechanisms.

Training and support services provide researchers with the knowledge and assistance needed to effectively utilize the platform’s features and analyze genomic data. Training sessions, documentation and customer support services are offered to help users navigate the platform and troubleshoot any issues they may encounter.

Custom report generation allows users to generate tailored reports summarizing analysis results for easy interpretation and presentation. Users can select specific parameters and metrics of interest to be included in the reports, which may include visualizations, statistical summaries, and key findings from the scientifics.