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GET IN TOUCHBoth 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.
Read MoreSNP 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.
Read MoreCopy 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.
Read MoreDifferential gene expression analysis can be implemented, when (coding) RNA sequencing is performed under two or more conditions. Due to the quantitative nature of RNA sequencing, the gene expression levels of every sequenced gene (e.g. all protein coding genes) can be compared between the conditions simultaneously. A wide range of conditions can be compared: case-control studies, the effect of different treatments to the same population, gene expression differences between different tissues from the same individuals, time-series data etc. Appropriate sample sizes under each conditions are compulsory.
Read MoreChromatin immunoprecipitation followed by second generation sequencing (ChIP-seq) can be used to investigate the binding behaviour and its quantitative changes of DNA binding proteins in case-control studies, under different treatment conditions (e.g. under the effect of certain antibiotics) or the effect of the same treatment between different genotypes, etc. Multiple conditions can be also investigated.
Read MoreFunctional annotation involves adding biological context to genomic variants, elucidating their potential impact on gene function or regulation. A functional annotation database provides information on the functional consequences of variants, including potential protein domain disruptions, regulatory element alterations, and associations with known biological pathways.
Read MoreThe primary aim of artificial selection is to genetically improve populations in one or more characteristics from generation to generation (e.g. increase milk production in dairy cattle, select for disease resistance, etc.) The sum of these characteristics form the breeding goal. Given this breeding goal, breeding value estimation is the process, in which each selection candidates’ value in those characteristics is predicted. This allows breeders to order animals based on their (additive) genetic merits and select those that best befit their breeding goals.
Read MoreWe can help you design the bioinformatics analysis, including method recommendation, sample size estimation, pipeline development etc. We are experienced in laboratory experiments, genetic data analysis, statistics, genomics, population and quantitative genetics alike.
Read MoreOur 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.
Read MoreCollaborative 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.
Read MoreInteractive 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.
Read MoreIntegrative 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.
Read MoreTraining 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.
Read MoreCustom 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.
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Whether you want advice about the most suitable analyzes to further your research or a quote for a specific service, feel free to contact us and we will get back to you within 48 hours.
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