Omics are becoming the most precise tool for diagnostics, pushing the boundaries of rare and inherited diseases, infectious diseases, metabolic disorders and cancer. We offer advanced omics tools to improve data analysis and interpretation through customized software and tailored solutions. Thus, we create valuable insights for a more precise decision making process in healthcare.
SOLUTIONS FOR DIAGNOSTICS
Genetic Diseases
Precision medicine demands a deeper understanding of the genetic mechanisms underlying diseases, which is enabled by multi-omics approaches.
Metabolic Diseases
Complex diseases involve multiple genes and regulations that can be better understood by omics.
Infectious Diseases
Pathogen identification and antimicrobial resistance make use of omics to enable more precise and scalable solutions.
Cancer
Gene regulations and mutations are key drivers in cancer. Multi-omics approaches lead the way to personalised diagnostics tools.
RELATED ANALYSES
Capture sequencing analysis
Whole Exome Sequencing analysis
Whole Genome Sequencing analysis
Variant interpretation
Single-cell analysis
Multi-omics data integration
RELATED CLOUD SOFTWARE
GINO is a user-friendly, accurate, fast and scalable software that boosts the exploitation of genomics data. It performs automated variant analysis and interpretation, working with NGS data from targeted sequencing, Whole Exome Sequencing and Whole Genome Sequencing.
GINO features customised variant filtering, virtual panels generation, trios analysis and can analyze both germline and somatic variants. It automatically generates curated reports adapted to its user’s desired outputs. GINO is a cloud-based software, allowing its users to access from any point and avoiding the need of large computational infrastructures.
GINO can answer several questions, such as:
- Which variants are present in a given individual?
- Which variants are likely to be pathogenic?
- What is the probability of inheriting a pathogenic variant?
MICK is a digital decision support platform that helps doctors integrate microbiome analysis into clinical practice, to help guide therapeutic decisions and provide targeted prevention to patients.
MICK is designed to help healthcare professionals offer personalized medical advice to patients through microbiome analysis.
- MICK automatically identifies the microorganisms in a sample, analyses key microbiome health status patterns and functionality, and performs an in-depth characterization of identified pathogens
- Monitor patients’ microbiome status over time. Record key aspects of patients’ health. Unlock Global Health Index and Nutrition Index to go deeper into your analysis
- Access a ready-to-use report to easily communicate and share the results with patients online and offline. Customize the report to fit your needs.
GAIA is a highly reliable and versatile metagenomics data analysis software. It performs taxonomic and functional analyses, delivering clear and precise results. GAIA works with Amplicon (16S/18S/ITS), Shotgun and metatranscriptomics data from any type of sample (including human, soil and water microbiomes), any sequencing platform (Illumina, PacBio, Oxford Nanopore), and any type of organism (prokaryotes, eukaryotes and viruses).
GAIA is user-friendly, cloud-based and dynamic. It can be used by anyone, since it doesn’t require any coding skills or computational infrastructure; and enables experimental design, results navigation, real time comparisons, data re-analysis and storage.
GAIA can answer several questions:
- Which species are present in my samples?
- Which gene functions are present in my samples?
- Which species are responsible for a specific function in my samples?
AIR, Artificial Intelligence RNAseq, is the most revolutionary software to run transcriptomics experiments.
AIR is fast, secure, intuitive, and will manage your data in real-time.
Thanks to our powerful cloud we created a software able to perform automatic Differential gene expression analysis with the same precision as a human curated one. It will leave you speechless.
AIR can answer several questions:
- Which genes are differentially expressed across my samples?
- Which is the function of the differentially expressed genes across my samples?
- Are my samples characterised by a specific transcriptome expression profile?
