Larkbio is a bioinformatics SME with offices in Austria and Hungary. Our focus areas are IT systems and data analysis for health and omics research.
We have extensive experience in the following fields:
• bioinformatics, biostatistics, data analysis and data mining for high-throughput ‘omics’ data, including genomics, transcriptomics, epigenomics, metagenomics,
proteomics and metabolomics
• biobank systems for large-scale data gathering, data integration, visualization and data interpretation
• data collection and analysis during the evaluation of existing health programmes aimed at providing tools for policymakers to make informed decisions
Our staff includes software engineers, medical doctors, bionformaticians, molecular biologists and project managers. Because we work with data that is of paramount importance for you, professional services delivered by Larkbio is only a part of our offering, the relationship between your team and our team must be built on trust.
Areas of our expertise
Bioinformatics data analysis
We started to develop algorithms and workflows for next generation sequencing in 2009. Ever since, we have been working in several bioinformatics projects analyzing and processing NGS data (DNA-Seq, RNA-Seq) coming from different high-end sequencers (Illumina, Roche, etc.). Apart from genomic data, we have also gained expertise in the analysis of transcriptomics, proteomics, metabolomics and metagenomics data.
Larkbio builds and maintains data management information systems that can handle samples as well as related experimental, clinical, environmental and lifestyle data.
Data management and integration
We have the capacity and know-how to process data produced by different high-throughput technologies and integrate our results with the outcomes of large-scale genomic, proteomic or metabolomic population studies.
With the rapid advancement of high-throughput technologies, the application areas of biostatistics have widened considerably. Our research team can provide assistance in choosing and performing the most appropriate biostatistical algorithms for a particular project.
Data mining and machine learning
Machine learning and data mining are two related research areas that offer efficient and effective computational tools when traditional statistical methods are inadequate. Applications of data mining to bioinformatics include biological sequence analysis, gene expression analysis and data mining in structural bioinformatics.
Evaluating existing health programmes
By participating in different programs and disseminating information, we contribute to maintaining and improving population health. We participate in actions aimed at the following: building health public policy, creating supportive environments, strengthening community action, developing personal skills and reorienting health services.