Larkbio offers a wide variety of bioinformatic services while handling every project individually.
Many people – in academic as well as business circles – consider bioinformatics as an application discipline, merely using IT solutions to help solve biological problems. This could not be further from the truth, especially when we reach the data interpretation phase. Here bioinformatics becomes almost pure science with a wide range of serious scientific questions waiting to be answered. The key lessons we learned from our many high-throughput data analysis projects are the following:
Our team of researchers (medicine, molecular biology, mathematics) and software engineers make sure that the collaboration between the different fields is as fluent as possible.
The tools applied in a particular project depend on the methods used (sequencing, microarray etc.), applications (de novo sequencing, ChIPSeq etc.), subtypes (exon array, gene array, tiling array etc.) and brands of instruments (Illumina, SOLiD/Ion, Roche etc.).
Due to the often enormous size of the datasets, we need a robust and almost infinitely scalable technology behind these operations. Apart from having our own powerful infrastructure, we also use the framework provided by cloud computing, harnessing its storage and computational capacities.
The purpose of data analysis is the biological interpretation of data – providing an answer to the original research questions.
Larkbio adds value to your research by:
integrate your experimental data with external datasources to a single datawarehouse
perform biostatistics and data mining in the data warehouse to „make sense” of your experience
data visualization and exploration tools make it easier to understand and interpret results of the analysis
In the last few years technology development has become one of the main drivers of genomics and it will remain so in the foreseeable future. With decreasing prices and increasing accuracy, Next Generation Sequencing (NGS) is already a mainstream application, the rising demand by researchers being matched by a constantly growing number of suppliers.
Having analyzed raw or preprocessed data originating from all types of NGS platforms (including Illumina, Roche 454, Ion Torrent, Applied Biosystems’ SOLiD, Pacific Biosciences and Complete Genomics), we can offer you end-to-end sequencing packages that promise the best possible results – considering your research needs, budget and special requirements/concerns.
Microarray technology is often still the preferred choice of researchers and for good reasons. Apart from having a long and proven track record and providing significant advantages when working with a large number of samples, it is simply better suited for many types of experiments.
We developed experimental design for several customers. During the data analysis process we usually perform quality control, low level data analysis and statistical analysis of microarray data in Affymetrix data sets. The resulting differential expression tables are usually annotated, and further analysed to identify pathways and biological processes related to the groups of the study.
Natural variations found in our genes can influence how well a patient might respond to a particular drug. Personalized medicine uses these variations to develop new, safe and effective treatments for genetically defined sub-groups of patients. In order for personalized medicine to be used effectively by healthcare providers and their patients, experimental findings must be translated into precise diagnostic tests and targeted therapies. This has already begun to happen in certain areas, such as testing patients genetically to predict the therapeutic effect of various cancer drugs.
Currently only a few hundred proteins are targeted by all the drugs available in the world combined. Bioinformatics can help us better understand disease mechanisms and identify novel drug targets. Better targeted and more specific medicines have the potential of (1) being more effective and at the same time (2) reducing the number of unwanted side effects.
Recently, biofuels have attracted great interest as an alternative, renewable source of energy in the face of the ongoing depletion of fossil fuels. Energy genomics uses microbial and plant genomic data, high-throughput analytical technologies, and modelling and simulation to develop a predictive understanding of biological systems behavior relevant to solving energy and environmental challenges. The emerging field of metagenomics offers particular value in the identification of alternative energy resources.