The concept of an agile bioinformatics consulting company was initially conceived by three trailblazers in the bioinformatics world. Wim Van Criekinge, a world authority in the bioinformatics fields and professor in computational biology in University of Ghent, Gerben Menschaert, a faculty member of the same university, and Jan Van den Berghe, an entrepreneur and a close friend of Wim.
The industry is becoming extremely data-driven, and the integration of heterogeneous data in a dynamic environment is becoming central to success. At BioLizard we provide services tailored to our clients needs.
We are an expert-team of scientists and engineers who provide innovative solutions for even the most complex biological datasets. Our speciality is gathering a diverse range of expertise tailor-made to your needs and implementing the most up-to-date technological tools within the sphere of bioinformatics, biostatistics, machine learning and AI. We work fully customised, depending on your needs and timelines.
Our experts are highly competent in RNA and DNA seq data analysis, but also proteomics, epigenetics, proteogenomics, actually any multi-omics data analysis, as well as epitope prediction and developing and optimizing pipelines, tools, interfaces and apps. We also focus on multimodality testing.
Repurposing historical data and existing real-world data to build effective models able to predict clinical outcomes. We use federated learning techniques to develop artificial intelligence based trial design, improve patient enrolment, predict retention and drive artificial intelligence enabled clinical trial analytics. We can also use real-world evidence to build synthetic control arms for clinical trials.
Biological data is highly complex with multiple variables contributing to observed trends. Analysis and interpretation of this type of data requires the use of appropriate tools and methods in order to harness the most out of the data, and highlight significant correlations and causal links. We develop and apply a range of statistical methods tailored to each specific problem setting, that can be used for both clinical and non-clinical datasets. Our models take into account experimental/clinical design as well as all relevant biological data. We have specific expertise in combining multi-omics data with other data sources that can be seamlessly integrated into our AI-based algorithms.
Exploring RNA-sequence data using tailor-made pipelines that are reproducible and robust to accurately identify transcriptional differences. Transcriptomes are investigated using a combination of in-house developed and publicly available tools to effectively assemble and align RNA-sequence reads. This can be done for samples with or without a reference genome. Differential expression is determined at a gene or transcript level and potential pathways are highlighted through gene enrichment analysis. Our pipeline, toRNAdo, is a Nextflow based pipeline that allows fast scaling processing on different cloud technologies (e.g. Google cloud, Amazon AWS). The fully automated toRNAdo provides a final output as well as a QC report based on the MultiQC tool. Due to the modular nature of the MultiQC tool, modifications can be made for seamless adaption to a range of biological data, such as variant calling data.
Both global and local DNA methylation changes have been associated to disease, and in particular to cancer. While methylation is the most well studied for of epigenetic modification, other non-methylation epigenetic mechanisms include histone modifications, micro-RNA interactions and chromatin remodelling complexes. We build robust computational pipelines for full epigenetic analysis to detect differences in methylation and non-methylation mechanisms for selected treatments and link these results to gene expression.
Protein alteration is prevalent in disease therefore they may be more or less active, have an altered function. Small molecule drugs may exist that modify a protein’s function by activating, inhibiting or altering the protein. We generate structural models of the disease altered proteins of interest, implementing a high throughput in silico workflow to screen multiple databases and score relevance of small molecules as clinical drug candidates.
We perform multi-omics mapping of full microbial communities to determine species composition and examine the causal role of a specific microbiome in selected disease states. Our workflows are used for human, animal, plant and aquatic systems. We implement state-of-the-art statistics to calculate causal statistics by comparative weighting of genetic, microbial and external factors to predict disease development, progression and resistance. By using a combination of a biostatistical frameworks and artificial intelligence tools we examine modifiable risk factors to establish the most viable drug targets and biomarkers in microbiome-associated illnesses as well as develop bioremediation approaches.
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Biolizard is an inventive company with expertise across different domains in bioinformatics, software development, and others.
A friendly coherent team with flexible set-ups!
The lizards are great to work with because of their strong and clear communication skills, their ability to emphasize with the situation, and to provide input and the necessary solutions based on your requirements and needs.