Empower the Biologist. Liberate the Bioinformatician.

Tercen is a rapid, flexible, drag and drop, data analytics platform that requires no coding knowledge.

Empower the Biologist. Liberate the Bioinformatician.

Data in biomedical science unquestionably more important than ever. Will biomedical researchers also have to learn data science?

Biomedical sciences has been revolutionised by high-throughput, high-dimensional data. However, most biomedical researchers have not trained as data scientists and have become more dependent on bioinformaticians to analyse the results of their experiments. As a result, bioinformaticians must spend more time executing simple tasks for researchers. Time they could use to advance research and develop better techniques.

Is coding the only solution to this dilemma?

The impact of Big Data in biomedical sciences

We have entered the era of Big Data. New technologies mean scientific instruments generate substantial volumes of measurements. Biomedical sciences are increasingly reliant on the processing, analysis, and interpretation of large volumes of data.

DNA sequencing now measures whole genomes and advances in Fluidics allows for the sorting of thousands of cells using technologies like Flow Cytometry. Combining both fields allows single-cell sequencing techniques that detect the expression levels of thousands of genes across thousands of cells. Wearable devices that capture real-time physiological signals provide further increases in data volume and complexity.

This wealth of data creates significant opportunities to discover and understand the critical interplay among diverse domains such as genomics, proteomics and metabolomics. Phenomics is a discipline that now encompasses imaging, biometrics, and clinical data. Concomitant advances in processing power and storage have allowed for sophisticated computational analyses of this data. The Bioinformatic community has developed many open-source libraries to process this specialised biological data.

Going “Data-driven”

Artificial intelligence (AI) has a growing role to play in biomedical research and healthcare. Image Analysis in particular is using AI to augment the ability of professionals in the context of diagnosis and treatment. Visual analytics has grown from the scientific visualisation field, and its ability to collect and store data is increasing at a faster rate than the scientific communities ability to analyze it. Researchers with interdisciplinary training are needed to provide the skillset to process and interpret big data derived form the fundamental and clinical sciences.

Impact on education: Online resources

While education in biology is traditionally split into a wet-lab or dry-lab scientist track, more and more interdisciplinary programs are aiming to close this gap.

Skilled dry-lab scientists know how to handle data end-to-end: from the moment it is output by the experiment to the point of extraction of knowledge.  They are well-versed in dealing with missing values and normalizing across different platforms, two perennial problems in biological data. They have strong knowledge of statistics, understand issues related to sample size, power, multiple hypothesis testing, classification (unsupervised learning), and generalised regression techniques (supervised learning).

Data integration is crucial to the research on molecular biomarkers of diseases, and a powerful way to leverage the existing knowledge base in the biological sciences. Data can originate from the same population cohort (multi-omics) or between heterogeneous populations (meta-omics). Relationships between omics, clinical, and phenotypes require data integration and the use of correlations and survival analysis.

Most researchers specialising in the biological sciences now have no choice but to address the techniques of quantitative science. Luckily, access to data analytics learning is easy. Multiple online resources and platforms are available to researchers. However, the learning-curve associated with coding means that time-pressed wet-lab scientists often do not extend their expertise. Tercen has developed an analytics platform that can be used without any prior coding knowledge.

Duel-skill scientists: A seller’s market

Biomedical data management has become a critical resource for many academic and commercial research institutions.

However, candidates that have combined biomedical knowledge, data science, and statistics skills are still a rarity. It is a field that offers many career opportunities. Interdisciplinary scientists get hired as quickly as they can be trained. The employers are more competitive and the market drives up salaries for both industrial and faculty staff. 

Skilled bioinformaticians often look for laboratories at the bleeding edge of technical development where new technologies can address some of science’s big, unanswered questions. In a way they are drawn upwards in the ecosystem of scientific analysis

Shortage of  high-throughput scientists

There are many laboratories that do excellent research in their field of study but do not develop new genomic or high-throughput techniques due to a shortage of data capabilities. In those labs, the biologists know the questions to ask to elicit knowledge from their data but, lack the computational tools to do so. Therefore they experience a slower, iterative, process where computational scientists devote their resources to routine processing and visualisation, and where wet-bench scientists have to vie for the attention of their overworked computational colleagues.

This is where Tercen comes in. A platform that allows wet-lab scientists to analyse their data without needing to code. Tercen empowers the wet-lab scientist to do their own data analysis and make their own plots. It frees the computational scientist to focus on more complex tasks.

Tercen: Analysing biological data without having to code

Tercen is an open platform for anyone who wants to get meaning from scientific data. It offers non-coders the ability to access high-end data science techniques to generate insights into multivariate datasets. The platform promotes a social and collaborative approach to data science. It has a unique visual programming paradigm. Tercen is aimed towards life-science projects but can handle any kin of data analysis.

On Tercen, researchers visually customize a workflow without the aid of a bioinformatician. A workflow is a data analysis pipeline composed of computation and annotation steps in a sequence. Standard workflows for each of molecular readouts such as RNAseq, FlowCyto, and Mass-spec. It is easy to add powerful computation, statistical, or visualization plug-ins to a pipeline. 

Team members collaborate on the same workflow and data, preventing duplication. They can generate reports for presentations or publication. Reports contain not only the conclusions but an automatic description of the process (e.g. normalization, statistics testing, clustering, and functional annotation). Essential for reproducible science.