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AI in Life Sciences

Next generation sequencing

The individual cell represents the fundamental unit of life. Its manner of functioning has been the focus of biomedical research for centuries. In recent years, advances in high throughput so-called single-cell sequencing techniques have made it possible to study individual cells and their genetic profile. This enables revolutionary new insights into tissue composition, cell–cell interactions and dynamic processes in health and disease. These groundbraking technologies are summarized under the name Next Generation Sequencing and were pioneered in 2009. Since then, single-cell sequencing has developed and matured – until it became a powerful tool and in 2018 was nominated Science Breakthrough of the Year.

A Big Data Problem

The resulting profile data, e.g. from single cell transcriptomics, however, provide analysts with new challenges: data sets are typically very large, noisy and highly interconnected with other annotation data, making them unsuitable for established procedures. The setting calls for the application of novel algorithms originating from the field of artificial intelligence, which are adapted to deal with this type of challenge.

Artificial Intelligence in single-cell genomics

Together, single cell sequencing and artificial intelligence can be considered powerful tools for biomedical research, enabling insights at the highest resolution. A recent article of Dickten et al. (2019) provides a short overview of the recent developments in both technologies and gives examples for their impact on medical applications. Subsequently, they demonstrate how methods from artificial intelligence can be successfully applied for the analysis of single cell transcriptomics data. Since the successful application of such methods still requires a detailed understanding of their requirements, an even stronger interaction between specialists of both disciplines may become necessary in the future. This is exactly where FASTGenomics comes into play.

A new global market

Insights gained from single-cell transcriptomics will not only revolutionarize the landscape of cancer genomics, immunology and whole-organism science [1]. They will also continue to create a new global market of personalized medicine that is estimated to reach more than $5 billion by 2025 [2].

For more information on the topic, the interested reader is referred to this interesting blog article of Harvard University.

what FASTGenomics offers

  • Intuitive analysis environment with cloud access from everywhere
  • Support of multiple data formats (mtx, rds, hdf5, h5ad, loom, csv, tsv)
  • R and Python: use Seurat and ScanPy with full support of their associated data objects
  • Data management: share your data and metadata with colleagues, coworkers or publish
  • State-of-the-art analytics: constantly growing number of analyses from renowned working groups
  • Interactive analyses using Jupyter notebooks
  • Showcase of results: make your analysis available to the community
  • Reproducibility and reusability of your results through dockerized environments