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Genomics, Epigenomics and Bioinformatics

VIOPLOTS

VIOPLOTS

IPA Treemap

IPA Treemap

Automatic Outliers

Automatic Outliers

QC Coverage Report

QC Coverage Report

In the last decade, the fields of genomics, proteomics, imaging and neuro-recording have seen extraordinary advances in high-throughput genome-scale data acquisition and analysis capabilities. The data so acquired enable unprecedented descriptions of complex biological systems (cells, tissues, organisms) through quantitative and qualitative modeling.
Such high-end bioinformatics applications push the envelope of both computational speed and storage space. With the advent of supercomputers, faster and more capable computer architectures enable researchers to tackle some of their most challenging computational problems.

Next-generation Sequencing Applications



DNA sequencing is used for far more than actually reading the DNA sequence. The advent of massive parallel DNA sequencing (synonyms: shotgun sequencing, next-generation sequencing, deep sequencing) has enabled researchers to do quantitative counting of RNA and DNA molecules, which can be combined with more spophisticated biochemical set-ups allowing the interrogation of active transcription (RNA-seq, PRO-seq, GRO-seq) as well as egpigenetic DNA modifications (bisulfite sequencing), insights about protein-DNA interactions and histone mark distributions (ChIP-seq), the 3D structure of chromatin (ChIA-PET, ChIP-seq), and many more. Even antibody-stainings typically used for FACS analyses can now be translated into DNA reads (CITE-seq).

The figure above taken from Frese et al., 2013, highlights some of the most commonly applied seq-based applications. For more details about the different high-throughput sequencing platforms and applications, see Goodwin et al., 2016 and Reuter et al., 2015.

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