Abstract
Differential gene and protein expression analysis reveals clinically significant insights that are crucial, e.g., for systems medicine approaches. However, processing of data still needs expertise of a computational biologist and existing bioinformatics tools are developed to answer only one research question at a time. As a result, current automated analysis pipelines and software platforms are not fully suited to help research-oriented clinicians answering their hypotheses arising during their clinical routine. Thus, we conducted user interviews in order to identify software requirements and evaluate our research prototype of an application that (i) automates the complete preprocessing of RNA sequencing data in a way that enables rapid hypothesis testing, (ii) can be run by a clinician and (iii) helps interpreting the data. In our contribution, we share details of our preprocessing pipeline, software architecture of our first prototype and the identified functionalities needed for rapid and clinically relevant hypothesis testing.
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Acknowledgement
Parts of this work were generously supported by a grant of the German Federal Ministry of Education and Research (031A427B).
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Kraus, M. et al. (2019). DEAME - Differential Expression Analysis Made Easy. In: Gadepally, V., Mattson, T., Stonebraker, M., Wang, F., Luo, G., Teodoro, G. (eds) Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2018 2018. Lecture Notes in Computer Science(), vol 11470. Springer, Cham. https://doi.org/10.1007/978-3-030-14177-6_13
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DOI: https://doi.org/10.1007/978-3-030-14177-6_13
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