On October 30th Xiaokang Zhang successfully defended his PhD thesis with the title: Biomarker Discovery Using Statistical and Machine Learning Approaches on Gene Expression Data.

Cod is an important fish for Norway and is used as a model organism to learn about how environmental toxicants affect biological systems. In the dCod1.0 project, we have studied how fish react to toxicants at molecular level. Using sequencing technology, we have measured the expression of several thousand genes in liver samples from cods exposed in the laboratory or from contaminated environments. An interesting question is which genes are activated when the fish are exposed to environmental toxicants.

One technology for measuring gene expression is RNA sequencing. The data that is produced must go through a series of steps to obtain gene expression. There are many tools to automate this process. However, many of them are made for special applications and for data from model organisms such as humans or mice. Therefore, we have developed a workflow called RASflow that can be easily used without special programming skills and with different research interests.

When the gene expression profiles are clear, both traditional statistical hypothesis testing and machine learning methods can be applied to find out the individual genes or gene sets that show a reaction to the toxicants. The performance of the individual method is very dependent on the data. In addition, the methods are often unstable when the number of samples is low and the number of genes is high. Motivated by this, we developed a framework that makes it possible to combine different methods to identify relevant genes and that shows stable behavior across all the data sets we have analyzed.

Publications

RASflow: an RNA-Seq analysis workflow with Snakemake (https://doi.org/10.1186/s12859-020-3433-x)

Zhang, X., & Jonassen, I.

BMC Bioinformatics, 21(1), 1-9.

An Ensemble Feature Selection Framework Integrating Stability (https://doi.org/10.1109/BIBM47256.2019.8983310)

Zhang, X., & Jonassen, I.

In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2792-2798). IEEE.

A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-Treated Atlantic Cod (Gadus morhua) Liver (https://doi.org/10.1007/978-3-030-35664-4_11)

Zhang, X., & Jonassen, I.

In Symposium of the Norwegian AI Society, Communications in Computer and Information Science (pp. 114-123). Springer, Cham.

ReCodLiver0.9: Overcoming challenges in genome-scale metabolic reconstruction of a non-model species (https://doi.org/10.3389/fmolb.2020.591406)

Hanna, E. M.*, Zhang, X.*, Eide, M., Fallahi, S., Furmanek, T., Yadetie, F., Zielinski, D. C., Goksøyr, A., & Jonassen, I.

Frontiers in Molecular Biosciences.

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision- cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol (https://doi.org/10.1016/j.aquatox.2018.06.003)

Yadetie, F., Zhang, X., Hanna, E. M., Aranguren-Abadía, L., Eide, M., Blaser, N., Brun, M., Jonassen, I., Goksøyr, A., & Karlsen, O. A.

Aquatic Toxicology, 201, 174-186.

Quantitative transcriptomics, and lipidomics in evaluating ovarian developmental effects in Atlantic cod (Gadus morhua) caged at a capped marine waste disposal site (https://doi.org/10.1016/j.envres.2020.109906)

Khan, E. A., Zhang , X., Hanna, E. M., Bartosova , Z., Yadetie, F., Jonassen , I., Goksøyr, A., & Arukwe, A.

Environmental Research, 189, 109906.

Application of quantitative transcriptomics in evaluating the ex vivo effects of per- and polyfluoroalkyl substances on Atlantic cod (Gadus morhua) ovarian physiology (https://doi.org/10.1016/j.scitotenv.2020.142904)

Khan, E. A., Zhang , X., Hanna, E. M., Yadetie, F., Jonassen , I., Goksøyr, A., & Arukwe, A.

Science of The Total Environment.

Proteomics and lipidomics analyses reveal modulation of lipid metabolism by perfluoroalkyl substances in liver of Atlantic cod (Gadus morhua) (https://doi.org/10.1016/j.aquatox.2020.105590)

Dale, K., Yadetie, F., Müller, M. B., Pampanin, D. M., Gilabert, A., Zhang, X., Tairova, Z., Haarr, A., Lille-Langøy, R., Lyche, J. L., Porte, C., Karlsen, O. A., & Goksøyr, A.

Aquatic Toxicology, 227, 105590.

*These authors contributed equally to this work.