Anders Group: Bioinformatics tools for omics data
Thanks to modern assay techniques, biology is transforming into a “data-rich” science. With high-throughput sequencing, mass spectrometry, automated perturbation screens and other “big data” assay technologies, we can now get at the same time a “bird's eye” overview as well as plenty of detail on a set of biological samples. The vast amount of raw data produced this way is, however, of little use without powerful bioinformatics and biostatistics methods to process, analyse, and interpret them.
The new bioinformatics group that I am setting up ZMBH focuses on developing the computational tools that biologists need to find the needles of biological insights in the haystacks of high-throughput assay data.
We are working on methods to analyse high-throughput sequencing data, to visually and interactively explore big interlinked data sets, e.g. from omics studies, and on the use of transcriptomcis and proteomics data in functional genomics and systems medicine.
For more details on our research scope and topics, please see our projects page.
T Roider, J Seufert, A Uvarovskii, F Frauhammer, …, S Anders*, S Dietrich* (2019): Dissecting intratumor heterogeneity of nodal B cell lymphomas on the transcriptional, genetic, and drug response level. BioRxiv preprint 850438 [Link]
C Ahlmann Eltze, S Anders (2019): proDA: Probabilistic dropout analysis for identifying differentially abundant proteins in label-free mass spectrometry. bioRxiv preprint 661496 [Link]
S Ovchinnikova, S Anders (2019): Exploring dimension-reduced embeddings with Sleepwalk. BioRxiv preprint 603589 [Link]
M Cardoso-Moreira, J Halbert, D Valloton, B Velten, C Chen, Y Shao, A Liechti, K Ascenção, C Rummel, S Ovchinnikova, P V Mazin, I Xenarios, K Harshman, M Mort, D N Cooper, C Sandi, M J Soares, P G Ferreira, S Afonso, M Carneiro, J M Turner, J L VandeBerg, A Fallahshahroudi, P Jensen, R Behr, S Lisgo, S Lindsay, P Khaitovich, W Huber, J Baker, S Anders, Y W Zhang, H Kaessmann H. (2019): Gene expression across mammalian organ development. Nature 571, 505-509 [Link]
W Huber, V Carey, R Gentleman, S Anders, M Carlson, …, M Morgan (2015): Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods 12:115. [Link]
S Anders, P T Pyl, W Huber (2015): HTSeq – A Python framework to work with high-throughput sequencing data. Bioinformatics 31:166-169. [Link]
M I Love, W Huber, S Anders (2014): Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology 15:550. [Link]
P Brennecke*, S Anders*, J K Kim*, A A Ko?odziejczyk, X Zhang, V Proserpio, B Baying, V Benes, S A Teichmann, J C Marioni, M G Heisler (2013): Accounting for technical noise in single-cell RNA-seq experiments. Nature Methods 10: 1093. [Link]
A Reyes*, S Anders*, R J Weatheritt, T J Gibson, L M Steinmetz, W Huber (2013): Drift and conservation of differential exon usage across tissues in primate species. PNAS 110: 15377. [Link]
S Anders*, A Reyes*, W Huber (2012): Detecting differential usage of exons from RNA-seq data. Genome Research 22: 2008-2017. [Link]
S Anders, W Huber (2010): Differential expression analysis for sequence count data. Genome Biology 11: R106. [Link]
S Anders (2009): Visualization of genomic data with the Hilbert curve. Bioinformatics 25: 1231-1235. [Link]
* = equal contribution
Dr. Simon Anders
ZMBH, INF 282