Anders, Simon

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 95,106

HTSeq—a Python framework to work with high-throughput sequencing data

Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics

Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

Cited by 21,464

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Genome Biology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 15,581

Detecting differential usage of exons from RNA-seq data

Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research

RNA Research and SplicingGenomics and Phylogenetic Studies

Cited by 1,545

Accounting for technical noise in single-cell RNA-seq experiments

Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 979

Gene expression across mammalian organ development

Cardoso-Moreira, M., Halbert, J., Valloton, D., Velten, B., Chen, C., Shao, Y., Liechti, A., Ascenção, K., Rummel, C., Ovchinnikova, S., et al. (2019) · Nature

Developmental Biology and Gene RegulationGenomics and Phylogenetic Studies

Cited by 838

Quiescence Modulates Stem Cell Maintenance and Regenerative Capacity in the Aging Brain

Kalamakis, G., Brüne, D., Ravichandran, S., Bolz, J., Fan, W., Ziebell, F., Stiehl, T., Catalá-Martinez, F., Kupke, J., Zhao, S., et al. (2019) · Cell

Neurogenesis and neuroplasticity mechanismsMesenchymal stem cell research

Cited by 399

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Nature Precedings

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 328

Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines

Ahlmann-Eltze, C., Huber, W., & Anders, S. (2024) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 268

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

Ahlmann-Eltze, C., Huber, W., & Anders, S. (2025) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 238

A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

Kurzawa, N., Becher, I., Sridharan, S., Franken, H., Mateus, A., Anders, S., Bantscheff, M., Huber, W., & Savitski, M. M. (2020) · Nature Communications

Advanced Proteomics Techniques and Applicationsthermodynamics and calorimetric analyses

Cited by 53

Lung endothelium exploits susceptible tumor cell states to instruct metastatic latency

Jakab, M., Lee, K. H., Uvarovskii, A., Ovchinnikova, S., Kulkarni, S. R., Jakab, S., Rostalski, T., Spegg, C., Anders, S., & Augustin, H. G. (2024) · Nature Cancer

Cancer Cells and MetastasisMechanisms of cancer metastasis

Cited by 39

Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial (Preprint)

Deckert, A., Anders, S., Morales, I., De Allegri, M., Nguyen, H. T., Souares, A., McMahon, S., Meurer, M., Burk, R., Lou, D., et al. (2022) · JMIR Public Health and Surveillance

SARS-CoV-2 detection and testingCOVID-19 epidemiological studies

Cited by 3