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1.
Bradfeld, J.P.* et al.: Author Correction: Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes. Genome Biol. 25:129 (2024)
2.
Bradfield, J.P.* et al.: Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes. Genome Biol. 25:22 (2024)
3.
Curion, F. et al.: hadge: A comprehensive pipeline for donor deconvolution in single-cell studies. Genome Biol. 25:109 (2024)
4.
Curion, F. et al.: Panpipes: A pipeline for multiomic single-cell and spatial transcriptomic data analysis. Genome Biol. 25:181 (2024)
5.
Karollus, A.* et al.: Species-aware DNA language models capture regulatory elements and their evolution. Genome Biol. 25:83 (2024)
6.
Lange, M. et al.: Mapping lineage-traced cells across time points with moslin. Genome Biol. 25:277 (2024)
7.
Critical Assessment of Genome Interpretation Consortium (Müller, N.S.) & Critical Assessment of Genome Interpretation Consortium (Eraslan, G.): CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biol. 25:46 (2024)
8.
Oksza-Orzechowski, K.* et al.: CaClust: Linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants. Genome Biol. 25:286 (2024)
9.
Sarfraz, I.* et al.: MAMS: Matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data. Genome Biol. 25:205 (2024)
10.
Wang, W. ; Wang, Y.* ; Lyu, R.* & Grün, D.*: Scalable identification of lineage-specific gene regulatory networks from metacells with NetID. Genome Biol. 25:275 (2024)
11.
Yu, Y.* et al.: Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration. Genome Biol. 25:13 (2024)
12.
Horlacher, M. et al.: Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning. Genome Biol. 24:180 (2023)
13.
Karollus, A.* ; Mauermeier, T.* & Gagneur, J.: Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers. Genome Biol. 24, 56:56 (2023)
14.
Li, S.* et al.: Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. Genome Biol. 24:80 (2023)
15.
Sánchez Quant, E.S. ; Richter, M. ; Colomé-Tatché, M. & Martinez Jimenez, C.P.: Single-cell metabolic profiling reveals subgroups of primary human hepatocytes with heterogeneous responses to drug challenge. Genome Biol. 24:234 (2023)
16.
Galle, E.* et al.: H3K18 lactylation marks tissue-specific active enhancers. Genome Biol. 23:207 (2022)
17.
Janjic, A.* et al.: Prime-seq, efficient and powerful bulk RNA sequencing. Genome Biol. 23:88 (2022)
18.
Kanoni, S.* et al.: Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis. Genome Biol. 23:268 (2022)
19.
Azodi, C.B* ; Zappia, L. ; Oshlack, A.* & McCarthy, D.J.*: splatPop: Simulating population scale single-cell RNA sequencing data. Genome Biol. 22:341 (2021)
20.
Buchka, S.* ; Hapfelmeier, A.* ; Gardner, P.P.* ; Wilson, R. & Boulesteix, A.L.*: On the optimistic performance evaluation of newly introduced bioinformatic methods. Genome Biol. 22:152 (2021)
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