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Biology subjects

Lapohos, O.

Publications and source records attributed to Lapohos, O..

2 recordsLinked to original sources

AmalgaMo: flexible DNA motif merging

MotivationInference of candidate upstream regulators via motif enrichment analysis is a common step in the interpretation of genomic data. However, redundancy in motif databases can negatively impact predictive value, especially when relying on regression-based motif enrichment analysis. Although various forms of motif clustering have been used to mitigate problems caused by redundancy, an algorithm optimized for downstream regression-based analysis is needed. ResultsWe introduce AmalgaMo, an efficient and flexible command line tool for merging highly similar motifs. Using publicly available human datasets, we demonstrate that merging motifs with our optimized settings greatly benefits regression-based motif enrichment analysis and provide detailed documentation that can serve as a reference for researchers inferring upstream regulators from genomic data. AvailabilitySource code is available on GitHub at https://github.com/lapohosorsolya/AmalgaMo.

genomics↗

Refining sequence-to-expression modelling with chromatin accessibility

MotivationSequence-to-expression models typically do not consider chromatin accessibility, a major factor limiting gene regulation. We hypothesized that supplying accessibility as an input feature would allow a sequenceto-expression model to focus on important open regions of the genome. ResultsWe found that the performance of such an augmented model was significantly better than that of sequence-only or accessibility-only models with similar architectures. Specifically, its ability to predict the expression of highly variable genes and gene expression in other cell types improved, and higher attribution scores in the input DNA sequences of the augmented model conformed to accessibility, enabling the learning of cell type-specific sequence patterns. Additionally, we show that fine-tuning a pre-trained sequence-only model with both sequence and accessibility can boost performance further and highlight the importance of sequencing depth in sequence-toexpression prediction. Availability and ImplementationSource code is available on GitHub at https://github.com/lapohosorsolya/accessible_seq2exp.

bioinformatics↗