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Номер журнала: 2019.3

Заголовок статьи: GPU-based algorithm for context analysis of the core promoter region of mouse genes differently expressed in hypothalamic energy-sensing neurons in response to weight-loss

Резюме

De novo motif discovery in the regulatory regions of eukaryotic genes poses a complex computational problem due to the large size of datasets and huge diversity of motifs. This article suggests a new algorithm for measuring the presence of degenerate oligonucleotide motifs written as a 15-letter IUPAC code in a DNA dataset. Its performance has increased 10 times compared with the previous one. There are three key ingredients of this method. The first one is the prefix trees. The second is the relation between motif prefixes and hash ranges in the analyzed nucleotide sequences. The third consists of applying CUDA framework to the massive parallelization allowing to use affordable graphic accelerators.
The context analysis of promoter regions of mouse genes differently expressed (DEG) in hypothalamic AGRP neurons after food deprivation was performed with the proposed method. When an animal is deprived of food, AGRP neurons produce molecules that in-crease appetite and stimulate weight gain. The understanding of how AGRP neurons re-spond to weight loss is important to confront the obesity. Nowadays, this hereditary dis-ease lacks methods of treatment and intervention strategies which would be both safe and efficient in the long term. The performed analysis revealed relevant oligonucleotide motifs that were associated with starvation.

Авторы

A. Bocharnikov, E. Ignatieva, O. Vishenvskiy

Ключевые слова

oligonucleotide motif, GPGPU, obesity

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