Fengzhu Sun | Search bioRxiv

bioRxiv2020

CRAFT: Compact genome Representation towards largescale Alignment-Free daTabase

MotivationRapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption. ResultsWe report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing (HTS) data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102 - 104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures. AvailabilityCRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https://github.com/jiaxingbai/CRAFT. Contactwangying@xmu.edu.cn; fsun@usc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

Brain aging trajectories among those of the same chronological age can vary significantly. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age, with imaging data. Recently, convolutional neural networks (CNNs) have shown the potential to more accurately predict brain age. We trained a CNN on 16,998 UK Biobank subjects, and in validation tests found that it was more accurate than a regression model for predicting brain age. A genome-wide association study was conducted on CNN-derived predicted brain age whereby we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging. One locus has been previously reported to be associated with brain aging. The three other loci were novel. Our results suggest that a more accurate brain age prediction enables the discovery of novel genetic associations, which may be valuable for identifying other lifestyle factors associated with brain aging.Competing Interest StatementThe authors have declared no competing interest.

neuroscience↗