Biology subjects
Publications and source records attributed to Kathiresan, N..
A high-performance computing genome variant calling workflow was designed to run GATK on HPC platforms. This workflow efficiently called an average of 27.3 M, 32.6 M, 168.9 M, and 16.2 M SNPs for rice, sorghum, maize, and soybean, respectively, on the most recently released high-quality reference sequences. Analysis of a rice pan-genome reference panel revealed 2.1 M novel SNPs that have yet to be publicly released.
Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Utilizing 16 high-quality genomes that represent the subpopulation structure of Asian rice (O. sativa), plus the genomes of two close relatives (O. rufipogon and O. punctata), we built a pan-genome inversion index of 1,054 non-redundant inversions that span an average of [~] 14% of the O. sativa cv. Nipponbare reference genome sequence. Using this index we estimated an inversion rate of 1,100 inversions per million years in Asian rice, which is 37 to 73 times higher than previously estimated for plants. Detailed analyses of these inversions showed evidence of their effects on gene regulation, recombination rate, linkage disequilibrium and agronomic trait performance. Our study uncovers the prevalence and scale of large inversions ([≥] 100 kb) across the pan-genome of Asian rice, and hints at their largely unexplored role in functional biology and crop performance.
MotivationStructural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. ResultsWe developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. Availabilityhttps://github.com/bio-ontology-research-group/DeepSVP Contactrobert.hoehndorf@kaust.edu.sa
Sustainable food production in the context of climate change necessitates diversification of agriculture and a more efficient utilization of plant genetic resources. Fonio millet (Digitaria exilis) is an orphan African cereal crop with a great potential for dryland agriculture. Here, we established high-quality genomic resources to facilitate fonio improvement through molecular breeding. These include a chromosome-scale reference assembly and deep re-sequencing of 183 cultivated and wild Digitaria accessions, enabling insights into genetic diversity, population structure, and domestication. Fonio diversity is shaped by climatic, geographic, and ethnolinguistic factors. Two genes associated with seed size and shattering showed signatures of selection. Most known domestication genes from other cereal models however have not experienced strong selection in fonio, providing direct targets to rapidly improve this crop for agriculture in hot and dry environments.
The success of deep learning has been shown in various fields including computer vision, speech recognition, natural language processing and bioinformatics. The advance of Deep Learning in Computer Vision has been an important source of inspiration for other research fields. The objective of this work is to adapt known deep learning models borrowed from computer vision such as VGGNet, Resnet and AlexNet for the classification of biological sequences. In particular, we are interested by the task of splice site identification based on raw DNA sequences. We focus on the role of model architecture depth on model training and classification performance.\n\nWe show that deep learning models outperform traditional classification methods (SVM, Random Forests, and Logistic Regression) for large training sets of raw DNA sequences. Three model families are analyzed in this work namely VGGNet, AlexNet and ResNet. Three depth levels are defined for each model family. The models are benchmarked using the following metrics: Area Under ROC curve (AUC), Number of model parameters, number of floating operations. Our extensive experimental evaluation show that shallow architectures have an overall better performance than deep models. We introduced a shallow version of ResNet, named S-ResNet. We show that it gives a good trade-off between model complexity and classification performance.\n\nAuthor summaryDeep Learning has been widely applied to various fields in research and industry. It has been also succesfully applied to genomics and in particular to splice site identification. We are interested in the use of advanced neural networks borrowed from computer vision. We explored well-known models and their usability for the problem of splice site identification from raw sequences. Our extensive experimental analysis shows that shallow models outperform deep models. We introduce a new model called S-ResNet, which gives a good trade-off between computational complexity and classification accuracy.