Eichten, S. R. | Search bioRxiv

bioRxiv2018

Population structure of the Brachypodium species complex and genome wide association of agronomic traits in response to climate.

The development of model systems requires a detailed assessment of standing genetic variation across natural populations. The Brachypodium species complex has been promoted as a plant model for grass genomics with translational to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands of Brachypodium accessions from around the globe were collected and sequenced using genotyping by sequencing (GBS). Overall, 1,897 samples were classified into two diploid or allopolyploid species and then further grouped into distinct inbred genotypes. A core set of diverse B. distachyon diploid lines were selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22 and 47 QTLs were identified for flowering time, early vigour and energy traits, respectively. Overall, the results highlight the genomic structure of the Brachypodium species complex and allow powerful complex trait dissection within this new grass model species.

plant biology↗

bioRxiv2017

HOME: A histogram based machine learning approach for effective identification of differentially methylated regions

BackgroundThe development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.\n\nResultsWe present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organisms dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.\n\nConclusionHOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

bioinformatics↗