Biology subjects

Denton, A. K.

Publications and source records attributed to Denton, A. K..

6 recordsLinked to original sources

bioRxiv2023

Predmoter - Cross-species prediction of plant promoter and enhancer regions

MotivationThe identification of cis-regulatory elements (CREs) is crucial for the analysis of gene regulatory networks in plants. Several next generation sequencing (NGS)-based methods were developed to identify CREs. However, these methods can be time-consuming and costly. They also involve creating sequencing libraries for the entire genome. Since many research efforts only focus on specific genomic loci, this presents a considerable expenditure. Computational prediction of the outputs of specialized NGS methods to analyze CREs, like Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq), would significantly cut costs and time investment. Yet, no such method is available to date. ResultsWe present Predmoter, a deep neural network able to predict base-wise ATAC-seq and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We evaluated our model on two plant genomes, the genome of the dicot Arabidopsis thaliana and of the monocot Oryza sativa. We trained our models on 10 species with publicly available ATAC-seq data and 15 species with ChIP-seq data. Our best models showed accurate predictions in peak positions and the overall pattern of peaks for ATAC- and Histone H3 trimethylated at lysine 4 (H3K4me3) ChIP-seq. Annotating putatively accessible chromatin regions provides valuable input for the identification of CREs. In conjunction with other in silico data, such as predicted binding affinities for transcription factors (TFs), this can significantly narrow down the search space to a manageable number of experimentally verifiable DNA-protein interaction pairs. Availability and ImplementationThe source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter along with documentation for installation and usage. Predmoter uses a single-command inference, Predmoter.py, for both training and prediction. Predmoter takes a fasta file as input and outputs an h5 file and optionally bigWig and bedGraph files. HighlightPredmoter will help identifying CREs and so gaining further insight into gene regulatory networks in plants.

bioinformatics↗

bioRxiv2023

The genome of Talinum fruticosum

Research on crassulacean acid metabolism (CAM) has in recent years focused on obligate CAM species, such as Kalanchoe fedtschenkoi and pineapple (Ananas comosus). To fully understand the plasticity of the CAM pathway, its evolutionary trajectory and regulation, genomic resources of additional species, including facultative CAM species are desirable. To this end, we sequenced the genome and full-length transcripts (Iso-Seq) of the facultative CAM dicot Talinum fruticosum. The provided resources may aid in CAM engineering as an approach to improving crop water-use efficiency.

plant biology↗

bioRxiv2023

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗

bioRxiv2022

Transposable elements contribute to the establishment of the glycine shuttle in Brassicaceae species

C3-C4 intermediate photosynthesis has evolved at least five times convergently in the Brassicaceae, despite this family lacking bona fide C4 species. The establishment of this carbon concentrating mechanism is known to require a complex suite of ultrastructural modifications as well as changes in spatial expression patterns, which are both thought to be underpinned by a reconfiguration of existing gene-regulatory networks. However, to date, the mechanisms which underpin the reconfiguration of these gene networks are largely unknown. In this study, we used a pan-genomic association approach to identify genomic features that could confer differential gene expression toward the C3-C4 intermediate state by analysing eight C3 species and seven C3-C4 species from five independent origins in the Brassicaceae. We found a strong correlation between transposable element (TE) insertions in cis-regulatory regions and the C3-C4 intermediacy. Specifically, our study revealed 113 gene models in which presence of a TE within a gene correlates with C3-C4 intermediate photosynthesis. In this set, genes involved in the photorespiratory glycine shuttle are enriched, including the glycine decarboxylase P-protein whose expression domain undergoes a spatial shift during the transition to C3-C4 photosynthesis. When further interrogating this gene, we discovered independent TE insertions in its upstream region which we conclude to be responsible for causing the spatial shift in GLDP1 gene expression. Our findings hint at a pivotal role of TEs in the evolution of C3-C4 intermediacy, especially in mediating differential spatial gene expression.

plant biology↗

bioRxiv2020

A Solanum lycopersicoides reference genome facilitates biological discovery in tomato

Wild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, including Solanum lycopersicoides, have been crossed to S. lycopersicum for the development of ordered introgression lines (ILs). Despite the utility of these wild relatives and their associated ILs, limited finished genomes have been produced to aid genetic and genomic studies. We have generated a chromosome-scale genome assembly for Solanum lycopersicoides LA2951 using PacBio sequencing, Illumina, and Hi-C. We identified 37,938 genes based on Illumina and Isoseq and compared gene function to the available cultivated tomato genome resources, in addition to mapping the boundaries of the S. lycopersicoides introgressions in a set of cv. VF36 x LA2951 introgression lines (IL). The genome sequence and IL map will support the development of S. lycopersicoides as a model for studying fruit nutrient/quality, pathogen resistance, and environmental stress tolerance traits that we have identified in the IL population and are known to exist in S. lycopersicoides.

genomics↗

bioRxiv2017

LOGAN: A framework for LOssless Graph-based ANalysis of high throughput sequence data

Recent massive growth in the production of sequencing data necessitates matching improve-ments in bioinformatics tools to effectively utilize it. Existing tools suffer from limitations in both scalability and applicability which are inherent to their underlying algorithms and data structures. We identify the key requirements for the ideal data structure for sequence analy-ses: it should be informationally lossless, locally updatable, and memory efficient; requirements which are not met by data structures underlying the major assembly strategies Overlap Layout Consensus and De Bruijn Graphs. We therefore propose a new data structure, the LOGAN graph, which is based on a memory efficient Sparse De Bruijn Graph with routing information. Innovations in storing routing information and careful implementation allow sequence datasets for Escherichia coli (4.6Mbp, 117x coverage), Arabidopsis thaliana (135Mbp, 17.5x coverage) and Solanum pennellii (1.2Gbp, 47x coverage) to be loaded into memory on a desktop computer in seconds, minutes, and hours respectively. Memory consumption is competitive with state of the art alternatives, while losslessly representing the reads in an indexed and updatable form. Both Second and Third Generation Sequencing reads are supported. Thus, the LOGAN graph is positioned to be the backbone for major breakthroughs in sequence analysis such as integrated hybrid assembly, assembly of exceptionally large and repetitive genomes, as well as assembly and representation of pan-genomes.

bioinformatics↗