Biology subjects

Wegrzyn, J.

Publications and source records attributed to Wegrzyn, J..

3 recordsLinked to original sources

bioRxiv2022

Welcome to the big leaves: best practices for improving genome annotation in non-model plant genomes

* Premise of the studyRobust standards to evaluate quality and completeness are lacking for eukaryotic structural genome annotation. Genome annotation software is developed with model organisms and does not typically include benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. Plant genomes are particularly challenging with their large genome sizes, abundant transposable elements (TEs), and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and approach on protein-coding gene prediction. * MethodsThe impact of repeat masking, long-read, and short-read inputs, de novo, and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. Annotations were benchmarked for structural traits and sequence similarity. * ResultsBenchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long-reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended. * DiscussionWhile annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation, and present a more robust set of metrics to evaluate the resulting predictions.

bioinformatics↗

bioRxiv2019

Comparative Genomics of Six Juglans Species Reveals Patterns of Disease-associated Gene Family Contractions.

Juglans (walnuts), the most speciose genus in the walnut family (Juglandaceae) represents most of the familys commercially valuable fruit and wood-producing trees and includes several species used as rootstock in agriculture for their resistance to various abiotic and biotic stressors. We present the full structural and functional genome annotations of six Juglans species and one outgroup within Juglandaceae (Juglans regia, J. cathayensis, J. hindsii, J. microcarpa, J. nigra, J. sigillata and Pterocarya stenoptera) produced using BRAKER2 semi-unsupervised gene prediction pipeline and additional in-house developed tools. For each annotation, gene predictors were trained using 19 tissue-specific J. regia transcriptomes aligned to the genomes. Additional functional evidence and filters were applied to multiexonic and monoexonic putative genes to yield between 27,000 and 44,000 high-confidence gene models per species. Comparison of gene models to the BUSCO embryophyta dataset suggested that, on average, genome annotation completeness was 89.6%. We utilized these high quality annotations to assess gene family evolution within Juglans and among Juglans and selected Eurosid species, which revealed significant contractions in several gene families in J. hindsii including disease resistance-related Wall-associated Kinase (WAK) and Catharanthus roseus Receptor-like Kinase (CrRLK1L) and others involved in abiotic stress response. Finally, we confirmed an ancient whole genome duplication that took place in a common ancestor of Juglandaceae using site substitution comparative analysis. SIGNIFICANCEHigh-quality full genome annotations for six species of walnut (Juglans) and a wingnut (Pterocarya) outgroup were constructed using semi-unsupervised gene prediction followed by gene model filtering and functional characterization. These annotations represent the most comprehensive set for any hardwood genus to date. Comparative analyses based on the gene models uncovered rapid evolution in multiple gene families related to disease-response and a whole genome duplication in a Juglandaceae common ancestor.

genomics↗

bioRxiv2018

gFACs: Filtering, analysis, and conversion to unify genome annotations across alignment and gene prediction frameworks.

Published genome annotations are filled with erroneous gene models that represent issues associated with frame, start side identification, splice sites, and related structural features. The source of these inconsistencies can often be traced to translated text file formats designed to describe long read alignments and predicted gene structures. The majority of gene prediction frameworks do not provide downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. In addition, these frameworks lack consideration for functional attributes, such as the presence or absence of protein domains which can be used for gene model validation. To provide oversight to the increasing number of published genome annotations, we present gFACs as a software package to filter, analyze, and convert predicted gene models and alignments. gFACs operates across a wide range of alignment, analysis, and gene prediction software inputs with a flexible framework for defining gene models with reliable structural and functional attributes. gFACs supports common downstream applications, including genome browsers and generates extensive details on the filtering process, including distributions that can be visualized to further assess the proposed gene space.

bioinformatics↗