Biology subjects

Reijnders, M. J.

Publications and source records attributed to Reijnders, M. J..

6 recordsLinked to original sources

bioRxiv2021

Functional constraints on insect immune system components govern their evolutionary trajectories

The role of constraints in shaping evolutionary outcomes is often investigated in the contexts of developmental biology and population genetics, in terms of the capacity to generate new variants as well as how selection either limits or promotes consequent phenotypic change. Comparative genomics also recognises the role of constraints, in terms of shaping the evolution of gene and genome architectures, sequence evolutionary rates, and gene gains and losses, as well as on molecular phenotypes. Characterising patterns of genomic change where putative functions and interactions of system components are relatively well-described offers opportunities to explore whether genes with similar or analogous roles exhibit similar evolutionary trajectories, possibly governed by common constraints. Using insect innate immunity as our study system, we hypothesise that quantitative characterisation of gene evolutionary histories can define distinct dynamics associated with different functional roles. We develop metrics that quantify gene evolutionary histories, employ these to characterise evolutionary features of immune gene repertoires, and explore relationships between gene family evolutionary profiles and their roles in immunity to understand how different constraints may relate to distinct dynamics. We identified three main axes of evolutionary trajectories characterised by gene duplication and synteny, maintenance/stability and sequence conservation, and loss and sequence divergence, highlighting similar and contrasting patterns across these axes amongst subsets of immune genes. Our results indicate that where and how genes participate in immune responses limit the range of possible evolutionary scenarios they exhibit. Comparative genomics approaches therefore offer opportunities to characterise how functional constraints on different components of biological systems govern their evolutionary trajectories.

evolutionary biology↗

bioRxiv2020

Summary visualisations of Gene Ontology terms with GO-Figure!

The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large- scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualisations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualisation tools and resources there is a lack of standalone open-source software solutions that facilitate systematic comparisons of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together GO terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar GO terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualisations of lists of GO terms, designed to support exploratory data analyses and multiple dataset comparisons.

bioinformatics↗

bioRxiv2020

Wei2GO: weighted sequence similarity-based protein function prediction

BackgroundProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases. MethodsThis paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations. ResultsWei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes. AvailabilityWei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO

bioinformatics↗

bioRxiv2019

CrowdGO: a wisdom of the crowd-based Gene Ontology annotation tool

BackgroundCharacterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. ResultsTo exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the communitys best performing individual methods. ConclusionCrowdGO offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations. Availability and ImplementationCrowdGO is implemented in Python3, and is freely available from https://gitlab.com/mreijnders/CrowdGO, with a Snakemake workflow and pre-trained models.

bioinformatics↗

bioRxiv2018

Leveraging evolutionary relationships to improve Anopheles genome assemblies

While new sequencing technologies have lowered financial barriers to whole genome sequencing, resulting assemblies are often fragmented and far from finished. Subsequent improvements towards chromosomal-level status can be achieved by both experimental and computational approaches. Requiring only annotated assemblies and gene orthology data, comparative genomics approaches that aim to capture evolutionary signals to predict scaffold neighbours (adjacencies) offer potentially substantive improvements without the costs associated with experimental scaffolding or re-sequencing. We leverage the combined detection power of three such gene synteny-based methods applied to 21 Anopheles mosquito assemblies with variable contiguity levels to produce consensus sets of scaffold adjacency predictions. Three complementary validations were performed on subsets of assemblies with additional supporting data: six with physical mapping data; 13 with paired-end RNA sequencing (RNAseq) data; and three with new assemblies based on re-scaffolding or incorporating Pacific Biosciences (PacBio) sequencing data. Improved assemblies were built by integrating the consensus adjacency predictions with supporting experimental data, resulting in 20 new reference assemblies with improved contiguities. Combined with physical mapping data for six anophelines, chromosomal positioning of scaffolds improved assembly anchoring by 47% for A. funestus and 38% A. stephensi. Reconciling an A. funestus PacBio assembly with synteny-based and RNAseq-based adjacencies and physical mapping data resulted in a new 81.5% chromosomally mapped reference assembly and cytogenetic photomap. While complementary experimental data are clearly key to achieving high-quality chromosomal-level assemblies, our assessments and validations of gene synteny-based computational methods highlight the utility of applying comparative genomics approaches to improve community genomic resources.

genomics↗

bioRxiv2018

Genome sequence of the wheat stem sawfly, Cephus cinctus, a primitive hymenopteran and wheat pest, illuminates evolution of hymenopteran chemoreceptors

The wheat stem sawfly, Cephus cinctus, is a major pest of wheat and key ecological player in the grasslands of western North America. It also represents a distinctive lineage of sawflies that appeared early during the hymenopteran radiation, but after the clade of Eusymphyta sawflies that is the sister lineage of all other Hymenoptera. We present a high-quality draft genome assembly of 162 Mbp in 1,976 scaffolds with a scaffold N50 of 622 kbp. Automated gene annotation identified 11,210 protein-coding gene models and 1,307 non-coding RNA models. Thirteen percent of the assembly consists of ~58,000 transposable elements partitioned equally between Class-I and Class-II elements. Orthology analysis reveals that 86% of Cephus proteins have identifiable orthologs in other insects. Phylogenomic analysis of conserved subsets of these proteins supports the placement of the Cephidae between the Eusymphyta and the parasitic woodwasp superfamily Orussoidea. Manual annotation and phylogenetic analysis of families of odorant, gustatory, and ionotropic receptors, plus odorant binding proteins, shows that Cephus has representatives for most conserved and expanded gene lineages in the Apocrita (wasps, ants, and bees). Cephus has also maintained several insect gene lineages that have been lost from the Apocrita, most prominently the carbon dioxide receptor subfamily. Furthermore, Cephus encodes a few small lineage-specific chemoreceptor gene family expansions that might be involved in adaptations to new grasses including wheat. These comparative analyses identify gene family members likely to have been present in the hymenopteran ancestor and provide a new perspective on the evolution of the chemosensory gene repertoire.

genomics↗