Tuesday, 10 December 2019

Edwards Lab at GIW/ABACBS2019

The Edwards Lab and close affiliates have five posters at GIW-ABACBS2019 this year. Come and check them out during the two poster sesstions:


Poster #26: Comparative performance of long-read whole genome assembly tools in diploid eukaryotes

Åsa Pérez-Bercoff, Paris Thompson & Richard J. Edwards [PDF]

As long-read (single molecule) sequencing from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) is getting more common, more long-read assemblers are emerging. Whilst a few benchmarking studies have been performed, there is no clear “best” assembly tool, and the choice is often a combination of compute resource availability and anecdotal reports of relative performance in similar organisms. Here, we have de novo assembled PacBio long-read sequencing data from 13 diploid Saccharomyces cerevisiae (baker’s yeast) strains (11 diploid, 1 haploid and 1 tetraploid) using four different assemblers: Canu v1.8, Flye v2.4.2, WTDBG2 (a.k.a. Redbean) v2.4 and Ra v20181211. Assembly performance statistics have been generated by comparing assemblies to the reference yeast genome (SGD R64.2.1) using QUAST v5.0.2, and rating each assembly for accuracy, coverage, and contiguity.

The latest generation of assembly tools decide how to process reads based on a genome size parameter. For heterozygous diploid organisms, it is not clear whether this should be the haploid or diploid genome size, or something in-between based on heterozygosity. In this study, we used three genome size settings: haploid (13.1Mb), diploid (26.2Mb) and half-way in-between (19.65Mb). This will enable us to establish how the genome size parameter influences the quality of the resulting genome assembly.


Poster #33: Estimating genome size using long read depth profiles and single copy regions of draft genome assemblies

Timothy G Amos, Ziying Zhang & Richard J Edwards [PDF]

Estimating the size of a eukaryote genome is a fundamental task in genome assembly. As well as informing decisions on sequencing technology and depth, greater accuracy in genome size prediction can assist in assessing the completeness and duplication of a genome assembly. Genome sizes can be estimated through both experimental and genome sequencing approaches. Experimental methods include densitometry, flow cytometry analysis of stained nuclei and quantitative PCR (qPCR) of single copy genes. Genome sequencing approaches include short-read k-mer distributions. However, these methods can give variable results, and are prone to inaccurate predictions for genomes with abundant repetitive sequences.

Here, we show that the read depth of long reads in a draft genome can be used to provide a relatively accurate estimate of genome size across various model organisms. In general, diploid genome assemblies will consist of regions of haploid, diploid and incorrect (or multi-copy organelle) depth. Repeats and assembly errors are likely to be highly variable in terms of genome vs assembly copy number and, therefore, coverage. The modal read depth of conserved single copy orthologues should therefore approximate the sequencing depth of the input data, which can be extrapolated to estimate genome size. We found that our method can provide comparable, if not more accurate, estimates than short-read k-mer distributions.


Poster #92: Using genomics to reveal drivers of invasion success

Katarina Stuart, Lee Ann Rollins, William Sherwin, Richard Edwards, Natalie Hofmeister & Yuanyuan Cheng [PDF]

Invasive species are a global concern due to their negative impacts on the economy and local ecosystems. However, well-documented invasions provide a useful system in which to pose biologically interesting questions regarding short time scale evolution. Answering these questions will further our knowledge of evolutionary mechanisms, as well as inform specific management strategies for the invasive population. The European starling (Sturnus vulgaris) is a global pest that was introduced into Australia’s south-eastern states in the 1860’s and has since greatly expanded its range. Previous research on multiple introduced starling populations has demonstrated that their morphology has undergone subtle shifts following colonisation. My research applies a range of sequencing techniques to investigate genomic variation across Australia’s starling population. We are combining long-, short- and linked-read whole genome sequencing to assemble a high-quality starling reference genome. This genome will be annotated using Iso-seq long-read (PacBio) whole transcriptome sequencing in order to properly identify putatively evolving loci. Population sequencing data (whole genome and DArTSeq) will be mapped onto this reference to identify functional SNPs and reveal potential drivers of the rapid phenotypic divergences across the Australian population.


Poster #93: Advancing genomic resources for myrtle rust research and management

Stephanie Chen, Jason Bragg & Richard Edwards [PDF]

Myrtle rust is a plant disease caused by an invasive fungal pathogen (Austropuccinia psidii) first detected in Australia in 2010. Over 350 native species from the family Myrtaceae, which includes eucalypts, paperbarks, tea-trees, and lillipillies, are known hosts. Detailed genetic information needed for an effective and coordinated response encompassing conservation management is lacking. We performed de novo genome assembly and annotation for two species which exhibit a spectrum of resistance – Rhodamnia argentea (malletwood) and Syzygium oleosum (blue lilly pilly). Reference genomes have been generated using 10X linked reads, and are being supplemented with long reads and a hybrid assembly approach. These genomes will complement reduced representation sequencing (DArTseq) and rust resistance assays from different genotypes across the landscape. Together, these data will facilitate the characterisation of genetic structure and rust resistance across space as well as within and among species. This research is crucial for the management of at-risk species through optimising methods of improving disease resistance and adaptation to future climates in addition to increasing our understanding of myrtle rust which is a pressing concern to native biodiversity.


Poster #168: BUSCOMP: BUSCO Compilation and Comparison for Assessing Completeness in Multiple Genome Assemblies

Richard J. Edwards [PDF]

Advances in DNA sequencing technology and bioinformatics tools have placed de novo genome assembly of complex organisms firmly in the domain of individual labs and small consortia. Nevertheless, the assemblies produced are often fragmented and incomplete. Optimal assembly depends on the size, repeat landscape, ploidy and heterozygosity of the genome, which are often unknown. It is therefore common practice to try multiple strategies, and there is a bottleneck in assessing and comparing assemblies.

BUSCO [1] is a powerful and popular tool that estimates genome completeness using gene prediction and curated models of single-copy protein orthologues. However, results can be counterintuitive: adding/removing scaffolds can alter BUSCO predictions elsewhere in the assembly, while low sequence quality may reduce “completeness” scores and miss genes that are present in the assembly [2].

BUSCOMP (BUSCO Compilation and Comparison) complements BUSCO to identify/overcome these issues. BUSCOMP compiles a non-redundant set of the highest-scoring single-copy BUSCO complete sequences, rapidly searches these against assemblies, and robustly re-rates genes as Complete (Single/Duplicated), Fragmented/Partial or Missing. On test data from three organisms (yeast, cane toad and mainland tiger snake), BUSCOMP (1) gives consistent results when re-running the same assembly, (2) is not affected by adding or removing non-BUSCO-containing scaffolds, and (3) is minimally affected by assembly quality. This makes BUSCOMP ideal to run alongside BUSCO when trying to compare and rank genome assemblies, even in the absence of error-correction.

Available at: >https://github.com/slimsuite/buscomp>.

  1. Simão FA et al. (2015) Bioinformatics 31:3210–3212
  2. Edwards RJ et al. (2018) GigaScience 7:giy095

Tuesday, 9 July 2019

We’ve been funded! ARC Linkage - Optimising plant populations for ecological restoration and resilience

We are very happy to report another successful ARC Linkage grant application.

LP180100721: Optimising plant populations for ecological restoration and resilience

Dr Richard Edwards; Professor Justin Borevitz; Dr Jason Bragg; Dr Maurizio Rossetto; Dr Brett Summerell; Dr Marlien van der Merwe

When choosing individual plants for restoration populations, there is potentially a trade-off between maximising genetic diversity (‘adaptability’) and selection for desirable properties (‘adaptation’). This project aims to develop pioneering methods to quantify this trade-off, and facilitate the design of optimised populations, with a focus on two Australian rainforest trees that are being impacted by myrtle rust infection: Rhodamnia argentea and Rhodamnia rubescens. By studying the genetic variation in each species, and how this relates to myrtle rust resistance and climate, this project aims to design populations that are genetically diverse, maximally resistant to myrtle rust, and adapted to future climate.

We are collaborating with the Royal Botanic Garden and Domain Trust to apply genomics to challenges of conservation for rainforest trees in the face of climate change and invasive pathogens.

There will be job and studentship opportunities associated with this grant, so watch this space (or get in touch)!

Friday, 5 July 2019

Research snapshot - July 2019

One of the most important, interesting and challenging questions in biology is how new traits evolve at the molecular level. My lab employs sequence analysis techniques to interrogate protein and DNA sequences for the signals left behind by evolution. We are a bioinformatics lab but like to incorporate bench data through collaboration wherever possible.

Main Research

The core research in the lab is broadly divided into two main themes:

1. Evolutionary Genomics.

Since moving to UNSW, a major focus of the lab has been the exploitation of genomic and post-genomic data to understand biological function and adaptation to novel environments. We work closely with the Ramaciotti Centre for Genomics and are involved in numerous de novo whole genome sequencing and assembly projects, using short read (Illumina), long read (PacBio & Nanopore) and linked read (10x Chromium) sequencing. The biggest of these is leading the bioinformatics and assembly effort in a consortium to sequence the cane toad genome, and leading the BABS Genome project to sequence two iconic Australian snakes. We are a member of the Oz Mammals Genomics initiative, assisting with the sequencing and assembly of Australia’s unique marsupial fauna. In 2018, we were selected as part of a team to sequence the Waratah genome as part of the pilot phase for the new Genomics of Australian Plants initiative.

We enjoy bringing our bioinformatics to bear on a variety of collaborative research projects. Most notably, we have an ARC Linkage Grant with Microbiogen Pty Ltd to understand how a strain of Saccharomyces cerevisiae has evolved to efficiently use xylose as a sole carbon source: something vital for second-generation biofuel production that wild yeast cannot do. We are combining comparative genomics, evolutionary genetics, RNA-Seq transcriptomics, and competition assays to understand how the novel metabolism evolved. Through deep Illumina resequencing of evolving populations, and assembling reliable complete genomes of the founding ancestors, the ultimate goal is to trace how mutations have interacted with existing genetic variation during adaptive evolution. More recently, we have received an ARC Linkage Grant with the Royal Botanic Gardens and Domain Trust, to apply genomics approaches to the challenges of rainforest tree conservation in the face of climate change and invasive pathogens. We are also collaborating with industrial and academic partners to de novo sequence, assemble, annotate and interrogate the genomes of a selection of microbes with interesting metabolic abilities.

2. Short Linear Motifs (SLiMs).

Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues. Their small size enables high functional density and evolutionary plasticity, making them frequent products of convergent “ex nihilo” evolution. It also makes them challenging to identify, both experimentally and computationally. A major focus of the lab is the computational prediction of SLiMs from protein sequences. Of particular interest is the study of rapidly evolving pathogens that exploit host SLiMs and use them to hijack cellular processes. Methods are made available through the SLiMSuite bioinformatics package and webservers.

Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues. Their small size enables high functional density and evolutionary plasticity, making them frequent products of convergent “ex nihilo” evolution. It also makes them challenging to identify, both experimentally and computationally.

A major focus of the lab is the computational prediction of SLiMs from protein sequences. This research originated with Rich’s postdoctoral research, during which he developed a sequence analysis methods for the rational design of biologically active short peptides. He subsequently developed SLiMDisc, one of the first algorithms for successfully predicting novel SLiMs from sequence data - and coined the term “SLiM” into the bargain. This subsequently lead to the development of SLiMFinder, the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions. SLiMFinder greatly increased the reliability of predictions. SLiMFinder has since spawned a number of motif discovery tools and webservers and is still arguably the most successful SLiM prediction tool on benchmarking data. Methods are made available through the SLiMSuite bioinformatics package and webservers.

Current research is looking to develop these SLiM prediction tools further and apply them to important biological questions. Of particular interest is the molecular mimicry employed by viruses to interact with host proteins and the role of SLiMs in other diseases, such as cancer. Other work is concerned with the evolutionary dynamics of SLiMs within protein interaction networks.

OTHER RESEARCH PROJECTS

In addition to the main research in the lab, the lab has a number of interdisciplinary collaborative projects applying bioinformatics tools and molecular evolution theory to experimental biology, often using large genomic, transcriptomic and/or proteomic datasets. These projects often involve the development of bespoke bioinformatics pipelines and a number of open source bioinformatics tools have been generated as a result. Please see the Publications and Lab software pages for more detail, or get in touch if something catches your eye and you want to find out more. We frequently have small collaborations and/or undergraduate student research projects. Many of these are “on hold” waiting for the right person, or sometimes data, to come along. If you think that you have what it needs, get in touch!

Tuesday, 2 July 2019

#GSA2019 - BUSCOMP: BUSCO Compilation and Comparison for Assessing Completeness in Multiple Genome Assemblies

Richard J. Edwards

If you are at the Genetics Society of Australasia Conference 2019, then come and hear me talk at 11:30 in Symposium 4B – Genomics & Bioinformatics (1). If you cannot make it, or loved the talk so much you want to look at it again, the slides are available on F1000Research:

  • Edwards RJ (2019): BUSCOMP: BUSCO compilation and comparison – Assessing completeness in multiple genome assemblies [version 1; not peer reviewed]. F1000Research 8:995 (slides)
    (doi: 10.7490/f1000research.1116972.1)

Abstract

Advances in DNA sequencing technology and free availability of bioinformatics tools have placed de novo genome assembly of complex organisms firmly in the domain of individual labs and small consortia. Nevertheless, the assemblies produced are often fragmented and incomplete. Optimal assembly depends on the size, repeat landscape, ploidy and heterozygosity of the genome, which are often unknown. It is therefore common practice to try multiple strategies, and there is a bottleneck in assessing and comparing assemblies.

BUSCO [1] is a powerful and popular tool that estimates genome completeness using gene prediction and curated models of single-copy protein orthologues. BUSCO assessments combine genome completeness, contiguity, and accuracy to rate genes as “Complete (Single Copy)”, “Duplicated”, “Fragmented” or “Missing”. However, results can be counterintuitive and lack robustness when comparing multiple assemblies of the same genome. Adding/removing scaffolds can alter the BUSCO genes returned by the rest of the assembly [2], while low sequence quality may reduce “completeness” scores and miss genes that are present in the assembly [3].

BUSCOMP (BUSCO Compilation and Comparison) is designed to complement BUSCO and identify/overcome these issues. BUSCOMP first compiles a non-redundant maximal set of the highest-scoring single-copy complete sequences for as many BUSCO genes as possible. These are then searched against assemblies using Minimap2 [4], converted into global alignment statistics, and used to robustly re-rate genes as Complete (Single/Duplicated), Fragmented/Partial or Missing. On test data from three organisms (yeast, cane toad and mainland tiger snake), BUSCOMP (1) gives consistent results when re-running the same assembly, (2) is not affected by adding or removing non-BUSCO-containing scaffolds, and (3) is minimally affected by assembly quality. This makes BUSCOMP ideal to run alongside BUSCO when trying to compare and rank genome assemblies, even in the absence of error-correction.

BUSCOMP is freely available at https://github.com/slimsuite/buscomp under a GNU GPL v3 license.

  1. Simão FA et al. (2015) Bioinformatics 31:3210–3212
  2. Edwards RJ et al. (2018) F1000Research 7:753
  3. Edwards RJ et al. (2018) GigaScience 7:giy095
  4. Li H (2018) Bioinformatics 34:3094-3100

Thursday, 6 June 2019

PhD available: Developing genomic resources to advance the molecular ecology of invasions

Expressions of interest are now open for a Scientia PhD Scholarship, in collaboration between the Edwards Lab, Lee Ann Rollins, and Marc Wilkins:

Developing genomic resources to advance the molecular ecology of invasions

These are exciting four-year scholarships to start in 2020 with full fees covered, a generous stipend, and career development funds. Please click on the toad or get in touch if you want to know more!

Closing date: 12 July 2019.
Location: University of New South Wales, Sydney, Australia

PROJECT DESCRIPTION

Invasive species pose a major challenge to biodiversity worldwide but also provide the unique opportunity to study evolution in action. Rapid changes are often associated with invaders’ introduction to novel environments. Understanding how molecular mechanisms drive these changes enables the creation of innovative solutions to controlling invasions and managing native species’ response to climatic change. The iconic Australian cane toad invasion is one of the best studied globally and is an emerging model for invasion genomics. This project will use whole genome sequencing, novel bioinformatic approaches and proteomics to identify molecular drivers of invasion success.

IDEAL CANDIDATE

We seek a highly motivated, curiosity-driven student with an interest in evolutionary biology and bioinformatics, who would like to understand why invasive species flourish. Ideally, candidates will have demonstrated computer literacy and be willing to learn new approaches to analysing genomic and proteomic data. Strong writing skills will be an asset. We will consider applicants coming from either a computing background who want to work in evolutionary biology or those with evolutionary biology backgrounds and a keen interest in bioinformatics. The supervisory team offer a high level of support in the fields of evolutionary biology, genomics, proteomics and bioinformatics. This project provides the opportunity for the successful applicant to develop the most current skills and build a successful career in these fields while contributing to solutions for two major global issues: loss of biodiversity and species’ response to climate change.

SUPERVISORY TEAM:

  • Dr Lee Ann Rollins, UNSW Scientia Fellow
  • Dr Richard Edwards, Senior Lecturer in Genomics & Bioinformatics
  • Prof Marc Wilkins, Professor of Systems Biology

HOW TO APPLY:

Complete an expression of interest at: https://www.scientia.unsw.edu.au/scientia-phd-scholarships/developing-genomic-resources-advance-molecular-ecology-invasions

The strongest expressions will receive an invitation to submit a full application to the scholarship competition.

CONTACT

To discuss the project and related opportunities, please contact Lee Ann Rollins (l.rollins[at]unsw.edu.au / @rollins_lee) or Rich Edwards (richard.edwards[at]unsw.edu.au / @slimsuite).

Monday, 3 June 2019

Anissa Benkaza (undergrad student)

Anissa Benkaza is a final year genetics student in the School of BABS. She is volunteering in the lab to annotate snake venom proteins as part of the BABS Genome Project.