Ants, Bees, Genomes & Evolution @ Queen Mary University London

Published: 30 April 2011

Behind the Scenes of an Ant Genome Project

Yannick Wurm

Formosan Entomologist, 2011, 31:149-56


Dramatic improvements in DNA sequencing technologies have led to a more than 1,000-fold reduction in sequencing costs over the past five years. Genome-wide research approaches can thus now be applied beyond medically relevant questions to examine the molecular-genetic basis of behavior, development and unique life histories in almost any organism. A first step for an emerging model organism is usually establishing a reference genome sequence. I offer insight gained from the fire ant genome project. First, I detail how the project came to be and how sequencing, assembly and annotation strategies were chosen. Subsequently, I describe some of the issues linked to working with data from recently sequenced genomes. Finally, I discuss an approach undertaken in a follow-up project based on the fire ant genome sequence.


Dramatic improvements in DNA sequencing technologies have led to an over 1,000-fold reduction in sequencing costs per basepair over the past five years (Stein, 2010). For example, the human genome published in 2001 cost an estimated 3 billion USD (Collins et al., 2003) and sequencing the honey bee genome completed in 2006 cost 8 million USD (Huang, 2007). More recently, several ant genomes were sequenced each for only tens to several hundred thousand USD (Chris R. Smith, private communication; Smith et al., 2011; Wurm et al., 2011) and prices continue to fall.

As this trend continues, there is increasing interest to obtain sequence information from “emerging model organisms” for which research communities are small in comparison with those that exist for established laboratory species such as Drosophila melanogaster or Caenorhabditis elegans. Many emerging plant and animal models have unique life histories and studying them at molecular-genomic levels provides new opportunities to answer original ecological and evolutionary questions (Straalen & Roelofs, 2006; Hudson, 2008; Tautz et al., 2010).

The first steps in developing genomic tools for an emerging model organism today are sequencing its genome and cataloging all protein-coding genes. I was recently involved in such a project for the fire ant Solenopsis invicta (Wurm et al., 2011). I share the steps and approaches taken for the fire ant genome project. First, I detail how the project came to be and how sequencing, assembly and annotation strategies were chosen. Subsequently, I describe some of the issues linked to working with data from recently sequenced genomes. Finally, I describe a sequencingbased follow-up project that uses the fire ant genome sequence as a reference.


Initiation & Consortium

The idea of sequencing the fire ant genome first materialized in 2005 with a white paper sequencing proposal (Ross et al., 2005) submitted to the United States Department of Energy Joint Genome Institute. But the proposal remained unfunded. The project resurfaced as sequencing prices plummeted and made it feasible to envision a genome project with “standard” laboratory funding. In summer 2008, University of Lausanne’s Genomics Technologies Facility acquired an Illumina sequencer and ran some sequencing tests… including with fire ant DNA. Preliminary assemblies at the Swiss Institute of Bioinformatics’ Vital-IT Center for High Performance Computing identified the full S. invicta mitochondrial sequence, suggesting that de novo genome assembly was feasible. Several months later, we decided to go ahead with the genome project in collaboration with DeWayne Shoemaker at USDA in Gainesville, Florida, John Wang, Laurent Keller and myself at University of Lausanne, Ioannis Xenarios at Vital-IT and Laurent Farinelli from sequencing provider Fasteris SA.

At the January 2009 Past-PresentFuture of Ant Genomics meeting at Arizona State University (Smith et al., 2009), we announced the creation of the fire ant genome consortium in which each participating laboratory would contribute at least 20,000 USD for sequencing costs. The participation of more than ten laboratories provided sufficient funds to experiment with sequencing approaches.

Sequencing, Assembly and Automated Gene Prediction Strategies

Sequencing. A sequencing project usually focuses on a single inbred strain or line because variations within a normal diploid genome are difficult to resolve during assembly. For example, assembly software may be unable to correctly merge sequence reads from two alleles of a single gene, and may be similarly confused when the two haplotypes differ by an insertion or deletion (Kelley & Salzberg, 2010). However, no strains or lines exist for fire ants. We were nevertheless able to circumvent this problem because the sex of an ant is determined by ploidy: Unfertilized haploid eggs become males (Hölldobler & Wilson, 1990). To minimize ambiguities, the bulk of our sequencing effort thus focused on a single haploid male. His DNA was sequenced using two methods: we obtained 20x genome coverage in single-ended reads from a Roche 454 sequencer (average length: 314 basepairs (bp)), and 44x genome coverage from an Illumina sequencer (paired reads separated by 352 bp; each read 32 to 100 bp long). Clearly, these kinds of sequences cannot be used to resolve long repetitive regions. Because eukaryotes in general and S. invicta in particular harbors many repetitive regions (Li & Heinz, 2000), we additionally obtained 6x genome coverage in pairs of Roche 454 sequences separated by 8,000 and 20,000 bp. Doing this required very large amounts of DNA that we obtained from pools of brothers of the focal male. Together, these pools contained the diploid genome of their mother. Roche 454 sequencing was performed at University of Florida’s Interdisciplinary Center for Biotechnology Research, while Fasteris SA in Geneva generated Illumina sequence.

Assembly. When we obtained sequence data (mid to late 2009), there were still no reports of assemblies or assembly approaches for eukaryotic genomes. Our initial attempts using assembly tools including Euler (Pevzner et al., 2001), Velvet (Zerbino & Birney, 2008), ABYSS (Simpson et al., 2009), cap3 (Huang & Madan, 1999) and Roche 454 newbler were unsuccessful: some required memory or processor resources impossible to meet, and others seemed like they might work but assembled only very few sequences. This shed doubt on the feasibility of the project. Subsequently hiring bioinformatics engineer Oksana Riba-Grognuz facilitated the systematic testing of many strategies for data filtering and assembly. Additionally, the performance of assembly algorithms improved dramatically during our few months of tests. Assemblies were compared using graphs that simultaneously indicated numbers of contigs or scaffolds, their N50 and maximal sizes, and the total amount of sequence in the resulting assemblies. In addition, assembly completeness and accuracy was estimated based on the presence of a repertoire of protein-coding genes that are conserved throughout eukaryotes (Parra et al., 2009) as well as using a specifically developed visual method based on alignment of assembled mRNA reads to the genome (Riba-Grognuz et al., in prep.). The retained approach creatively combined the different data types and two assembly tools:

  1. Stringent quality filtering and trimming of Illumina sequence.
  2. SOAPdenovo (Li, Zhu et al., 2010) which was used for the Panda genome (Li et al., 2010) to iteratively assemble the Illumina sequence.
  3. Cutting the resulting assembled Illumina data up into partially overlapping “imitation single-ended Roche 454 sequences” 300 bp long.
  4. Assembling the real and imitation single-ended Roche 454 sequences with Roche 454 newbler software using extremely stringent parameters that helped resolve many repeats as well as the “-large” parameter required for assemblies of large genomes to converge.
  5. Adding the pairs of sequences separated by 8,000 and 20,000 bp in turn with Roche 454 newbler using even more stringent parameters to bioinformatically select only the sequences matching the haplotypes of the focal haploid male

Gene prediction. Automated gene prediction algorithms are based on similarity between the genome sequence and known protein or mRNA sequences, or discover genes ab initio, using models of the theoretical structure of genes, or both. Performance of gene identification software varies among algorithms and among gene and genomes. Genome projects thus often rely on predictions by several different programs that are may subsequently be merged (Elsik et al., 2007). To identify protein-coding genes in the assembled fire ant genome, we opted for the MAKER (Cantarel et al., 2008) software which is becoming the “BLAST of genome annotation” because it is easy to set up and transparently takes care of optimally running multiple gene prediction algorithms and creating consensus gene sets. Some S. invicta gene models were subsequently further refined using Genewise (Birney et al., 2004). Overall, gene identification greatly benefited from the use of protein sequence from other insects and of independently obtained S. invicta mRNA sequence.

Identifying unique features of a genome It is challenging to identify what may be interesting or unique in the large fasta files resulting from genome assembly and gene prediction. A first approach relies on candidate genes or pathways and comparing their presence/absence or evolution with what is known from related insects. For example, this approach helped determine that the transformer/feminizer sexdetermination gene was independently duplicated in an ancestor of S. invicta and in an ancestor of the honey bee Apis mellifera (Wurm et al., 2011).

The second approach relies on unbiased automated comparisons. Within a species, mRNA sequence can be used to identify genes with extreme numbers of splice-forms. Between genomes of related species, comparison of sizes of gene families or protein domain families can identify differences between species. The latter approach using PFAM (Finn et al., 2010) and Prosite (Sigrist et al., 2010) domain analyses highlighted several unique aspects of the fire ant genome that are likely linked to the complex social behavior of this species. For example, its more than 400 putative olfactory receptors constitute the largest repertoire found in insects, and the genome also harbors an expansion of lipid-processing genes that may be used in the production of pheromones. These gene family expansions likely reflect the importance of chemical communication in ants (Wurm et al., 2011).


Issues with Young Genome Projects

While recent technological progress has made genome sequencing, assembly and annotation widely accessible, caution must be taken when working with the obtained data. First, the new sequencing technologies remain young: they provide only short sequence reads with relatively high error rates. Similarly, algorithms and bioinformatics tools to deal with the new data types are still improving. Furthermore, de novo assemblies of eukaryotic genomes consist in many pieces of contiguous sequence (contigs) arranged in thousands of scaffolds instead of small numbers of chromosomes (Salzberg & Yorke, 2005). Labor-intensive mapping and finishing steps to reduce fragmentation were normally part of Sanger-based genome projects but are now generally foregone. The exons of a gene may thus be split across multiple contigs or scaffolds. Finally, while gene prediction tools have matured, adjacent genes may be incorrectly joined into a single gene model and single genes may be incorrectly truncated or split into multiple gene models. Studying the molecular evolution of specific gene families thus requires case-by-case inspection and often editing of the relevant gene models (Lumi Viljakainen, personal communication).

A follow-up study based on highresolution genotyping

Explaining how interactions between genes and the environment influence social behavior is a fundamental question, yet there is limited relevant information for species exhibiting natural variation in social organization. S. invicta is characterrized by a remarkable social polymerphism: The presence of one or multiple reproductive queens within a colony as well as other phenotypic and behavioral differences are completely associated with allelic variation at a single Mendelian factor marked by the gene Gp-9 (Ross & Keller, 1998). Furthermore, the b allele at Gp-9 is a rare example of a “green beard gene” because workers carrying the b allele favor the reproduction of queens carrying b by executing queens that do not carry b. This selfish allele has not reached fixation because of balancing selection: The phenotypes associated with b are counter-selected in certain environments, and bb homozygotes are lethal (Keller & Ross, 1998; Ross & Keller, 1998).

When it was identified, Gp-9 was one of only fifteen examined allozyme markers, leaving open the question of whether other genes may be tightly linked to and thus co-segregating with Gp-9. John Wang, DeWayne Shoemaker, Laurent Keller and I are now following up on this question using the fire ant genome sequence and RADseq genotyping (Baird et al., 2008; Davey & Blaxter, 2011). The RADseq approach consists in extracting the same 0.01% of the genome of many individuals and sequencing the extracted sequence from all individuals within a single Illumina sequencing reaction while keeping track of which sequence comes from which individual. Individual genotypes at several thousand polymorphic sites should thus be found (Davey & Blaxter, 2011). By analyzing the resulting data, we should be able to determine whether any genes are completely linked to Gp-9. Such genes could be responsible for the phenotypes associated with Gp-9.


Recent improvements in sequencing technology have drastically increased accessibility of molecular and genomic investigations. However, the technologies remain young and continue to improve: Between July 2009 and September 2010, the length of useable Illumina sequence reads more than doubled, and the quality and the quantity of sequence obtained from a single lane increased more than 10-fold (my unpublished data). Similarly, software for assembling, mapping and analyzing the new data types is maturing and performs far better than before. Given the quick improvements of both sequencing and analysis tools, one continues to wonder whether new sequencing projects should be initiated immediately, or whether a few months delay may lead to better data and easier analysis to the extent of obtaining results more rapidly.


I thank John Wang and Chin Cheng Yang for comments on this manuscript; Oksana Riba-Grognuz, Laurent Keller, DeWayne Shoemaker, Laurent Farinelli, John Wang, Ioannis Xenarios, Krista Ingram and the many members of the fire ant genome consortium for their help and support. I am funded by a Swiss National Science foundation grant and an ERC grant to Laurent Keller.


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