Skip to main content
  • Research article
  • Open access
  • Published:

An improved genome of the model marine alga Ostreococcus tauri unfolds by assessing Illumina de novo assemblies



Cost effective next generation sequencing technologies now enable the production of genomic datasets for many novel planktonic eukaryotes, representing an understudied reservoir of genetic diversity. O. tauri is the smallest free-living photosynthetic eukaryote known to date, a coccoid green alga that was first isolated in 1995 in a lagoon by the Mediterranean sea. Its simple features, ease of culture and the sequencing of its 13 Mb haploid nuclear genome have promoted this microalga as a new model organism for cell biology. Here, we investigated the quality of genome assemblies of Illumina GAIIx 75 bp paired-end reads from Ostreococcus tauri, thereby also improving the existing assembly and showing the genome to be stably maintained in culture.


The 3 assemblers used, ABySS, CLCBio and Velvet, produced 95% complete genomes in 1402 to 2080 scaffolds with a very low rate of misassembly. Reciprocally, these assemblies improved the original genome assembly by filling in 930 gaps. Combined with additional analysis of raw reads and PCR sequencing effort, 1194 gaps have been solved in total adding up to 460 kb of sequence. Mapping of RNAseq Illumina data on this updated genome led to a twofold reduction in the proportion of multi-exon protein coding genes, representing 19% of the total 7699 protein coding genes. The comparison of the DNA extracted in 2001 and 2009 revealed the fixation of 8 single nucleotide substitutions and 2 deletions during the approximately 6000 generations in the lab. The deletions either knocked out or truncated two predicted transmembrane proteins, including a glutamate-receptor like gene.


High coverage (>80 fold) paired-end Illumina sequencing enables a high quality 95% complete genome assembly of a compact ~13 Mb haploid eukaryote. This genome sequence has remained stable for 6000 generations of lab culture.


Unicellular marine photosynthetic eukaryotic organisms represent much of the untapped genetic diversity reservoir of our planet [1, 2]. Their ecological importance in the global carbon cycle [3, 4] and their biotechnological potential as possible sources of biofuels and dietary «omega-3» lipid food supplements, have fostered several genome projects to gain knowledge into their diversity and metabolic potential [510]. Ostreococus tauri is the smallest photosynthetic eukaryote known and its genome was the first marine green algal genome to be sequenced. It has a simple cellular organization with a single mitochondrion and a single chloroplast [11, 12], all orchestrated by a 13 Mb haploid nuclear genome [7]. Its compact genome, ease of culture and genetic transformation by homologous recombination promoted O. tauri as an ideal model for cell biology [13, 14]. It has been successfully used to gain knowledge into fundamental cellular processes such as the cell cycle [1517], the circadian clock [1820], lipid [21] and starch synthesis [22], as well as the mechanisms of genome evolution [2325].

High throughput technologies approaches are revolutionizing research on phytoplanktonic eukaryotes [26]. Illumina, among the market leaders for low cost nucleotide sequencing [27], has been broadly adopted for sequencing phytoplanktonic eukaryotes. To what extent this approach delivers worthy genome sequence therefore merits critical appraisal. Comparative studies to assess the quality of de novo assemblies are scarce and suggest that assembly quality varies widely from one species to another and from one assembler to another [28]. Even fewer studies have been made to evaluate the quality and accuracy of de novo scaffolds [29] (Table 1), as the major limiting step is the availability of a high quality reference genome sequence to benchmark an assembly resulting from processing short reads.

Table 1 Quality assessment statistics of de novo assemblies of high throughput sequencing studies

DNA was extracted from the O. tauri strain in 2001 (OT-2001) and in 2009 (OT-2009) and 40 millions paired-end DNAseq reads were generated from each extraction. These datasets were used to compare the output of three de novo assembly algorithms. The resulting assemblies were benchmarked against the O. tauri sequenced genome to estimate their quality and the percent of the genome covered. Combined with RNAseq data, this data led to a significant improvement of the reference genome sequence by resolving 1194 gaps corresponding to 460 kb and resulting in a remarkable improvement of the 7699 protein coding genes models.

Genetic selection pressures differ between organisms that grow in the wild, that are subject for example to limiting environmental conditions (such as nutrient supply) and in the laboratory, where mutations favouring growth in culture are expected to become fixed over time [30]. Previous studies on a few genes have revealed amino-acid differences that result in marked differences in the phenotype of the S. cereviseae lab strain as compared to wild strains [31, 32]. More recently, Illumina sequencing allowed scientists to track 120 mutations in yeast during three experiments selecting for increased growth rates in a constant environment [33]. The O. tauri strain has been maintained in laboratory culture conditions since its isolation in 1995 [11]. The comparison between the 2001 and 2009 sequence data enabled us to investigate genome stability of O. tauri over approximately 6000 generations of lab subculturing.



We used the O. tauri whole genome sequence as a reference (GenBank accession number CAID01000001 to CAID01000020), sequenced on two BAC and five shotgun libraries [7]. The scaffolding was improved by using information about the location of each contig in a BAC library hybridized to macroarrays [7], leading to 20 scaffolds representing a total of 12.56 Mb, corresponding to 20 chromosomes. The reference genome assembly contained 1671 gaps as a consequence of low coverage (7X).

The culture used for the reference genome sequence came from a natural sample of O. tauri isolated 1995 in the Thau Lagoon [11, 12] and maintained by serial subcultures using 50 ml plastic tissue culture flasks in 20 ml K medium at 20°C under 100 μE s-1 m-2 constant light in Banyuls sur mer. Every 2 to 3 weeks the cells reach a stationary phase (at a concentration of approximately 3 107 and 20 μl (approx. 6 105 cells) is sub-cultured in fresh K media. This culture was cloned in 2005 on agar plate and the cloned culture was maintained in the lab.

DNA extraction was performed on the 2001 and the 2009 culture as previously described [7]. Genomic DNA of the Ot strain from 2001 (OT-2001), from the same extraction sample that was used for Sanger sequencing, and 2009 (OT-2009) was randomly sheared into ~250-bp fragments. The libraries created from these fragments were sequenced on an Illumina GAIIx system at the Joint Genome Institute. The sequencing experiment produced 43 millions and 41 millions 76 bp paired-end reads with an average insert size of 250 nucleotides. The alignment of these paired-end reads against the reference genome sequence (BWA version 0.6.1-r104 with default parameters [34]), produced an average coverage of 175 and 205 reads per reference base pair in OT-2001 and OT-2009, respectively. Both 2001 and 2009 cultures were non-axenic and contained bacteria, as judged from the presence of bacterial contigs in the assemblies [35]. As the OT-2009 dataset corresponded to a clonal strain, this dataset was used for analysis of de novo assemblers and genome update. The clonal strain resequenced in 2009 has been submitted to the Roscoff Culture Collection under accession number RCC4221. The Illumina dataset have been deposited in the SRA archive under accession numbers: SRX026855 and SRX030853.

De novo assemblies of O. taurigenome

We used 3 de novo assemblers Velvet [36] (version 1.0.18), ABySS [37] (version 1.2.6) and CLCbio (version 4.06.beta) ( These tools have a De-Bruijn graph based algorithm and are well suited for short paired-end reads. During the scaffolding step, the number of paired-end reads required to join 2 contigs into a scaffold was set to 10 for both Velvet and ABySS. As there is no scaffolding step for CLCbio we used SSPACE [38]. Among the assemblies build with different k-mer sizes, the assembly with the highest weighted median length, N50, was kept for comparison between assemblers. The quality of de novo assemblies was assessed in terms of contiguity and correctness on scaffolds with a size greater than 500 bp. To remove bacterial sequences, contigs with less than 70% nucleotide identity (blastn) with available Mamiellales genome sequences were eliminated [39]. These comprised: Bathycoccus prasinos, Micromonas pusilla, Micromonas RCC299, Ostreococcus RCC809 and Ostreococcus lucimarinus and Ostreococcus tauri. Contiguity statistics were the number of scaffolds, the N50, the assembly size and the percentage of the reference genome covered by the scaffolds (estimated from the number of aligned bases in the dnadiff report of NUCmer alignments see below).

Assessing assembly error rates of de novoscaffolds

Scaffolds were aligned against the reference genome using NUCmer from MUMmer v3.20 [40] with default options except for "-maxmatch -l 30 -banded -D 5". A minimum exact-match anchor size was set to 30 bp and a minimum combined anchor length to 65 bp per cluster. Following Salzberg et al. [28], we discarded alignments with less than 95% identity, or more than 95% overlap with another alignment using delta-filter. From these alignments we tallied the correctness statistics using dnadiff[41] from MUMmer v3.20. The output was filtered by removing all regions corresponding to repeated elements (transposable elements and tandem duplications). The correctness statistics are: the number of mis-joins (translocation, relocation or inversion) as defined in Salzberg et al. [28]. A mis-join is defined when subparts of a scaffold align on two different chromosomes (translocation), on the same chromosome in a different order (relocation) or are inverted compared to the reference (inversion). The error rate was computed as the mean number of mis-joins per scaffolds and as the proportion of scaffolds having at least one mis-join. To assess precisely how coding sequences (CDS) were represented in de novo assemblies, we calculated the percent of aligned bases in the CDS from dnadiff after a NUCmer alignment of the scaffolds against the CDS sequences. The number of complete CDS (start to stop) present in the assembly was obtained from the show-coords files (-l -c -b -T -o –r).

Improving a historical reference genome

Gap closing was performed in 4 steps using the OT-2009 dataset (1) de novo assembly, (2) IMAGE and (3) PCR sequencing (4) CRAC. De novo scaffolds recruitment to close gaps in the reference genome sequence was done as follows. De novo scaffolds were aligned onto the reference genome sequence using blastn. If the scaffold aligned onto the reference over 200 bp with 95% identity and with at least 50 bp on each side of a gap, the sequence of the scaffold was used to close the gap. As de novo assemblers may discard some informative low copy reads, we also used raw reads to improve the reference genome with two further steps. In a second step, we performed local iterative de novo assemblies using IMAGE [42] (version 2.1) and the 41 millions paired-end reads. We divided the genome into 597 super-contigs corresponding to n = 577 gaps and chromosomes (n = 20). IMAGE aligned the 41 M paired-end reads Illumina dataset against these super contigs using BWA (with default parameters). IMAGE subsequently gathered paired-end reads for which only one of the paired reads mapped at the end of one of two super contigs separated by a gap. If at least 10 paired-end reads were gathered, IMAGE performed a local assembly of these paired-end reads to elongate contigs iteratively.

Since the publication of the first version of the genome, primers have been designed manually to fill additional gaps, especially around coding regions. DNA from PCR were sent to sequencing platforms and this enabled 134 additional gaps in the updated genome version to be closed.

As a last step we used CRAC, a sensitive mapping method that uses a k-mer profiling approach of reads onto a reference genome [43]. CRAC first collects for each k-mer in the read its locations on the genome and its support (which is a proxy of the read coverage), then analyses both the variation of location and of support within the read: this enables the precise detection of deletions, insertions or translocations with DNA-seq data. This enabled us to extract paired reads that align on two different scaffolds and that could have been omitted in the previous approaches. We manually checked the positions mapped by these paired end reads on the reference genome and performed a manual assembly when possible. This enabled the filling of 34 additional gaps and the identification of two errors in the assembly that corresponded to inversions of one scaffold relative to its neighboring scaffolds.

The mapping of the Illumina reads onto the reference (BWA, [34]) enabled the identification of nucleotide insertions/deletions (indels) variants compared to the reference genome sequence. A base in the reference was considered to be incorrect if at least a minimum of 10 reads scored the nucleotide differently (with both DNAseq and RNAseq). The incorrect nucleotide was then changed to the most occurring nucleotide if occurring in more than 90% of DNA reads. Previous analysis on SNP-calling on O. tauri mitochondrial and chloroplast genomes enabled us to estimate empirically that these coverage thresholds corresponded to 100% correct SNP predictions [25]. We applied the same cut-off for insertion/deletion correction of the reference genome sequence.

Genome evolution between 2001 and 2009

OT-2001 and OT-2009 reads were aligned on the reference genome with BWA with default parameters [34]. We used custom C scripts to scan the pileup files to call variants. There were 11 760 029 sites covered by a minimum of 10 reads and a maximum, which was chosen as 220 for OT-2001 and 256 for OT-2009 reads were retained for the analysis of the OT-2001 (corresponding to 125% of the average genome coverage for each library), to discard low covered regions and possible duplicated regions in the reference genome. Candidate substitutions were identified when 99% of the OT-2001 reads were consistent with the reference nucleotide and 99% of the OT-2009 reads were consistent with the variant. This led to 12 candidate substitutions. In order to confirm each of these substitutions, we designed primers (Additional file 1: Table S1) to sequence 100 bp each side of the substitution in the OT-2001 and the OT-2009 samples. The position of the substitution within the gene and the type of mutations (non-synonymous, synonymous, non-coding, nonsense) was obtained from manual inspection of the alignments of the 2001 and 2009 coding sequences. We used TMHMM to identify transmembrane domains ( [44]. The information about the gene families (number and presence of homologous genes) within sequenced green alga and land plants genomes, corresponding to the genes containing non-sense or frameshift mutations, were retrieved from the pico-PLAZA database [45]. The absence of a homologous gene was further confirmed by a tblastn against the genome sequence.

To investigate copy number variations between 2001 and 2009, we analysed the coverage over 50 bp windows along the chromosomes. Whenever we found a two fold or higher increase in coverage (as compared to the average genome coverage) with the OT-2009 reads, it was compared with the OT-2001 coverage.

Updated genome sequence annotation

RNAseq data was obtained from cells grown under diurnal LD cycles (12L12D). As most genes are expressed rhythmically in these conditions [46], we isolated RNA every 3 hours over a 24 hours cycle and pooled the samples for sequencing. RNA was extracted using the RNeasy-Plus Mini kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Contaminating DNA was removed using RQ1 RNAse-free DNAse (Promega Corporation, Madison, US). Poly-A RNA was isolated and paired-end librairies were generated following the protocol from the Illumina mRNA-Seq Sample Prep Kit. Sequencing was carried out on a single lane of Illumina GIIx and 76 bp paired-end reads were obtained.

RNAseq data was used to guide the annotation procedure using the annotation pipeline developed at Gent University. Similar scripts can be downloaded from The updated genomic sequence of O. tauri was annotated by using the EuGene [47, 48] gene finding system. Both Eugene (ab initio) as well as Splice-Machine [49] were specifically trained for O. tauri datasets. This pipeline integrates homology information derived from proteins sets of other microalgae from the Mamiellophyceae family; Bathycoccus prasinos RCC4222 (a clonal lineage re-isolated from RCC1105, [10]), Micromonas pusilla RCC299 and CCMP1545 [9], Ostreococcus lucimarinus[8], ESTs and full-length transcripts from Ostreococcus tauri that could be collected from NCBI, and all junctions present from mapping the present RNAseq dataset. Given the high density of the gene content in O. tauri, no RNAseq assembly was performed aiming at obtaining additional (full-length) transcripts. A trial-assembly of the RNAseq resulted in too many concatenated transcripts due to overlapping UTRs. A final thorough manually curation of the predicted gene models was performed by the authors using the ORCAE interface [50].


De novo assemblies of O. tauri’sgenome

We generated de novo assemblies of the O. tauri genome using 41 million paired-end 76 bp Illumina reads from the OT-2009 strain. The three different assembly algorithms produced between 1402 to 2080 scaffolds, with a weighted median size length (N50) of 9,539 to 14,550 bp (Table 2). The total assembly size varied from 12.3 to 12.8 Mb and corresponds to 94 to 96% of the complete Genbank reference genome sequence. Among the three assemblies, ABySS and CLCbio produced assemblies with better contiguity; they had fewer scaffolds (CLC: 1402 and Ab: 1490) with greater N50 (Ab 14550, CLC 14519) and both covered 96% of the Genbank reference genome sequence. ABySS produced the longest assembly of 12.8 Mb, closest to the expected O. tauri genome size. The alignment of the de novo assembly generated by ABySS on to the O. tauri reference genome sequence is presented in Figure 1 (outer circle).

Table 2 Assembly Statistics of de novo assemblers in O. tauri
Figure 1
figure 1

Illumina DNAseq and RNAseq aligned against Ostreococcus tauri reference genome sequence. Colored numbered lines represent the 20 chromosomes of Ostreococcus tauri. The contiguity of the de novo assembly along the chromosomes ranges from 0 (white) to 28 scaffolds per 30 kb window (red). The inner blue track is the DNAseq coverage (from 0 to 582 reads per bp). The inner purple track is the RNAseq coverage averaged across 10 kb windows (from 0 to 1947 reads per bp). Figure generated with the RCircos software [51].

It is essential to assess the correctness of de novo assemblies as contiguity may come with a trade off in correctness [28]. dnadiff tools from the NUCmer alignment of each set of de novo scaffolds detected 5, 8 and 6 translocations, 3, 4 and 7 relocations for Velvet, ABySS and CLCbio respectively. The average number of mis-joins per scaffolds was less than 0.009 and the percentage of mis-assembled scaffolds was less than 0.9 percent for the three assemblers (Table 3).

Table 3 Correctness Statistics of each assembly assessed with dnadiff

The coding sequence representation in these assemblies, measured as the percentage of coding sequence base pairs in the original assembly that align against a de novo scaffold is 96.1, 97.6 and 98.2 (Velvet, ABySS and CLCbio) (Table 3). This is significantly higher than that observed for intergenic regions (86.8, 84.9, 87.2 for ABySS, Velvet and CLCbio respectively, Fisher exact test: p-value < 2.2x10-16 for all 3 assemblers). The number of CDS included from start to stop codon within a scaffold was 3101 (41.5%) for Velvet, 3363 (42.7%) for ABySS and 3274 (41.5%) for CLCbio.

To estimate the impact of sequencing depth on reference genome coverage and de novo assembly, we randomly sampled paired-end reads from our dataset to produce seven subsets corresponding to a 10, 25, 80, 125, 200, 225 and 250 fold sequencing depth. The different sampled paired-end reads sets were aligned against the reference genome using BWA and reassembled de novo. The obtained scaffolds were aligned against the reference genome using NUCmer. Figure 2 shows the relationship between raw reads and scaffolds genome coverage, and sequencing depth. Coverage changed from 99.5% to 94.8% when the sequencing depth decreased from 250X to 10X. It decreased more dramatically for de novo assembly, from 95% for sequencing depth greater or equal to 80X, down to 69% for a sequencing depth of 10X. This suggests that 80 fold sequencing depth is optimal for de novo genome assembly with this approach.

Figure 2
figure 2

Saturation curve of coverage along the GenBank reference genome sequence. BWA alignment of 41 M Illumina paired-end reads subsets representing different sequencing depth (black line) and after NUCmer alignment of de novo scaffolds produced by a Velvet de novo assembly of these same paired-end reads subset (grey line).

Improving a historical genome sequence

The reference genome contained 1671 gaps, of which 930 could be resolved using de novo assembly and 92 could be further resolved by IMAGE [42]. In depth analysis of the remaining reads using CRAC identified 50 adjacent contigs linked by paired reads, of which 34 could be fixed, while two indicated clear assembly errors in the reference genome. These errors consisted of two inversions in the reference assembly. Fixing these two inversions closed 4 additional gaps. Additional 134 gaps were filled by PCR sequencing effort. The analysis of the alignment of the raw reads onto the updated genome sequence confirmed that the 477 still remaining gaps could not be joined by paired-end reads, as expected if they correspond to regions larger than 100 bp, or if the Illumina library did not contain the corresponding sequence. The 477 remaining gaps have a random distribution across the chromosomes (the distribution of the distances between gaps is not significantly different from expectations, Chi2 test, p = 0.43). The updated genome sequence is thus 12,916,858 nucleotides long, 460.5 kb longer than the historical reference genome sequence [7]. Alignment of paired-end reads against the reference genome sequence enabled 2126 single nucleotides and 3342 indel differences to be identified and corrected in the updated genome sequence.

Genome evolution between 2001 and 2009

Comparison of the OT-2001 and OT-2009 datasets enabled us to identify 8 nucleotide substitutions, 2 deletions and 1 insertion that had occurred in this strain between the 2001 and 2009 cultures (Table 4). All except the insertion were confirmed by independent Sanger sequencing on the OT-2001 DNA and the OT-2009 DNA. The predicted insertion in the first 145 bp of chromosome 9 could not be amplified because of its proximity to the telomere CCCTAAA repeats. One substitution is synonymous, 6 are non-synonymous and one corresponds to a nonsense mutation (Table 4). In total, two substitutions result in the introduction of a stop codon in a coding sequence (the nonsense mutation and one of the deletions). This may lead to a gene knockout, or alternatively, cause a shorter protein by initiation of translation from a downstream methionine (Figure 3). For both genes, the entire genomic region is covered by RNAseq data.The analysis of read coverage over 50 bp windows along the chromosomes led to the identification of two large duplicated regions encompassing 80 kb on chromosome 19 and 30 kb on chromosome 2 (Figure 1 inner circle). Local peaks on chromosome 12 and 18 correspond to the region containing the rRNA operon (ch12) and a single gene with unknown function, ostta18g00700 (ch18). Using coverage to estimate the number of gene copies, we predicted that there are 4 copies of the ribosomal gene « operon » and 5 copies of the ostta18g00700 gene. However, there is no evidence for copy number variations between 2001 and 2009 as no coverage variations have been identified between OT-2001 and OT-2009.

Table 4 Evolution of the Genome sequence between 2001 and 2009
Figure 3
figure 3

Localization of the substitutions between 2001 and 2009 within two genes. A: ostta06g00130 (Ot06g00160), B: gene organization of ostta12g00065 (Ot12g00160), C: Transmembrane organization of the two encoded proteins, left : Arabidopsis glutamate-like receptors homologous to Ot12g00160 from Lam et al. [58], right : TMHMM prediction for Ot06g00160.

Annotation update

Gene prediction from the updated genome sequence, followed by manual editing by experts, led to the annotation of 7699 protein coding genes, 39 tRNA and 319 transposable elements (TE) (Table 5). Compared to the annotation of the historical genome sequence, (protein-coding) genes are longer and contain fewer introns (Table 5), while the proportion of validated introns - as measured by RNAseq - has risen drastically from 7% to 89%. The complete updated genome sequence has been submitted to Genbank and is available under accession numbers CAID01000001 to CAID01000020. Old gene model names are provided as synonyms in new gene models and the link between updated gene and the previous annotations can be browsed via ORCAE (

Table 5 Genome annotation update of O. tauri


There have been several concerns about the sequence quality of de novo assembled genomes using next generation sequencing technology [52, 53]. Here we use the compact 13 Mb genome of a haploid eukaryote to assess the quality of de novo Illumina PE genome sequences generated with three widely used assemblers. Velvet, ABySS and CLCBio show moderate differences in contiguity and quality, providing a genome sequence fragmented into 1402 to 2080 scaffolds of a median length from 9539 to 14550 bp (Figure 1). Random sampling of paired-ends reads enabled us to estimate that a 80-fold coverage is required to obtain an assembly of 95% of the complete genome sequence (Figure 2). The assemblies had low levels of mis-assembly with values per scaffolds ranging from 0.4% (Velvet) to under 0.9% (CLCBio). These values are close to the lower mis-assembly values obtained in other species (Table 1).

Which sequences are lacking in the de novogenome assembly and in the reference genome sequence?

The comparison of the de novo assemblies with the reference genome enabled us to investigate the features of sequences absent from the de novo assemblies. These sequences tend to contain significantly more intergenic regions. This is in line with previous studies showing an increased coverage in exons [54, 55]. This may be due to the higher proportion of low complexity sequences in intergenic regions, as these produce assembly forks that stop the contig elongation in the assembler [56]. Another assembly-independent reason is the lack of reads in the library for these regions. The genome sequence with no read coverage had an average GC content of 80%, consistent with an underrepresentation of extreme GC sequences in Illumina sequencing data [57]. Reciprocally, de novo assemblies closed 930 gaps (56%) in the historical genome sequence, these resolved gaps had an average length of 386 bp.

Genome evolution under laboratory conditions between 2001 and 2009

O. tauri was isolated in 1995 from the Thau lagoon in the NW Mediterranean Sea and conserved in the lab since. When introduced into the lab, organisms may evolve as a consequence of selection for better growth and as a consequence of the loss of selective pressures that are present in the wild [30, 33]. In this study, the comparison of the DNAseq data from 2001 and 2009 gave us an insight into the genome evolution of a lab-adapted strain. There is no evidence for copy number variations and our analysis revealed 8 substitutions, 2 deletions and possibly one insertion, suggesting a high level of genome stability within this timeframe, which corresponds to approximately 6000 generations. These substitutions occur within 8 protein coding genes and one intergenic region (Table 4). Interestingly, 2 substitutions and one deletion occurred in the same gene (Ot12g00990) annotated as a membrane receptor related to the Glutamate-like receptor gene family (GLR). GLR are homologs of mammalian ionotropic glutamate receptors, glutamate-activated ion channels involved in rapid synaptic transmission. Their initial discovery in Arabidopsis thaliana raised intriguing questions about the physiological functions of neurotransmitter-gated channels in plants and provided an insight into why plants make chemicals that act on human brain [58]. The function and ligand of plant GLR is an intense area of research ([59] for a review) and they are hypothesized to be potential amino acid sensors. The deletion induced a frameshift and splits the gene into one 146 aa and one 380 aa open reading frames, thus shortening one of the ligand fixation regions predicted to be outside the cell (Figure 3). In the second gene annotated as a membrane protein (Ot06g00160), the open reading frame was shortened from 678 to 591 amino acids. High throughput transcript analysis in S. cerevisiae suggests that 60% of genes have transcript isoforms, with several cases of downstream methionine initiation [60]. While we do not know the extent of transcriptional heterogeneity from isoform profiling in O. tauri, the substitutions we report here may have been either compensated by the initiation of the gene from a downstream methionine or may have caused a knock out of this gene. While Ot06g00160 has homologous genes in the two other Ostreococcus spp. genomes sequenced, the orthologous gene family of Ot12g00990 does not include any gene from the species O. lucimarinus, suggesting that this gene is dispensable if knocked out. Subculturing produces a bottleneck of 6 105 cells per subculture, a population size that should be sufficiently large to prevent the fixation of deleterious mutations as a consequence of drift, suggesting that these substitutions between the strains are either neutral or advantageous in the lab environment. Kvitek and Sherlock [33] have tracked mutations in one strain of S. cerevisiae evolving in a constant environment and provided evidence that many of the mutations led to the loss of signalling pathways that usually sense a changing environment. When these mutant cells were faced with uncertain environments, the mutations proved to be deleterious. Consistent with this, the knock-out of two transmembrane genes may lead to altered perception of environmental signals, but this is difficult to test experimentally without knowledge of the signalling pathways that might be affected.


Although the de novo assemblies are fragmented in nature, we show that less than 5% of the genome is lacking from any de novo assembly. We took advantage of this data to improve the reference genome sequence of this model marine alga significantly and we show that only 9 substitutions have occurred within 6000 generations of lab culture.


  1. Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, Sullivan M, Arendt D, Benzoni F, Claverie J-M, Follows M, Gorsky G, Hingamp P, Iudicone D, Jaillon O, Kandels-Lewis S, Krzic U, Not F, Ogata H, Pesant S, Reynaud EG, Sardet C, Sieracki ME, Speich S, Velayoudon D, Weissenbach J, Wincker P, Consortium Tara Oceans: A holistic approach to marine eco-systems biology. PLoS Biol. 2011, 9: e1001177-10.1371/journal.pbio.1001177.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, Armbrust EV, Archibald JM, Bharti AK, Bell CJ, Beszteri B, Bidle KD, Cameron CT, Campbell L, Caron DA, Cattolico RA, Collier JL, Coyne K, Davy SK, Deschamps P, Dyhrman ST, Edvardsen B, Gates RD, Gobler CJ, Greenwood SJ, Guida SM, Jacobi JL, Jakobsen KS, James ER, Jenkins B, et al: The marine microbial eukaryote transcriptome sequencing project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014, 12: e1001889-10.1371/journal.pbio.1001889.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Falkowski PG, Barber RT, Smetacek V: Biogeochemical controls and feedbacks on ocean primary production. Science. 1998, 281: 200-206.

    Article  CAS  PubMed  Google Scholar 

  4. Field CB, Behrenfeld MJ, Randerson JT, Falkowski P: Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998, 281: 237-240.

    Article  CAS  PubMed  Google Scholar 

  5. Scala S, Carels N, Falciatore A, Chiusano ML, Bowler C: Genome properties of the diatom Phaeodactylum tricornutum. Plant Physiol. 2002, 129: 993-1002. 10.1104/pp.010713.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, Zhou S, Allen AE, Apt KE, Bechner M, Brzezinski MA, Chaal BK, Chiovitti A, Davis AK, Demarest MS, Detter JC, Glavina T, Goodstein D, Hadi MZ, Hellsten U, Hildebrand M, Jenkins BD, Jurka J, Kapitonov VV, Kröger N, Lau WW, Lane TW, Larimer FW, Lippmeier JC, Lucas S, et al: The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science. 2004, 306: 79-86. 10.1126/science.1101156.

    Article  CAS  PubMed  Google Scholar 

  7. Derelle E, Ferraz C, Rombauts S, Rouzé P, Worden AZ, Robbens S, Partensky F, Degroeve S, Echeynié S, Cooke R: Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc Natl Acad Sci. 2006, 103: 11647-11652. 10.1073/pnas.0604795103.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Palenik B, Grimwood J, Aerts A, Rouzé P, Salamov A, Putnam N, Dupont C, Jorgensen R, Derelle E, Rombauts S, Zhou K, Otillar R, Merchant SS, Podell S, Gaasterland T, Napoli C, Gendler K, Manuell A, Tai V, Vallon O, Piganeau G, Jancek S, Heijde M, Jabbari K, Bowler C, Lohr M, Robbens S, Werner G, Dubchak I, Pazour GJ, et al: The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc Natl Acad Sci. 2007, 104: 7705-7710. 10.1073/pnas.0611046104.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Worden AZ, Lee J-H, Mock T, Rouzé P, Simmons MP, Aerts AL, Allen AE, Cuvelier ML, Derelle E, Everett MV, Foulon E, Grimwood J, Gundlach H, Henrissat B, Napoli C, McDonald SM, Parker MS, Rombauts S, Salamov A, Von Dassow P, Badger JH, Coutinho PM, Demir E, Dubchak I, Gentemann C, Eikrem W, Gready JE, John U, Lanier W, Lindquist EA, et al: Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes micromonas. Science. 2009, 324: 268-272. 10.1126/science.1167222.

    Article  CAS  PubMed  Google Scholar 

  10. Moreau H, Verhelst B, Couloux A, Derelle E, Rombauts S, Grimsley N, Bel MV, Poulain J, Katinka M, Hohmann-Marriott MF, Piganeau G, Rouzé P, Da Silva C, Wincker P, Van de Peer Y, Vandepoele K: Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 2012, 13: R74-10.1186/gb-2012-13-8-r74.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Courties C, Vaquer A, Troussellier M, Lautier J, Chrétiennot-Dinet MJ, Neveux J, Machado C, Claustre H: Smallest eukaryotic organism. Nature. 1994, 370: 255-255.

    Article  Google Scholar 

  12. Chrétiennot-Dinet M-J, Courties C, Vaquer A, Neveux J, Claustre H, Lautier J, Machado MC: A new marine picoeucaryote: Ostreococcus tauri gen. et sp. nov. (Chlorophyta, Prasinophyceae). Phycologia. 1995, 34: 285-292. 10.2216/i0031-8884-34-4-285.1.

    Article  Google Scholar 

  13. van Ooijen G, Knox K, Kis K, Bouget FY, Millar AJ: Genomic transformation of the picoeukaryote Ostreococcus tauri. J Vis Exp. 2012, 65: e4074-

    PubMed  Google Scholar 

  14. Lozano JC, Schatt P, Botebol H, Vergé V, Lesuisse E, Blain S, Carré IA, Bouget FY: Efficient gene targeting and removal of foreign DNA by homologous recombination in the picoeukaryote Ostreococcus. Plant J. 2014, 78 (6): 1073-83. 10.1111/tpj.12530.

    Article  CAS  PubMed  Google Scholar 

  15. Robbens S, Khadaroo B, Camasses A, Derelle E, Ferraz C, Inzé D, de Peer YV, Moreau H: Genome-wide analysis of core cell cycle genes in the unicellular green alga Ostreococcus tauri. Mol Biol Evol. 2005, 22: 589-597.

    Article  CAS  PubMed  Google Scholar 

  16. Moulager M, Corellou F, Vergé V, Escande M-L, Bouget F-Y: Integration of light signals by the retinoblastoma pathway in the control of S phase entry in the picophytoplanktonic cell ostreococcus. PLoS Genet. 2010, 6: e1000957-10.1371/journal.pgen.1000957. Takahashi JS, editor

    Article  PubMed Central  PubMed  Google Scholar 

  17. Gan L, Ladinsky MS, Jensen GJ: Organization of the smallest eukaryotic spindle. Curr Biol. 2011, 21 (18): 1578-83. 10.1016/j.cub.2011.08.021.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Corellou F, Schwartz C, Motta J-P, Djouani-Tahri EB, Sanchez F, Bouget F-Y: Clocks in the Green Lineage: comparative functional analysis of the Circadian architecture of the Picoeukaryote ostreococcus. Plant Cell. 2009, 21: 3436-3449. 10.1105/tpc.109.068825.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. O’Neill JS, van Ooijen G, Dixon LE, Troein C, Corellou F, Bouget F-Y, Reddy AB, Millar AJ: Circadian rhythms persist without transcription in a eukaryote. Nature. 2011, 469: 554-558. 10.1038/nature09654.

    Article  PubMed Central  PubMed  Google Scholar 

  20. Dixon LE, Hodge SK, van Ooijen G, Troein C, Akman OE, Millar AJ: Light and circadian regulation of clock components aids flexible responses to environmental signals. New Phytol. 2014, 203 (2): 568-77. 10.1111/nph.12853.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Wagner M, Hoppe K, Czabany T, Heilmann M, Daum G, Feussner I, Fulda M: Identification and characterization of an acyl-CoA:diacylglycerol acyltransferase 2 (DGAT2) gene from the microalga O. tauri. Plant Physiol Biochem. 2010, 48: 407-416. 10.1016/j.plaphy.2010.03.008.

    Article  CAS  PubMed  Google Scholar 

  22. Sorokina O, Corellou F, Dauvillée D, Sorokin A, Goryanin I, Ball S, Bouget F-Y, Millar AJ: Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus. BMC Syst Biol. 2011, 5: 36-10.1186/1752-0509-5-36.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Jancek S, Gourbière S, Moreau H, Piganeau G: Clues about the genetic basis of adaptation emerge from comparing the proteomes of two Ostreococcus ecotypes (Chlorophyta, Prasinophyceae). Mol Biol Evol. 2008, 25: 2293-2300. 10.1093/molbev/msn168.

    Article  CAS  PubMed  Google Scholar 

  24. Michely S, Toulza E, Subirana L, John U, Cognat V, Maréchal-Drouard L, Grimsley N, Moreau H, Piganeau G: Evolution of codon usage in the smallest photosynthetic eukaryotes and their giant viruses. Genome Biol Evol. 2013, 5: evt053

    Google Scholar 

  25. Blanc-Mathieu R, Sanchez-Ferandin S, Eyre-Walker A, Piganeau G: Organellar inheritance in the Green Lineage: insights from Ostreococcus tauri. Genome Biol Evol. 2013, 5 (8): 1503-1. 10.1093/gbe/evt106.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Kim KM, Park JH, Bhattacharya D, Yoon HS: Applications of next-generation sequencing to unravelling the evolutionary history of algae. Int J Syst Evol Microbiol. 2014, 64: 333-45. 10.1099/ijs.0.054221-0.

    Article  PubMed  Google Scholar 

  27. Earl D, Bradnam K, St John J, Darling A, Lin D, Fass J, Yu HOK, Buffalo V, Zerbino DR, Diekhans M, Nguyen N, Ariyaratne PN, Sung WK, Ning Z, Haimel M, Simpson JT, Fonseca NA, Birol I, Docking TR, Ho IY, Rokhsar DS, Chikhi R, Lavenier D, Chapuis G, Naquin D, Maillet N, Schatz MC, Kelley DR, Phillippy AM, Koren S, et al: Assemblathon 1: a competitive assessment of de novo short read assembly methods. Genome Res. 2011, 21: 2224-2241. 10.1101/gr.126599.111.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Salzberg SL, Phillippy AM, Zimin A, Puiu D, Magoc T, Koren S, Treangen TJ, Schatz MC, Delcher AL, Roberts M, Marçais G, Pop M, Yorke JA: GAGE: a critical evaluation of genome assemblies and assembly algorithms. Genome Res. 2012, 22: 557-567. 10.1101/gr.131383.111.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Narzisi G, Mishra B: Comparing de novo genome assembly: the long and short of it. PLoS One. 2011, 6: e19175-10.1371/journal.pone.0019175.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Aguilar C, Escalante A, Flores N, de Anda R, Riveros-McKay F, Gosset G, Morett E, Bolívar F: Genetic changes during a laboratory adaptive evolution process that allowed fast growth in glucose to an Escherichia coli strain lacking the major glucose transport system. BMC Genomics. 2012, 13: 385-10.1186/1471-2164-13-385.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Liu H, Styles CA, Fink GR: Saccharomyces cerevisiae S288C has a mutation in FLO8, a gene required for filamentous growth. Genetics. 1996, 144: 967-978.

    CAS  PubMed Central  PubMed  Google Scholar 

  32. Bonhivers M, Carbrey JM, Gould SJ, Agre P: Aquaporins in Saccharomyces: genetic and functional distinctions between laboratory and wild-type strains. J Biol Chem. 1998, 273: 27565-27572. 10.1074/jbc.273.42.27565.

    Article  CAS  PubMed  Google Scholar 

  33. Kvitek DJ, Sherlock G: Whole genome, whole population sequencing reveals that loss of signaling networks is the major adaptive strategy in a constant environment. PLoS Genet. 2013, 9: e1003972-10.1371/journal.pgen.1003972.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Abby S, Touchon M, De Jode A, Grimsley N, Piganeau G: Bacteria in Ostreococcus tauri cultures – friends, foes or hitchhikers?. Front Microbiol. 2014, 5: 505-

    Article  PubMed Central  PubMed  Google Scholar 

  36. Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008, 18: 821-829. 10.1101/gr.074492.107.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM, Birol I: ABySS: a parallel assembler for short read sequence data. Genome Res. 2009, 19: 1117-1123. 10.1101/gr.089532.108.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W: Scaffolding pre-assembled contigs using SSPACE. Bioinforma Oxf Engl. 2011, 27: 578-579. 10.1093/bioinformatics/btq683.

    Article  CAS  Google Scholar 

  39. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-10. 10.1016/S0022-2836(05)80360-2.

    Article  CAS  PubMed  Google Scholar 

  40. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL: Versatile and open software for comparing large genomes. Genome Biol. 2004, 5: R12-10.1186/gb-2004-5-2-r12.

    Article  PubMed Central  PubMed  Google Scholar 

  41. Phillippy AM, Schatz MC, Pop M: Genome assembly forensics: finding the elusive mis-assembly. Genome Biol. 2008, 9: R55-10.1186/gb-2008-9-3-r55.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Tsai IJ, Otto TD, Berriman M: Improving draft assemblies by iterative mapping and assembly of short reads to eliminate gaps. Genome Biol. 2010, 11: R41-10.1186/gb-2010-11-4-r41.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Philippe N, Salson M, Commes T, Rivals E: CRAC: an integrated approach to the analysis of RNA-seq reads. Genome Biol. 2013, 14: R30-10.1186/gb-2013-14-3-r30.

    Article  PubMed Central  PubMed  Google Scholar 

  44. Sonnhammer EL, von Heijne G, Krogh A: A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998, 6: 175-82.

    CAS  PubMed  Google Scholar 

  45. Vandepoele K, Van Bel M, Richard G, Van Landeghem S, Verhelst B, Moreau H, Van de Peer Y, Grimsley N, Piganeau G: pico-PLAZA, a genome database of microbial photosynthetic eukaryotes. Environ Microbiol. 2013, 15: 2147-2153. 10.1111/1462-2920.12174.

    Article  CAS  PubMed  Google Scholar 

  46. Monnier A, Liverani S, Bouvet R, Jesson B, Smith JQ, Mosser J, Corellou F, Bouget F-Y: Orchestrated transcription of biological processes in the marine picoeukaryote Ostreococcus exposed to light/dark cycles. BMC Genomics. 2010, 11: 192-10.1186/1471-2164-11-192.

    Article  PubMed Central  PubMed  Google Scholar 

  47. Schiex T, Moisan A, Rouzé P: Eugène: An Eukaryotic Gene Finder That Combines Several Sources of Evidence. Computational Biology. Lecture Notes in Computer Science. Edited by: Gascuel O, Sagot M-F. 2001, Berlin Heidelberg: Springer, 111-125. Available from:,

    Google Scholar 

  48. Foissac S, Schiex T: Integrating alternative splicing detection into gene prediction. BMC Bioinformatics. 2005, 6: 25-10.1186/1471-2105-6-25.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Degroeve S, Saeys Y, Baets BD, Rouzé P, de Peer YV: SpliceMachine: predicting splice sites from high-dimensional local context representations. Bioinformatics. 2005, 21: 1332-1338. 10.1093/bioinformatics/bti166.

    Article  CAS  PubMed  Google Scholar 

  50. Sterck L, Billiau K, Abeel T, Rouzé P, Van de Peer Y: ORCAE: online resource for community annotation of eukaryotes. Nat Methods. 2012, 9 (11): 1041-10.1038/nmeth.2242.

    Article  CAS  PubMed  Google Scholar 

  51. Zhang H, Meltzer P, Davis S: RCircos: an R package for Circos 2D track plots. BMC Bioinformatics. 2013, 14: 244-10.1186/1471-2105-14-244.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Salzberg SL, Yorke JA: Beware of mis-assembled genomes. Bioinformatics. 2005, 21: 4320-4321. 10.1093/bioinformatics/bti769.

    Article  CAS  PubMed  Google Scholar 

  53. Baker M: De novo genome assembly: what every biologist should know. Nat Methods. 2012, 9: 333-337. 10.1038/nmeth.1935.

    Article  CAS  Google Scholar 

  54. Cheung M-S, Down TA, Latorre I, Ahringer J: Systematic bias in high-throughput sequencing data and its correction by BEADS. Nucleic Acids Res. 2011, 39: e103-e103. 10.1093/nar/gkr425.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Ekblom R, Smeds L, Ellegren H: Patterns of sequencing coverage bias revealed by ultra-deep sequencing of vertebrate mitochondria. BMC Genomics. 2014, 15: 467-10.1186/1471-2164-15-467.

    Article  PubMed Central  PubMed  Google Scholar 

  56. Miller JR, Koren S, Sutton G: Assembly algorithms for next-generation sequencing data. Genomics. 2010, 95: 315-327. 10.1016/j.ygeno.2010.03.001.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Benjamini Y, Speed TP: Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 2012, 40: e72-10.1093/nar/gks001.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  58. Lam HM, Chiu J, Hsieh MH, Meisel L, Oliveira IC, Shin M, Coruzzi G: Glutamate-receptor genes in plants. Nature. 1998, 396 (6707): 125-6. 10.1038/24066.

    Article  CAS  PubMed  Google Scholar 

  59. Price MB, Jelesko J, Okumoto S: Glutamate receptor homologs in plants: functions and evolutionary origins. Front Plant Sci [Internet]. 2012, 3: 235-

    Google Scholar 

  60. Pelechano V, Wei W, Steinmetz LM: Extensive transcriptional heterogeneity revealed by isoform profiling. Nature. 2013, 497 (7447): 127-31. 10.1038/nature12121.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references


We would like to thank the GenoToul bioinformatic platform (, the ATGC Bioinformatics Platform and the computing center HPC@LR for access to computing facilities. The Illumina sequence data analyzed in this study were produced by the US Department of Energy Joint Genome Institute within the Community Sequencing Program CSP-129. PCR Sequencing was performed on the sequencing platform of the LGDB Lab in Perpignan (Richard Cooke) and on the BIO2MAR platform in Banyuls sur mer. We are grateful to Sebastien Peuchet for help with the PCR resequencing of predicted substitutions, Elodie Desgranges and Jérémie Heurtault for help with manual annotations. Part of this work was funded by ANR-12-BSV7-0006-01 "REVIREC" (NG) and the European Community’s 7th Framework program FP7 under grant agreement n°254619 (GP and AEW). BV, SR and YVDP would like to acknowledge the support of Ghent University (Multidisciplinary Research Partnership ‘Bioinformatics: from nucleotides to networks’; ER and AC acknowledge the support from the Défi MASTODONS SePhHaDe CNRS, the Labex NUMev, and the Projet Investissements d'Avenir France Génomique. RBM acknowledge Nice-Sophia University for funding.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Gwenaël Piganeau.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

RBM and SR performed de novo assemblies. RBM carried out statistical analysis. BV, SR and YVDP carried out annotation and analysis. Expert manual annotation was done by BV, FYB, GP, HM and NG. BP annotated Transposable Elements. RBM, WS, AC and ER participated in the improvement of the reference genome sequence. IC and FYB provided RNAseq data. WS and GP identified substitutions between 2001 and 2009. ED designed and performed DNA extraction, PCR gap closing and experimental validation of candidate substitutions. RBM and GP wrote the manuscript. All authors contributed to manuscript editing. All authors read and approved the final manuscript.

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blanc-Mathieu, R., Verhelst, B., Derelle, E. et al. An improved genome of the model marine alga Ostreococcus tauri unfolds by assessing Illumina de novo assemblies. BMC Genomics 15, 1103 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: