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BackgroundThe marine dinoflagellate, Symbiodinium, is a well-known photosynthetic partner for coral and other diverse, non-photosynthetic hosts in subtropical and tropical shallows, where it comprises an essential component of marine ecosystems. Using molecular phylogenetics, the genus Symbiodinium has been classified into nine major clades, A-I, and one of the reported differences among phenotypes is their capacity to synthesize mycosporine-like amino acids (MAAs), which absorb UV radiation. However, the genetic basis for this difference in synthetic capacity is unknown. To understand genetics underlying Symbiodinium diversity, we report two draft genomes, one from clade A, presumed to have been the earliest branching clade, and the other from clade C, in the terminal branch.ResultsThe nuclear genome of Symbiodinium clade A (SymA) has more gene families than that of clade C, with larger numbers of organelle-related genes, including mitochondrial transcription terminal factor (mTERF) and Rubisco. While clade C (SymC) has fewer gene families, it displays specific expansions of repeat domain-containing genes, such as leucine-rich repeats (LRRs) and retrovirus-related dUTPases. Interestingly, the SymA genome encodes a gene cluster for MAA biosynthesis, potentially transferred from an endosymbiotic red alga (probably of bacterial origin), while SymC has completely lost these genes.ConclusionsOur analysis demonstrates that SymC appears to have evolved by losing gene families, such as the MAA biosynthesis gene cluster. In contrast to the conservation of genes related to photosynthetic ability, the terminal clade has suffered more gene family losses than other clades, suggesting a possible adaptation to symbiosis. Overall, this study implies that Symbiodinium ecology drives acquisition and loss of gene families.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4857-9) contains supplementary material, which is available to authorized users.

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Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.