BackgroundSoybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding.ResultsTo understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits.ConclusionsThis study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1289-9) contains supplementary material, which is available to authorized users.

Compound identification is a key component of data analysis in the applications of gas chromatography–mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semi-partial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standard Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semi-partial correlations always outperform both the dot-product and its composite measure. The mixture similarity with semi-partial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53, 1.3) for fragment ion intensity and m/z value, respectively.

Background Huperzia serrata (H. serrata) is an economically important traditional Chinese herb with the notably medicinal value. As a representative member of the Lycopodiaceae family, the H. serrata produces various types of effectively bioactive lycopodium alkaloids, especially the huperzine A (HupA) which is a promising drug for Alzheimer’s disease. Despite their medicinal importance, the public genomic and transcriptomic resources are very limited and the biosynthesis of HupA is largely unknown. Previous studies on comparison of 454-ESTs from H. serrata and Phlegmariurus carinatus predicted putative genes involved in lycopodium alkaloid biosynthesis, such as lysine decarboxylase like (LDC-like) protein and some CYP450s. However, these gene annotations were not carried out with further biochemical characterizations. To understand the biosynthesis of HupA and its regulation in H. serrata, a global transcriptome analysis on H. Serrata tissues was performed.ResultsIn this study, we used the Illumina Highseq4000 platform to generate a substantial RNA sequencing dataset of H. serrata. A total of 40.1 Gb clean data was generated from four different tissues: root, stem, leaf, and sporangia and assembled into 181,141 unigenes. The total length, average length, N50 and GC content of unigenes were 219,520,611 bp, 1,211 bp, 2,488 bp and 42.51%, respectively. Among them, 105,516 unigenes (58.25%) were annotated by seven public databases (NR, NT, Swiss-Prot, KEGG, COG, Interpro, GO), and 54 GO terms and 3,391 transcription factors (TFs) were functionally classified, respectively. KEGG pathway analysis revealed that 72,230 unigenes were classified into 21 functional pathways. Three types of candidate enzymes, LDC, CAO and PKS, responsible for the biosynthesis of precursors of HupA were all identified in the transcripts. Four hundred and fifty-seven CYP450 genes in H. serrata were also analyzed and compared with tissue-specific gene expression. Moreover, two key classes of CYP450 genes BBE and SLS, with 23 members in total, for modification of the lycopodium alkaloid scaffold in the late two stages of biosynthesis of HupA were further evaluated.ConclusionThis study is the first report of global transcriptome analysis on all tissues of H. serrata, and critical genes involved in the biosynthesis of precursors and scaffold modifications of HupA were discovered and predicted. The transcriptome data from this work not only could provide an important resource for further investigating on metabolic pathways in H. serrata, but also shed light on synthetic biology study of HupA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3615-8) contains supplementary material, which is available to authorized users.