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Summary Arabica coffee (Coffea arabica) has a small gene pool limiting genetic improvement. Selection for caffeine content within this gene pool would be assisted by identification of the genes controlling this important trait. Sequencing of DNA bulks from 18 genotypes with extreme high‐ or low‐caffeine content from a population of 232 genotypes was used to identify linked polymorphisms. To obtain a reference genome, a whole genome assembly of arabica coffee (variety K7) was achieved by sequencing using short read (Illumina) and long‐read (PacBio) technology. Assembly was performed using a range of assembly tools resulting in 76 409 scaffolds with a scaffold N50 of 54 544 bp and a total scaffold length of 1448 Mb. Validation of the genome assembly using different tools showed high completeness of the genome. More than 99% of transcriptome sequences mapped to the C. arabica draft genome, and 89% of BUSCOs were present. The assembled genome annotated using AUGUSTUS yielded 99 829 gene models. Using the draft arabica genome as reference in mapping and variant calling allowed the detection of 1444 nonsynonymous single nucleotide polymorphisms (SNPs) associated with caffeine content. Based on Kyoto Encyclopaedia of Genes and Genomes pathway‐based analysis, 65 caffeine‐associated SNPs were discovered, among which 11 SNPs were associated with genes encoding enzymes involved in the conversion of substrates, which participate in the caffeine biosynthesis pathways. This analysis demonstrated the complex genetic control of this key trait in coffee.

International audience; Chloroplast sequences are widely used for phylogenetic analysis due to their high degree of conservation in plants. Whole chloroplast genomes can now be readily obtained for plant species using new sequencing methods, giving invaluable data for plant evolution However new annotation methods are required for the efficient analysis of this data to deliver high quality phylogenetic analyses. In this study, the two main tools for chloroplast genome annotation were compared. More consistent detection and annotation of genes were produced with GeSeq when compared to the currently used Dogma. This suggests that the annotation of most of the previously annotated chloroplast genomes should now be updated. GeSeq was applied to species related to coffee, including 16 species of the Coffea and Psilanthus genera to reconstruct the ancestral chloroplast genomes and to evaluate their phylogenetic relationships. Eight genes in the plant chloroplast pan genome (consisting of 92 genes) were always absent in the coffee species analyzed. Notably, the two main cultivated coffee species (i.e. Arabica and Robusta) did not group into the same clade and differ in their pattern of gene evolution. While Arabica coffee (Coffea arabica) belongs to the Coffea genus, Robusta coffee (Coffea canephora) is associated with the Psilanthus genus. A more extensive survey of related species is required to determine if this is a unique attribute of Robusta coffee or a more widespread feature of coffee tree species.

Coffee is an important crop that provides a livelihood to millions of people living in developing countries. Production of genotypes with improved coffee quality attributes is a primary target of coffee genetic improvement programmes. Advances in genomics are providing new tools for analysis of coffee quality at the molecular level. The recent report of a genomic sequence for robusta coffee, Coffea canephora, is a major development. However, a reference genome sequence for the genetically more complex arabica coffee (C. arabica) will also be required to fully define the molecular determinants controlling quality in coffee produced from this high quality coffee species. Genes responsible for control of the levels of the major biochemical components in the coffee bean that are known to be important in determining coffee quality can now be identified by association analysis. However, the narrow genetic base of arabica coffee suggests that genomics analysis of the wild relatives of coffee (Coffea spp.) may be required to find the phenotypic diversity required for effective association genetic analysis. The genomic resources available for the study of coffee quality are described and the potential for the application of next generation sequencing and association genetic analysis to advance coffee quality research are explored. (C) 2016 Society of Chemical Industry