Pineapple (Ananas comosus (L.) Merr.) is the most economically valuable crop possessing crassulacean acid metabolism (CAM), a photosynthetic carbon assimilation pathway with high water use efficiency, and the second most important tropical fruit after banana in terms of international trade. We sequenced the genomes of pineapple varieties ‘F153’ and ‘MD2’, and a wild pineapple relative A. bracteatus accession CB5. The pineapple genome has one fewer ancient whole genome duplications than sequenced grass genomes and, therefore, provides an important reference for elucidating gene content and structure in the last common ancestor of extant members of the grass family (Poaceae). Pineapple has a conserved karyotype with seven pre rho duplication chromosomes that are ancestral to extant grass karyotypes. The pineapple lineage has transitioned from C3 photosynthesis to CAM with CAM-related genes exhibiting a diel expression pattern in photosynthetic tissues using beta-carbonic anhydrase (βCA) for initial capture of CO2. Promoter regions of all three βCA genes contain a CCA1 binding site that can bind circadian core oscillators. CAM pathway genes were enriched with cis-regulatory elements including the morning (CCACAC) and evening (AAAATATC) elements associated with regulation of circadian-clock genes, providing the first link between CAM and the circadian clock regulation. Gene-interaction network analysis revealed both activation and repression of regulatory elements that control key enzymes in CAM photosynthesis, indicating that CAM evolved by reconfiguration of pathways preexisting in C3 plants. Pineapple CAM photosynthesis is the result of regulatory neofunctionalization of preexisting gene copies and not acquisition of neofunctionalized genes via whole genome or tandem gene duplication.

Several new genomics technologies have become available that offer long-read sequencing or long-range mapping with higher throughput and higher resolution analysis than ever before. These long-range technologies are rapidly advancing the field with improved reference genomes, more comprehensive variant identification and more complete views of transcriptomes and epigenomes. However, they also require new bioinformatics approaches to take full advantage of their unique characteristics while overcoming their complex errors and modalities. Here, we discuss several of the most important applications of the new technologies, focusing on both the currently available bioinformatics tools and opportunities for future research.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Background: The use of high throughput genome-sequencing technologies has uncovered a large extent of structural variation in eukaryotic genomes that makes important contributions to genomic diversity and phenotypic variation. When the genomes of different strains of a given organism are compared, whole genome resequencing data are typically aligned to an established reference sequence. However, when the reference differs in significant structural ways from the individuals under study, the analysis is often incomplete or inaccurate.Results: Here, we use rice as a model to demonstrate how improvements in sequencing and assembly technology allow rapid and inexpensive de novo assembly of next generation sequence data into high-quality assemblies that can be directly compared using whole genome alignment to provide an unbiased assessment. Using this approach, we are able to accurately assess the 'pan-genome' of three divergent rice varieties and document several megabases of each genome absent in the other two.Conclusions: Many of the genome-specific loci are annotated to contain genes, reflecting the potential for new biological properties that would be missed by standard reference-mapping approaches. We further provide a detailed analysis of several loci associated with agriculturally important traits, including the S5 hybrid sterility locus, the Sub1 submergence tolerance locus, the LRK gene cluster associated with improved yield, and the Pup1 cluster associated with phosphorus deficiency, illustrating the utility of our approach for biological discovery. All of the data and software are openly available to support further breeding and functional studies of rice and other species. BackgroundRice (Oryza sativa) provides 20% of the world's dietary energy supply and is the predominant staple food for 17 countries in Asia, 9 countries in North and South America and 8 countries in Africa. Within O. sativa, there are two major varietal groups, Indica and Japonica, that can be further subdivided into five major subpopulations: indica and aus share ancestry within the Indica varietal group, and tropical japonica, temperate japonica and aromatic (Group V) share ancestry within the Japonica varietal group (Figure 1 The time since divergence of the ancestral Indica and Japonica gene pools is estimated at 0.44 million years, based on sequence comparisons between cv Nipponbare (Japonica) and cv . This time estimate pre-dates the domestication of O. sativa by several hundred thousand years, suggesting that rice cultivation proceeded from multiple, pre-differentiated ancestral pools [1,[9][10][11][12][13]. This is consistent with genome-wide estimates of divergence based on gene content [14], transcript levels [15], single nucleotide polymorphisms (SNPs) [3,16], and

BackgroundThe use of high throughput genome-sequencing technologies has uncovered a large extent of structural variation in eukaryotic genomes that makes important contributions to genomic diversity and phenotypic variation. When the genomes of different strains of a given organism are compared, whole genome resequencing data are typically aligned to an established reference sequence. However, when the reference differs in significant structural ways from the individuals under study, the analysis is often incomplete or inaccurate.ResultsHere, we use rice as a model to demonstrate how improvements in sequencing and assembly technology allow rapid and inexpensive de novo assembly of next generation sequence data into high-quality assemblies that can be directly compared using whole genome alignment to provide an unbiased assessment. Using this approach, we are able to accurately assess the ‘pan-genome’ of three divergent rice varieties and document several megabases of each genome absent in the other two.ConclusionsMany of the genome-specific loci are annotated to contain genes, reflecting the potential for new biological properties that would be missed by standard reference-mapping approaches. We further provide a detailed analysis of several loci associated with agriculturally important traits, including the S5 hybrid sterility locus, the Sub1 submergence tolerance locus, the LRK gene cluster associated with improved yield, and the Pup1 cluster associated with phosphorus deficiency, illustrating the utility of our approach for biological discovery. All of the data and software are openly available to support further breeding and functional studies of rice and other species.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0506-z) contains supplementary material, which is available to authorized users.

Background Sugarcane cultivars are polyploid interspecific hybrids of giant genomes, typically with 10–13 sets of chromosomes from 2 Saccharum species. The ploidy, hybridity, and size of the genome, estimated to have >10 Gb, pose a challenge for sequencing. Results Here we present a gene space assembly of SP80-3280, including 373,869 putative genes and their potential regulatory regions. The alignment of single-copy genes in diploid grasses to the putative genes indicates that we could resolve 2–6 (up to 15) putative homo(eo)logs that are 99.1% identical within their coding sequences. Dissimilarities increase in their regulatory regions, and gene promoter analysis shows differences in regulatory elements within gene families that are expressed in a species-specific manner. We exemplify these differences for sucrose synthase (SuSy) and phenylalanine ammonia-lyase (PAL), 2 gene families central to carbon partitioning. SP80-3280 has particular regulatory elements involved in sucrose synthesis not found in the ancestor Saccharum spontaneum. PAL regulatory elements are found in co-expressed genes related to fiber synthesis within gene networks defined during plant growth and maturation. Comparison with sorghum reveals predominantly bi-allelic variations in sugarcane, consistent with the formation of 2 “subgenomes” after their divergence ∼3.8–4.6 million years ago and reveals single-nucleotide variants that may underlie their differences. Conclusions This assembly represents a large step towards a whole-genome assembly of a commercial sugarcane cultivar. It includes a rich diversity of genes and homo(eo)logous resolution for a representative fraction of the gene space, relevant to improve biomass and food production.

Motivation: Genomics is expanding from a single reference per species paradigm into a more comprehensive pan-genome approach that analyzes multiple individuals together. A compressed de Bruijn graph is a sophisticated data structure for representing the genomes of entire populations. It robustly encodes shared segments, simple single-nucleotide polymorphisms and complex structural variations far beyond what can be represented in a collection of linear sequences alone. Results: We explore deep topological relationships between suffix trees and compressed de Bruijn graphs and introduce an algorithm, splitMEM, that directly constructs the compressed de Bruijn graph in time and space linear to the total number of genomes for a given maximum genome size. We introduce suffix skips to traverse several suffix links simultaneously and use them to efficiently decompose maximal exact matches into graph nodes. We demonstrate the utility of splitMEM by analyzing the nine-strain pan-genome of Bacillus anthracis and up to 62 strains of Escherichia coli, revealing their core-genome properties. Availability and implementation: Source code and documentation available open-source http://splitmem.sourceforge.net.

Motivation: Genome resequencing and short read mapping are two of the primary tools of genomics and are used for many important applications. The current state-of-the-art in mapping uses the quality values and mapping quality scores to evaluate the reliability of the mapping. These attributes, however, are assigned to individual reads and do not directly measure the problematic repeats across the genome. Here, we present the Genome Mappability Score (GMS) as a novel measure of the complexity of resequencing a genome. The GMS is a weighted probability that any read could be unambiguously mapped to a given position and thus measures the overall composition of the genome itself.Results: We have developed the Genome Mappability Analyzer to compute the GMS of every position in a genome. It leverages the parallelism of cloud computing to analyze large genomes, and enabled us to identify the 5–14% of the human, mouse, fly and yeast genomes that are difficult to analyze with short reads. We examined the accuracy of the widely used BWA/SAMtools polymorphism discovery pipeline in the context of the GMS, and found discovery errors are dominated by false negatives, especially in regions with poor GMS. These errors are fundamental to the mapping process and cannot be overcome by increasing coverage. As such, the GMS should be considered in every resequencing project to pinpoint the ‘dark matter’ of the genome, including of known clinically relevant variations in these regions.Availability: The source code and profiles of several model organisms are available at http://gma-bio.sourceforge.netContact: hlee@cshl.eduSupplementary Information: Supplementary data are available at Bioinformatics online.