Reference-Guided De Novo Genome Assembly to Dissect a QTL Region for Submergence Tolerance Derived from Ciherang-Sub1

Global climate change has increased the number of severe flooding events that affect agriculture, including rice production in the U.S. and internationally. Heavy rainfall can cause rice plants to be completely submerged, which can significantly affect grain yield or completely destroy the plants. Recently, a major effect submergence tolerance QTL during the vegetative stage, qSub8.1, which originated from Ciherang-Sub1, was identified in a mapping population derived from a cross between Ciherang-Sub1 and IR10F365. Ciherang-Sub1 was, in turn, derived from a cross between Ciherang and IR64-Sub1. Here, we characterize the qSub8.1 region by analyzing the sequence information of Ciherang-Sub1 and its two parents (Ciherang and IR64-Sub1) and compare the whole genome profile of these varieties with the Nipponbare and Minghui 63 (MH63) reference genomes. The three rice varieties were sequenced with 150 bp pair-end whole-genome shotgun sequencing (Illumina HiSeq4000), followed by performing the Trimmomatic-SOAPdenovo2-MUMmer3 pipeline for genome assembly, resulting in approximate genome sizes of 354.4, 343.7, and 344.7 Mb, with N50 values of 25.1, 25.4, and 26.1 kb, respectively. The results showed that the Ciherang-Sub1 genome is composed of 59–63% Ciherang, 22–24% of IR64-Sub1, and 15–17% of unknown sources. The genome profile revealed a more detailed genomic composition than previous marker-assisted breeding and showed that the qSub8.1 region is mostly from Ciherang, with some introgressed segments from IR64-Sub1 and currently unknown source(s).

Listeria monocytogenes clones in Italian food products: virulence and environmental adaptation

Background Food is the main source of Listeria monocytogenes (Lm) infection. Lm is a highly heterogeneous species composed of hypervirulent and hypovirulent clones. Understanding the distribution of Lm clonal complexes (CCs) in different food categories has strong implications for risk assessment. The aim of this work was to analyse collection of Lm strains of National Reference Laboratory (NRL Lm) in order to assess link between genetic profile and matrices and the level of pathogenicity of circulating strains based on CCs. Methods NRL Lm database actually consists of 906 sequenced strains isolated in 10 years from 5 food compartments (meat, fish, dairy, vegetables and composite dishes). Epidata were analysed to remove redundant strains based on the same epidemiological description. After that, WGS data from 465 Lm strains were investigated. In silico MLST was defined and Roary 3.12.0 was used to obtain a pan-genome profile. Genes were later uploaded to Pasteur Institute platform for characterization. Results In silico MLST identified 36 CCs and 6 singleton. CC9 (23.0%), CC8 (15.3%) and CC121 (13.3%) were the prevalent CCs. In particular, CC9 was present in 35.2% of meat samples and CC8 in 25.8% of fish samples. Pan genome profile revealed high prevalence (>98%) of genes related to biofilm formation and resistance to environmental stress in CC9 strains and genes involved in tolerance to quaternary ammonium compounds in CC121 strains. Conclusions Results, in particular for meat products, confirmed in Italy, the prevalence of hypovirulent Lm strains previously observed at European Union level. The high presence of stress resistance and disinfectant tolerance genes in these strains could make them able to persist in food-production environment and should be taken into account evaluating the health hazards. In fish product is also relevant the prevalence of CC8 strains which are potentially highly pathogenic and have been responsible of recent European multi country outbreak. Key messages Pangenome of Listeria monocytogenes isolated from Italian food revealed high presence of disinfectant tolerance and stress resistance genes in meat products and virulence genes in fish products. Listeria monocytogenes CC9 and CC121 prevalence in Italian meat product confirms occurrence of hypovirulent strains detected at European Union level.

Efficient merging of genome profile alignments

Motivation Whole-genome alignment (WGA) methods show insufficient scalability toward the generation of large-scale WGAs. Profile alignment-based approaches revolutionized the fields of multiple sequence alignment construction methods by significantly reducing computational complexity and runtime. However, WGAs need to consider genomic rearrangements between genomes, which make the profile-based extension of several whole-genomes challenging. Currently, none of the available methods offer the possibility to align or extend WGA profiles. Results Here, we present genome profile alignment, an approach that aligns the profiles of WGAs and that is capable of producing large-scale WGAs many times faster than conventional methods. Our concept relies on already available whole-genome aligners, which are used to compute several smaller sets of aligned genomes that are combined to a full WGA with a divide and conquer approach. To align or extend WGA profiles, we make use of the SuperGenome data structure, which features a bidirectional mapping between individual sequence and alignment coordinates. This data structure is used to efficiently transfer different coordinate systems into a common one based on the principles of profiles alignments. The approach allows the computation of a WGA where alignments are subsequently merged along a guide tree. The current implementation uses progressiveMauve and offers the possibility for parallel computation of independent genome alignments. Our results based on various bacterial datasets up to several hundred genomes show that we can reduce the runtime from months to hours with a quality that is negligibly worse than the WGA computed with the conventional progressiveMauve tool. Availability and implementation GPA is freely available at https://lambda.informatik.uni-tuebingen.de/gitlab/ahennig/GPA. GPA is implemented in Java, uses progressiveMauve and offers a parallel computation of WGAs. Supplementary information Supplementary data are available at Bioinformatics online.