Motivation Solanum sitiens is a self-incompatible wild relative of tomato, characterised by salt and drought resistance traits, with the potential to contribute through breeding programmes to crop improvement in cultivated tomato. This species has a distinct morphology, classification and ecotype compared to other stress resistant wild tomato relatives such as S. pennellii and S. chilense. Therefore, the availability of a reference genome for S. sitiens will facilitate the genetic and molecular understanding of salt and drought resistance. Results A high-quality de novo genome and transcriptome assembly for S. sitiens (Accession LA1974) has been developed. A hybrid assembly strategy was followed using Illumina short reads (∼159X coverage) and PacBio long reads (∼44X coverage), generating a total of ∼262 Gbp of DNA sequence. A reference genome of 1,245 Mbp, arranged in 1,483 scaffolds with a N50 of 1.826 Mbp was generated. Genome completeness was estimated at 95% using the Benchmarking Universal Single-Copy Orthologs (BUSCO) and the K-mer Analysis Tool (KAT). In addition, ∼63 Gbp of RNA-Seq were generated to support the prediction of 31,164 genes from the assembly, and to perform a de novo transcriptome. Lastly, we identified three large inversions compared to S. lycopersicum, containing several drought resistance related genes, such as beta-amylase 1 and YUCCA7. Availability S. sitiens (LA1974) raw sequencing, transcriptome and genome assembly have been deposited at the NCBI’s Sequence Read Archive, under the BioProject number “PRJNA633104”. All the commands and scripts necessary to generate the assembly are available at the following github repository: https://github.com/MCorentin/Solanum_sitiens_assembly. Supplementary information Supplementary data are available at Bioinformatics online.

Background Fusarium langsethiae is a T-2 and HT-2 mycotoxins producing species firstly characterised in 2004. It is commonly isolated from oats in Northern Europe. T-2 and HT-2 mycotoxins exhibit immunological and haemotological effects in animal health mainly through inhibition of protein, RNA and DNA synthesis. The development of a high-quality and comprehensively annotated assembly for this species is therefore essential in providing the molecular understanding and the mechanism of T-2 and HT-2 biosynthesis in F. langsethiae to help develop effective control strategies. Results The F. langsethiae assembly was produced using PacBio long reads, which were then assembled independently using Canu, SMARTdenovo and Flye. A total of 19,336 coding genes were identified using RNA-Seq informed ab-initio gene prediction. Finally, predicting genes were annotated using the basic local alignment search tool (BLAST) against the NCBI non-redundant (NR) genome database and protein hits were annotated using InterProScan. Genes with blast hits were functionally annotated with Gene Ontology. Conclusions We developed a high-quality genome assembly of a total length of 59 Mb and N50 of 3.51 Mb. Raw sequence reads and assembled genome is publicly available and can be downloaded from: GenBank under the accession JAFFKB000000000. All commands used to generate this assembly are accessible via GitHub: https://github.com/FadyMohareb/fusarium_langsethiae.

Summary Bionano optical mapping is a technology that can assist in the final stages of genome assembly by lengthening and ordering scaffolds in a draft assembly by aligning the assembly to a genomic map. However, currently, tools for visualization are limited to use on a Windows operating system or are developed initially for visualizing large-scale structural variation. MapOptics is a lightweight cross-platform tool that enables the user to visualize and interact with the alignment of Bionano optical mapping data and can be used for in depth exploration of hybrid scaffolding alignments. It provides a fast, simple alternative to the large optical mapping analysis programs currently available for this area of research. Availability and implementation MapOptics is implemented in Java 1.8 and released under an MIT licence. MapOptics can be downloaded from https://github.com/FadyMohareb/mapoptics and run on any standard desktop computer equipped with a Java Virtual Machine (JVM). Supplementary information Supplementary data are available at Bioinformatics online.

Summary In recent years, the ability to generate genomic data has increased dramatically along with the demand for easily personalized and customizable genome browsers for effective visualization of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provides means for creating multiple visualization instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customizable web application for interactive visualization of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualization instances in parallel while allowing users to control and customize multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customizations made by the users allowing quick navigation between instances. Currently, the browser supports visualizing a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalized tracks, providing a whole new level of customization both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualization instance. Availability and implementation CRAMER is implemented in JavaScript and is publicly available on GitHub on https://github.com/FadyMohareb/cramer. The application is released under an MIT licence and can be deployed on any server running Linux or Mac OS. Contact f.mohareb@cranfield.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundHuman milk (HM) is the ideal source of nutrients for infants. Its composition is highly variable according to the infant's needs. When not enough own mother's milk (OMM) is available, the administration of pasteurized donor human milk (DHM) is considered a suitable alternative for preterm infants. This study protocol describes the NUTRISHIELD clinical study. The main objective of this study is to compare the % weight gain/month in preterm and term infants exclusively receiving either OMM or DHM. Other secondary aims comprise the evaluation of the influence of diet, lifestyle habits, psychological stress, and pasteurization on the milk composition, and how it modulates infant's growth, health, and development.Methods and designNUTRISHIELD is a prospective mother-infant birth cohort in the Spanish-Mediterranean area including three groups: preterm infants <32 weeks of gestation (i) exclusively receiving (i.e., >80% of total intake) OMM, and (ii) exclusively receiving DHM, and (iii) term infants exclusively receiving OMM, as well as their mothers. Biological samples and nutritional, clinical, and anthropometric characteristics are collected at six time points covering the period from birth and until six months of infant's age. The genotype, metabolome, and microbiota as well as the HM composition are characterized. Portable sensor prototypes for the analysis of HM and urine are benchmarked. Additionally, maternal psychosocial status is measured at the beginning of the study and at month six. Mother-infant postpartum bonding and parental stress are also examined. At six months, infant neurodevelopment scales are applied. Mother's concerns and attitudes to breastfeeding are registered through a specific questionnaire.DiscussionNUTRISHIELD provides an in-depth longitudinal study of the mother-infant-microbiota triad combining multiple biological matrices, newly developed analytical methods, and ad-hoc designed sensor prototypes with a wide range of clinical outcome measures. Data obtained from this study will be used to train a machine-learning algorithm for providing dietary advice to lactating mothers and will be implemented in a user-friendly platform based on a combination of user-provided information and biomarker analysis. A better understanding of the factors affecting milk's composition, together with the health implications for infants plays an important role in developing improved strategies of nutraceutical management in infant care.Clinical trial registrationhttps://register.clinicaltrials.gov, identifier: NCT05646940.