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Abstract Genome sequencing for agriculturally important Rosaceous crops has made rapid progress both in completeness and annotation quality. Whole genome sequence and annotation give breeders, researchers, and growers information about cultivar-specific traits such as fruit quality and disease resistance, and inform strategies to enhance postharvest storage. Here we present a haplotype-phased, chromosomal-level genome of Malus domestica, ‘WA 38’, a new apple cultivar released to market in 2017 as Cosmic Crisp®. Using both short and long-read sequencing data with a k-mer-based approach, chromosomes originating from each parent were assembled and segregated. This is the first pome fruit genome fully phased into parental haplotypes in which chromosomes from each parent are identified and separated into their unique, respective haplomes. The two haplome assemblies, ‘Honeycrisp’ originated HapA and ‘Enterprise’ originated HapB, are about 650 Megabases each, and both have a BUSCO score of 98.7% complete. A total of 53,028 and 54,235 genes were annotated from HapA and HapB, respectively. Additionally, we provide genome-scale comparisons to ‘Gala’, ‘Honeycrisp’, and other relevant cultivars highlighting major differences in genome structure and gene family circumscription. This assembly and annotation was done in collaboration with the American Campus Tree Genomes project that includes ‘WA 38’ (Washington State University), ‘d’Anjou’ pear (Auburn University), and many more. To ensure transparency, reproducibility, and applicability for any genome project, our genome assembly and annotation workflow is recorded in detail and shared under a public GitLab repository. All software is containerized, offering a simple implementation of the workflow. 

Abstract Background Lung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms. Results We built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer. Conclusions Our approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species. 

Abstract Background Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. Results GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Conclusions Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions. 

Abstract Cultivated pear consists of several Pyrus species with Pyrus communis (European pear) representing a large fraction of worldwide production. As a relatively recently domesticated crop and perennial tree, pear can benefit from genome-assisted breeding. Additionally, comparative genomics within Rosaceae promises greater understanding of evolution within this economically important family. Here, we generate a fully phased chromosome-scale genome assembly of P. communis ‘d’Anjou.’ Using PacBio HiFi and Dovetail Omni-C reads, the genome is resolved into the expected 17 chromosomes, with each haplotype totaling nearly 540 Megabases and a contig N50 of nearly 14 Mb. Both haplotypes are highly syntenic to each other and to the Malus domestica ‘Honeycrisp’ apple genome. Nearly 45,000 genes were annotated in each haplotype, over 90% of which have direct RNA-seq expression evidence. We detect signatures of the known whole-genome duplication shared between apple and pear, and we estimate 57% of d’Anjou genes are retained in duplicate derived from this event. This genome highlights the value of generating phased diploid assemblies for recovering the full allelic complement in highly heterozygous crop species. 

Abstract Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL https://www.agbiodata.org/databases 

Abstract Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow. 

Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4 ) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.