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  1. 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 accessmore »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.« less
    Free, publicly-accessible full text available December 1, 2023
  2. Abstract Host-specific interactions can maintain genetic and phenotypic diversity in parasites that attack multiple host species. Host diversity, in turn, may promote parasite diversity by selection for genetic divergence or plastic responses to host type. The parasitic weed purple witchweed [ Striga hermonthica (Delile) Benth.] causes devastating crop losses in sub-Saharan Africa and is capable of infesting a wide range of grass hosts. Despite some evidence for host adaptation and host-by- Striga genotype interactions, little is known about intraspecific Striga genomic diversity. Here we present a study of transcriptomic diversity in populations of S. hermonthica growing on different hosts (maize [ Zea mays L.] vs. grain sorghum [ Sorghum bicolor (L.) Moench]). We examined gene expression variation and differences in allelic frequency in expressed genes of aboveground tissues from populations in western Nigeria parasitizing each host. Despite low levels of host-based genome-wide differentiation, we identified a set of parasite transcripts specifically associated with each host. Parasite genes in several different functional categories implicated as important in host–parasite interactions differed in expression level and allele on different hosts, including genes involved in nutrient transport, defense and pathogenesis, and plant hormone response. Overall, we provide a set of candidate transcripts that demonstratemore »host-specific interactions in vegetative tissues of the emerged parasite S. hermonthica . Our study shows how signals of host-specific processes can be detected aboveground, expanding the focus of host–parasite interactions beyond the haustorial connection.« less