Fine mapping and cloning of the major seed protein quantitative trait loci on soybean chromosome 20

Soybean [Glycine max (L.) Merr.] is a unique crop species because it has high levels of both protein and oil in its seed. Of the many quantitative trait loci (QTL) controlling soybean seed protein content, alleles of the cqSeed protein-003 QTL on chromosome 20 exert the greatest additive effect. The high-protein allele exists in both cultivated and wild soybean (Glycine soja Siebold & Zucc.) germplasm. Our objective was to fine map this QTL to enable positional-based cloning of its underlying causative gene(s). Fine mapping was achieved by developing and testing a series of populations in which the chromosomal region surrounding the segregating high- versus low-protein alleles was gradually narrowed, using marker-based detection of recombinant events. The resultant 77.8 kb interval was directly sequenced from a G. soja source and compared with the reference genome to identify structural and sequence polymorphisms. An insertion/deletion variant detected in Glyma.20G85100 was found to have near-perfect +/- concordance with high/low-protein allele genotypes inferred for this QTL in parents of published mapping populations. The indel structure was concordant with an evolutionarily recent insertion of a TIR transposon into the gene in the low-protein lineage. Seed protein was significantly greater in soybean expressing an RNAi hairpin downregulation element in two independent events relative to control null segregant lineages. We conclude that a transposon insertion within the CCT domain protein encoded by the Glyma.20G85100 gene accounts for the high/low seed protein alleles of the cqSeed protein-003 QTL

Design considerations for workflow management systems use in production genomics research and the clinic

Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance