Soybean Cyst Nematode Resistance Quantitative Trait Locus cqSCN-006 Alters the Expression of a γ-SNAP Protein.

Soybean cyst nematode (SCN) is the most economically damaging pathogen of soybean and host resistance is a core management strategy. The SCN resistance quantitative trait locus cqSCN-006, introgressed from the wild relative Glycine soja, provides intermediate resistance against nematode populations, including those with increased virulence on the heavily used rhg1-b resistance locus. cqSCN-006 was previously fine-mapped to a genome interval on chromosome 15. The present study determined that Glyma.15G191200 at cqSCN-006, encoding a γ-SNAP, contributes to SCN resistance. CRISPR/Cas9-mediated disruption of the cqSCN-006 allele reduced SCN resistance in transgenic roots. There are no encoded amino acid polymorphisms between resistant and susceptible alleles. However, other cqSCN-006-specific DNA polymorphisms in the Glyma.15G191200 promoter and gene body were identified, and we observed differing induction of γ-SNAP protein abundance at SCN infection sites between resistant and susceptible roots. We identified alternative RNA splice forms transcribed from the Glyma.15G191200 γ-SNAP gene and observed differential expression of the splice forms 2 days after SCN infection. Heterologous overexpression of γ-SNAPs in plant leaves caused moderate necrosis, suggesting that careful regulation of this protein is required for cellular homeostasis. Apparently, certain G. soja evolved quantitative SCN resistance through altered regulation of γ-SNAP. Previous work has demonstrated SCN resistance impacts of the soybean α-SNAP proteins encoded by Glyma.18G022500 (Rhg1) and Glyma.11G234500. The present study shows that a different type of SNAP protein can also impact SCN resistance. Little is known about γ-SNAPs in any system, but the present work suggests a role for γ-SNAPs during susceptible responses to cyst nematodes.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

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

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.