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      The transcriptome of soybean reproductive tissues subjected to water deficit, heat stress, and a combination of water deficit and heat stress

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          SUMMARY

          Global warming and climate change are driving an alarming increase in the frequency and intensity of extreme climate events, such as droughts, heat waves, and their combination, inflicting heavy losses to agricultural production. Recent studies revealed that the transcriptomic responses of different crops to water deficit (WD) or heat stress (HS) are very different from that to a combination of WD + HS. In addition, it was found that the effects of WD, HS, and WD + HS are significantly more devastating when these stresses occur during the reproductive growth phase of crops, compared to vegetative growth. As the molecular responses of different reproductive and vegetative tissues of plants to WD, HS, or WD + HS could be different from each other and these differences could impact many current and future attempts to enhance the resilience of crops to climate change through breeding and/or engineering, we conducted a transcriptomic analysis of different soybean ( Glycine max) tissues to WD, HS, and WD + HS. Here we present a reference transcriptomic dataset that includes the response of soybean leaf, pod, anther, stigma, ovary, and sepal to WD, HS, and WD + HS conditions. Mining this dataset for the expression pattern of different stress response transcripts revealed that each tissue had a unique transcriptomic response to each of the different stress conditions. This finding is important as it suggests that enhancing the overall resilience of crops to climate change could require a coordinated approach that simultaneously alters the expression of different groups of transcripts in different tissues in a stress‐specific manner.

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          Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

          Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT2, for representing and searching an expanded model of the human reference genome, in which a large catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate two initial applications of HISAT2: HLA typing, a critical need in human organ transplantation, and DNA fingerprinting, widely used in forensics. These applications are part of HISAT-genotype, with performance not only surpassing earlier computational methods, but matching or exceeding the accuracy of laboratory-based assays.
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            Is Open Access

            Twelve years of SAMtools and BCFtools

            Abstract Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.
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              Is Open Access

              g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

              Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                The Plant Journal
                The Plant Journal
                Wiley
                0960-7412
                1365-313X
                April 14 2023
                Affiliations
                [1 ] Division of Plant Science and Technology, College of Agriculture Food and Natural Resources and Interdisciplinary Plant Group University of Missouri Columbia Missouri 65211 USA
                [2 ] Christopher S. Bond Life Sciences Center University of Missouri Columbia Missouri 65211 USA
                [3 ] Department of Biology, Biochemistry and Environmental Sciences University Jaume I Av. de Vicent Sos Baynat, s/n Castelló de la Plana 12071 Spain
                [4 ] Institute for Data Science and Informatics and Interdisciplinary Plant Group University of Missouri Columbia Missouri 65211 USA
                [5 ] Department of Health Management and Informatics, and Christopher S. Bond Life Sciences Center University of Missouri Columbia Missouri 65211 USA
                [6 ] Department of Surgery University of Missouri School of Medicine, Christopher S. Bond Life Sciences Center, University of Missouri Columbia Missouri 65201 USA
                Article
                10.1111/tpj.16222
                dd1ad488-a7f3-45a9-ba5f-044a9f5358f8
                © 2023

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