A reference-grade wild soybean genome

Xie, Min; Chung, Claire Yik Lok; Li, Man-Wah; Wong, Fuk Ling; Wang, Xin; Liu, Ailin; Wang, Zhili; Leung, Alden King-Yung; Wong, Tin Hang; Tong, Suk Wah; Xiao, Zhixia; Fan, Kejing; Ng, Ming Sin; Qi, Xiaofeng; Yang, L.; Deng, Tianquan; He, Li; Chen, Lu; Fu, Aisi; Ding, Qiong; He, Jun; Chung, Gyuhwa; Isobe, Sachiko; Tanabata, Takanari; Valliyodan, Babu; Nguyen, Henry T.; Cannon, Steven B.; Foyer, Christine H.; Chan, Ting‐Fung; Lam, Hon‐Ming

doi:10.1038/s41467-019-09142-9

Cited by 193 publications

(178 citation statements)

References 71 publications

(116 reference statements)

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“…In the reference-based method, quality-and adaptertrimmed RNA-Seq data from individual samples were mapped to a reference genome of either Williams 82 (Schmutz et al, 2010) or W05 ( Fig. 1A; Supplemental Table S2; Xie et al, 2019), followed by transcript assembly ( Fig. 1B) and annotation (Fig.…”

Section: Overview Of Lncrna Identificationmentioning

confidence: 99%

“…E, The same set of clean reads as in (A) were assembled by the program Trinity (Grabherr et al, 2011) using the de novo approach. F, Another set of de novo transcripts generated by PacBio Iso-Seq were retrieved from Xie et al (2019). G, De novo transcripts from (E) and (F) were mapped to the reference genome with the software GMAP (Wu and Watanabe, 2005).…”

Section: Te Composition Of Lncrnasmentioning

confidence: 99%

“…We also investigated lncRNA transcriptional responses with respect to time by making use of previously published RNA-Seq datasets corresponding to timed salt treatment of two of our in-house soybean germplasms, C08 (Liu et al, 2018) and W05 (Xie et al, 2019). In a previous study from our group, it was shown that sodium ion started to accumulate in root tissues within the first hour of salt treatment, while it was detected in leaf tissues after 24 h of salt treatment (Liu et al, 2018).…”

Section: Stress Response Of Lncrna Genes In Different Soybean Germplamentioning

confidence: 99%

“…Reference genomes and gene annotations were publicly available for Williams 82 (Wm82.a2.v1, G. max; Schmutz et al, 2010) and W05 (G. soja; Xie et al, 2019; Supplemental Table S2). We used two soybean reference genomes here because our group has previously reported the substantial genomic differences between G. max and G. soja (Lam et al, 2010;Xie et al, 2019).…”

Section: Reference Genomes and Annotationsmentioning

confidence: 99%

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Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Lin

et al. 2019

Plant Physiol.

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Long non-coding RNAs (lncRNAs) are defined as non-protein-coding transcripts that are at least 200 nucleotides long. They are known to play pivotal roles in regulating gene expression, especially during stress responses in plants. We used a large collection of in-house transcriptome data from various soybean (Glycine max and Glycine soja) tissues treated under different conditions to perform a comprehensive identification of soybean lncRNAs. We also retrieved publicly available soybean transcriptome data that were of sufficient quality and sequencing depth to enrich our analysis. In total, RNA-sequencing data of 332 samples were used for this analysis. An integrated reference-based, de novo transcript assembly was developed that identified ;69,000 lncRNA gene loci. We showed that lncRNAs are distinct from both protein-coding transcripts and genomic background noise in terms of length, number of exons, transposable element composition, and sequence conservation level across legume species. The tissue-specific and time-specific transcriptional responses of the lncRNA genes under some stress conditions may suggest their biological relevance. The transcription start sites of lncRNA gene loci tend to be close to their nearest proteincoding genes, and they may be transcriptionally related to the protein-coding genes, particularly for antisense and intronic lncRNAs. A previously unreported subset of small peptide-coding transcripts was identified from these lncRNA loci via tandem mass spectrometry, which paved the way for investigating their functional roles. Our results also highlight the present inadequacy of the bioinformatic definition of lncRNA, which excludes those lncRNA gene loci with small open reading frames from being regarded as protein-coding.

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Section: Overview Of Lncrna Identificationmentioning

confidence: 99%

Section: Te Composition Of Lncrnasmentioning

confidence: 99%

Section: Stress Response Of Lncrna Genes In Different Soybean Germplamentioning

confidence: 99%

Section: Reference Genomes and Annotationsmentioning

confidence: 99%

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Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Lin

et al. 2019

Plant Physiol.

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“…and it was only present in two of the eight high SNP varieties that harbored either the I, i k , or i wild alleles ( Figure 5). Finally, a recent reference quality assembly of a black-seeded (i genotype) G. soja wild soybean line (Xie et al, 2019) contained eight CHS genes in the chr 8 locus region which is in good agreement with the digital PCR results for nonadapted Peking and Sooty varieties with i genotype (Figure 2b) and lower than the number of copies found in the dominant silencing i i allele with 11 CHS copies estimated by digital PCR (Figure 2b) and 12 copies found in the modified Wm82.a2 genome (Figure 1). The ancestral G. soja genome also is reported to contain an inversion relative to the domesticated Williams 82 cultivar sequences having the i i allele.…”

Section: The Levels Of Snps Define the I And I K Alleles As Distincmentioning

confidence: 99%

Nonallelic homologous recombination events responsible for copy number variation within an RNA silencing locus

2019

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The structure of chalcone synthase ( CHS ) gene repeats in different alleles of the I (inhibitor) locus in soybean spawns endogenous RNA interference (RNAi) that leads to phenotypic change in seed coat color of this major agronomic crop. Here, we examined CHS gene copy number by digital PCR and single nucleotide polymorphisms (SNPs) through whole genome resequencing of 15 cultivars that varied in alleles of the I locus ( I , i i , i k , and i ) that control the pattern distribution of pigments in the seed coats. Lines homozygous for the i i allele had the highest copy number followed by the I and i k cultivars which were more related to each other than to the lines with i i alleles. Some of the recessive i alleles were spontaneous mutations, and each revealed a loss of copy number by digital PCR relative to the parent varieties. Amplicon sequencing and whole genome resequencing determined that the breakpoints of several i i to i mutations resulted from nonallelic homologous recombination (NAHR) events between CHS genes located in segmental duplications leading to large 138‐kilobase deletions that erase the structure generating the CHS siRNAs along with eight other non‐ CHS genes. Functional hybrid CHS genes (designated CHS5:1 ) were formed in the process and represent rare examples of NAHR in higher plants that have been captured by examining spontaneous mutational events in isogenic mutant lines.

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The future of legume genetic data resources: Challenges, opportunities, and priorities

et al. 2019

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Legumes, comprising one of the largest, most diverse, and most economically important plant families, are the subject of vibrant research and development worldwide. Continued improvement of legume crops will benefit from the recent proliferation of genetic (including genomic) resources; but the diversity, scale, and complexity of these resources presents challenges to those managing and using them. A workshop held in March of 2019 addressed questions of data resources and priorities for the legumes. The workshop identified various needs and recommendations: (a) Develop strategies to effectively store, integrate, and relate genetic resources collected in different projects. (b) Leverage information collected across many legume species by standardizing data formats and ontologies, improving the state of metadata about datasets, and increasing use of the FAIR data principles. (c) Advocate for the critical role that curators exercise in integrating complex datasets into databases and adding high value metadata that enable downstream analytics and facilitate practical applications. (d) Implement standardized software and database development practices to best leverage limited developer time and expertise gained from the various legume (and other) species. (e) Develop tools and databases that can manage genetic information for the world's plant genetic resources, enabling efficient incorporation of important traits into breeding programs. (f) Centralize information on databases, tools, and training materials and establish funding streams to support training and outreach.

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A reference-grade wild soybean genome

Cited by 193 publications

References 71 publications

Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Nonallelic homologous recombination events responsible for copy number variation within an RNA silencing locus

The future of legume genetic data resources: Challenges, opportunities, and priorities

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