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R-loop in soybean

ABSTRACT: R-loop in soybean

PROVIDER: PRJNA893374 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR22016985_1.fastq.gz	Fastqsanger.gz
	SRR22016985_2.fastq.gz	Fastqsanger.gz
	SRR22016986_1.fastq.gz	Fastqsanger.gz
	SRR22016986_2.fastq.gz	Fastqsanger.gz

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R-loop in soybean

Project description:The R-loop is a common chromatin feature presented from prokaryotic to eukaryotic genomes and has been revealed to be involved in multiple cellular processes and associated with many human diseases. Here, we take the advantage of our recently developed ssDRIP-seq method to profile genome-wide R-loop levels of soybean (Glycine max).

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Transcriptional response to soybean aphid infestation in susceptible and resistant soybean plants

Project description:Soybean aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. We used microarrays to characterize the soybean plant's transcriptional defense against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured trancript levels in leaves after one and seven days of aphid infestation.

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Transcriptome profiling of interaction effects of soybean cyst nematodes and soybean aphids on soybean.

Project description:Purpose: Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae) and soybean cyst nematode, Heterodera glycines Ichinohe, (SCN) are the two most economically important pests of soybean, Glycine max (L.) Merr., in the Midwest. Although the soybean aphid is an aboveground pest and SCN is a belowground pest there is evidence that concomitant infestations result in improved SCN reproduction. This study is aimed to characterize the three-way interactions among soybean, soybean aphid and SCN using demographic and genetic datasets. Results: More than 1.1 billion reads (61.4 GB) of transcriptomic data were yielded from 47 samples derived from the experiment using whole roots of G. max. The phred quality scores per base for all the samples were higher than 30. The GC content ranged from 43 to 45% and followed the normal distribution. After trimming, more than 99% of the reads were retained as the clean and good quality reads. Upon mapping these reads, we obtained high mapping rate ranging from 73.8% to 94.3%. Among the mapped reads, 67.1% to 87.6% reads were uniquely mapped. Conclusions: The comprehensive understanding of these transcriptome data would help in understanding the molecular interactions among soybean, A. glycines, and H. glycines. The use of multifaceted bioinformatics approaches could facilitate finding candidate genes and their function that might play a crucial role in various pathways for host resistance against both soybean aphids and SCN. For differential gene expression analysis, EdgeR, limma, and DEseq2 could be used. Apart from standalone tools like iDEP, Galaxy (https://usegalaxy.org), CyVerse (http://www.cyverse.org), and MeV (http://mev.tm4.org) could also be used for both analysis and visualization of RNA- seq data.

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