RNA-Seq-Mediated Transcriptome Analysis of a Fiberless Mutant Cotton and Its Possible Origin Based on SNP Markers

Abstract Background Maize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines. Results A total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic. Conclusions These high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.

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Transcriptome Analysis Reveals HgCl2 Induces Apoptotic Cell Death in Human Lung Carcinoma H1299 Cells through Caspase-3-Independent Pathway

International Journal of Molecular Sciences ◽

10.3390/ijms22042006 ◽

2021 ◽

Vol 22 (4) ◽

pp. 2006

Author(s):

Mi Jin Kim ◽

Jinhong Park ◽

Jinho Kim ◽

Ji-Young Kim ◽

Mi-Jin An ◽

...

Keyword(s):

Cell Cycle ◽

Cell Death ◽

Transcriptome Analysis ◽

Caspase 3 ◽

Apoptotic Cell ◽

Expression Patterns ◽

Nitrogen Compound ◽

Apoptotic Cell Death ◽

Mercury Chloride ◽

Rna Seq

Mercury is one of the detrimental toxicants that can be found in the environment and exists naturally in different forms; inorganic and organic. Human exposure to inorganic mercury, such as mercury chloride, occurs through air pollution, absorption of food or water, and personal care products. This study aimed to investigate the effect of HgCl2 on cell viability, cell cycle, apoptotic pathway, and alters of the transcriptome profiles in human non-small cell lung cancer cells, H1299. Our data show that HgCl2 treatment causes inhibition of cell growth via cell cycle arrest at G0/G1- and S-phase. In addition, HgCl2 induces apoptotic cell death through the caspase-3-independent pathway. Comprehensive transcriptome analysis using RNA-seq indicated that cellular nitrogen compound metabolic process, cellular metabolism, and translation for biological processes-related gene sets were significantly up- and downregulated by HgCl2 treatment. Interestingly, comparative gene expression patterns by RNA-seq indicated that mitochondrial ribosomal proteins were markedly altered by low-dose of HgCl2 treatment. Altogether, these data show that HgCl2 induces apoptotic cell death through the dysfunction of mitochondria.

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184 Whole-transcriptome analysis by RNA-Seq for genetic diagnosis of Mendelian skin disorders in the context of consanguinity

Journal of Investigative Dermatology ◽

10.1016/j.jid.2021.02.204 ◽

2021 ◽

Vol 141 (5) ◽

pp. S32

Author(s):

L. Youssefian ◽

A. Saeidian ◽

P. Fortina ◽

A. South ◽

J. Uitto ◽

...

Keyword(s):

Transcriptome Analysis ◽

Genetic Diagnosis ◽

Rna Seq ◽

Skin Disorders ◽

Whole Transcriptome Analysis ◽

Whole Transcriptome

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De novo RNA-Seq based transcriptome analysis of Papiliotrema laurentii strain RY1 under nitrogen starvation

Gene ◽

10.1016/j.gene.2017.12.014 ◽

2018 ◽

Vol 645 ◽

pp. 146-156 ◽

Cited By ~ 5

Author(s):

Soumyadev Sarkar ◽

Somnath Chakravorty ◽

Avishek Mukherjee ◽

Debanjana Bhattacharya ◽

Semantee Bhattacharya ◽

...

Keyword(s):

Transcriptome Analysis ◽

Nitrogen Starvation ◽

De Novo ◽

Rna Seq

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Transcriptome Analysis Based on RNA-Seq in Understanding Pathogenic Mechanisms of Diseases and the Immune System of Fish: A Comprehensive Review

International Journal of Molecular Sciences ◽

10.3390/ijms19010245 ◽

2018 ◽

Vol 19 (1) ◽

pp. 245 ◽

Cited By ~ 39

Author(s):

Arun Sudhagar ◽

Gokhlesh Kumar ◽

Mansour El-Matbouli

Keyword(s):

Immune System ◽

Transcriptome Analysis ◽

Rna Seq ◽

Comprehensive Review ◽

Pathogenic Mechanisms

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De Novo Transcriptome Analysis of Medicinally Important Plantago ovata Using RNA-Seq

PLoS ONE ◽

10.1371/journal.pone.0150273 ◽

2016 ◽

Vol 11 (3) ◽

pp. e0150273 ◽

Cited By ~ 14

Author(s):

Shivanjali Kotwal ◽

Sanjana Kaul ◽

Pooja Sharma ◽

Mehak Gupta ◽

Rama Shankar ◽

...

Keyword(s):

Transcriptome Analysis ◽

De Novo ◽

Rna Seq ◽

Plantago Ovata ◽

De Novo Transcriptome

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Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

Scientific Reports ◽

10.1038/s41598-021-87281-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Go-Eun Yu ◽

Younhee Shin ◽

Sathiyamoorthy Subramaniyam ◽

Sang-Ho Kang ◽

Si-Myung Lee ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Feature Selection Method ◽

Flower Color ◽

Classification Performance ◽

Snp Markers ◽

Rna Seq ◽

Color Classification ◽

Dna Pcr ◽

Selection Of

AbstractBellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

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