Back

De novo assembly and characterization of transcriptome towards understanding molecular mechanism associated with MYMIV-resistance in Vigna mungo - A computational study

Gupta, M. K.; Donde, R.; Gouda, G.; Vadde, R.; Behera, L.

2019-11-16 bioinformatics
10.1101/844639 bioRxiv
Show abstract

The fast climate change affects yield in Vigna mungo via enhancing both biotic and abiotic stresses. Out of all factors, the yellow mosaic disease has the most damaging effect. However, due to lack of reference genome of Vigna mungo, the complete mechanism associated with MYMIV (Mungbean Yellow Mosaic Indian Virus) resistance in Vigna mungo remain elusive to date. Considering this, the authors made an attempt to release new transcriptome and its annotation by employing computational approaches. Quality assessment of the generated transcriptomes reveals that it successfully aligned with 99.03% of the raw reads and hence can be employed for future research. Functional annotation of the transcriptome reveals that 31% and [~]14% of the total transcripts encode lncRNAs and protein-coding sequences, respectively. Further, analysis reveals that, out of total transcripts, only 4536 and 78808 are significantly down and up-regulated during MYMIV infection in Vigna mungo, respectively. These significant transcripts are mainly associated with ribosome, spliceosome, glycolysis /gluconeogenesis, RNA transport, oxidative phosphorylation, protein processing in the endoplasmic reticulum, MAPK signaling pathway - plant, methionine and cysteine metabolism, purine metabolism and RNA degradation. Unlike the previous study, this is for the first time, the present study identified these pathways may play key role in MYMIV resistance in Vigna mungo. Thus, information and transcriptomes data available in the present study make a significant contribution to understanding the genomic structure of Vigna mungo, enabling future analyses as well as downstream applications of gene expression, sequence evolution, and genome annotation.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Frontiers in Plant Science
256 papers in training set
Top 0.3%
12.8%
2
Physiologia Plantarum
39 papers in training set
Top 0.1%
12.4%
3
Plant Direct
95 papers in training set
Top 0.4%
6.7%
4
PLOS ONE
5266 papers in training set
Top 26%
6.2%
5
Plant Science
31 papers in training set
Top 0.1%
5.1%
6
BMC Plant Biology
57 papers in training set
Top 0.2%
4.3%
7
Frontiers in Genetics
230 papers in training set
Top 0.7%
4.3%
50% of probability mass above
8
PeerJ
308 papers in training set
Top 2%
4.0%
9
BMC Genomics
406 papers in training set
Top 2%
3.2%
10
Plant Molecular Biology
20 papers in training set
Top 0.2%
3.2%
11
Genes
144 papers in training set
Top 1.0%
2.8%
12
Scientific Reports
3612 papers in training set
Top 40%
2.6%
13
Plants
43 papers in training set
Top 0.5%
2.6%
14
Plant Methods
42 papers in training set
Top 0.4%
1.9%
15
Gene
46 papers in training set
Top 0.8%
1.7%
16
Horticulture Research
47 papers in training set
Top 0.5%
1.7%
17
Genomics
64 papers in training set
Top 0.9%
1.5%
18
The Plant Journal
215 papers in training set
Top 3%
1.5%
19
Molecular Biology Reports
21 papers in training set
Top 0.5%
1.4%
20
Gene Reports
14 papers in training set
Top 0.5%
1.1%
21
The Plant Genome
57 papers in training set
Top 0.8%
1.0%
22
International Journal of Molecular Sciences
494 papers in training set
Top 13%
1.0%
23
Gigabyte
62 papers in training set
Top 1%
0.9%
24
Plant Physiology
238 papers in training set
Top 3%
0.8%
25
Journal of Proteomics
28 papers in training set
Top 0.5%
0.8%
26
Journal of Biotechnology
11 papers in training set
Top 0.3%
0.6%
27
Plant and Cell Physiology
52 papers in training set
Top 2%
0.6%
28
Plant Biotechnology Journal
64 papers in training set
Top 1%
0.6%