scholarly journals Computational identification and characterization of conserved miRNAs and their target genes in beet (Beta vulgaris)

2015 ◽  
Vol 14 (3) ◽  
pp. 9103-9108 ◽  
Author(s):  
J.L. Li ◽  
J. Cui ◽  
D.Y. Cheng
Keyword(s):  
Beta Vulgaris ◽  
Target Genes ◽  
Computational Identification ◽  
Identification And Characterization

Computational identification and characterization of conserved miRNAs and their target genes in garlic (Allium sativum L.) expressed sequence tags

Gene ◽  
2014 ◽  
Vol 537 (2) ◽  
pp. 333-342 ◽  
Author(s):  
Debashis Panda ◽  
Budheswar Dehury ◽  
Jagajjit Sahu ◽  
Madhumita Barooah ◽  
Priyabrata Sen ◽  
...  
Keyword(s):  
Expressed Sequence Tags ◽  
Allium Sativum ◽  
Target Genes ◽  
Computational Identification ◽  
Identification And Characterization ◽  
Expressed Sequence

scholarly journals Computational identification and characterization of miRNAs and their target genes from five cyprinidae fishes

2017 ◽  
Vol 24 (6) ◽  
pp. 1126-1135 ◽  
Author(s):  
Yong Huang ◽  
Hong-Tao Ren ◽  
Quan Zou ◽  
Yu-Qin Wang ◽  
Ji-Liang Zhang ◽  
...  
Keyword(s):  
Target Genes ◽  
Computational Identification ◽  
Identification And Characterization

Identification and characterization of microRNAs and their target genes in tobacco (Nicotiana tabacum)

Planta ◽  
2010 ◽  
Vol 232 (6) ◽  
pp. 1289-1308 ◽  
Author(s):  
Taylor P. Frazier ◽  
Fuliang Xie ◽  
Andrew Freistaedter ◽  
Caitlin E. Burklew ◽  
Baohong Zhang
Keyword(s):  
Nicotiana Tabacum ◽  
Target Genes ◽  
Identification And Characterization

scholarly journals Identification and characterization of the binding sequences and target genes of p53 lacking the 1st transactivation domain

2020 ◽  
Vol 111 (2) ◽  
pp. 451-466
Author(s):  
Shiori Suzuki ◽  
Shuichi Tsutsumi ◽  
Yu Chen ◽  
Chikako Ozeki ◽  
Atsushi Okabe ◽  
...  
Keyword(s):  
Target Genes ◽  
Transactivation Domain ◽  
Identification And Characterization

scholarly journals Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Lin Liu ◽  
Guangyu Wang ◽  
Liguo Wang ◽  
Chunlei Yu ◽  
Mengwei Li ◽  
...  
Keyword(s):  
Computational Identification ◽  
Identification And Characterization

scholarly journals Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Peanut

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 536 ◽  
Author(s):  
Xiaobo Zhao ◽  
Liming Gan ◽  
Caixia Yan ◽  
Chunjuan Li ◽  
Quanxi Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Target Genes ◽  
Sequencing Data ◽  
Regulatory Processes ◽  
Genome Wide ◽  
Non Coding Rnas ◽  
Identification And Characterization ◽  
Lower Expression ◽  
Weighted Correlation

Long non-coding RNAs (lncRNAs) are involved in various regulatory processes although they do not encode protein. Presently, there is little information regarding the identification of lncRNAs in peanut (Arachis hypogaea Linn.). In this study, 50,873 lncRNAs of peanut were identified from large-scale published RNA sequencing data that belonged to 124 samples involving 15 different tissues. The average lengths of lncRNA and mRNA were 4335 bp and 954 bp, respectively. Compared to the mRNAs, the lncRNAs were shorter, with fewer exons and lower expression levels. The 4713 co-expression lncRNAs (expressed in all samples) were used to construct co-expression networks by using the weighted correlation network analysis (WGCNA). LncRNAs correlating with the growth and development of different peanut tissues were obtained, and target genes for 386 hub lncRNAs of all lncRNAs co-expressions were predicted. Taken together, these findings can provide a comprehensive identification of lncRNAs in peanut.

scholarly journals MyDGR: a server for identification and characterization of diversity-generating retroelements

2019 ◽  
Vol 47 (W1) ◽  
pp. W289-W294 ◽  
Author(s):  
Fatemeh Sharifi ◽  
Yuzhen Ye
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Web Server ◽  
Nucleotide Sequences ◽  
Bacterial Genomes ◽  
Fasta Format ◽  
Reverse Transcriptases ◽  
Identification And Characterization ◽  
The Web

Abstract MyDGR is a web server providing integrated prediction and visualization of Diversity-Generating Retroelements (DGR) systems in query nucleotide sequences. It is built upon an enhanced version of DGRscan, a tool we previously developed for identification of DGR systems. DGR systems are remarkable genetic elements that use error-prone reverse transcriptases to generate vast sequence variants in specific target genes, which have been shown to benefit their hosts (bacteria, archaea or phages). As the first web server for annotation of DGR systems, myDGR is freely available on the web at http://omics.informatics.indiana.edu/myDGR with all major browsers supported. MyDGR accepts query nucleotide sequences in FASTA format, and outputs all the important features of a predicted DGR system, including a reverse transcriptase, a template repeat and one (or more) variable repeats and their alignment featuring A-to-N (N can be C, T or G) substitutions, and VR-containing target gene(s). In addition to providing the results as text files for download, myDGR generates a visual summary of the results for users to explore the predicted DGR systems. Users can also directly access pre-calculated, putative DGR systems identified in currently available reference bacterial genomes and a few other collections of sequences (including human microbiomes).

Sign in / Sign up

Export Citation Format

Share Document