scholarly journals Revealing New Landscape of Turbot (Scophthalmus maximus) Spleen Infected with Aeromonas salmonicida through Immune Related circRNA-miRNA-mRNA Axis

Biology ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 626
Author(s):  
Ting Xue ◽  
Yiping Liu ◽  
Min Cao ◽  
Mengyu Tian ◽  
Lu Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Target Genes ◽  
Scophthalmus Maximus ◽  
Aeromonas Salmonicida ◽  
Circular Rnas ◽  
Sequencing Data ◽  
Turbot Scophthalmus Maximus ◽  
Immune Related Genes

Increasing evidence suggests that non-coding RNAs (ncRNA) play an important role in a variety of biological life processes by regulating gene expression at the transcriptional and post-transcriptional levels. Turbot (Scophthalmus maximus) has been threatened by various pathogens. In this study, the expression of circular RNAs (circRNAs), microRNAs (miRNAs), and mRNA in the immune organs spleen of turbot infected with Aeromonas salmonicida was analyzed by high-throughput sequencing, and a circRNA-miRNA-mRNA network was constructed, so as to explore the function of non-coding RNA in the immune system of teleost. Illumina sequencing was performed on the uninfected group and infected group. A total of 119 differential expressed circRNAs (DE-circRNAs), 140 DE-miRNAs, and 510 DE-mRNAs were identified in the four infected groups compared with the uninfected group. Most DE-mRNAs and the target genes of DE-ncRNAs were involved in immune-related pathways. The quantitative real-time PCR (qRT-PCR) results verified the reliability and accuracy of the high-throughput sequencing data. Ninety-six differentially expressed circRNA-miRNA-mRNA regulatory networks were finally constructed. Among them, 15 circRNA-miRNA-mRNA were presented in the form of “up (circRNA)-down (miRNA)-up (mRNA)” or “down-up-down”. Immune-related genes gap junction CX32.2, cell adhesion molecule 3, and CC chemokine were also found in these networks. These results indicate that ncRNA may regulate the expression of immune-related genes through the circRNA-miRNA-mRNA regulatory network and thus participate in the immune response of turbot spleen after pathogen infection.

scholarly journals seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data

2020 ◽  
Vol 16 (10) ◽  
pp. e1008338
Author(s):  
Mohamed Chaabane ◽  
Kalina Andreeva ◽  
Jae Yeon Hwang ◽  
Tae Lim Kook ◽  
Juw Won Park ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

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