scholarly journals A comprehensive co-expression network analysis in Vibrio cholerae

2020 ◽  
Author(s):  
Cory D. DuPai ◽  
Claus O. Wilke ◽  
Bryan W. Davies
Keyword(s):  
Network Analysis ◽  
Vibrio Cholerae ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Model Organism ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Virulent Strains ◽  
Micro Array ◽  
The Impact

AbstractResearch into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high throughput sequencing data to drive molecular analyses. The steady accumulation of these datasets now provides a unique opportunity for in silico hypothesis generation via co-expression analysis. Here we leverage all published V. cholerae RNA-sequencing data, in combination with select data from other platforms, to generate a gene co-expression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g. Tn-seq and ChIP-seq), and expands upon micro-array based findings in related gram-negative bacteria.ImportanceCholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite this importance, roughly one third of V. cholerae genes are functionally un-annotated, leaving large gaps in our understanding of this microbe. Through co-expression network analysis of existing RNA-sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

scholarly journals A Comprehensive Coexpression Network Analysis in Vibrio cholerae

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Cory D. DuPai ◽  
Claus O. Wilke ◽  
Bryan W. Davies
Keyword(s):  
Network Analysis ◽  
Vibrio Cholerae ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Model Organism ◽  
Coexpression Network ◽  
Sequencing Data ◽  
Content Type ◽  
The Impact ◽  
Coexpression Network Analysis

ABSTRACT Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for in silico hypothesis generation via coexpression analysis. Here, we leverage all published V. cholerae RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria. IMPORTANCE Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

scholarly journals tRNA-Derived Small RNAs: Biogenesis, Modification, Function and Potential Impact on Human Disease Development

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 607 ◽  
Author(s):  
Vera Oberbauer ◽  
Matthias Schaefer
Keyword(s):  
Human Disease ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Current Knowledge ◽  
Biologically Active ◽  
Data Sets ◽  
Disease Development ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
The Impact

Transfer RNAs (tRNAs) are abundant small non-coding RNAs that are crucially important for decoding genetic information. Besides fulfilling canonical roles as adaptor molecules during protein synthesis, tRNAs are also the source of a heterogeneous class of small RNAs, tRNA-derived small RNAs (tsRNAs). Occurrence and the relatively high abundance of tsRNAs has been noted in many high-throughput sequencing data sets, leading to largely correlative assumptions about their potential as biologically active entities. tRNAs are also the most modified RNAs in any cell type. Mutations in tRNA biogenesis factors including tRNA modification enzymes correlate with a variety of human disease syndromes. However, whether it is the lack of tRNAs or the activity of functionally relevant tsRNAs that are causative for human disease development remains to be elucidated. Here, we review the current knowledge in regard to tsRNAs biogenesis, including the impact of RNA modifications on tRNA stability and discuss the existing experimental evidence in support for the seemingly large functional spectrum being proposed for tsRNAs. We also argue that improved methodology allowing exact quantification and specific manipulation of tsRNAs will be necessary before developing these small RNAs into diagnostic biomarkers and when aiming to harness them for therapeutic purposes.

scholarly journals High-throughput sequencing data and the impact of plant gene annotation quality

2018 ◽  
Vol 70 (4) ◽  
pp. 1069-1076 ◽  
Author(s):  
Aleksia Vaattovaara ◽  
Johanna Leppälä ◽  
Jarkko Salojärvi ◽  
Michael Wrzaczek
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Gene Annotation ◽  
Sequencing Data ◽  
Plant Gene ◽  
High Throughput Sequencing Data ◽  
The Impact ◽  
Annotation Quality

scholarly journals BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

2014 ◽  
Vol 30 (9) ◽  
pp. 1214-1219 ◽  
Author(s):  
C. Ye ◽  
C. Hsiao ◽  
H. Corrada Bravo
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Blind Deconvolution ◽  
Sequencing Data ◽  
Base Calling ◽  
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 deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

scholarly journals Improving gene function predictions using independent transcriptional components

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Urzúa-Traslaviña ◽  
Vincent C. Leeuwenburgh ◽  
Arkajyoti Bhattacharya ◽  
Stefan Loipfinger ◽  
Marcel A. T. M. van Vugt ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Sequencing Data ◽  
New Members ◽  
High Throughput Sequencing Data ◽  
Gene Sets ◽  
Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

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.

scholarly journals A meta-analysis reveals the environmental and host factors shaping the structure and function of the shrimp microbiota

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5382 ◽  
Author(s):  
Fernanda Cornejo-Granados ◽  
Luigui Gallardo-Becerra ◽  
Miriam Leonardo-Reza ◽  
Juan Pablo Ochoa-Romo ◽  
Adrian Ochoa-Leyva
Keyword(s):  
Developmental Stage ◽  
High Throughput Sequencing ◽  
Meta Analysis ◽  
Biological Factor ◽  
Rrna Gene ◽  
Wild Type ◽  
Biological Factors ◽  
Sequencing Data ◽  
Freshwater Environment ◽  
The Impact

The shrimp or prawn is the most valuable traded marine product in the world market today and its microbiota plays an essential role in its development, physiology, and health. The technological advances and dropping costs of high-throughput sequencing have increased the number of studies characterizing the shrimp microbiota. However, the application of different experimental and bioinformatics protocols makes it difficult to compare different studies to reach general conclusions about shrimp microbiota. To meet this necessity, we report the first meta-analysis of the microbiota from freshwater and marine shrimps using all publically available sequences of the 16S ribosomal gene (16S rRNA gene). We obtained data for 199 samples, in which 63.3% were from marine (Alvinocaris longirostris, Litopenaeus vannamei and Penaeus monodon), and 36.7% were from freshwater (Macrobrachium asperulum, Macrobrachium nipponense, Macrobranchium rosenbergii, Neocaridina denticulata) shrimps. Technical variations among studies, such as selected primers, hypervariable region, and sequencing platform showed a significant impact on the microbiota structure. Additionally, the ANOSIM and PERMANOVA analyses revealed that the most important biological factor in structuring the shrimp microbiota was the marine and freshwater environment (ANOSIM R = 0.54, P = 0.001; PERMANOVA pseudo-F = 21.8, P = 0.001), where freshwater showed higher bacterial diversity than marine shrimps. Then, for marine shrimps, the most relevant biological factors impacting the microbiota composition were lifestyle (ANOSIM R = 0.341, P = 0.001; PERMANOVA pseudo-F = 8.50, P = 0.0001), organ (ANOSIM R = 0.279, P = 0.001; PERMANOVA pseudo-F = 6.68, P = 0.001) and developmental stage (ANOSIM R = 0.240, P = 0.001; PERMANOVA pseudo-F = 5.05, P = 0.001). According to the lifestyle, organ, developmental stage, diet, and health status, the highest diversity were for wild-type, intestine, adult, wild-type diet, and healthy samples, respectively. Additionally, we used PICRUSt to predict the potential functions of the microbiota, and we found that the organ had more differentially enriched functions (93), followed by developmental stage (12) and lifestyle (9). Our analysis demonstrated that despite the impact of technical and bioinformatics factors, the biological factors were also statistically significant in shaping the microbiota. These results show that cross-study comparisons are a valuable resource for the improvement of the shrimp microbiota and microbiome fields. Thus, it is important that future studies make public their sequencing data, allowing other researchers to reach more powerful conclusions about the microbiota in this non-model organism. To our knowledge, this is the first meta-analysis that aims to define the shrimp microbiota.

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