scholarly journals Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes

2021 ◽  
Vol 22 (13) ◽  
pp. 6791
Author(s):  
Yeonhwa Jo ◽  
Chang-Gi Back ◽  
Kook-Hyung Kim ◽  
Hyosub Chu ◽  
Jeong Hun Lee ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
In Silico Analysis ◽  
Sequencing Data ◽  
Yield And Quality ◽  
Bulbous Plant ◽  
Virus C ◽  
Dominant Bacteria ◽  
Garlic Virus ◽  
Silico Analysis

Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study.

In Silico Analysis of Micro-RNA Sequencing Data

2021 ◽  
pp. 231-251
Author(s):  
Ernesto Aparicio-Puerta ◽  
Bastian Fromm ◽  
Michael Hackenberg ◽  
Marc K. Halushka
Keyword(s):  
Rna Sequencing ◽  
In Silico ◽  
In Silico Analysis ◽  
Micro Rna ◽  
Sequencing Data ◽  
Silico Analysis

scholarly journals The pattern of gene copy number alteration (CNAs) in hepatocellular carcinoma: an in silico analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Arman Shahrisa ◽  
Maryam Tahmasebi-Birgani ◽  
Hossein Ansari ◽  
Zahra Mohammadi ◽  
Vinicio Carloni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
In Silico ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Gene Copy Number ◽  
In Silico Analysis ◽  
Gene Copy ◽  
Sequencing Data ◽  
Silico Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer that occurs predominantly in patients with previous liver conditions. In the absence of an ideal screening modality, HCC is usually diagnosed at an advanced stage. Recent studies show that loss or gain of genomic materials can activate the oncogenes or inactivate the tumor suppressor genes to predispose cells toward carcinogenesis. Here, we evaluated both the copy number alteration (CNA) and RNA sequencing data of 361 HCC samples in order to locate the frequently altered chromosomal regions and identify the affected genes. Results Our data show that the chr1q and chr8p are two hotspot regions for genomic amplifications and deletions respectively. Among the amplified genes, YY1AP1 (chr1q22) possessed the largest correlation between CNA and gene expression. Moreover, it showed a positive correlation between CNA and tumor grade. Regarding deleted genes, CHMP7 (chr8p21.3) possessed the largest correlation between CNA and gene expression. Protein products of both genes interact with other cellular proteins to carry out various functional roles. These include ASH1L, ZNF496, YY1, ZMYM4, CHMP4A, CHMP5, CHMP2A and CHMP3, some of which are well-known cancer-related genes. Conclusions Our in-silico analysis demonstrates the importance of copy number alterations in the pathology of HCC. These findings open a door for future studies that evaluate our results by performing additional experiments.

scholarly journals Finding of regulatory codes in 5`-UTR of A. thaliana mRNAs by polysome profiling method

2020 ◽  
Author(s):  
K. V. Kabardaeva ◽  
O. N. Mustafaev ◽  
I. V. Deineko ◽  
A. V. Suhorukova ◽  
I. V. Goldenkova-Pavlova
Keyword(s):  
Rna Sequencing ◽  
In Silico ◽  
In Silico Analysis ◽  
Translational Efficiency ◽  
Mrna Pool ◽  
Polysome Profiling ◽  
Silico Analysis

The polysome profiling method was used to separate mRNAs depending on their loading by ribosomes into polysomal and monosomal fractions. Pools separation of such mRNAs and analysis of transcripts (mRNAs) which are associated with each mRNA pool due to RNA sequencing allowed to get an idea of the translational efficiency of individual mRNAs. Moreover, subsequent in silico analysis make possible searching of regulatory contexts in the 5'-UTR of plant A. thaliana, which may be potentially important for efficient translation of mRNA.

scholarly journals Identification and removal of sequencing artifacts produced by mispriming during reverse transcription in multiple RNA-seq technologies

2018 ◽  
Author(s):  
Haridha Shivram ◽  
Vishwanath R. Iyer
Keyword(s):  
Reverse Transcriptase ◽  
Rna Sequencing ◽  
Reverse Transcription ◽  
Group Ii Intron ◽  
Computational Pipeline ◽  
Rna Seq ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Specific Priming

AbstractThe quality of RNA sequencing data relies on specific priming by the primer used for reverse transcription (RT-primer). Non-specific annealing of the RT-primer to the RNA template can generate reads with incorrect cDNA ends and can cause misinterpretation of data (RT mispriming). This kind of artifact in RNA-seq based technologies is underappreciated and currently no adequate tools exist to computationally remove them from published datasets. We show that mispriming can occur with as little as 2 bases of complementarity at the 3’ end of the primer followed by intermittent regions of complementarity. We also provide a computational pipeline that identifies cDNA reads produced from RT mispriming, allowing users to filter them out from any aligned dataset. Using this analysis pipeline, we identify thousands of mispriming events in a dozen published datasets from diverse technologies including short RNA-seq, total/mRNA-seq, HITS-CLIP and GRO-seq. We further show how RT-mispriming can lead to misinterpretation of data. In addition to providing a solution to computationally remove RT-misprimed reads, we also propose an experimental solution to avoid RT-mispriming by performing RNA-seq using thermostable group II intron derived reverse transcriptase (TGIRT-seq).

scholarly journals Leaf nutrient content and transcriptomic analyses of endive (Cichorium endivia) stressed by downpour-induced waterlog reveal a gene network regulating kestose and inulin contents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Giulio Testone ◽  
Anatoly Petrovich Sobolev ◽  
Giovanni Mele ◽  
Chiara Nicolodi ◽  
Maria Gonnella ◽  
...  
Keyword(s):  
In Silico Analysis ◽  
Nutrient Content ◽  
Gene Subset ◽  
Short Term ◽  
Yield And Quality ◽  
Term Enrichment ◽  
Cichorium Endivia ◽  
Gene Correlation ◽  
Total Carbohydrates ◽  
Silico Analysis

AbstractEndive (Cichorium endivia L.), a vegetable consumed as fresh or packaged salads, is mostly cultivated outdoors and known to be sensitive to waterlogging in terms of yield and quality. Phenotypic, metabolic and transcriptomic analyses were used to study variations in curly- (‘Domari’, ‘Myrna’) and smooth-leafed (‘Flester’, ‘Confiance’) cultivars grown in short-term waterlog due to rainfall excess before harvest. After recording loss of head weights in all cultivars (6-35%), which was minimal in ‘Flester’, NMR untargeted profiling revealed variations as influenced by genotype, environment and interactions, and included drop of total carbohydrates (6–50%) and polyols (3–37%), gain of organic acids (2–30%) and phenylpropanoids (98–560%), and cultivar-specific fluctuations of amino acids (−37 to +15%). The analysis of differentially expressed genes showed GO term enrichment consistent with waterlog stress and included the carbohydrate metabolic process. The loss of sucrose, kestose and inulin recurred in all cultivars and the sucrose-inulin route was investigated by covering over 50 genes of sucrose branch and key inulin synthesis (fructosyltransferases) and catabolism (fructan exohydrolases) genes. The lowered expression of a sucrose gene subset together with that of SUCROSE:SUCROSE-1-FRUCTOSYLTRANSFERASE (1-SST) may have accounted for sucrose and kestose contents drop in the leaves of waterlogged plants. Two anti-correlated modules harbouring candidate hub-genes, including 1-SST, were identified by weighted gene correlation network analysis, and proposed to control positively and negatively kestose levels. In silico analysis further pointed at transcription factors of GATA, DOF, WRKY types as putative regulators of 1-SST.

Sign in / Sign up

Export Citation Format

Share Document