With the development of next-generation sequencing technology (NGS), the ability to generate large amount of data through sequencing has revolutionized the field of microbial transcriptomics. However, most viruses have a relatively small genome. And the low relative abundance of viral RNA when compared to host-derived transcriptomes in any given samples limits the detection of viral transcriptomes or viral RNA genomes. We provide a robust viral RNA sequencing using NGS and/or long-read sequencing to bring new insights into the study of viral transcriptomes.
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RNA sequencing (RNA-Seq) is a powerful and high-throughput transcriptome analysis technique utilizing NGS platforms. It can provide accurate estimates of isoform abundance over a large dynamic range. A wide dynamic range allows us to determine both high and low expression levels in individual viral RNA sequencing experiments, greatly increasing the analysis efficiency. Hence, RNA-seq has been very important in basic research. Viral RNA-seq refers to the sequencing of all transcriptional information of viruses, which is time-saving and economical. Furthermore, the use of long-read sequencing (LRS) to solve the complex transcriptome structure has become a popular research method. Here, we have established standardized viral RNA-seq platforms based on NGS, PacBio SMRT, and Oxford nanopore instruments to identify common and novel transcripts without bias. PacBio SMRT sequencing and Oxford nanopore sequencing can generate much longer reads, and detect more transcript overlaps, polycistronic RNAs, splice and transcript variants.
Viral RNA sequencing is important for understanding pathogen-host immune interactions, exploring resistance to specific antibiotics, quantifying changes in gene expression, and monitoring disease progression. Its importance in research has been increasingly recognized. At present, viral RNA-seq using next-generation sequencing and/or long-read sequencing has become a standard method for analyzing viral transcriptome or genome information.
Our bioinformatics analysis includes read QC, transcript assembly, functional profiling analysis, and advanced analysis, which is flexible to your needs. Please feel free to contact us to discuss your project.
|Read QC||Filtering, trimming, optional error correction if possible|
|Genome/Transcriptome Assembly||Mapping to known genomes or transcriptomes; de novo assembly and annotation to protein database; outcome evaluation; Transcript and isoform quantification|
|Functional Profiling||GO, DAVID, KEGG, TrEMBL, Pfam, InterPro, SignalP, Blast2GO; Molecular functions or pathways in which differentially expressed genes|
|Advanced Analysis||Detection of eQTL/sQTL, Chromatin, TF binding, Proteomics/metabolomics; Analysis of integration with other technologies|