Small RNAs are a type of non-coding RNA (ncRNA) molecules that are less than 200nt in length. They are often involved in gene silencing and post- transcriptional regulation of gene expression. Small RNA sequencing is used to discover novel small RNAs, examine the dierential expression of all small RNAs and to characterize variations with single-base resolution.
Unique Molecular Identifiers (UMIs) can be utilized to eliminate undesirable PCR duplicates derived from a single molecule. After PCR, molecules sharing a UMI are assumed to be derived from the same input molecule. As such, UMI counts offer superior results to counting reads, leading to more accurate estimates of quantitative small RNA expression[1]. UMI technology is especially beneficial to customers undertaking research on rare and precious samples or samples containing less RNAs, such as exosomes.
DNBSEQ™ is an innovative high-throughput sequencing solution, developed by BGI’s Complete Genomics subsidiary in Silicon Valley. The system is powered by combinatorial Probe-Anchor Synthesis (cPAS), linear isothermal Rolling-Circle Replication and DNA Nanoballs (DNB™) technology, followed by high-resolution digital imaging[2].
The combination of linear amplification and DNB technology reduces the error rate while enhancing the signal. The size of the DNB is controlled in such a way that only one DNB is bound per active site on the flow cell. This densely patterned array technology provides optimal sequencing accuracy and increases flow cell utilization.
Our DNBseq™ Small RNA sequencing service with optional UMI technology delivers accurate, affordable and high-quality sequencing data to support your academic and clinical research applications.
To demonstrate the high technical reproducibility of the DNBSEQ™ technology platform for small RNA sequencing, six human brain samples, two heart samples and two blood samples were sequenced [3]. Reproducibility was assessed by using six technical replicates of human brain sample. The median correlation between the six replicates was 0.98, and the 25% and 75% quantile were 0.98 and 0.99, respectively.
[1] Fu Y, Wu PH, Beane T, Zamore PD, Weng Z: Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genomics 2018;19:531.
[2] Drmanac R et al. : Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 2010;327:78-81.
[3] Fehlmann T, Reinheimer S, Geng C, Su X, Drmanac S, Alexeev A, Zhang C, Backes C, Ludwig N, Hart M, An D, Zhu Z, Xu C, Chen A, Ni M, Liu J, Li Y, Poulter M, Li Y, Stahler C, Drmanac R, Xu X, Meese E, Keller A: cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs. Clin Epigenetics 2016;8:123.
We care for your samples from the start through to the result reporting. Highly experienced laboratory professionals follow strict quality procedures to ensure the integrity of your results.
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DNBSEQ™ Small RNA Sequencing Services are performed with the DNBSEQ™ technology, featuring cPAS and DNA Nanoballs (DNBTM) technology for superior data quality[2].
We can process your human, plant, animal or microbial samples, with the following general requirements:
Regular Samples
Mass: ≥1μg Concentration: ≥50ng/μl
Sample Volume: 15 μl
RIN≥6.5 (plant) RIN≥8.0 (human/animal)
FFPE RNA
Mass: ≥1μg Concentration: ≥50ng/μl
Sample Volume: 15 μl
RIN≥2.0 DV200≥30%
Small RNA of
Plasma/Serum/Exosome
Mass: ≥20μg Concentration: ≥2ng/μl
Sample Volume: 10 μl
N/A
In addition to data output, BGI offers a range of standard and customized bioinformatics pipelines for your small RNA sequencing project.
Reports and output data files are delivered in industry standard FASTQ, and Excel file formats with publication-ready tables and figures.
Sequencing data filtering
Small RNA length distribution
miRNA quantification
Differentially expressed miRNA detection
Clustering analysis
miRNA target gene analysis
GO and Pathway annotation for target genes
Transcription factor prediction (For Animal TFDB/Plant TFDB)
GSEA Analysis
Rfam、Pfam、Reactome、COG、EggNOG and InterPro annotation;
miRNA-mRNA interaction, lncRNA-mRNA interaction analysis
Protein-Protein Interaction (PPI) analysis
Co-expression Interaction Network Analysis
Multiple data base annotation (TCGA、ARCHS4)
Key driver analysis (KDA)
Custom dataset table upload
