Small RNA Sequencing Data Analysis

What is Small RNA Sequencing Data Analysis?

Small RNA refers to endogenous RNA with a length of 18 to 30 nt, including miRNA, siRNA and piRNA. They play an important role in the regulation of mRNA transcription and post-transcription levels, participating in various biological processes such as cell growth, differentiation, and metabolism. The function of small RNA in the normal development of the organism and the disease process is also vital.

Small RNA sequencing data analysis refers to the large-scale detection of small RNAs of target samples based on high-throughput sequencing platforms, combined with bioinformatics mining, to conduct a comprehensive analysis of miRNA, siRNA, piRNA in samples. This type of analysis can identify known small RNA, predict new small RNA and predict the target genes of small RNAs, providing a powerful means for studying the function, structure and regulatory mechanism of small RNA.

Fig 1. Differentially expressed miRNAs in colorectal cancer samples (p<0.05). (Srinivas V K, et al. 2017)

Application of Small RNA Data Analysis

Cancer research

Disease Research

Animal and Plant Research

Disease biomarkers

Molecular diagnostic testing

Advantages of CD Genomics

Rich experience in small RNA analysis sequencing data analysis.

Reliable analysis results.

Comprehensive analysis

Flexible custom analysis

CD Genomics Data Analysis Pipeline

Sample Submission Guidelines of Sequencing

Bioinformatics Analysis Content

CD Genomics uses authoritative algorithms in academia to systematically classify and annotate miRNA, siRNA, piRNA, and unknown small RNA, and then fully explore the regulatory functions of small RNA through base editing analysis, expression level analysis, and target gene prediction.

For small RNA sequencing data analysis, if you have other sequencing data, such as circular RNA sequencing data, we can provide you with interaction analysis of miRNA and circular RNA based on your data. If you have other analysis needs, we will negotiate and determine the most suitable analysis content based on your data and needs. For analysis content, price, cycle, if you have any questions, please click online inquiry.

Example Data Analysis Report

To showcase the quality and detail of a CD Genomics report for shotgun metagenomic sequencing data analysis, we offer a sample report upon request. You can contact us to obtain this report. Additionally, refer to a client-published article for more insights. " Simultaneous carbon catabolite repression governs sugar and aromatic co-utilization in Pseudomonas putida M2." which includes some of the data we provided.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics has successfully conducted small RNA sequencing data analysis on a variety of species. Experienced teams of scientists, researchers, and technicians, we provide fast turnaround, high-quality data reports at competitive prices for worldwide customers. Customers can contact our employees directly and we will respond promptly. If you are interested in our services, please contact us for more detailed information.

Reference

1. Srinivas V K, et al. Exploration of small RNA-seq data for small non-coding RNAs in Human Colorectal Cancer. Journal of Genomics, 2017; 5: 16-31.

What Does Data Analysis of Small RNA Sequencing Show?

smRNA Expression Distribution

Fig 2. Boxplot for each sample.

Fig 3. Density distribution.

Correlation between Samples.

Fig 4. Correlation analysis between samples. The scatter diagrams demonstrate the correlation coefficient between samples.

Differential Expression Analysis

PCA Analysis

Fig 5. PCA _ plot.

MA Analysis

Fig 6. GeneMA

Volcano Plot of Differential Expression Transcripts

Fig 7. Volcano plot of significantly differentially expressed smRNA (Plus_vs_minus).

Classification of GO for Differentially Expressed miRNA Target Genes

Fig 8. Statistics results of GO annotation for Plus_vs_minus.

KEGG Enrichment Analysis

Fig 9. Statistics results of KEGG enrichment for Plus_vs_minus.

Title: A Fecal MicroRNA Signature by Small RNA Sequencing Accurately Distinguishes Colorectal Cancers: Results From a Multicenter Study

Publication: Gastroenterology

Main Methods: Small RNA sequencing

Abstract: This study evaluated stool microRNA profiles as biomarkers for colorectal cancer (CRC) detection. Small RNA sequencing of fecal samples from Italian and Czech cohorts identified a 5-miRNA signature (miR-149-3p, miR-607-5p, miR-1246, miR-4488, miR-6777-5p) distinguishing CRC patients from controls with high accuracy (AUC up to 0.96). The signature also differentiated low-/high-stage tumors and advanced adenomas. Comparable miRNA profiles in tissues and other gastrointestinal diseases underscored specificity for CRC. These findings suggest fecal miRNAs as promising noninvasive biomarkers for CRC diagnosis and screening.

Study Design:

Fig 10. Representation of the study design.

Main Research Results:

Altered Stool MicroRNA Profiles in Colorectal Cancer Patients: Findings from Two European Populations:

Small RNA sequencing of stool samples from colorectal cancer (CRC) patients and controls identified an average of 479 miRNAs per sample. Analysis revealed 25 miRNAs consistently altered in CRC across Italian and Czech cohorts, correlating with clinical parameters like tumor size and disease stage. Functional analysis suggested their involvement in cancer-related processes, underscoring their potential as biomarkers for CRC diagnosis.

Fig 11. (A) Scatterplots of stool miRNA levels in CRC patients vs. controls from IT and CZ cohorts, colored by log2 fold change and sized by adjusted P values. (B) Correlations of 25 DEmiRNAs across cohorts. (C) Heatmap of DEmiRNA levels with clinical data. (D) DEmiRNA levels by CRC clinical parameters. (E) Line plot of classifier performance using different miRNA combinations. (F) ROC curves for CRC classification using the miRNA signature in training and validation cohorts

A Fecal MicroRNA Signature Distinguishes Colorectal Cancer Patients From Control Individuals:

An explainable machine learning approach identified a minimal set of 5 miRNAs as a signature for CRC detection. These miRNAs—miR-607-5p, miR-6777-5p, miR-4488, miR-149-3p, and miR-1246—achieved an AUC of 0.87 in distinguishing CRC patients from controls in the training cohort. Including age and sex improved classification (AUC 0.86), validated similarly in an independent cohort (AUC 0.91–0.96). RT-qPCR confirmed detection and expression patterns of these miRNAs, supporting their potential as noninvasive CRC biomarkers.

Stool Differentially Expressed MicroRNA Profiles Mirror Those of Primary Colorectal Cancer and Adenoma Tissues:

A paired differential expression analysis compared tumor tissues with adjacent mucosa from 102 CRC patients, revealing 14 of the 25 stool DEmiRNAs as differentially expressed (adjusted P < .05). Up-regulated miRNAs in tumors included miR-21-5p, miR-1246, miR-1290, miR-148a-3p, miR-4488, miR-149-3p, and miR-12114, consistent with their levels in stool. Down-regulated miRNAs in stool and tissues included miR-607-5p, miR-6777-5p (part of the 5-miRNA signature), miR-6076, miR-922-5p, and miR-9899. Adenoma tissues also showed distinct miRNA expression profiles.

Fig 12. (A) Median levels of 25 fecal DEmiRNAs in CRC and adenoma tissues, showing differential expression compared to adjacent mucosa. (B) Comparison of miRNA levels in CRC stool samples and various gastrointestinal disorders vs. controls. (C) Analysis of DEmiRNAs in FIT leftover samples from CRC screening, including detection rates and differential expression in CRC-positive vs. negative cases. (D) Box plots of miR-1246 and miR-607-5p levels across all cohorts and sample types.

A Subset of Stool Differentially Expressed MicroRNAs Is Specifically Dysregulated in Colorectal Cancer Patients but Not in Those With Other GI Diseases:

Comparative analysis of CRC DEmiRNAs with other GI disorders in the IT and CZ cohorts revealed significant dysregulation across various diseases. Most CRC DEmiRNAs showed altered levels in ulcerative colitis, diverticulitis, nAA, and AA. The 5-miRNA CRC signature effectively distinguished CRC from controls and adenomas (AUC up to 0.82), with miRNA expression patterns differing among disease types. miR-6777-5p was specific to CRC, while miR-149-3p and miR-607-5p showed differential expression in AA patients.

MicroRNAs Are Detectable in Fecal Immunochemical Test Leftover Samples by Small RNA Sequencing:

Sequencing analysis of 185 FIT leftover samples detected an average of 618 miRNAs per sample, with all 25 stool DEmiRNAs identified. Among these, miR-607-5p, miR-1246, let-7a-3p, and miR-922 were consistently detected in all samples. The 5-miRNA signature (miR-607-5p, miR-1246, miR-6777-5p, miR-149-3p, miR-4488) showed high detection rates (>95%) in CRC screening samples. Differential expression analysis between CRC-negative and positive cases revealed significant differences in several miRNAs. Strong correlations were observed between miRNA levels in FIT leftovers and stool samples, supporting their utility in CRC classification (AUC up to 0.93).

Conclusion:

Sequencing analysis of 185 FIT leftover samples identified an average of 618 miRNAs per sample, including all 25 stool DEmiRNAs. Key miRNAs like miR-607-5p, miR-1246, let-7a-3p, and miR-922 were consistently detected across all samples. The 5-miRNA signature (miR-607-5p, miR-1246, miR-6777-5p, miR-149-3p, miR-4488) exhibited robust detection (>95%) in CRC screening samples. Differential expression analysis between CRC-negative and positive cases highlighted significant miRNA differences. Strong correlations between miRNA levels in FIT leftovers and stool samples underscore their potential for effective CRC classification (AUC up to 0.93).

1. What are the special requirements for sample extraction for small RNA sequencing?

When extracting total RNA, it is advised to avoid using a column kit or LiCl precipitation to prevent the loss of small RNA fragments. If small RNA samples are provided directly, a small RNA extraction kit should be used for extraction.

2. What are the principles of Small RNA sequencing?

Small RNA sequencing involves isolating small RNA molecules, ligating adapters to their ends, reverse transcribing them into cDNA, amplifying the cDNA via PCR, and then sequencing the library on a high-throughput platform. The sequencing data is then analyzed to identify and quantify the small RNA species.

3. What are the sample requirements for small RNA sequencing analysis?

Small RNA sequencing requires 1-5 µg of high-quality total RNA with a RIN > 7 and A260/A280 ratio of ~2.0. Samples should be free of DNA, phenol, and other contaminants. Both total RNA and enriched small RNA fractions are suitable.

4. What samples are suitable for Small RNA sequencing data analysis?

Suitable samples for small RNA sequencing include total RNA from various sources like tissues, cells, or body fluids, enriched for small RNAs. High-quality RNA with minimal degradation (RIN > 7) and no contaminants is essential for reliable small RNA profiling and analysis.

5. What are the differences among LncRNA, CirRNA, and SmallRNA?

LncRNA are long, linear RNAs over 200 nucleotides involved in gene regulation. circRNA are covalently closed circular RNAs that act as microRNA sponges. Small RNA are short, linear RNAs (18-30 nucleotides) involved in post-transcriptional gene regulation, including miRNAs, siRNAs, and piRNAs.