From Raw Reads to Biological Insights: What's Included in Our Exosomal RNA Analysis Service

Introduction

For many researchers, RNA sequencing feels like reaching the finish line—samples are shipped, libraries are prepared, and data is finally delivered. But in reality, sequencing is just the beginning. The true value lies not in the terabytes of raw reads, but in the biological meaning they contain.

In a recent survey, over 90% of research scientists reported that their greatest challenge in RNA-seq projects was not data quality, but data interpretation. Receiving a folder of FASTQ files without guidance or analysis support often leads to frustration, delays, and missed discoveries.

That's why our exosomal RNA analysis service was designed with one goal in mind: to turn your sequencing data into actionable biological insights.

From initial quality control to transcript quantification, differential expression, and functional enrichment analysis, we provide a complete, publication-ready data interpretation workflow. Every step is performed by expert bioinformaticians using industry-standard tools—and every deliverable is tailored to help you make sense of your results.

In this article, we'll walk you through each stage of our analysis pipeline. You'll learn exactly what we do with your raw reads, what kind of outputs you'll receive, and how our comprehensive approach helps you go beyond data files—toward meaningful conclusions.

Raw Reads QC & Filtering

Before any biological interpretation can begin, it's critical to ensure that your raw sequencing data meets stringent quality standards. Low-quality reads, adapter contamination, or sequence artifacts can distort downstream analysis and lead to unreliable conclusions. That's why we start every project with a robust quality control and filtering step.

✅ What We Do

We perform comprehensive quality assessment using FastQC, an industry-standard tool for raw read inspection. Key metrics evaluated include:

• Phred quality scores across all bases
• GC content distribution
• Sequence duplication levels
• Adapter contamination detection
• Overrepresented sequences

Based on these metrics, we apply filtering and trimming procedures to:

• Remove low-quality reads (based on user-defined or default quality thresholds)
• Eliminate adapter sequences
• Discard reads that are too short to be reliably mapped or interpreted

Our goal is to retain only high-quality "clean reads" that are suitable for accurate mapping and quantification.

Deliverables You'll Receive

• QC Summary Report: A detailed PDF with figures and explanations of all quality metrics
• Clean Reads Statistics: Excel spreadsheet listing per-sample stats, including:
  • Total reads before/after filtering
  • Percentage of reads removed
  • Read length distribution

Client FAQ: "Can I see how much data was removed from each sample?"

✅ Yes. Our clean reads report includes a side-by-side table of before-and-after read counts for each sample.

By beginning with a clean, high-quality dataset, we ensure that all downstream analyses are built on a solid foundation—minimizing noise and maximizing confidence in the biological patterns you uncover.

Mapping & Quantification

With clean reads in hand, the next critical step is to determine where these sequences originate in the genome or transcriptome—and how abundantly each RNA species is expressed. This stage transforms raw sequences into meaningful transcript-level data.

What We Do

We use high-performance alignment algorithms tailored to the characteristics of exosomal RNA, which often contains a diverse mix of coding and non-coding transcripts, including miRNAs, lncRNAs, and circRNAs.

Alignment Tools

Depending on your project's focus and RNA types, we select the optimal mapping strategy:

• STAR or Bowtie2 for alignment to the reference genome
• miRDeep2 for known and novel miRNA detection
• CIRCexplorer2 or similar tools for circular RNA analysis (optional)

We ensure high alignment efficiency and detailed classification of mapped vs. unmapped reads.

Quantification

Once mapped, expression levels are quantified and reported in multiple formats, depending on your downstream needs:

• Raw counts (for differential expression analysis)
• TPM (Transcripts Per Million) and FPKM (Fragments Per Kilobase per Million) for normalized expression
• Separate expression matrices for each RNA biotype (e.g., mRNA, miRNA, lncRNA, circRNA)

We also support novel transcript discovery, helping you detect unannotated RNAs that may play roles in exosome-mediated regulation.

Deliverables You'll Receive

• Alignment Summary Report
  Includes:
  • Mapping rates for each sample (e.g., uniquely mapped %, multi-mapped %)
  • Reference genome version used
  • Read distribution across genomic features (e.g., exons, introns, intergenic)
• Expression Matrix Files
  • Excel or tab-delimited text files with raw count, TPM, and FPKM values
  • Separate sheets or files for each RNA type (if applicable)
• Visualizations
• Alignment rate bar charts
• Heatmap of top-expressed RNAs

This step delivers the foundational data needed to ask the key question: Which RNAs are changing, and by how much?

These quantified results are the input for the next step: differential expression analysis.

Differential Expression

Once transcript expression levels are quantified, the next step is to identify which RNAs show significant changes between experimental conditions. Differential expression (DE) analysis enables you to uncover exosome-enriched RNAs that may serve as biomarkers, regulatory elements, or functional mediators in your system of interest.

What We Do

We perform rigorous statistical comparisons using well-established methods tailored to RNA-seq count data. Depending on your experimental design and sample size, we may use one or more of the following:

• DESeq2 – robust for small sample sizes and commonly used for RNA-seq
• EdgeR – ideal for datasets with biological replicates
• Limma + voom – suited for large-scale or complex designs

All analyses account for multiple testing correction (e.g., Benjamini-Hochberg) to control false discovery rates (FDR).

You can define your comparison groups based on project needs—for example:

• Treatment vs. Control
• Timepoint comparisons
• Responder vs. Non-responder (non-clinical models)

Each comparison generates a list of significantly upregulated and downregulated RNAs, along with fold change and adjusted p-values.

Deliverables You'll Receive

Differential Expression Tables

• Full results table with log₂ fold change, p-value, adjusted p-value (FDR)
• Filterable by custom thresholds (e.g., |log₂FC| ≥ 1, FDR < 0.05)

Publication-Ready Visualizations

• Volcano plot: highlights significantly changed RNAs at a glance
• MA plot: shows expression vs. fold change trends
• Hierarchical Clustering Heatmap: visualizes sample clustering based on DE RNAs
• PCA plot (optional): captures variance across replicates and conditions

Client FAQ: "Can I download the volcano plot and heatmap for my manuscript?"

✅ Absolutely. All figures are delivered in high-resolution PNG and vector (PDF/SVG) formats.

This step reveals the RNAs that matter most—those whose expression is truly altered and statistically significant. It provides a focused list of candidates for downstream pathway analysis, network modeling, or validation experiments.

Functional Annotation & Pathway Enrichment

Identifying differentially expressed RNAs is only part of the story. To translate these results into biological understanding, we annotate the RNAs and map them to functional categories, pathways, and regulatory networks. This is where raw data becomes biological insight.

What We Do

We perform comprehensive functional enrichment analyses using established bioinformatics tools and databases to help answer questions such as:

• What biological processes are most affected?
• Which signaling pathways are involved?
• Are there regulatory interactions among the different RNA types?

Gene Ontology (GO) Enrichment

We classify and enrich differentially expressed genes or predicted targets into three GO categories:

• Biological Process (BP) – e.g., immune response, cell proliferation
• Molecular Function (MF) – e.g., RNA binding, ATPase activity
• Cellular Component (CC) – e.g., exosome, cytoplasm

KEGG Pathway Enrichment

We identify enriched molecular pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, especially:

• Cancer-related pathways
• Metabolic signaling
• Immune system modulation
• Exosome biogenesis and uptake

Network Analysis (Optional Add-ons)

• miRNA–mRNA interaction networks – based on TargetScan, miRDB, or miRTarBase predictions
• circRNA–miRNA–mRNA regulatory axes – predicted using tools like circBank or RNAhybrid
• Exosome–target cell interaction prediction – explores downstream effects of RNA cargo in recipient cells (research-use only)

Deliverables You'll Receive

Enrichment Result Tables

GO and KEGG tables with gene counts, enrichment scores, and FDR-adjusted p-values

Separate Excel sheets for each enrichment type

• High-Quality Figures
• Bar plots and dot plots for top enriched GO terms and KEGG pathways
• Interaction network diagrams (if applicable)
• All images available as high-resolution files (PNG + PDF)
• Annotated Gene List

Each gene labeled with corresponding GO terms, KEGG IDs, and pathway involvement

Functional annotation closes the loop between data and discovery—offering hypotheses about how exosomal RNAs contribute to specific biological responses or systems-level regulation.

Advanced Analysis(Optional)

For research projects requiring deeper biological interpretation or exploratory biomarker discovery, we offer a suite of optional advanced analysis modules. These value-added services go beyond standard pipelines—empowering you to extract more meaning, generate new hypotheses, or prepare for high-impact publications.

Machine Learning for Biomarker Discovery

We apply supervised machine learning models to identify exosomal RNAs that best distinguish between conditions. Popular algorithms include:

• Support Vector Machine (SVM)
• Random Forest Classification

These models help rank RNAs by importance, suggesting potential non-invasive biomarker candidates or mechanistic regulators in your system.

Deliverables:

• Ranked feature importance table
• ROC curve plots and model performance metrics
• Customizable prediction model output

Time-Series & Paired Data Analysis

If your experiment includes time points or paired samples (e.g., before/after treatment in animal models), we implement:

• Time-course modeling (e.g., spline-based trends)
• Paired differential analysis (e.g., patient-derived xenografts at two stages)

These approaches improve sensitivity by accounting for within-subject variance and temporal dynamics.

Deliverables:

• Time-resolved expression plots
• DE tables adjusted for time or pairing
• Trajectory cluster visualizations

circRNA Discovery & Validation

For circular RNA-focused projects, we can:

• Predict back-splice junctions from unmapped reads (e.g., using CIRI2, find_circ)
• Validate circRNA candidates via read distribution and junction depth

Deliverables:

• Predicted circRNA list with annotation and counts
• Junction-spanning read plots and validation metrics

Exosome–Target Cell Interaction Prediction

This exosome-specific module leverages databases like ExoCarta and miRNet to explore how your exosomal RNAs may affect recipient cells:

• Target gene prediction for miRNAs
• Integration with KEGG or GO functional impacts
• Optional integration with published uptake pathway databases

Deliverables:

• Target interaction network diagrams
• Functional impact tables
• Research-use-only insights into downstream regulatory potential

Service Delivery & Customer Support

We understand that insightful results are only as valuable as your ability to interpret and use them. That's why our deliverables are not just technically complete—but also research-ready, publication-friendly, and reviewer-compliant.

What You'll Receive

Each exosomal RNA sequencing analysis project includes the following deliverables:

1. Comprehensive Analysis Report (PDF)

A fully annotated document containing:

• Workflow overview
• QC results with interpretation
• Differential expression summaries
• Functional enrichment figures & explanations
• Optional modules (e.g., ML outputs, networks)

2. Data Packages

We provide all underlying data in organized formats:

Conclusion

Most sequencing service providers stop at data delivery. We go further.

At CD Genomics, we believe that clean reads alone aren't the destination—biological insight is. Our exosomal RNA sequencing analysis service is designed to close the gap between raw data and real results, so you can:

• Identify novel regulatory RNAs
• Generate mechanistic hypotheses
• Pinpoint functional pathways and biomarkers
• Produce publication-quality figures and findings

From rigorous QC to differential analysis, functional enrichment, and optional advanced modules like machine learning and target cell prediction, we provide a comprehensive solution that's not only scientifically robust, but also researcher-friendly.

✅ Don't settle for fastq files.

✅ Don't get stuck interpreting Excel sheets alone.

✅ Let us turn your sequencing data into scientific value.