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194 Bioinformatics of Autoimmune Diseases
FIGURE 5.6 RNA-Seq gene expression of GeneX: Positive versus negative anti-CCP.
molecular functions that are overrepresented. Rather than analyzing genes in isolation, functional
enrichment looks for statistically signicant associations between input gene sets and annotated
databases such as Gene Ontology (GO), KEGG pathways, and Reactome. These databases group
genes based on shared biological processes, molecular functions, or cellular components, enabling
researchers to understand the broader biological context of their ndings.
The analysis typically begins with a list of genes that are signicantly upregulated or downregulated in a condition of interest. Functional enrichment tools, such as g:Profiler, use statistical
models to determine whether certain pathways or functions appear more frequently in the input
list than would be expected by chance. These enriched terms can highlight common mechanisms
shared among the genes, such as immune signaling, cytokine activity, apoptosis, or cell cycle regulation. The output helps to prioritize pathways for further investigation and can provide mechanistic
insights that are not immediately apparent from the gene-level data alone.
In the context of autoimmune diseases, functional enrichment is particularly valuable because
these conditions often involve complex and dysregulated immune pathways. Diseases such as rheumatoid arthritis, lupus, or MS involve numerous genes that act together within signaling cascades,
inammatory responses, or immune cell activation. Functional enrichment can reveal that differentially expressed genes in patient samples converge on pathways such as TNF signaling, interferon
responses, or antigen processing. This not only enhances the understanding of disease mechanisms
but also supports the identication of potential biomarkers or therapeutic targets. Furthermore,
since autoimmune diseases may affect diverse tissues and cell types, enrichment analysis allows
researchers to lter and interpret gene expression data through a systems biology lens, helping to
make sense of the molecular heterogeneity that characterizes these conditions.
The run _ enrichment.py program is a standalone script designed to perform functional
enrichment analysis using the g:Profile r A PI. It takes as input a le containing a list of gene
symbols, typically generated from RNA-Seq differential expression analysis, and submits this list to
the g:P rofile r service to identify overrepresented biological functions, pathways, or molecular
annotations. The script is exible and designed to work with gene lists formatted as CSV or plain
195 RNA Sequencing
text les, where the genes are typically listed in the rst column. It supports enrichment across a
range of biological sources, including Gene Ontology (GO), KEGG, and Reactome, and is set to
query the human genome by default.
Upon execution, the script validates that the gene list is non-empty and proceeds to prepare a
structured query for g:Proler’s REST API. It submits the gene list and receives a response containing any enriched terms along with relevant metadata such as p-values, term size, and the number
of overlapping genes. These results are parsed and saved in CSV format to a designated output le,
making them easy to review or incorporate into downstream analyses. The script also provides
summary feedback in the terminal, including the number of genes queried and a preview of the
top enriched terms grouped by source. This helps users to quickly assess whether any meaningful
biological patterns were detected.
The expected output from run_enrichment.py is a structured CSV le listing enriched pathways
or functional terms, each accompanied by their associated p-value, term size, and intersection size.
This output enables researchers to understand which biological processes or molecular mechanisms
are most prominently represented among their genes of interest. In practice, this helps translate statistical gene-level results into interpretable biological insights. For example, an analysis involving
genes upregulated in rheumatoid arthritis might reveal enrichment in pathways such as cytokine
signaling, interferon response, or antigen presentation, pointing to dysregulated immune activity at
the systems level. The script’s design ensures that such insights are easily accessible and formatted
in a way that is compatible with visualization tools or further statistical summarization.
Table 5.7 is a sample table that represents what the output of the run _ enrichment.py
script might look like after querying g:P rofile r with a list of human gene symbols. The table
reects enriched terms from different biological databases, along with relevant statistical and biological metadata.
Each row represents an enriched term, including the biological source (GO:BP for Biological
Process, GO:MF for Molecular Function, KEGG, or Reactome), the term name, the associated
p-value indicating statistical signicance, the total number of genes annotated to that term (term_
size), and how many of those genes were present in the input gene list (intersection_size).
This table helps the researcher interpret which biological themes are most relevant to the gene
list being studied, making it a key component in translating raw RNA-Seq data into meaningful
insights about cellular pathways or disease mechanisms.
For example, the rst row in the enrichment table indicates that the biological process “response
to interferon-gamma” is signicantly overrepresented in the list of genes. Out of all the genes known
to be involved in this process, which number 65 in the background set, 7 were found among the
TABLE 5.7
Functional Enrichment of RNA-Seq Top Expressed Genes
Source Name p-Value Term Size Intersection Size
GO:BP Response to interferon-gamma 0.000021 65 7
GO:BP Regulation of cytokine production 0.000134 120 9
GO:MF Chemokine receptor binding 0.000215 20 4
KEGG TNF signaling pathway 0.000742 95 6
REAC Interferon signaling 0.001102 88 5
GO:BP Leukocyte migration 0.0034 142 6
GO:MF Cytokine activity 0.004501 35 4
REAC Antigen processing and presentation 0.00911 53 3
GO:MF Nicotinamide-nucleotide adenylyltransferase activity 0.014936 3 2
GO:MF Protein ADP-ribosyltransferase-substrate adaptor activity 0.029815 4 2
196 Bioinformatics of Autoimmune Diseases
genes we submitted for enrichment analysis. The associated adjusted p-value is 0.000021, showing
a very strong statistical signicance after correcting for multiple testing.
This result suggests that our gene set is highly involved in the cellular response to interferongamma, a cytokine that plays a central role in immune activation. Interferon-gamma is particularly
important in the regulation of immune responses, especially in the context of inammation and host
defense mechanisms. The enrichment of this term implies that the underlying biological condition
we are studying may involve heightened interferon-gamma signaling.
Given that our RNA-Seq data is related to an autoimmune disease such as rheumatoid arthritis, this nding is biologically meaningful. Interferon-gamma has been implicated in autoimmune
pathogenesis, where it can contribute to the activation of macrophages, upregulation of antigen presentation, and the promotion of inammatory pathways. The presence of this enrichment strengthens the interpretation that immune system dysregulation, particularly through interferon-gamma
signaling, is a prominent feature in your dataset.
5.6 SMALL RNA DATA ANALYSIS
Small RNAs are a diverse class of non-coding RNAs, typically ranging in length from 18 to
30nucleotides, that play essential roles in regulating gene expression at both transcriptional and
post-transcriptional levels. The most studied types include microRNAs (miRNAs), small interfering
RNAs (siRNAs), and PIWI-interacting RNAs (piRNAs). Among these, miRNAs are particularly
signicant in the context of human diseases, including autoimmune disorders. As illustrated in
Figure 5.7, the miRNA pathway begins with the transcription of a gene encoding a primary miRNA
(pri-miRNA). The nuclear enzyme Drosha processes the pri-miRNA into a precursor miRNA
(pre-miRNA), which is then exported to the cytoplasm. There, the enzyme Dicer further cleaves
the pre-miRNA into a mature miRNA duplex. One strand of this duplex is incorporated into the
miRNA-induced silencing complex (miRISC). The mature miRNA guides the complex to target
mRNAs through base pairing. Perfect complementarity typically results in mRNA degradation,
while partial complementarity leads to translational repression.
In the immune system, miRNAs are critical regulators of immune cell development and function. For example, specic miRNAs control the differentiation of T and B lymphocytes, the activation of dendritic cells, and the production of cytokines. Dysregulation of these miRNAs can
FIGURE 5.7 M icroRNA pathway.
197 RNA Sequencing
disturb immune homeostasis, potentially leading to autoimmunity. In autoimmune diseases, aberrant expression proles of miRNAs have been consistently observed. Certain miRNAs are overexpressed and act as pro-inammatory agents, enhancing the activation of immune cells and the
production of inammatory cytokines. Others are under-expressed, leading to a failure in suppressing inappropriate immune responses. These changes contribute to the breakdown of self-tolerance
and the development of chronic inammation characteristic of autoimmune conditions.
Emerging evidence suggests that miRNAs may also serve as biomarkers for diagnosis and prognosis in autoimmune diseases. For instance, altered levels of circulating miRNAs in patient plasma
or serum can reect disease activity and therapeutic response, offering a non-invasive tool for monitoring disease progression. Additionally, specic miRNAs have been shown to correlate with the
severity of joint damage in rheumatoid arthritis or the presence of autoantibodies in lupus. Their
stability in body uids and sensitivity to pathological changes make them attractive candidates for
early detection and personalized treatment strategies.
From a therapeutic perspective, modulating miRNA expression is an exciting frontier. Strategies
such as miRNA mimics to restore downregulated miRNAs, or antagomirs to inhibit overexpressed
ones, are being explored in preclinical and clinical studies. However, challenges remain in ensuring the specicity, delivery, and safety of such approaches. Nonetheless, the ability of small RNAs
to inuence key pathways in immune regulation offers a promising avenue for developing targeted
therapies in autoimmune diseases, potentially shifting the paradigm from broad immunosuppression to precision medicine.
5.6.1 SMALL RNA DIFFERENTIAL ANALYSIS
The above-discussed RNA-Seq pipelines were designed for standard transcriptome analysis, primarily focusing on mRNA. To adapt this pipeline for small RNA-Seq, several critical modications can be implemented to accommodate the unique properties of small RNA molecules such
as miRNAs, piRNAs, and other non-coding RNA species. Unlike mRNAs, which are typically
hundreds to thousands of nucleotides in length and often spliced, small RNAs are short (18–30 nt)
and unspliced. This necessitates changes in read processing, alignment strategies, and expression
quantication methods.
One of the primary changes involved customizing the read trimming step. While mRNA pipelines often use fastp or Trimmomatic with generic adapter trimming, small RNA libraries
require precise removal of 3′ adapter sequences due to the short length of the reads. These adapters,
if not removed accurately, can dominate the sequence and interfere with mapping. The pipeline
therefore retains fastp but incorporates specic adapter sequences commonly used in small RNA
library preparation, ensuring only high-quality and adapter-free reads are passed to the alignment
step.
The alignment phase shifts from STAR, a spliced aligner optimized for long reads, to Bowtie1,
which is tailored for short, unspliced sequences and allows for strict mismatch control. This is
essential because small RNAs require exact or near-exact matches to reference sequences due to
their short length; even a single mismatch can signicantly reduce mapping accuracy. The reference genome used for alignment can be replaced or augmented with a specialized index, such as
one built from miRbase (publicly accessible online database), depending on the small RNA species
under study.
Gene-level quantication with tools like featureCounts, which rely on GTF annotations, is
not suitable for small RNA. Instead, the pipeline performs sequence-based quantication by counting the frequency of each unique sequence mapped in the BAM les. This approach is critical for
detecting small RNA expression since many small RNAs do not correspond to annotated genes, and
their regulatory effects are sequence-specic rather than gene-specic. The sequence-level count
matrix is then normalized using CPM, and genes with low expression across samples are ltered out
to reduce noise and false positives in downstream analysis.
198 Bioinformatics of Autoimmune Diseases
Differential expression analysis remains similar in concept but is applied to unique sequences
rather than annotated gene IDs. In srna _ pipeline _ ttest.py, statistical comparisons
are performed using t-tests between biological groups, followed by p-value adjustment using the
Benjamini-Hochberg method to control for false discovery rate. The interpretation of the results,
however, focuses on identifying differentially expressed small RNA sequences that may correspond
to functionally important non-coding RNAs, such as miRNAs that are known to regulate gene
expression post-transcriptionally.
In srna _ pipeline _ 1wayA nova.py, the differential _ expression function
was modied to accommodate the characteristics of small RNA-Seq data and support one-way
ANOVA across multiple biological groups. Instead of comparing two groups using a t-test as in
standard RNA-Seq analysis, the updated function performs an ANOVA test to evaluate expression differences among more than two groups dened by a metadata column, such as ethnicity.
This is essential in studies with more complex experimental designs. Additionally, the function
was adapted to operate on unique small RNA sequences rather than gene identiers, reecting the
sequence-centric nature of small RNA data. To aid in biological interpretation, log2 fold changes
are calculated between either the top two most expressed groups or relative to a specied control
group. The function returns a ranked table of sequences with raw and adjusted p-values, allowing
for identication of differentially expressed small RNAs across diverse sample categories.
In srna _ pipeline _ 1wAnova _ ctrl.py, the differential _ expression func-
tion was modied to analyze small RNA expression at the sequence level across multiple groups
using one-way ANOVA, while also incorporating log2 fold change calculations. Instead of operating on annotated gene symbols, the function now uses unique small RNA sequences as identiers,
which is more appropriate given that many small RNAs do not correspond to known gene annotations. For each sequence, expression values are grouped by a metadata column such as ethnicity, and
ANOVA is used to detect overall differences across the groups. If a control group is specied, the
function computes log2 fold changes comparing each other group to the control, enabling targeted
biological interpretation. If no control is provided, the log2 fold change is calculated between the
two groups with the highest mean expression for each sequence. This structure enables ne-grained
comparison of small RNA expression patterns across heterogeneous populations while preserving
statistical rigor through multiple testing correction.
This modied pipeline reects a tailored approach that respects the biological and technical
differences between mRNA and small RNA data. It enhances the accuracy of read processing and
quantication, ensuring that the distinct characteristics of small RNAs are appropriately captured,
ultimately leading to more biologically meaningful and reproducible results.
5.6.2 VISUALIZING DIFFERENTIAL SMALL RNA EXPRESSION
5.6.2.1 PCA Plot
In the sRNA _plot _pca.py program, the PCA plotting function was modied to suit small
RNA-Seq data by shifting the input from gene-level expression proles to sequence-level normalized counts, which is more appropriate for small RNA analysis (Figure 5.8). While the overall structure of the PCA computation remains the same (standardizing the data, performing dimensionality
reduction with PCA, and visualizing sample clustering) the underlying biological entities are different. Instead of genes, the principal components now reect variance across short RNA sequences,
many of which may correspond to miRNAs, piRNAs, or other non-coding RNAs. This shift affects
interpretation: the clusters seen in the PCA plot represent similarities or differences in small RNA
expression proles across samples, which could reect biological groupings like treatment or phenotype, but at the regulatory RNA level rather than transcript abundance. As many small RNAs
have precise and targeted regulatory roles, clustering patterns may reveal subtle yet biologically
meaningful differences in post-transcriptional regulation that may not be captured by mRNA-level
199 RNA Sequencing
FIGURE 5.8 PCA plot of small RNA (rheumatoid arthritis).
PCA. Thus, while the visualization approach is technically the same, the biological interpretation
focuses on regulatory signatures rather than transcriptomic structure.
5.6.2.2 Volcano Plot
In sR N A _ plot _ volc ano.p y, the volcano plot (Figure 5.9) was modied to reect the charac-
teristics of small RNA-Seq data by replacing gene-level identiers with sequence-level identiers,
typically referred to as “Sequence” in the differential expression output. In standard mRNA-Seq
volcano plots, each point represents a gene, often annotated with gene symbols, and the plot highlights genes with signicant changes in expression between conditions. For small RNA, however,
many features of interest are not genes but short RNA sequences such as miRNAs, piRNAs, or
other non-coding RNA fragments. The updated plot handles these by labeling and coloring points
based on log2 fold change and adjusted p-value, exactly as in the original, but using sequence identiers instead of gene symbols.
This change ensures that the volcano plot remains informative and statistically rigorous while
being biologically appropriate for small RNA studies. The overall structure of the plot remains
the same (log2 fold change on the x-axis and negative log10 adjusted p-value on the y-axis) but the
interpretation shifts from gene-level transcriptional regulation to sequence-level insights into regulatory RNA dynamics. The labeled top sequences are the most statistically signicant small RNAs,
and depending on annotation, they could represent miRNAs with known targets or novel regulatory molecules. This makes the volcano plot a powerful tool not just for identifying differentially
expressed RNAs but also for generating hypotheses about post-transcriptional regulation and small
RNA-mediated pathways.
200 Bioinformatics of Autoimmune Diseases
FIGURE 5.9 Volcano plot of small RNA (rheumatoid arthritis).
5.6.2.3 MA Plot
In s R NA _ plot _ MA.py, the MA plot (Figure 5.10) was modied to accommodate the charac-
teristics of small RNA-Seq data by shifting its focus from gene-level expression to sequence-level
expression. In the original mRNA-Seq version, genes were used as identiers and the x-axis represented the average log2 CPM of gene expression, while the y-axis showed the log2 fold change
between groups. In the small RNA version, the same structure is preserved, but the identiers are
unique small RNA sequences, which may or may not correspond to annotated genes. This change
ensures that the plot reects the actual biology of small RNAs, many of which function as individual regulatory molecules and are not captured in standard gene annotations.
The interpretation of the MA plot in the small RNA context also shifts slightly. In mRNA
analysis, differentially expressed genes often indicate changes in transcriptional output or shifts in
broader functional pathways. In contrast, differentially expressed small RNAs may suggest changes
in regulatory activity, such as miRNA-mediated repression or modulation of post-transcriptional
networks. Therefore, while the visual interpretation remains the same (spotting sequences that
are highly expressed and differentially regulated) the biological conclusions focus more on gene
regulation and interaction networks rather than protein-coding functions. The use of small RNA
sequences on the y-axis also means that additional care must be taken when annotating or interpreting individual points, as these may correspond to miRNAs, piRNAs, or uncharacterized short
RNAs with distinct biological roles.
5.6.2.4 Heatmap Plot
In sR NA _ plot _ he atm ap.p y, the heatmap plot (Figure 5.11) was adapted from an mRNA-
based format to accommodate small RNA-Seq data by shifting from gene symbols to sequencelevel identiers. In the original version, the heatmap displayed normalized expression levels of top
differentially expressed genes using gene symbols as row labels. For small RNA-Seq, many features
201 RNA Sequencing
FIGURE 5.10 MA plot of small RNA (rheumatoid arthritis).
FIGURE 5.11 Heatmap plot of small RNA (rheumatoid arthritis).
202 Bioinformatics of Autoimmune Diseases
of interest are short RNA sequences that do not map directly to known genes. The updated version
reads a list of top small RNA sequences, lters the normalized expression matrix accordingly, and
uses these sequences as the row identiers in the heatmap. The expression values are standardized
across samples, and the samples are annotated with group labels from the metadata, allowing for
visual clustering of both small RNA expression and sample grouping.
The interpretation also shifts slightly. While an mRNA heatmap often highlights coordinated
changes in gene expression related to specic biological pathways or cellular processes, a small
RNA heatmap reects changes in post-transcriptional regulation and small RNA-mediated control
mechanisms. The rows now represent individual small RNAs (such as miRNAs or other regulatory
fragments) which may target multiple genes and inuence gene expression indirectly. Consequently,
the heatmap allows researchers to visually assess patterns of regulatory RNA activity across experimental groups. This can reveal subtle regulatory shifts that may not be apparent in mRNA proles, especially in cases where transcriptional changes are minimal but regulatory mechanisms are
actively altered through small RNA expression.
5.7 SUMMARY
This chapter provides a comprehensive overview of RNA sequencing (RNA-Seq) and its critical
role in understanding gene expression in autoimmune diseases, with a particular emphasis on rheumatoid arthritis (RA). It begins by exploring how tightly regulated gene expression governs immune
responses and how its dysregulation contributes to autoimmune pathogenesis. The text illustrates
how RNA-Seq enables the identication of gene signatures and pathways involved in diseases like
SLE, RA, MS, and IBD, offering insights into mechanisms and guiding personalized therapeutic
strategies. Examples such as the type I interferon signature in SLE and the synovial expression proles in RA underscore the utility of transcriptomic data in clinical contexts.
The chapter transitions into practical applications, detailing how to extract raw sequencing data
and associated metadata from public repositories like the NCBI SRA. Using a specic RA study as
a case example, it walks through the scripting and methodological process of retrieving and organizing BioProject metadata. Clinical variables such as anti-CCP status, CRP, ESR, pathotype, and
joint counts are explained in depth to underscore their biological relevance and role in shaping gene
expression patterns.
A signicant portion is devoted to implementing a robust RNA-Seq data analysis pipeline using
Python. It outlines every step from preprocessing raw FASTQ les, reference genome indexing,
read alignment, and quantication to gene ltering and normalization. The chapter further delves
into statistical analyses for differential expression, including t-tests, one-way and two-way ANOVA,
and post hoc comparisons using Tukey’s HSD. Each statistical approach is framed with examples
tailored to autoimmune disease research, emphasizing the importance of appropriate experimental
design and interpretation.
Visualization techniques, such as PCA plots and volcano plots, are introduced as indispensable
tools for interpreting differential gene expression results. These methods help to assess clustering,
identify signicant genes, and communicate ndings effectively. Finally, the chapter underscores
the power of integrating RNA-Seq with clinical and phenotypic metadata to uncover complex geneenvironment interactions and supports the advancement of precision medicine in autoimmune
diseases. Through a combination of theory, application, and coding practices, the chapter equips
readers with a practical and intuitive foundation for RNA-Seq analysis in biomedical research.
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