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Gene-Level Comparisons Within Each Platform
Gene-Level Comparisons Within Each Platform
Nanopore Direct RNA Sequencing
Nanopore Direct RNA Sequencing
Detection Sensitivity:
Detection Sensitivity:
The Nanopore dataset identified pseudouridine modification signals in a large number of genes, with 847 genes detected in PM-exposed lung cells and 989 genes in the control condition.
Among these, approximately 450 genes were common to both conditions.
BID‑Seq
BID‑Seq
Selective Detection:
Selective Detection:
BID‑Seq showed a more selective detection approach, with 258 genes identified in PM samples and 246 in controls.
Only 22 genes were shared between PM and control conditions within BID‑Seq, implying a stricter threshold and lower background signal.
Shared Genes Across Platforms
Shared Genes Across Platforms
PM Condition:
PM Condition:
Merging the gene-level data from Nanopore and BID‑Seq revealed 34 shared genes. These high-confidence, consistently modified genes serve as strong candidates for further functional investigation.
Figure 1. Gene-level Scatter Plot for PM-Exposed Lung Cells.
This scatter plot compares the mean modification probability obtained from Nanopore direct RNA sequencing against the mean ratio from BID‑Seq for genes in PM-exposed lung cells. Each point represents a gene detected as modified by both platforms. The red dashed line (y = x) indicates the line of perfect agreement.
Control Condition:
Control Condition:
In control samples, 57 genes were shared between the two platforms.
Figure 2. Gene-level Scatter Plot for Control Lung Cells.
This plot presents the gene-level comparison between Nanopore modification probability and BID‑Seq ratio values for control (media-only) lung cells. Each data point corresponds to a gene detected by both methods in control samples, with the red dashed line representing the line of identity (y = x).
Correlation Analysis:
PM condition: Pearson r = 0.28 (p = 0.113)
Control condition: Pearson r = –0.17 (p = 0.207)
Correlation Analysis:
PM condition: Pearson r = 0.28 (p = 0.113)
Control condition: Pearson r = –0.17 (p = 0.207)
Neither correlation is statistically significant, indicating only modest agreement under PM exposure and poor concordance at baseline. Nanopore probabilities cluster high (0.8–1.0) while BID‑Seq ratios remain low, reflecting different dynamic ranges and sensitivities. The negative control correlation means that genes with higher Nanopore scores tend to have lower BID‑Seq ratios in the absence of PM.
Discussion of Platform-Specific Differences and Bias
Discussion of Platform-Specific Differences and Bias
Sensitivity vs. Specificity:
Sensitivity vs. Specificity:
The analysis indicates that Nanopore sequencing is more sensitive, capturing a greater number of modified genes, whereas BID‑Seq appears more selective, reporting fewer genes with modification signals.
This discrepancy may partly reflect the differences in how each technology detects RNA modifications: Nanopore detects changes in electrical current across RNA molecules, capturing a broader dynamic range, while BID‑Seq relies on chemical treatments that could result in a lower baseline signal.
Methodological Challenges:
Methodological Challenges:
Merging the data at a gene level simplifies the integration, but it also aggregates isoform-specific differences that might be biologically significant. Hence, while the gene-level analysis offers a robust foundation, some transcript-level nuances are necessarily lost—a point to be addressed in future work.
Discussion of Biological Implications and Future Directions
Discussion of Biological Implications and Future Directions
Environmental Response:
Environmental Response:
The shared genes across platforms under PM exposure represent a subset of high-confidence candidates that might be involved in the lung cell response to environmental stress. For example, genes like ADAM15 and AK2, which consistently appear modified in both assays, could play pivotal roles in stress signaling or cellular adaptation.
Potential for Differential Analysis:
Potential for Differential Analysis:
Although the current analysis focuses on absolute signals, assessing fold changes (PM vs. control) on a per-gene basis might reveal which genes respond most strongly to PM exposure. This differential analysis could be explored in future iterations.
Future Direction for Transcript-Level Analysis:
Future Direction for Transcript-Level Analysis:
Extend the project to analyze isoform-specific modification patterns by mapping BID‑Seq genomic coordinates directly onto transcript coordinates. This would help resolve whether differences in isoform expression contribute to the biases between methods.
Conclusions
Conclusions
Overall, the gene-level analysis reveals that while both Nanopore and BID‑Seq identify overlapping sets of modified genes under PM and control conditions, there are significant differences in sensitivity, dynamic range, and detection thresholds. These findings highlight the need to consider both the strengths and biases of each platform in RNA modification studies, and they lay the groundwork for more detailed transcript-level analyses and functional studies in the future.
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