在秀麗隱桿線蟲中，piRNA 與 miRNA 負責調控 mRNA 表現。piRNA 經由與 PIWI 蛋白結合形成 piRNA-PIWI 複合體，能精準識別並抑制特定基因；miRNA 會與ALG-1蛋白結合後形成miRISC並透過與 mRNA 結合達成基因沉默。這些小分子 RNA 在發育、代謝與免疫等生理過程中扮演關鍵角色。由於線蟲與人類在基因調控機制上的高度相似性，線蟲已成為研究 RNA 調控的重要模式生物。
本研究重新分析 NCBI 公開的定序資料，聚焦 PRG-1 與 ALG-1 所影響的 small RNA- target RNA 調控關係，利用 CLASH 與 iCLIP 實驗得到的結果深入探討這樣的現象。這些技術可取得大量轉錄層級的 small RNA- target RNA 調控 配對資訊，並搭配定序片段數量、結合分數評估調控強度與可信度。
傳統上，crosslink-induced mutation sites (CIMS) 常被忽略，然而本實驗延續先前研究，發現 CIMS 現象高度對應於真實的 small RNA- target RNA 調控事件。本研究將突變分析從單一位點擴展至配對層級 (pair-level)，即將所有屬於同一組 regulator-target 的 hybrid reads 作為整體，並探討其在不同定序片段數量與結合分數下的表現。
結果顯示，pair-level 的 CIMS 指標與 hybrid-level 分析高度一致，且在資料量有限或配對強度較低時表現出更佳敏感度與可靠性。此外，多數 hybrid-level 顯著突變現象在 pair-level 分析中亦可重現，強化 CIMS 作為小 RNA 調控現象的可信度。
本研究所建立之分析策略與 pair-level 評估框架，未來可進一步應用於其他small RNA 調控通路、不同 RNA 結合蛋白 (RBP) 資料集與多種物種的轉錄組資料上，驗證其於更廣泛演化背景下的適用性與通用性。此成果不僅有助於解析 piRNA 與 miRNA 複雜的調控網絡，也為 RNA 研究中如何有效利用突變訊號提供嶄新思路。

High-throughput CLASH/iCLIP experiments reveal thousands of small-RNA–target pairings, but distinguishing functional from spurious pairs is difficult when read support or predicted binding is weak. This study evaluates three orthogonal indicators of regulatory significance—readcount, binding score, and crosslink-induced mutation sites (CIMS)—using a pair-level framework that aggregates all hybrid reads for each regulator–target pair. We reanalyzed C. elegans PRG-1 (piRNA, CLASH) and ALG-1 (miRNA, iCLIP) datasets. For PRG-1, functional output was quantified by 22G-RNA fold-change at WAGO-associated targets; for ALG-1, mRNA fold-change between mutant and wild-type provided a repression readout. Across stratifications by readcount and binding score, pairs carrying CIMS consistently exhibited stronger functional signatures than CIMS-negative pairs, with the largest gains in low-score/low-read regimes. These results indicate that CIMS is not experimental noise but a credible interaction-level signal. Integrating pair-level CIMS with readcount and binding score increases sensitivity to non-canonical or weakly supported interactions without over-filtering low-abundance data. The approach generalizes across piRNA and miRNA pathways and can be applied to other RNA-binding proteins and species to improve transcriptome-wide interaction maps

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