講演情報
[O4-3]大規模ChIP-seqデータで解明する転写因子認識配列に重なるSNPの転写因子結合影響予測とその細胞型依存性
○田原 沙絵子1,2, 土屋 貴穂1,3, 松本 拡高4,5, 尾崎 遼1,3,5 (1.筑波大学 医学医療系 バイオインフォマティクス研究室, 2.筑波大学 医学群 医学類, 3.筑波大学 人工知能科学センター, 4.長崎大学 情報データ科学部, 5.理化学研究所 生命機能科学研究センター バイオインフォマティクス研究開発ユニット)
More than 99% of single nucleotide polymorphisms (SNPs) are located in non-coding regions and localized in transcription factor (TF) binding regions. Elucidating the functions of SNPs in TF-binding regions and interpreting their effects on human diseases would provide the basis for precision medicine. However, it has been difficult to predict TFs and cell-type specificities that are strongly affected by SNPs.
Here, we aimed to develop a method to predict the effect of SNPs in TF-binding regions on TF-bindings and cell-type specificity.
We analyzed more than 10,000 human TF ChIP-seq data in a public database using a method called MOCCS2, which calculate TF-binding specificity score (MOCCS2score) of each short-length DNA sequence.
Using the MOCCS2 results, we developed an index “ΔMOCCS2score”, which is a difference of TF-binding specificities between reference and alternative alleles within TF-binding sequences. ΔMOCCS2score is anticipated to predict SNPs’ effects on TF binding. Indeed, we confirmed that the ΔMOCCS2scores correlated with SNP-SELEX (in vitro) and allele-specific binding (in vivo) results, indicating ΔMOCCS2score can be applicable for evaluating SNPs’ effects. Moreover, we applied the ΔMOCCS2score to GWAS-SNPs of Crohn's disease. SPI1 and FOS showed highly affected TFs and hematopoietic cells showed high specificity, consistent with previous studies.
In the future, we will develop a web service that anyone can predict affected TFs and cell types from user-provided SNP lists by calculating the ΔMOCCS2score from accumulated ChIP-seq data.
Here, we aimed to develop a method to predict the effect of SNPs in TF-binding regions on TF-bindings and cell-type specificity.
We analyzed more than 10,000 human TF ChIP-seq data in a public database using a method called MOCCS2, which calculate TF-binding specificity score (MOCCS2score) of each short-length DNA sequence.
Using the MOCCS2 results, we developed an index “ΔMOCCS2score”, which is a difference of TF-binding specificities between reference and alternative alleles within TF-binding sequences. ΔMOCCS2score is anticipated to predict SNPs’ effects on TF binding. Indeed, we confirmed that the ΔMOCCS2scores correlated with SNP-SELEX (in vitro) and allele-specific binding (in vivo) results, indicating ΔMOCCS2score can be applicable for evaluating SNPs’ effects. Moreover, we applied the ΔMOCCS2score to GWAS-SNPs of Crohn's disease. SPI1 and FOS showed highly affected TFs and hematopoietic cells showed high specificity, consistent with previous studies.
In the future, we will develop a web service that anyone can predict affected TFs and cell types from user-provided SNP lists by calculating the ΔMOCCS2score from accumulated ChIP-seq data.