Figure 4
WER targets inferred from WER perturbation followed by high-throughput sequencing. (A) The number distribution of WER perturbations followed by high-throughput sequencing datasets. (B) The intersection among three types of downstream effects upon WER perturbation, including alterations in gene expression, m6A and translation efficiency, corresponding to RNA-seq, MeRIP-seq and Ribo-seq, respectively. (C) The association of WER targets inferred from high-throughput sequencing upon WER perturbation. Blue nodes represent readers, purple nodes represent writers and green nodes represent erasers. Node size indicates the number of target associations identified for this WER. The thickness of the edge indicates the number of target associations that is validated by both WERs. (D) The gene type distribution of WER targets in the perturbation module of m6A2Target; others refer to those low frequency gene types, such as miscRNA and snoRNA.

WER targets inferred from WER perturbation followed by high-throughput sequencing. (A) The number distribution of WER perturbations followed by high-throughput sequencing datasets. (B) The intersection among three types of downstream effects upon WER perturbation, including alterations in gene expression, m6A and translation efficiency, corresponding to RNA-seq, MeRIP-seq and Ribo-seq, respectively. (C) The association of WER targets inferred from high-throughput sequencing upon WER perturbation. Blue nodes represent readers, purple nodes represent writers and green nodes represent erasers. Node size indicates the number of target associations identified for this WER. The thickness of the edge indicates the number of target associations that is validated by both WERs. (D) The gene type distribution of WER targets in the perturbation module of m6A2Target; others refer to those low frequency gene types, such as miscRNA and snoRNA.

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