MeRIPseqPipe: an integrated analysis pipeline for MeRIP-seq data based on Nextflow

Bao, Xiaoqiong; Zhu, Kaiyu; Liu, Xuefei; Chen, Zhihang; Luo, Ziwei; Zhao, Qi; Ren, Jian; Zuo, Zhixiang

doi:10.1093/bioinformatics/btac025

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…RSEM ( 26 ) was then used to generate gene counts, and DESeq2 ( 29 ) was used to obtain the differentially expressed genes ( P -value < 0.05, fold change > 1.5). For modification enrichment, MeRIPseqPipe ( 30 ) was used to automatically analyze MeRIP-seq data, where MACS2 and MeTPeak ( 31 ) were used for m6A peak calling and DESeq2 was set to compare the modification levels ( P -value < 0.05, fold change > 1.5). For m5C BS-seq, information about the modification sites and quantification results were downloaded from relevant GEO datasets.…”

Section: Methodsmentioning

confidence: 99%

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications

Bao

Zhang

et al. 2022

Nucleic Acids Research

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RNA modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Abnormal changes of WERs will disrupt the RNA modification homeostasis of their target genes, leading to the dysregulation of RNA metabolisms such as RNA stability and translation, and consequently to diseases such as cancer. A public repository hosting the regulatory relationships between WERs and their target genes will help in understanding the roles of RNA modifications in various physiological and pathological conditions. Previously, we developed a database named ‘m6A2Target’ to host targets of WERs in m6A, one of the most prevalent RNA modifications in eukaryotic cells. To host all RNA modification (RM)-related WER–target associations, we hereby present an updated database, named ‘RM2Target’ (http://rm2target.canceromics.org/). In this update, RM2Target encompasses 1 619 653 WER–target associations for nine RNA modifications in human and mouse, including m6A, m6Am, m5C, m5U, m1A, m7G, pseudouridine, 2′-O-Me and A-to-I. Extensive annotations of target genes are available in RM2Target, including but not limited to basic gene information, RNA modifications, RNA–RNA/RNA–protein interactions and related diseases. Altogether, we expect that RM2Target will facilitate further downstream functional and mechanistic studies in the field of RNA modification research.

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Section: Methodsmentioning

confidence: 99%

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications

Bao

Zhang

et al. 2022

Nucleic Acids Research

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“…In total, we got 20 725 DRACH m6A sites (including 17 702 RRACH sites) and considered any other DRACH site in the human genome as unmodified. For P.trichocarpa , we performed m6A prediction from the MeRIP-seq data from Gao et al (2021) by using the MeRIPseqPipe pipeline ( Bao et al 2022 ). We got 122 075 RRACH m6A sites in total (Note S1).…”

Section: Methodsmentioning

confidence: 99%

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads

Ni,

Xu,

Zhong

et al. 2024

Bioinformatics

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Motivation Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. Results Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%–9.9% and 5.5%–13.8% higher than other methods, respectively. Availability and implementation The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

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Soft computing techniques for biomedical data analysis: open issues and challenges

Houssein,

Hosney,

Emam

et al. 2023

Artif Intell Rev

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In recent years, medical data analysis has become paramount in delivering accurate diagnoses for various diseases. The plethora of medical data sources, encompassing disease types, disease-related proteins, ligands for proteins, and molecular drug components, necessitates adopting effective disease analysis and diagnosis methods. Soft computing techniques, including swarm algorithms and machine learning (ML) methods, have emerged as superior approaches. While ML techniques such as classification and clustering have gained prominence, feature selection methods are crucial in extracting optimal features and reducing data dimensions. This review paper presents a comprehensive overview of soft computing techniques for tackling medical data problems through classifying and analyzing medical data. The focus lies mainly on the classification of medical data resources. A detailed examination of various techniques developed for classifying numerous diseases is provided. The review encompasses an in-depth exploration of multiple ML methods designed explicitly for disease detection and classification. Additionally, the review paper offers insights into the underlying biological disease mechanisms and highlights several medical and chemical databases that facilitate research in this field. Furthermore, the review paper outlines emerging trends and identifies the key challenges in biomedical data analysis. It sheds light on this research domain’s exciting possibilities and future directions. The enhanced understanding of soft computing techniques and their practical applications and limitations will contribute to advancing biomedical data analysis and support healthcare professionals in making accurate diagnoses.

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MeRIPseqPipe: an integrated analysis pipeline for MeRIP-seq data based on Nextflow

Cited by 7 publications

References 27 publications

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads

Soft computing techniques for biomedical data analysis: open issues and challenges

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