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Background: N6-methyladenosine (m6A) is the most abundant modification of RNA in eukaryotic cells and play critical roles in cancer. While most related studies focus on m6A modifications in linear RNA, transcriptome-wide profiling and exploration of m6A modification in circular RNAs in cancer is still lacking. Methods: For the detection of m6A modification in circRNAs, we developed a new bioinformatics tools called Circm6A and applied it to the m6A-seq data of 77 tissue samples from 58 individuals with pancreatic ductal adenocarcinoma (PDAC). Results: Circm6A performs better than the existing circRNA identification tools, which achieved highest F1 score among these tools in the detection of circRNAs with m6A modifications. By using Circm6A, we identified a total of 8807 m6A-circRNAs from our m6A-seq data. The m6A-circRNAs tend to be hypermethylated in PDAC tumor tissues compared with normal tissues. The hypermethylated m6A-circRNAs were associated with a significant gain of circRNA-mRNA coexpression network, leading to the dysregulation of many important cancer-related pathways. Moreover, we found the cues that hypermethylated m6A-circRNAs may promote the circularization and translation of circRNAs. Conclusions: These comprehensive findings further bridged the knowledge gaps between m6A modification and circRNAs fields by depicting the m6A-circRNAs genomic landscape of PDAC patients and revealed the emerging roles played by m6A-circRNAs in pancreatic cancer. Circm6A is available at https://github.com/canceromics/circm6a. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) is the most abundant posttranscriptional modification in mammalian mRNA molecules and has a crucial function in the regulation of many fundamental biological processes. The m6A modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Different WERs might have different functions, and even the same WER might function differently in different conditions, which are mostly due to different downstream genes being targeted by the WERs. Therefore, identification of the targets of WERs is particularly important for elucidating this dynamic modification. However, there is still no public repository to host the known targets of WERs. Therefore, we developed the m6A WER target gene database (m6A2Target) to provide a comprehensive resource of the targets of m6A WERs. M6A2Target provides a user-friendly interface to present WER targets in two different modules: 'Validated Targets', referred to as WER targets identified from low-throughput studies, and 'Potential Targets', including WER targets analyzed from high-throughput studies. Compared to other existing m6A-associated databases, m6A2Target is the first specific resource for m6A WER target genes. M6A2Target is freely accessible at http://m6a2target.canceromics.org. [ABSTRACT FROM AUTHOR]

Modified reduced activation ferritic/martensitic steels with different amounts of Ta and Ti are fabricated with a thermo‐mechanical control process (TMCP). TMCP greatly enhances the refinement of both the precipitates and the martensite matrix. In addition, an increase in the dislocation density is achieved. The modified steels show excellent comprehensive mechanical properties compared with traditional reduced activation ferritic/martensitic steels. In addition, under the conditions of the TMCP, the effects of Ta and Ti on the microstructure and properties are analyzed. Simply increasing the amounts of Ta and Ti does not increase the phase fraction of MX, which is quite different from that derived from equilibrium thermodynamics theory or the typical hot rolling process for traditional reduced activation ferritic/martensitic steels. Without a high enough nucleation rate for MX, the excess Ta and Ti, which are not consumed by the formation of MX, promotes the formation of proeutectoid ferrite. This decreases the impact on toughness significantly. Herein, preliminary guidance is offered for a comprehensive design with a combination of both composition and rolling processing modification for the production of reduced activation ferritic/martensitic steels. [ABSTRACT FROM AUTHOR]

Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients. Availability and Implementation The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License. [ABSTRACT FROM AUTHOR]