版本1.2.0的变化------------------------新功能o通过UCSC goldenpath url添加对hg38, mm10和rn6的CpG注释的支持。o添加一个从AnnotationHub资源构建注释的函数build_ah_annots()。o从UCSC基因组浏览器中添加对chromHMM轨道(染色质状态)的支持。o如果需要，用户可以注释多个细胞系中的染色质状态。o使用rtracklayer::liftOver将hg19和mm9增强器提升到hg38和mm10。用户面对的变化添加minoverlaps参数到annotate_regions()，传递给GenomicRanges:: finoverllaps()。o将supported_annotations()和supported_genomes()更改为builtin_annotations()和builtin_genomes()。这为AnnotationHub注释提供了所需的更多灵活性。o增加了将annotate_regions()的结果强制到data.frame和基于基因符号的子集设置到小插图的文档。修正了强制GRanges到data.frame的错误，其中row.names可以被复制。 Thanks to @kdkorthauer. o Require GenomeInfoDb >= 1.10.3 because of changes to NCBI servers. o Change scale_fill_brewer() to scale_fill_hue() in plot_categorical() to enable more categories and avoid plotting abnormalities. o Fixed bug that mistakenly displayed some supported annotations. o Fixed a bug in lncRNA annotation building caused by incomplete reference. CHANGES IN VERSION 0.99.13 -------------------------- PKG FEATURES o annotatr is a package to quickly and flexibly annotate genomic regions to genomic annotations. o Genomic annotations include CpG features (island, shore, shelves, and open sea), genic features (1-5kb upstream of TSS, promoters, 5'UTRs, exons, introns, CDS, 3'UTRs, intron/exon boundaries, and exon/ intron boundaries), as well as enhancers from the FANTOM5 consortium for hg19 and mm9. o Annotations are built at runtime using the TxDb.*, AnnotationHub, and rtracklayer packages. Users can select annotations a la carte, or via shortcuts, such as hg19_basicgenes. o Annotations are currently available for hg19, mm9, mm10, dm3, dm6, rn4, rn5, and rn6. Any species is supported through custom annotations. o Genomic regions are read in using the rtracklayer::import() function, and the extraCols argument enables users to include an arbitrary number of categorical or numerical data with the genomic regions. o Annotations are determined via GenomicRanges::findOverlaps(), and all annotations are returned, rather than imposing a prioritization. o annotatr provides several helpful summarization (using dplyr) and plot functions (using ggplot2) to investigate trends in data associated with the genomic regions over annotations.