CHANGES IN VERSION 1.0.3
-------------------------

  o Require GenomeInfoDb >= 1.10.3 because of changes to NCBI servers.

CHANGES IN VERSION 1.0.2
-------------------------

  o Correct result of supported_annotations().

  o Add support for chromHMM tracks (chromatin state) from the UCSC Genome Browser.

    o Users may annotate to chromatin states in multiple cell lines, if desired.

  o Add support for CpG annotations for hg38, mm10, and rn6 via the UCSC
    goldenpath URLs.

    o Consequently, require readr as a prerequisite to get data from the URL.

  o Minor vignette updates.

  o Add minoverlaps parameter to annotate_regions() that is passed to
    GenomicRanges::findOverlaps().

CHANGES IN VERSION 1.0.1
-------------------------

  o Require GenomeInfoDb >= 1.10.1 because of a change to NCBI servers.

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.