This container track helps call out sections of the genome that often cause problems or confusion when working with the genome. The hg19 genome has a track with the same name, but with more subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist for hg38.
The Problematic Regions track contains the following subtracks:
The Highly Reproducible Regions track highlights regions and variants from eight samples that can be used to assess variant detection pipelines. The "Highly Reproducible Regions" subtrack comprises the intersection of the reproducible regions across all eight samples, while the "Variants" subtracks contain the reproducible variants from each assayed sample. Both tracks contain data from the following samples:
The Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the genome to evaluate variant calls in complex regions. It is designed for use with Global Alliance for Genomic Health (GA4GH) benchmarking tools like hap.py and includes regions with low complexity, segmental duplications, functional regions, and difficult-to-sequence areas. Developed in collaboration with GA4GH, the Genome in a Bottle (GIAB) consortium, and the Telomere-to-Telomere Consortium (T2T), the dataset aims to standardize the analysis of genetic variation by offering pre-defined BED files for stratifying true and false positives in genomic studies, facilitating accurate assessments in complex areas of the genome.
The creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to generate stratification BED files that categorize genomic regions based on specific challenges, such as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls. For more information on the pipeline and configuration used, please visit the following webpage: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md. If you have questions or comments, please write to Justin Zook (jzook@nist.gov).
The Panmask Easy 151b Regions subtrack contains a set of sample-agnostic easy regions where short-read variant calling reaches high accuracy. Easy regions are derived for variant filtration agnostic to individual samples. They are genomic intervals where general variant callers achieve high accuracy without sophisticated filtering.
A set of easy regions for ancient DNA variant filtering was generated by selecting 35-mers that could not be mapped elsewhere within one mismatch or gap. Read alignments from multiple samples were inspected to exclude regions with excessively high or low coverage or those enriched with low mapping quality alignments. The easy regions generated through this k-mer uniqueness procedure are referred to as pm151:lenient, where "pm" stands for panmask. In addition, low complexity regions identified by SDUST were removed.
The pm151 regions are used to filter spurious variant calls in centromeres, long repeats, and other genomic regions where short-read mapping is often problematic. They cover 88.2% of hg38, 92.2% of coding regions, and 96.3% of ClinVar pathogenic variants. The track can be used to filter variant calls for clinical or research human samples. Like the HighRepro track in this container (see above), it shows regions that are easy to sequence, not those that are problematic. The data was derived from the HPRC assemblies, and this track presents the 151b-easy panmask set.
++The Panmask Difficult 151b Regions subtrack is the complement of the Panmask Easy 151b Regions +track above: it marks the bases of the genome that Panmask did not classify as easy, so a +variant caller can expect lower accuracy there. Unlike Panmask Easy, this track directly +represents difficult regions, matching the rest of this container track, so it is the version +used in the Problematic Regions Recommended Track Set. It was built at UCSC, not downloaded, by +inverting the Panmask Easy 151b regions with the featureBits tool and removing assembly +gaps, restricted to the 24 chromosomes that Panmask itself covers.
+Each track contains a set of regions of varying length with no special configuration options. The UCSC Unusual Regions track has a mouse-over description, all other tracks have at most a name field, which can be shown in pack mode. The tracks are usually kept in dense mode.
The Hide empty subtracks control hides subtracks with no data in the browser window. Changing the browser window by zooming or scrolling may result in the display of a different selection of tracks.
The raw data can be explored interactively with the Table Browser or the Data Integrator.
For automated download and analysis, the genome annotation is stored in bigBed files that
can be downloaded from
our download server.
Individual
regions or the whole genome annotation can be obtained using our tool bigBedToBed
which can be compiled from the source code or downloaded as a precompiled
binary for your system. Instructions for downloading source code and binaries can be found
here.
The tool
can also be used to obtain only features within a given range, e.g.
bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout
Files were downloaded from the respective databases and converted to bigBed format. The procedure is documented in our hg38 makeDoc file.
Thanks to Anna Benet-Pagès, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo Navarro at the UCSC Genome Browser for planning, building, and testing these tracks. The underlying data comes from the ENCODE Blacklist and some parts were copied manually from the HGNC and NCBI RefSeq tracks.
Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019 Jun 27;9(1):9354. PMID: 31249361; PMC: PMC6597582
Dwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B, Olson ND et al. The GIAB genomic stratifications resource for human reference genomes. Nat Commun. 2024 Oct 19;15(1):9029. PMID: 39424793; PMC: PMC11489684
Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol. 2019 May;37(5):555-560. PMID: 30858580; PMC: PMC6699627
Li H. Finding easy regions for short-read variant calling from pangenome data. ArXiv. 2025 Aug 8;. PMID: 40799803; PMC: PMC12340882
Pan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et al. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing. Genome Biol. 2022 Jan 3;23(1):2. PMID: 34980216; PMC: PMC8722114