The ExAC browser: displaying reference data information from over 60 000 exomes

1Recently, large reference datasets, such as those from the 1000 Genomes Project Consortium, Exome Sequencing Project (ESP) and Exome Aggregation Consortium (ExAC), have become publicly available for the benefit of the biomedical community. These datasets are beneficial for many applications, including clinical as well as basic research. In particular, as a variant's frequency is among the best predictor of its deleteriousness, clinical geneticists use reference datasets to discern pathogenic mutations from benign polymorphisms. Additionally, genetics researchers rely on variant and allele frequency data to infer gene and variant function (e.g. whether a gene is essential) as well as for population genetics analyses. These large-scale projects release raw data in the form of variant call format (VCF) files, but these files require bioinformatics expertise to parse and synthesize. Genome browsers, such as the UCSC genome browser, have become a popular method for non-technical audiences to visualize large genome-scale datasets.

2Additionally, browsers of variation data, including the Exome Variant Server (EVS) from ESP and the 1000 Genomes Browser, have been developed to present population data, but these are limited in the data they display. For instance, deviations in coverage, which affect one's confidence of the absence of variation, are not natively shown: EVS contains a link to a coverage track on UCSC, but coverage is not visualized on the page itself.

3There are a number of practical considerations for the optimal display of reference data. Specifically, as one primary use case for genome browsers involves gene-level analyses, the display of gene summary information is a central view for a genome browser, including integration of summary statistics as well as data for individual single nucleotide variants (SNV), insertions and deletions (indel) and copy number variants (CNV). Of course, detailed information on each variant, including annotations and quality metrics, is of paramount importance. However, an equally important display is that of the absence of variation: whether a missing variant implies a lack of observed variation, low or no coverage in the genomic region, or variation filtered due to poor quality.

4The Exome Aggregation Consortium (ExAC) has collected, harmonized, and released exome sequence data from 60 706 individuals. Already, these data have proven useful in filtering variants for identifying causal variants for rare disease. Here, we present a visual browser of the ExAC dataset. The browser is intended for use by clinical geneticists researching variants of interest for patients as well as biologists exploring variation in specific genes.

5We designed the ExAC browser as an intuitive interface to enable clinical geneticists and biologists to explore variants and genes of interest. We built a scalable browser framework to display qualitative and quantitative information for genes and variants in the ExAC dataset (see Methods), including both quality control information as well as summary statistics. The front page of the browser includes a search bar, which is seeded with autocomplete suggestions based on gene symbols and aliases, as well as sample queries. From here, there are two central units of the ExAC browser: the gene (or transcript) page and the variant page.

6The ExAC browser gene page is an overview page for gene-level information, including summary statistics, coverage, and variants. The page begins with gene metadata and external references, along with constraint information, which summarizes the gene's intolerance to variation for multiple functional classes (Figure 1A). Next, we present single base-resolution coverage information for each exon for a number of metrics including mean, median, and proportion of individuals covered at a number of depth cutoffs (Figure 1B). Immediately below, an exon summary plot displays the position and frequency of each SNV and indel, as well as CNV count information broken down by population (Figure 1C). All individual CNV calls are provided in the form of UCSC tracks, which are linked at the top of the page and above the CNV display. Finally, the browser provides a comprehensive table for variant information, which includes the worst functional annotation across transcripts for each variant, as well as frequency information. The table is sortable and can be exported to a CSV format (Figure 1D).

7By default, the gene summary page presents a table of all variants in the gene, annotated with the worst consequence across all transcripts, as well as coverage information for the canonical transcript: we also present a transcript page that includes annotation and coverage information specific to that transcript.

8The variant page includes a diverse set of annotations for the given variant. First, a site overview (Figure 2A) and site- and genotype-level quality metrics are provided (Figure 2B). The user is notified whether any individual has another variant in the same codon (suggesting a multi-nucleotide variant, or MNV), whether the site is multi-allelic, or if a low number of individuals is covered at this locus. Functional annotations of the variant against each transcript including PolyPhen2, SIFT, and LOFTEE annotations, as well as a sortable table of population frequencies are provided (Figure 2C). Finally, for users that wish to evaluate the validity of specific variants, raw short-read data from a subset of individuals is available for each variant. We provide an IGVweb visualization of the read pileup of a 125 bp window around the variant (Figure 2D) for a random sample of individuals with each variant, as well as a sampling of homozygous individuals, if available. For the first time among genome browsers, we provide users with a mechanism to efficiently visualize the raw read support for a variant and make assessments of its quality that may not have been detected by variant calling algorithms.

9One important consideration for displaying genetic data includes the display of non-variant sites. In particular, if a variant or region is queried, we display metadata about the locus, whether or not variants are present in the dataset. When a user searches for variants or regions that are not covered in the ExAC dataset, the user is shown a page with coverage information for the general region for the variant.

10As current web browsers and connections benefit from smaller data transfers and footprints, we have developed a number of optimizations to the browser, including compression and caching of data for large genes (Methods). Finally, the browser is optimized for mobile browsing, where extraneous information is hidden when browsing from a mobile device.

11Here, we have described a browser for reference variation data, whose use has become widespread in clinical genetics laboratories across the world. As of this writing (8/1/2016), the browser has had over 5 million pageviews and 250 000 users spanning over 188 countries.

12The top 10 genes and top three variants visited by users are shown in Table 1.

13There are a number of limitations to the ExAC browser. First, the browser only displays data from the exome, or the coding regions of the genome. While these are typically of highest relevance for clinical genetics, there are many non-coding regions that are known to be important for human disease and studied by researchers. An updated version of the browser to allow whole-genome data will be useful for these applications, but additional scalability considerations will be required to display these expanded datasets. Accordingly, queries are limited to 100 kb to ensure a timely return of results. The addition of an optimized API would provide additional flexibility for the browser, as well as serve the needs of researchers needing to do bulk queries for larger-scale analyses. Finally, the quality of the database relies on the quality of the variant calls and annotations contained within ExAC: as new callsets become available, the browser will be updated with new versions that adopt new computational methods and sequencing technologies.

14The code is open-source and available at http://github.com/konradjk/exac_browser. The browser framework established can be privately cloned and used for internal sequencing projects, as well as extended to a number of applications, such as a browser for results from genome-wide association studies (GWAS).

15As of this writing (8/1/2016), version 0.3.1 of ExAC dataset, as described in, was used for the ExAC Browser. Variants were annotated using the Variant Effect Predictor (VEP) version 81 against the Gencode v19 transcript set. RSIDs were obtained from dbSNP version 142 and gene names and aliases were extracted from dbNSFP. Histograms for various genotype-specific quality metrics, such as per-sample genotype quality and depth, are pre-computed using a custom python script (https://github.com/macarthur-lab/exac_2015/blob/master/src/prepare_exac_sites_vcf.py). MNVs and constraint metrics are pre-calculated as described in.

16Reassembled read data was generated for each of the 9.8 million variants in ExAC v0.3.1 by running GATK HaplotypeCaller 3.1 (full version: v3.1-1-ga70dc6e) with the -bamout flag on each sample containing the particular variant (up to a limit of five homozygous and five heterozygous samples). Only samples with a read depth (DP) ≥10 and genotype quality (GQ) ≥20 were included. When a variant was present in more than five such samples, the five samples with the highest GQ were selected. Overall, HaplotypeCaller was run 22.3 million times to produce over 5 Tb of small BAM files—with each BAM file storing reassembled reads for a several-hundred base pair window around the variant. Batches of several thousand of these small BAM files were then combined into larger BAM files to improve compression ratios, while using read groups to keep track of the original source of each read. The final dataset comprised ∼23 000 BAMs and spanned 540 Gb. These BAM files were made directly available over the web and visualized in the ExAC browser using IGV.js.

17Besides the -bamout flag, these additional flags were passed to HaplotypeCaller to ensure that gVCF genotype calls matched the original ExAC gVCF genotypes, which are reproduced here to facilitate reproducibility: -ERC GVCF - -paddingAroundSNPs 300- -paddingAroundIndels 300- -max_alternate_alleles 3-A DepthPerSampleHC-A StrandBiasBySample - -maxNumHaplotypesInPopulation 200-stand_call_conf 30.0-stand_emit_conf 30.0 - -disable_auto_index_creation_and_locking_when_reading_rods- -minPruning 3- -variant_index_type LINEAR- -variant_index_parameter 128000

18-ERC GVCF - -paddingAroundSNPs 300- -paddingAroundIndels 300- -max_alternate_alleles 3

19- -paddingAroundSNPs 300

20- -paddingAroundIndels 300

21- -max_alternate_alleles 3

22-A DepthPerSampleHC

23-A StrandBiasBySample - -maxNumHaplotypesInPopulation 200

24- -maxNumHaplotypesInPopulation 200

25-stand_call_conf 30.0

26-stand_emit_conf 30.0 - -disable_auto_index_creation_and_locking_when_reading_rods- -minPruning 3- -variant_index_type LINEAR- -variant_index_parameter 128000

27- -disable_auto_index_creation_and_locking_when_reading_rods

28- -minPruning 3

29- -variant_index_type LINEAR

30- -variant_index_parameter 128000

31This data processing was managed by a python-based pipeline available here: https://github.com/macarthur-lab/exac_readviz_scripts.

32CNVs were generated using XHMM and based on GENCODE v19 coding regions: all details of CNV calling and quality control have been published previously. Gene summary CNV counts and related constraint scores are presented based on likelihoods of the CNV occurring within the genomic range of the gene, as described. Exon CNV counts and CNVs presented in the UCSC browser are based on all confidently called CNVs (XHMM SQ > 60) across the genome. All overlapping CNVs, regardless of amount of overlap, are included in Exon CNV counts.

33The ExAC browser was built primarily on open-source tools. On the server, a lightweight Flask framework serves content built on Python scripts available at http://github.com/konradjk/exac_browser. All variants and metadata are loaded into MongoDB (version 2.4.14). The major components loaded include the variant data (directly from the VCF format), coverage data (generated by a modified version of samtools, as described in), MNV and constraint information, as well as gene models from Gencode and RSID information from dbSNP.

34The HTML backbone was created based on Bootstrap version 3.1.1 (https://github.com/twbs/bootstrap) and JQuery version 1.11.1 (http://jquery.org). Plotting was performed using d3 version 3. Read visualization is powered by IGVweb version 0.9.3 (https://github.com/igvteam/igv.js/releases/tag/0.9.3).

35The entire system runs on a Linux virtual machine with eight cores, 32 GB RAM, and 2T of disk space using Apache 2.4.12. Page tracking is provided by Google Analytics (http://www.google.com/analytics/).

36Bootstrap is a mobile-first web framework, which enables the ExAC browser's optimizations for mobile browsing: specifically, much extraneous information (such as the coverage information or additional variant annotations) is hidden when the browser is used on a smaller screen. Additionally, the pages for large genes are pre-computed, allowing for faster load times for these genes. Finally, user search is optimized using typeahead version 0.10.2, with most search terms, including gene names and all aliases, populating the search bar. The single search bar is used to search for variants (formatted as RSIDs or in a chromosome and position format), genes and transcripts (symbols, aliases or Ensembl identifiers) and regions.