Introduction

Patient specific variant detection is of importance for the diagnosis and treatment of various diseases. DNA capture sequencing using Molecular Inversion Probes (MIPs) is a fast and efficient method for targeted sequencing of regions of interest and has been applied in various disease cohorts [1–3]. A number of protocols exist with minor deviations from the general workflow [4]. The general approach involves designing single stranded DNA probes containing two primer sequences complementary to the region of interest as well as a linker, serving as the backbone to physically link the two primers [5]. These probes are then hybridized to the target DNA and circularized upon polymerase fill-in and nick ligation. Degradation of non-circularized molecules enriches the target DNA and sample multiplexing is enabled by using sample specific barcodes in the linker or during an amplification reaction linearizing the DNA and adding required sequencing adapters.

Hemophilia A and B are X-linked recessive disorders resulting from more than 3,000 known DNA variants in the genes encoding coagulation factor VIII (F8) and factor IX (F9), respectively. Determination of the causative genetic variant is important for the patient's reproductive planning, for use in pregnancy and neonatal management, and also to inform risks of neutralizing antibody (inhibitor) formation and bleeding severity. Therapies targeted to specific patient variants are likely to become more common in the future [6]. The "My Life, Our Future" (MLOF) project is a multisector collaboration developed to provide wide-scale access to free hemophilia genotype analysis for patients in the United States and to create a research repository of associated samples and data to support scientific discovery and treatment advances. For the MLOF initiative, a novel MIP-based genotyping approach was developed for the F8 and F9 genes [6] including more than 450 MIPs (see S1 Text for details).

Here, we introduce hemoMIPs, an easy and efficient pipeline to analyze MIP target capture data generated on the Illumina sequencing platform. Using an easily adapted Snakemake workflow [7], hemoMIPs performs sample demultiplexing, overlap paired-end merging, alignment using BWA, MIP-arm trimming, variant calling using GATK, coverage analysis and HTML report generation for single end and paired end sequencing datasets. While hemoMIPs was developed to analyze targeted sequencing data of the MLOF Initiative, it can be applied to a broad set of MIP sequencing data sets. Currently various tools and individual pipelines are being used in the analysis and genotyping of Molecular Inversion Probe Data. While two pipelines [2,8] are publicly inaccessible, MIPgen tools [5], bwa-MIPs [9] and MIPWrangler [10] stop after alignment and MIP-arm trimming. Therefore, hemoMIPs is the first complete analysis workflow that is open source and easy to employ via workflow management.

Design and implementation

The hemoMIPs pipeline enables hemophilia screening of (typically) 384 patients on a single Illumina NextSeq run. Fig 1 outlines the general workflow and the following sections describe data processing and analysis in more detail. All steps are implemented in the workflow management software Snakemake [7] and rely on Conda predefined environments to manage software dependencies and easy deployment.

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Fig 1. Depiction of analysis workflow of the hemoMIPs pipeline.

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Report generation

Different HTML reports are generated for visualization, interpretation and better access to all information collected in previous steps. There are two entry points to this information, organized as two different HTML reports–one summarizing all variant calls and MIP performance across samples and the other summarizing per-sample results in an overview table. The first report (summary.html, S2 Fig) provides a more technical sample and variant summary, per region coverage and MIP performance statistics. This report across samples can be used to assess assay performance (e.g. underperforming MIPs could be redesigned in future assays) and allows identification of suspiciously frequent variants (common variants or systematic errors).

The second report (report.html) provides an overview of results for each sample (Fig 2), highlighting putative deleterious variants and taking previously defined common/known benign variants out of focus (gray font). Additional information is provided about potential structural variants and incompletely covered regions. This table also provides an overall sample status field with information about passing and failing samples, as well as flags indicating outlier MIP performances.

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Fig 2. HTML reports are generated for visualization, interpretation and better access to all information collected across the individual workflow steps.

Here, a section of report.html shows the obtained genotypes for the demultiplexed samples and highlights potential pathogenic variants, their location in the gene and which exon (E) or intron (I) is affected. Additionally incomplete called sites, predefined structural variants (columns INT1 and INT22 referring to inversions of F8‐intron 22 and F8‐intron 1 which are common causes of severe hemophilia A) and failed MIPs are reported. The multiplexed samples can be identified via their sampleID. This output is meant to give a general overview over the sample performances.

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Both reports provide links to individual report pages of each sample. The individual reports (ind_SAMPLENAME.html, S3 Fig), provide quality measures like overall coverage, target region coverage, read counts underlying the inferred sample sex and MIP performance statistics (over- or underperforming MIPs in this sample), but most importantly provide detailed information on the identified variants, structural variant call results and regions without coverage (potential deletions).

Results

We introduced an easy-to-use pipeline to analyze highly imbalanced, targeted next-generation sequencing data sets generated using MIP experiments. In a user-friendly HTML report, we summarize all analysis results including covered, incomplete or missing regions, called variants and their predicted effects.

Using the GATK3 version of hemoMIPs, the MLOF initiative screened 3,000 patients for hemophilia causative variants in 2017, sequencing the F8 and F9 genes for about 15% of the total hemophilia A and B population of the United States [6]. All F8 and F9 coding regions, splice sites, and upstream (450 bp for F8 and 300 bp for F9) and downstream (1838 bp for F8 and 1417 bp for F9) untranslated sequences were captured using 458 MIP probes, each with about 111 bp in target size. Additional eight probes were designed to capture reference or mutant sequences of large DNA inversions mediated through sequences in F8 intron 22 or F8 intron 1 and homologous sequences distal to the F8 gene, resulting in gene disruptions [15]. Finally, five probes are targeting SRY unique sequence to detect patient sex.

In 98.4% (2,952/3,000) of patients, the likely causative variant was identified from our results and confirmed using Sanger validation [6]. Of 924 unique variants observed in this hemophilia cohort, 285 novel variants were identified. In cases of severe hemophilia, predicted gene-disrupting variants were common while missense variants dominated for mild-to-moderate disease. Novel hemophilia DNA variants were detected continuously throughout the project, indicating that additional variation likely remains undiscovered.

We have extended our pipeline to use GATK4 for variant calling and coverage analysis. Results are highly concordant between the two versions, but GATK4 calling is 50 times faster (see also S1 Text). The hemoMIPs workflow can be easily adapted to other MIP designs due to the use of Snakemake as workflow management and Conda for managing software dependencies.

Availability and future directions

HemoMIPs is available on GitHub on https://github.com/kircherlab/hemoMIPs. Its source code is open and available for everyone to download and modify (MIT License). A manual can be found in the main repository together with example inputs and outputs to run the pipeline. All dependencies are handled by predefined conda environments available in the main repository.

As an open-source and community effort, the hemoMIPs pipeline will continue to evolve with best practice workflows (e.g. provided through GATK) as well as potential novel molecular inversion probe designs.

Supporting information

Acknowledgments

We thank current and previous members of the Shendure and Kircher laboratories for helpful discussions and suggestions. Further, we would like to acknowledge all staff and patients enrolled in the My Life, Our Future initiative. We would like to express a special thank you to Shelley N. Fletcher and Haley Huston for their input during the development of this pipeline.