<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY center_name="BioProject" alias="PRJNA775954" accession="SRP343612">
    <IDENTIFIERS>
      <PRIMARY_ID>SRP343612</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject" label="primary">PRJNA775954</EXTERNAL_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>RH long read sequencing</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_ABSTRACT>Long-read sequencing technologies may offer improvements in the characterization of genes that are currently difficult to assess. We used a combination of targeted DNA capture, long-read sequencing, and a customized bioinformatics pipeline to fully assemble the RH region which harbors variation relevant to red cell donor-recipient mismatch, particularly among patients with sickle cell disease. RHD and RHCE are a pair of duplicated genes located within a ~175kb region on human chromosome 1, with high sequence similarity and frequent structural variations. To achieve the assembly, we utilized palindrome repeats in PacBio SMRT reads to obtain consensus sequences of 2.1 to 2.9kb average length with over 99% accuracy. These long consensus sequences were used to identify 771 assembly markers and to phase the RHD-RHCE region with high confidence. The data set enabled direct linkage between coding and intronic variants, phasing of distant SNPs to determine RHD-RHCE haplotypes, and identification of known and novel structural variations along with the breakpoints. A limiting factor is the frequency of heterozygous assembly markers and therefore, was most successful in samples from African Black individuals with increased heterogeneity at the RH locus. Overall, this approach allows RH genotyping and de novo assembly in an unbiased and comprehensive manner that is necessary to expand application of NGS technology to high resolution RH typing.</STUDY_ABSTRACT>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <URL_LINK>
          <LABEL>Bioinformatics code</LABEL>
          <URL>https://github.com/zhezhangsh/PAClindrome</URL>
        </URL_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
  </STUDY>
</STUDY_SET>
