Full-resolution HLA and KIR gene annotations for human genome assemblies
Full-resolution HLA and KIR gene annotations for human genome assemblies is a dataset published in Genome Research (2024). On theSindex it has a DataRank of 0.761, placing it in the top 44.5% of the data-sharing corpus. It has been cited 25 times, with 24 citing works in its 1-hop citation network. Its calibrated FAIR score is 47/100.
Abstract
The human leukocyte antigen (HLA) genes and the killer cell immunoglobulin-like receptor (KIR) genes are critical to immune responses and are associated with many immune-related diseases. Located in highly polymorphic regions, it is difficult to study them with traditional short-read alignment-based methods. Although modern long-read assemblers can often assemble these genes, using existing tools to annotate HLA and KIR genes in these assemblies remains a nontrivial task. Here, we describe Immuannot, a new computation tool to annotate the gene structures of HLA and KIR genes and to type the allele of each gene. Applying Immuannot to 56 regional and 212 whole-genome assemblies from previous studies, we annotate 9931 HLA and KIR genes and found that almost half of these genes, 4068, have novel sequences compared with the current Immuno Polymorphism Database (IPD). These novel gene sequences are represented by 2664 distinct alleles, some of which contained nonsynonymous variations, resulting in 92 novel protein sequences. We demonstrate the complex haplotype structures at the two loci and report the linkage between HLA/KIR haplotypes and gene alleles. We anticipate that Immuannot will speed up the discovery of new HLA/KIR alleles and enable the association of HLA/KIR haplotype structures with clinical outcomes in the future.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.483
From this paper's citation signal
Citation Network Contribution
0.278
From 17 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- A global reference for human genetic variationNature201519,823 citationsDataRank 11.1Top 19%
- ModelFinder: fast model selection for accurate phylogenetic estimatesNature Methods201718,197 citationsDataRank 1.5
- Minimap2: pairwise alignment for nucleotide sequencesBioinformatics201816,325 citationsDataRank 1.5
- Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotypeNature Biotechnology201915,249 citationsDataRank 1.4
- The complete sequence of a human genomeScience20223,274 citationsDataRank 8.5Top 24%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 63% comes from its base citations and 37% from the citation network (17 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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