NGS-based HLA & KIR genotyping system
Introducing novoHLA, NGS-based HLA & KIR genotyping system to type HLA & KIR genes based on extensive known HLA & KIR alleles obtained from the IPD-IMGT/HLA Database.
| Sample | Gene | A | B |
|---|---|---|---|
| HG01459 | A | HLA-A*02:01:01:110 | HLA-A*02:01:01:110 |
| HG01459 | B | HLA-B*13:02:01 | HLA-B*38:01:01:18 |
| HG01459 | C | HLA-C*06:02:01 | HLA-C*12:03:01 |
| HG01459 | DPB1 | HLA-DPB1*04:01:01:03 | HLA-DPB1*17:01:01:01 |
| HG01459 | E | HLA-E*01:01:01:(30,62) | HLA-E*01:113 |
| HG01459 | F | HLA-F*01:01:01:(01,09) | HLA-F*01:01:01 |
| HG01459 | G | HLA-G*01:01:01 | HLA-G*01:05:01N |
| HG01459 | Y | HLA-Y*02:01 | HLA-Y*02:01 |
| HG01459 | DRA | HLA-DRA*01:01:01:(03,26) | HLA-DRA*01:01:01:(03,26) |
| HG01459 | DRB1 | HLA-DRB1*04:02:01 | HLA-DRB1*07:01:01 |
| HG01459 | DQA1 | HLA-DQA1*02:01:01:(01,02,03,09) | HLA-DQA1*03:01:01:01 |
| HG01459 | DQA2 | HLA-DQA2*01:01:01:04 | HLA-DQA2*01:01:02:(02,08) |
| HG01459 | DQB1 | HLA-DQB1*02:02:01:(01,16) | HLA-DQB1*03:02:01:01 |
| HG01459 | DQB2 | HLA-DQB2*01:01:01:01,HLA-DQB2*01:07:01:03 | HLA-DQB2*01:02:01:01,HLA-DQB2*01:09 |
| HG01459 | DPA1 | HLA-DPA1*01:03:01:04 | HLA-DPA1*02:01:01:03 |
| HG01459 | DMA | HLA-DMA*01:01:01:(01,11,41) | HLA-DMA*01:02:01:(01,07,09) |
| HG01459 | DMB | HLA-DMB*01:01:01:38 | HLA-DMB*01:02:01:01 |
| HG01459 | DOA | HLA-DOA*01:01:02:42 | HLA-DOA*01:01:02:45 |
| HG01459 | DOB | HLA-DOB*01:01:01:(01,24) | HLA-DOB*01:01:01:(24,28,29) |
| HG01459 | HFE | HLA-HFE*001:01:02 | HLA-HFE*001:01:03 |
| HG01459 | MICA | MICA*02:01:(01,19) | MICA*08:01:03 |
| HG01459 | MICB | MICB*02:01 | MICB*05:02 |
| HG01459 | TAP1 | TAP1*01:01:01:05 | TAP1*01:01:01:05 |
| HG01459 | TAP2 | TAP2*01:03:02:07 | TAP2*02:01:03:06 |
| HG01459 | KIR2DL1 | KIR2DL1*00302(04,05,66,82) | KIR2DL1*00302(04,05,66,82) |
| HG01459 | KIR2DL3 | KIR2DL3*0010114 | KIR2DL3*009{0010113} |
| HG01459 | KIR2DL4 | KIR2DL4*0010201 | KIR2DL4*0110101 |
| HG01459 | KIR2DS4 | KIR2DS4*0010109 | KIR2DS4*010 |
| HG01459 | KIR3DL1 | KIR3DL1*00501(01,03,14) | KIR3DL1*0290101 |
| HG01459 | KIR3DL2 | KIR3DL2*0010301 | KIR3DL2*0020106 |
| HG01459 | KIR3DL3 | KIR3DL3*00902 | KIR3DL3*04801 |
Key features
Experience precision, efficiency, & innovation with novoHLA
Support FASTQ and BAM
novoHLA supports both FASTQ and BAM as input.
Wide HLA & KIR genes prediction
novoHLA supports predictions of HLA class I, class I – pseudogenes, class II, class II – DRB and KIR genes
High fields resolution
novoHLA can infer HLA and KIR genes up to 4 fields resolution.
How It Works
Key Benefits
Favourable in clinical research setting
Provides statistical report
Unlock the power of data with our advanced statistical reporting solutions. From numbers to knowledge, we transform information into actionable insights.
Copy Number Normalisation plot
Explore your data with precision using our Copy Number Normalization (CNN) Plot. Gain valuable insights into copy number variations, identify trends, and make informed decisions with our interactive and intuitive visualization tool.
Genome browser visualisation
Enhance your genomic insights with Genome Browser Visualisation, seamlessly integrated with our novoHLA Unravel the genetic code and enhance healthcare with precision HLA typing integration.
Optimised read alignment
Optimisation of read alignments to HLA & KIR allele sequences
High performance
High typing performances
A pilot study was done by Institute For Medical Research Malaysia (IMR) on 46 healthy individuals of Bidayuh ethnicity identifying HLA-A and HLA-B via PCR-SSOP HLA typing, and we benchmarked against the results using in-silico HLA typing tools, novoHLA and T1K. For HLA-A assessment, novoHLA achieved best recall and precision for NGS HLA typing (The assessment were done up to 3rd field resolution (max field from PCR-SSOP).
Precision through collaboration
In collaboration with Simona Pagliuca @ CHRU de Nancy – University of Lorraine
Simona Pagliuca provided numerous NGS samples for HLA and KIR genotyping and offered valuable feedback on the accuracy of typing results and their presentation. Her insights contributed to refining our NGS-based genotyping system, ensuring reliable and precise analysis.
Recent publications
Leukemia relapse via genetic immune escape after allogeneic hematopoietic cell transplantation
Pagliuca, Simona, et al. Nature communications 14.1 (2023): 3153.
Molecular landscape of immune pressure and escape in aplastic anemia.
Pagliuca, Simona, et al. Leukemia 37.1 (2023): 202-211.
Unraveling Immunogenomic Features Germane to Pathobiology of Myelodysplastic Syndromes
Durmaz, Arda, et al. Blood 144 (2024): 2224.
Somatic and Germline HLA Determinants of Immune Surveillance and Escape in Myelodysplastic Syndromes
Gurnari, Carmelo, et al. Blood 142 (2023): 3220.
Reverse Engineering of Antigenic Peptides in LGL to Decipher Disease Triggers.
Durmaz, Arda, et al. Blood 142 (2023): 246.
Spectrum of molecular modes of immune escape in idiopathic aplastic anemia and paroxysmal nocturnal hemoglobinuria.
Pagliuca, Simona, et al. Blood 138.Supplement 1 (2021): 603-603.
Applications
Unleashing novoHLA in diverse applications
Whole genome sequenceWhole exome sequenceAmpliconRNA
novoHLA research areas
novoHLA can be used in numerous fields in the biological sciences.
Human Leukocyte Antigen
Cancer Research
Disease Association Study
