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Method: Pangenome-based genome inference using integer programming
- Ghanshyam Chandra,
- Md Helal Hossen,
- Stephan Scholz,
- Alexander T. Dilthey,
- Daniel Gibney,
- and Chirag Jain
Genome Res. December 2025 35: 2661-2670; Published in Advance August 21, 2025, doi:10.1101/gr.280567.125
...and Health Informatics (BCB '22), Article 51, pp. 1–9. Association for Computing Machinery, New York. doi:10.1145/3535508.3545556 ↵Vaddadi NSK, Mun T, Langmead B. 2023. Minimizing reference bias: the impute-first approach for personalized analysis. In Proceedings of the 14th ACM International Conference...
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Method: Proxy panels enable privacy-aware outsourcing of genotype imputation
- Degui Zhi,
- Xiaoqian Jiang,
- and Arif Harmanci
Genome Res. February 2025 35: 326-339; Published in Advance January 10, 2025, doi:10.1101/gr.278934.124
...to minimize the risks and evaluate them effectively. This process is very challenging when individual-level data sets (from both the client and the reference panel owners) are shared with the imputation servers. For instance, even the knowledge of rare variant positions can be used to identify an individual...
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Method: Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases
- Prashant S. Emani,
- Maya N. Geradi,
- Gamze Gürsoy,
- Monica R. Grasty,
- Andrew Miranker,
- and Mark B. Gerstein
Genome Res. December 2023 33: 2156-2173; Published in Advance December 14, 2023, doi:10.1101/gr.278322.123
...trajectory, through the reference haplotype space. Thus, whereas phasing and imputation already have highly efficient solutions to find the single most likely genotype at an unobserved locus based on sufficiently dense nearby SNPs, our method is optimized to find all equally likely trajectories pieced...
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Review: Genome graphs and the evolution of genome inference
- Benedict Paten,
- Adam M. Novak,
- Jordan M. Eizenga,
- and Erik Garrison
Genome Res. May 2017 27: 665-676; Published in Advance March 30, 2017, doi:10.1101/gr.214155.116
...Kingdom The human reference is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw...
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Method: Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
- Ron Schwessinger,
- Maria C. Suciu,
- Simon J. McGowan,
- Jelena Telenius,
- Stephen Taylor,
- Doug R. Higgs,
- and Jim R. Hughes
Genome Res. October 2017 27: 1730-1742; Published in Advance September 13, 2017, doi:10.1101/gr.220202.117
...Institute of Molecular Medicine, Oxford OX3 9DS, United Kingdom In the era of -wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory...
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Resource: PWAS Hub for exploring gene-based associations of common complex diseases
- Guy Kelman,
- Roei Zucker,
- Nadav Brandes,
- and Michal Linial
Genome Res. October 2024 34: 1674-1686; Published in Advance October 15, 2024, doi:10.1101/gr.278916.123
.... Accurate risk assessments are sensitive to sample size (Daetwyler et al. 2008). By selecting disease phenotypes with a minimal prevalence level of 3%–4%, we hope that biases due to cohort size are minimized. Some of the studied phenotypes include clinical conditions that are very broad (e.g., ICD-10 codes...
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Method: Assessing transcriptomic reidentification risks using discriminative sequence models
- Shuvom Sadhuka,
- Daniel Fridman,
- Bonnie Berger,
- and Hyunghoon Cho
Genome Res. July 2023 33: 1101-1112; Published in Advance August 4, 2023, doi:10.1101/gr.277699.123
...the DSM requires substantial memory (75 GB) and time (∼6 h) per window. These requirements depend on the size of the eQTL window and reference panel size (see Supplemental Fig. S4). In contrast, both EBL and GNB can be trained with minimal time and memory requirements. However, once trained, DSM performs...
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Method: Fast inference of genetic recombination rates in biobank scale data
- Ardalan Naseri,
- William Yue,
- Shaojie Zhang,
- and Degui Zhi
Genome Res. July 2023 33: 1015-1022; Published in Advance June 22, 2023, doi:10.1101/gr.277676.123
...that the total genetic length in centimorgans is known or specified by the user. This assumption is commonly held for indirect inference of recombination rates (Zhou et al. 2020). If the total genetic length is not correctly specified, the estimated rates will be biased toward the total genetic length bias...
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Method: Accurate estimation of pathway activity in single cells for clustering and differential analysis
- Daniel Davis,
- Avishai Wizel,
- and Yotam Drier
Genome Res. June 2024 34: 925-936; Published in Advance July 9, 2024, doi:10.1101/gr.278431.123
..., demonstrating its higher robustness across data types; however, AUCell missed three pathways and ssGSEA and VAM missed five pathways each, with most misses owing to biases toward specific metamodules. VAM showed bias toward the MES-like group (28 upregulated pathways compared to 20, 23, and 17 detected...
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Method: Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data
- Li Song,
- Gali Bai,
- X. Shirley Liu,
- Bo Li,
- and Heng Li
Genome Res. May 11, 2023 gr.277585.122; Published in Advance May 11, 2023, doi:10.1101/gr.277585.122
...incorporating assignments with lower scores. In the reference sequences for genomic sequencing data, we only include part of the introns flanking the exons and mark the remaining parts as gaps. This strategy could create alignment bias near the boundary. For example, if allele A has a deletion at position x...
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