Demo Data from SIGIR 2026 / reviewers in the wild / expert
Harald Ringbauer
dblp:326/4044
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-4884-9682ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › molecular evolution › evolutionary bioinformatics
ancient DNA analysis |
0.6 | 1 | 2022 | hapCon: estimating contamination of ancient genomes by copying from reference haplotypes · Bioinform. 2022 |
Bioinformatics and computational biology
genomics |
0.2 | 1 | 2022 | hapCon: estimating contamination of ancient genomes by copying from reference haplotypes · Bioinform. 2022 |
Bioinformatics and computational biology › sequence analysis › sequencing data analysis
low-coverage sequencing analysis |
0.2 | 1 | 2022 | hapCon: estimating contamination of ancient genomes by copying from reference haplotypes · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood estimation · 0.6li-stephens haplotype copying model · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | hapCon: estimating contamination of ancient genomes by copying from reference haplotypesabstractMOTIVATION: Human ancient DNA (aDNA) studies have surged in recent years, revolutionizing the study of the human past. Typically, aDNA is preserved poorly, making such data prone to contamination from other human DNA. Therefore, it is important to rule out substantial contamination before proceeding to downstream analysis. As most aDNA samples can only be sequenced to low coverages (<1× average depth), computational methods that can robustly estimate contamination in the low coverage regime are needed. However, the ultra low-coverage regime (0.1× and below) remains a challenging task for existing approaches. RESULTS: We present a new method to estimate contamination in aDNA for male modern humans. It utilizes a Li&Stephens haplotype copying model for haploid X chromosomes, with mismatches modeled as errors or contamination. We assessed this new approach, hapCon, on simulated and down-sampled empirical aDNA data. Our experiments demonstrate that hapCon outperforms a commonly used tool for estimating male X contamination (ANGSD), with substantially lower variance and narrower confidence intervals, especially in the low coverage regime. We found that hapCon provides useful contamination estimates for coverages as low as 0.1× for SNP capture data (1240k) and 0.02× for whole genome sequencing data, substantially extending the coverage limit of previous male X chromosome-based contamination estimation methods. Our experiments demonstrate that hapCon has little bias for contamination up to 25-30% as long as the contaminating source is specified within continental genetic variation, and that its application range extends to human aDNA as old as ∼45 000 and various global ancestries. AVAILABILITY AND IMPLEMENTATION: We make hapCon available as part of a python package (hapROH), which is available at the Python Package Index (https://pypi.org/project/hapROH) and can be installed via pip. The documentation provides example use cases as blueprints for custom applications (https://haproh.readthedocs.io/en/latest/hapCon.html). The program can analyze either BAM files or pileup files produced with samtools. An implementation of our software (hapCon) using Python and C is deposited at https://github.com/hyl317/hapROH. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Yilei Huang, Harald Ringbauer |
Bioinform. | 2 |