Biology subjects

Besenbacher, S.

Publications and source records attributed to Besenbacher, S..

12 recordsLinked to original sources

bioRxiv2026

Combining mutation detection with fragmentomics features leads to improved tumor-informed ctDNA detection

Liquid biopsy through circulating tumor DNA (ctDNA) analysis enables non-invasive detection of minimal residual disease (MRD) and early identification of cancer relapse, facilitating timely clinical intervention. However, detecting ctDNA in plasma samples with low tumor burden remains challenging due to the scarcity of mutant molecules, the background noise of sequencing errors and somatic mutations in normal cell-free DNA (cfDNA). Here, we present a mutation-informed fragmentomic framework and evaluate it on 90 stage III colorectal cancer patients with three years of follow-up. Using 712 serial whole-genome sequenced cfDNA samples (30x) with matched whole-genome sequencing of tumor tissue and germline DNA from buffycoat for each patient, we collected cfDNA fragments spanning tumor-derived somatic mutation positions and compared fragmentomic characteristics of mutation-bearing and non-mutated cfDNA fragments within the same sample. By leveraging fragment length and fragment end-motif patterns, our approach can distinguish cancer-positive from cancer-negative plasma samples without requiring model training or panel-of-normals calibration. The method achieved AUCs of 0.863 and 0.74 using fragment length and end motif features, respectively, and 0.871 when combined, outperforming tumor fraction estimates based on the frequency of mutated fragments (AUC=0.832). Integrating fragmentomic features with tumor fraction further improved performance, yielding an AUC of 0.873, indicating complementary signals between fragmentomic patterns and mutation burden. Aggregated analyses revealed ctDNA-specific patterns, including fragment shortening, motif enrichment of A/T ends, and depletion of C/G ends, directly linking fragmentomic features to tumor-derived cfDNA. Overall, mutation-informed fragmentomic profiling improves ctDNA detection beyond counting mutant reads and provides a scalable, training-free strategy for MRD assessment and early relapse detection while offering mechanistic insights into tumor-specific cfDNA biology.

bioinformatics↗

bioRxiv2025

A reference-free strategy for circulating tumor DNA detection from whole-genome sequencing data

Circulating tumor DNA (ctDNA) is emerging as a promising biomarker for postoperative monitoring of cancer patients. Precise estimation of circulating tumor fraction is crucial for evaluating treatment effects and timely detection of disease recurrence. All current ctDNA detection methods that utilize whole-genome sequencing (WGS) data rely on the reference genome alignment of sequencing reads and often apply separate tools for detecting different variant types. However, various bioinformatic analysis confounders and the application of external variant calling tools could be avoided by analyzing k-mers from unaligned sequencing reads. While k-mer-based methods have successfully been applied for somatic variant validation and detection, the potential of k-mer-based ctDNA detection is unexplored. We have developed a tumor-informed reference-free ctDNA detection tool called ctDNAmer that detects tumor-specific somatic variation directly from unaligned sequencing data by identifying k-mers unique to the tumor DNA. ctDNAmer detects variant information across the genome by comparing the primary tumor and germline WGS data and accounts for sample-specific germline variability and technical noise in the same framework. We tested the utility of ctDNAmer for tumor fraction estimation on postoperative plasma cfDNA WGS data (mean sequencing depth ~28x) from 90 stage III colorectal cancer patients with three years of follow-up. The tumor fraction (TF) estimates agreed with the available clinical information and ctDNA was detected in 77% (17/22) of recurring patients with a median lead time of 8 months compared to radiological imaging. We further validated ctDNAmers tumor fraction estimates based on a comparison with the mean cfDNA allele frequencies of somatic clonal SNVs identified from aligned primary tumor sequencing data. The TF estimates showed a strong Pearson correlation of 0.897 with the mean allele frequencies and improved ctDNA detection results across samples with an AUC of 0.79 compared to 0.75 if the mean allele frequency of clonal mutations is used.

bioinformatics↗

bioRxiv2024

Accurate calling of low-frequency somatic mutations by sample-specific modeling of error rates

Calling rare somatic variants from NGS data is more challenging than calling inherited variants, especially if the somatic variant is only present in a small fraction of the cells in the sequenced biopsy. In this case, having a good estimate of the error rate of a specific base in a particular read becomes essential. In paired-end sequencing, where some DNA fragments are shorter than twice the read length, the overlapping regions of the read pairs are an ideal resource for training models to discern context-dependent base error rates, as any discordant bases in the overlaps must be caused by a sequencing error or an alignment error. We have created a new tool named BBQ (an acronym for Better Base Quality) that uses overlapping reads to estimate the error rate conditional on the mutation type, sequence context, and base quality. We also estimate how much the error rate of concordant bases in overlapping reads is decreased compared to bases in non-overlapping reads. Results show that overlapping reads can remove sequencing errors induced by DNA damage and that the increased quality of overlapping reads differs between samples and mutation types, reflecting different damage patterns between samples. We use the error models to call rare somatic variants. Sequencing data from a testis biopsy and a cell-free DNA sample serve as a proof-of-concept for rare germ cell mutation calling and for detecting rare cancer mutations. We find that using the sample-specific error models of BBQ allows us to call rare somatic variants with fewer false positives than existing tools such as Mutect2 and Strelka2.

bioinformatics↗

bioRxiv2024

Estimating gene conversion tract length and rate from PacBio HiFi data

Gene conversions are broadly defined as the transfer of genetic material from a donor to an acceptor sequence and can happen both in meiosis and mitosis. They are a subset of non-crossover events and, like crossover events, gene conversion can generate new combinations of alleles and counteract mutation load by reverting germline mutations through GC-biased gene conversion. Estimating gene conversion rate and the distribution of gene conversion tract lengths remains challenging. We present a new method for estimating tract length, rate and detection probability of non-crossover events directly in HiFi PacBio long read data. The method can be used to make inference from sequencing of gametes from a single individual. The method is unbiased even under low single nucleotide variant (SNV) densities and does not necessitate any demographic or evolutionary assumptions. We test the accuracy and robustness of our method using simulated datasets where we vary length of tracts, number of tracts, the genomic SNV density and levels of correlation between SNV density and NCO event position. Our simulations show that under low SNV densities, like those found in humans, only a minute fraction ([~]2%) of NCO events are expected to become visible as gene conversions by moving at least one SNV. We finally illustrate our method by applying it to PacBio sequencing data from human sperm.

genomics↗

bioRxiv2024

Insights into gene conversion and crossing-over processes from long-read sequencing of human, chimpanzee and gorilla testes and sperm

Homologous recombination rearranges genetic information during meiosis to generate new combinations of variants. Recombination also causes new mutations, affects the GC content of the genome and reduces selective interference. Here, we use HiFi long-read sequencing to directly detect crossover and gene conversion events from switches between the two haplotypes along single HiFi-reads from testis tissue of humans, chimpanzees and gorillas as well as human sperm samples. Furthermore, based on DNA methylation calls, we classify the cellular origin of reads to either somatic or germline cells in the testis tissue. We identify 1692 crossovers and 1032 gene conversions in nine samples and investigate their chromosomal distribution. Crossovers are more telomeric and correlate better with recombination maps than gene conversions. We show a strong concordance between a human double-strand break map and the human samples, but not for the other species, supporting different PRDM9-programmed double-strand break loci. We estimate the average gene conversion tract lengths to be similar and very short in all three species (means 40-100 bp, fitted well by a geometric distribution) and that 95-98% of non-crossover events do not involve tracts intersecting with polymorphism and are therefore not detectable. Finally, we detect a GC bias in the gene conversion of both single and multiple SNVs and show that the GC-biased gene conversion affects SNVs flanking crossover events. This implies that gene conversion events associated with crossover events are much longer (estimated above 500 bp) than those associated with non-crossover events. Highly accurate long-read sequencing combined with the classification of reads to specific cell types provides a new, powerful way to make individual, detailed maps of gene conversion and crossovers for any species.

evolutionary biology↗

bioRxiv2024

Determinants of de novo mutations in extended pedigrees of 43 dog breeds

Intensive breeding of dogs has had dramatic effects on genetic variants underlying phenotypes. To investigate whether this also affected mutation rates, we deep-sequenced pedigrees from 43 different dog breeds representing 404 trios. We find that the mutation rate is remarkably stable across breeds and is predominantly influenced by variation in parental ages. The effect of paternal age per year on mutation rates is approximately 1.5 times greater in dogs than humans, suggesting that the elevated yearly mutation rate in dogs is only partially attributed to earlier reproduction. While there is no significant effect of breeds on the overall mutation rate, larger breeds accumulate proportionally more mutations earlier in development than small breeds. Interestingly, we find a 2.6 times greater mutation rate in CG Islands (CGIs) compared to the remaining genome in dogs, unlike humans, where there is no difference. Our estimated rate of mutation by recombination in dogs is more than 10 times larger than estimates in humans. We ascribe these to the fact that canids have lost PRDM9-directed recombination and draw away recombination from CGIs. In conclusion, our study sheds light on stability of mutation processes and disparities in mutation accumulation rates reflecting the influence of differences in growth patterns among breeds, and the impact of PRDM9 gene loss on the de novo mutations of canids.

evolutionary biology↗

bioRxiv2021

A method to build extended sequence context models of point mutations and indels

The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers using IUPAC patterns. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. Revealing that for some mutation types, the mutation rate of a position is significantly affected by nucleotides that are up to four base pairs away. As the first method of its kind, it does not only predict rates for point mutations but also indels. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong constraint, e.g. haploinsufficient genes.

bioinformatics↗

bioRxiv2021

Mutationathon: towards standardization in estimates of pedigree-based germline mutation rates

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a "Mutationathon", a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

evolutionary biology↗

bioRxiv2021

Discovering fragment length signatures of circulating tumor DNA using Non-negative Matrix Factorization

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of cfDNA fragments from a cancer patient can inform tumor load and type. Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as a novel method to study fragment lengths in cfDNA samples. Using shallow whole-genome sequencing (sWGS) of cfDNA from a cohort of patients with metastatic castration-resistant prostate cancer (mCRPC), we demonstrate how NMF accurately infers the true tumor fragment length distribution as an NMF component - and that the sample weights of this component correlate with ctDNA levels (r=0.75). We further demonstrate how using several NMF components enables accurate cancer detection on data from various early stage cancers (AUC=0.96). Finally, we show that NMF, when applied across genomic regions, can be used to discover fragment length signatures associated with open chromatin.

bioinformatics↗

bioRxiv2020

The germline mutational process in rhesus macaque and its implications for phylogenetic dating

Understanding the rate and pattern of germline mutations is of fundamental importance for understanding evolutionary processes. Here we analyzed 19 parent-offspring trios of rhesus macaques (Macaca mulatta) at high sequencing coverage of ca. 76X per individual, and estimated an average rate of 0.77 x 10-8 de novo mutations per site per generation (95 % CI: 0.69 x 10-8 - 0.85 x 10-8). By phasing 50 % of the mutations to parental origins, we found that the mutation rate is positively correlated with the paternal age. The paternal lineage contributed an average of 81 % of the de novo mutations, with a trend of an increasing male contribution for older fathers. About 3.5 % of de novo mutations were shared between siblings, with no parental bias, suggesting that they arose from early development (postzygotic) stages. Finally, the divergence times between closely related primates calculated based on the yearly mutation rate of rhesus macaque generally reconcile with divergence estimated with molecular clock methods, except for the Cercopithecidae/Hominoidea molecular divergence dated at 52 Mya using our new estimate of the yearly mutation rate.

evolutionary biology↗

bioRxiv2018

Direct estimation of mutations in great apes reveals significant recent human slowdown in the yearly mutation rate

The human mutation rate per generation estimated from trio sequencing has revealed an almost linear relationship with the age of the father and the age of the mother. The yearly trio-based mutation rate estimate of ~0.43x10-9 is markedly lower than prior indirect estimates of ~1x10-9 per year from phylogenetic comparisons of the great apes. This suggests either a slowdown over the past 10 million years or an inaccurate interpretation of the fossil record. Here we use sequencing of chimpanzee, gorilla and orangutan trios and find that each species has higher estimated mutation rates per year by factors of 1.67+/- 0.22, 1.54+/- 0.2 and 1.84+/- 0.19, respectively. These estimates suggest a very recent and appreciable slowdown in human mutation rate, and, if extrapolated over the great apes phylogeny, yields divergence estimates much more in line with the fossil record and the biogeography.

evolutionary biology↗

bioRxiv2017

A Site Specific Model And Analysis Of The Neutral Somatic Mutation Rate In Whole-Genome Cancer Data

BackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.\n\nResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.\n\nConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

bioinformatics↗