Biology subjects

Siren, J.

Publications and source records attributed to Siren, J..

17 recordsLinked to original sources

bioRxiv2026

Gene expression dynamics through time reveal low pleiotropy genes underlying local adaptation in silver birch (Betula pendula)

Understanding the molecular mechanisms underlying local adaptation in boreal trees is important for assessing their adaptive potential under climate change. The role of pleiotropy in polygenic local adaptation remains poorly understood. Here we studied growth and temporal transcriptomic responses to photoperiod in three photoperiod treatments in three populations of Betula pendula within a two-week time period to identify genes and co-expression networks underlying local adaptation and to estimate the level of pleiotropy in population specific gene expression. We used RNA sequencing to characterize the temporal transcriptomic responses which were analyzed with variance partitioning, differential gene expression and weighted co-expression network analyses. Photoperiod treatment and sampling time were the main drivers of gene expression variation both at the co-expression module and single gene level. The majority of circadian clock and flowering time genes exhibited striking similarity across populations. Nevertheless, we detected small but significant population effects in the temporal gene expression patterns which were concentrated on the periphery of the co-expression networks consistent with low pleiotropy effects. Our findings suggest that local adaptation to growing season length among populations exhibiting very low genetic differentiation along a latitudinal cline in B. pendula is likely polygenic and potentially governed through low pleiotropy genes.

evolutionary biology↗

bioRxiv2026

GBZ-base and GAF-base: Indexed pangenome file formats

MotivationExisting pangenome file formats are designed for batch processing. Graphs must be loaded into memory, and alignment files must be read sequentially. Indexed file formats that can be used directly from disk would be more appropriate for interactive applications. ResultsWe propose GBZ-base and GAF-base -- SQLite-backed file formats comparable to GBZ and GAF. GBZ-base supports efficient extraction of local subgraphs, and GAF-base lets us extract all alignments to the subgraph. Additionally, GAF-base is smaller than any other file format for sequence-to-graph alignments. Availability and implementationFrom https://github.com/jltsiren/gbz-base and https://crates.io/crates/gbz-base under the MIT license.

bioinformatics↗

bioRxiv2025

Rapid, accurate long- and short-read mapping to large pangenome graphs with vg Giraffe

1We previously introduced Giraffe, a short-read-to-pangenome graph mapper available in the vg pangenomics toolkit. Giraffe was fast and accurate for mapping short reads to human-scale pangenomes, but struggled with long reads. Long reads present a unique challenge to pangenome mapping algorithms due to their length and error profile, which allow them to take more topologically complex paths through the pangenome graph and increase the possible search space for the algorithm. We present updates to Giraffe that allow it to quickly and accurately map long reads to pangenome graphs. For both short and long reads, Giraffe mapping to a pangenome containing data from more than 450 human haplotypes, generated by the Human Pangenome Reference Consortium, is comparable in speed to linear mappers to human reference genomes; Giraffe is also over an order of magnitude faster than GraphAligner, the current state-of-the-art long-read-to-pangenome mapper. Its alignments produce similar or improved small and structural variant calling results, compared to those from commonly used graph-based and linear mappers. We additionally demonstrate using Giraffes long read alignments in a pangenome-guided assembly workflow, which is capable of producing more contiguous local assemblies than Hifiasm in our test regions.

bioinformatics↗

bioRxiv2025

Pangenome-aware DeepVariant

Population-scale genomics information provides valuable prior knowledge for various genomic analyses, especially variant calling. A notable example of such application is the human pangenome reference released by the Human Pangenome Reference Consortium, which has been shown to improve read mapping and structural variant genotyping. In this work, we introduce pangenome-aware DeepVariant, a variant caller that uses a pangenome reference alongside sample-specific read alignments. It generates pileup images of both reads and pangenome haplotypes near potential variants and uses a Convolutional Neural Network to infer genotypes. This approach allows directly using a pangenome for distinguishing true variant signals from sequencing or alignment noise. We assessed its performance on various short-read sequencing platforms and read mappers. Across all settings, pangenome-aware DeepVariant outperformed the linear-reference-based DeepVariant, reducing errors by up to 25.5%. We also show that Element reads with pangenome-aware DeepVariant can achieve 23.6% more accurate variant calling performance compared to existing methods.

bioinformatics↗

bioRxiv2025

Lossless Pangenome Indexing Using Tag Arrays

Pangenome graphs represent the genomic variation by encoding multiple haplotypes within a unified graph structure. However, efficient and lossless indexing of such structures remains challenging due to the scale and complexity of pangenomic data. We present a practical and scalable indexing framework based on tag arrays, which annotate positions in the Burrows-Wheeler transform (BWT) with graph coordinates. Our method extends the FM-index with a run-length compressed tag structure that enables efficient retrieval of all unique graph locations where a query pattern appears. We introduce a novel construction algorithm that combines unique k-mers, graph-based extensions, and haplotype traversal to compute the tag array in a memory-efficient manner. To support large genomes, we process each chromosome independently and then merge the results into a unified index using properties of the multi-string BWT and r-index. Our evaluation on the HPRC graphs demonstrates that the tag array structure compresses effectively, scales well with added haplotypes, and preserves accurate mapping information across diverse regions of the genome. This indexing method enables lossless and haplotype-aware querying in complex pangenomes and offers a practical indexing layer to develop scalable aligners and downstream graph-based analysis tools. The index additionally supports efficient one-to-all coordinate translation, enabling any interval on a haplotype to be mapped to its corresponding intervals across all other haplotypes in the graph.

bioinformatics↗

bioRxiv2025

RecAlign: A* recombination-aware sequence to graph mapping

Pangenomics and long reads bring the promise of integrating read mapping with variant calling, since a pangenome encodes a reference genome that incorporates evolutionary or population aspects, while even a single long read can provide a good evidence of different kinds of variants (not only the single nucleotide variants that can be easily observed by short reads). This promise needs to be fulfilled by the development of new read mapping approaches that are tailored for that purpose. This paper focuses on integrating recombination events, that are key in bacteria, into read mapping. A first approach in that direction [ACBC+24] provides an exact dynamic programming algorithm that is too slow to manage multiple recombinations or long reads. We present a novel A* algorithm for recombination-aware sequence-to-graph mapping that significantly reduces running time by incorporating haplotype information and an efficient heuristic function. Our tool, RecAlign, demonstrates up to a two-order magnitude improvement in time and space complexity over [ACBC+24] and efficiently handles multiple recombinations.

bioinformatics↗

bioRxiv2024

Opportunistic partner choice among arctic plants and root-associated fungi is driven by environmental conditions.

Interactions between plants and soil microbes play an important role in structuring plant communities. Yet, little is known about how fungal networks are structured on the one hand by fungal responses to their environment (including their host plant) and on the other by responses to each other. We quantified changes in plant-fungus networks along geographic and environmental gradients across the Arctic, assessing the degree to which plants and fungi showed preference for specific interaction partners and how specificity varies along environmental gradients. To this aim, we sampled roots of 12 widely distributed plant taxa: Saxifraga oppositifolia; Bistorta vivipara; Dryas spp.; Vaccinium tis-idaea; Vaccinium uliginosum; Vaccinium myrtillus; Empetrum nigrum; Betula nana; alix arctica; Salix polaris; Cassiope tetragona; and Silene acaulis. To quantify the pool of fungi from which plant roots may recruit association partners, we also sampled fungi in the surrounding soil. Identifying fungaI communities by DNA metabarcoding, we used Hierarchical Modelling of Species Communities (HMSC) to assess how fungal communities change along environmental gradients, and whether plants actively select their root-associated fungi from the pool of fungi present in the bulk soil. We found that although the fungal communities within the soil and rhizosphere share 85% of genera, their composition differs significantly from each other. The two community types show similar responses to the environment and taxa show low partner fidelity. Thus, the structure of fungal communities on plant rhizosphere is mainly driven by abiotic rather than biotic conditions. Overall, in comparison with null models, networks of plants and rhizosphere-associated fungi showed a distinctly non-random structure, responding strongly to pH and temperature gradients. Our findings suggest that the dynamics and structure of plant-root associated interactions might be severely altered by abiotic changes in the rapidly changing arctic environment. Open Research statementData are privately provided for peer review. The raw sequences for the soil and root samples generated during the current study will be available in the Sequence Read Archive repository, in the BioProject PRJNA1094865 upon acceptance. For review purposes, the code and datasets used for the analyses of this study are temporarily available in Figshare open access repository at https://figshare.com/s/1b074f1751682d3487cf upon acceptance.

ecology↗

bioRxiv2024

SAFARI: Pangenome Alignment of Ancient DNA Using Purine/Pyrimidine Encodings

Aligning DNA sequences retrieved from fossils or other paleontological artifacts, referred to as ancient DNA, is particularly challenging due to the short sequence length and chemical damage which creates a specific pattern of substitution (C[->]T and G[->]A) in addition to the heightened divergence between the sample and the reference genome thus exacerbating reference bias. This bias can be mitigated by aligning to pangenome graphs to incorporate documented organismic variation, but this approach still suffers from substitution patterns due to chemical damage. We introduce a novel methodology introducing the RYmer index, a variant of the commonly-used minimizer index which represents purines (A,G) and pyrimidines (C,T) as R and Y respectively. This creates an indexing scheme robust to the aforementioned chemical damage. We implemented SAFARI, an ancient DNA damage-aware version of the pangenome aligner vg giraffe which uses RYmers to rescue alignments containing deaminated seeds. We show that our approach produces more correct alignments from ancient DNA sequences than current approaches while maintaining a tolerable rate of spurious alignments. In addition, we demonstrate that our algorithm improves the estimate of the rate of ancient DNA damage, especially for highly damaged samples. Crucially, we show that this improved alignment can directly translate into better insights gained from the data by showcasing its integration with a number of extant pangenome tools.

bioinformatics↗

bioRxiv2023

Personalized Pangenome References

Pangenomes, by including genetic diversity, should reduce reference bias by better representing new samples compared to them. Yet when comparing a new sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with using allele frequency filters. However, this is a blunt heuristic that both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach, inspired by local ancestry inference methods, that imputes a personalized pangenome subgraph based on sampling local haplotypes according to k-mer counts in the reads. Our approach is tailored for the Giraffe short read aligner, as the indexes it needs for read mapping can be built quickly. We compare the accuracy of our approach to state-of-the-art methods using graphs from the Human Pangenome Reference Consortium. The resulting personalized pangenome pipelines provide faster pangenome read mapping than comparable pipelines that use a linear reference, reduce small variant genotyping errors by 4x relative to the Genome Analysis Toolkit (GATK) best-practice pipeline, and for the first time make short-read structural variant genotyping competitive with long-read discovery methods.

bioinformatics↗

bioRxiv2023

Extensive hybridisation between multiple differently adapted species may aid persistence in a changing climate

Hybridisation and gene flow can have both deleterious and adaptive consequences for natural populations and species. To better understand the extent and consequences of hybridisation in nature, information on naturally hybridising non-model organisms is required, including characterising the structure and extent of natural hybrid zones. Here we study natural populations of five keystone mound-building wood ant (Formica rufa group) species across Finland. No genomic studies across the species group exist and the extent of hybridisation and genomic differentiation in sympatry is unknown. Combining genome-wide and morphological data, we show that Formica rufa, F. aquilonia, F. lugubris, and F. pratensis form distinct gene pools in Finland. We demonstrate more extensive hybridisation than previously thought between all five species and reveal a mosaic hybrid zone between F. aquilonia, F. rufa and F. polyctena. We show that hybrids between these climatically differently adapted species occupy warmer habitats than the cold-adapted parent F. aquilonia. This suggests hybrids occupy a different microclimatic niche compared to the locally abundant parent. We propose that wood ant hybridisation may increase with a warming climate, and warm winters, in particular, may provide a competitive advantage for the hybrids over F. aquilonia in the future. In summary, our results demonstrate how extensive hybridisation may help persistence in a changing climate. Additionally, they provide an example on how mosaic hybrid zones can have significant ecological and evolutionary consequences because of their large extent and independent hybrid populations that face both ecological and intrinsic selection pressures.

evolutionary biology↗

bioRxiv2022

GBZ File Format for Pangenome Graphs

MotivationPangenome graphs representing aligned genome assemblies are being shared in the text-based Graphical Fragment Assembly format. As the number of assemblies grows, there is a need for a file format that can store the highly repetitive data space-efficiently. ResultsWe propose the GBZ file format based on data structures used in the Giraffe short read aligner. The format provides good compression, and the files can be efficiently loaded into in-memory data structures. We provide compression and decompression tools and libraries for using GBZ graphs, and we show that they can be efficiently used on a variety of systems. AvailabilityC++ and Rust implementations are available at https://github.com/jltsiren/gbwtgraph and https://github.com/jltsiren/gbwt-rs, respectively. Contactjouni.siren@iki.fi Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our novel toolchain can construct spliced pangenome graphs, map RNA-seq data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. This workflow improves accuracy over state-of-the-art RNA-seq mapping methods, and it can efficiently quantify haplotype-specific transcript expression without needing to characterize a samples haplotypes beforehand.

bioinformatics↗

bioRxiv2020

Genotyping common, large structural variations in 5,202 genomes using pangenomes, the Giraffe mapper, and the vg toolkit

We introduce Giraffe, a pangenome short read mapper that can efficiently map to a collection of haplotypes threaded through a sequence graph. Giraffe, part of the variation graph toolkit (vg)1, maps reads to thousands of human genomes at around the same speed BWA-MEM2 maps reads to a single reference genome, while maintaining comparable accuracy to VG-MAP, vgs original mapper. We have developed efficient genotyping pipelines using Giraffe. We demonstrate improvements in genotyping for single-nucleotide variants (SNVs), small insertions and deletions (indels) and structural variations (SVs) genome-wide. We use Giraffe to genotype about 167 thousand structural variants ascertained from long read studies in 5,202 human genomes sequenced with short reads, including the complete 1000 Genomes Project dataset, at an average cost of $1.50 per sample. We determine the frequency of these variations in diverse human populations, characterize their complex allelic variations and identify thousands of expression quantitative trait loci (eQTLs) driven by these variations.

bioinformatics↗

bioRxiv2019

Distance Indexing and Seed Clustering in Sequence Graphs

Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but much more complicated in a graph context. In read mapping algorithms, distance calculations are commonly used in a clustering step to determine if seed alignments could belong to the same mapping. Clustering algorithms are a bottleneck for some mapping algorithms due to the cost of repeated distance calculations. We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for mapping algorithms.

bioinformatics↗

bioRxiv2019

Genotyping structural variants in pangenome graphs using the vg toolkit

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmarked vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

bioinformatics↗

bioRxiv2019

Haplotype-aware graph indexes

MotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes. ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows-Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes. AvailabilityOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/gcsa2. Contactjouni.siren@iki.fi Supplementary informationSupplementary data are available.

bioinformatics↗

bioRxiv2017

Sequence variation aware genome references and read mapping with the variation graph toolkit

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individuals genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.

genomics↗