Biology subjects

Savolainen, O.

Publications and source records attributed to Savolainen, O..

12 recordsLinked to original sources

bioRxiv2024

Optimizing Exome Captures in Species with Large Genomes Using Species-specific Repetitive DNA Blocker

Large and highly repetitive genomes are common. However, research interests usually lie within the non-repetitive parts of the genome, as they are more likely functional, and can be used to answer questions related to adaptation, selection, and evolutionary history. Exome capture is a cost-effective method for providing sequencing data from protein-coding parts of the genes. C0t-1 DNA blockers consist of repetitive DNA and are used in exome captures to prevent the hybridization of repetitive DNA sequences to capture baits or bait-bound genomic DNA. Universal blockers target repetitive regions shared by many species, while species-specific c0t-1 DNA is prepared from the DNA of the studied species, thus perfectly matching the repetitive DNA contents of the species. So far the use of species-specific c0t-1 DNA has been limited to a few model species. Here, we evaluated the performance of blocker treatments in exome captures of Pinus sylvestris, a widely distributed conifer species with a large (> 20 Gbp) and highly repetitive genome. We compared treatment with a commercial universal blocker to treatments with species-specific c0t-1 (30,000 ng and 60,000 ng). Species-specific c0t-1 captured more unique exons than the initial set of targets, reduced sequencing of tandem repeats, and produced more target regions with high read coverage and narrower depth distribution than the universal blocker. Based on our results, we recommend optimizing exome captures by using at least 60,000 ng species-specific c0t-1 DNA. It is relatively easy and fast to prepare and can also be used with existing bait set designs.

genomics↗

bioRxiv2023

Does the seed fall far from the tree? - weak fine scale genetic structure in a continuous Scots pine population

Knowledge of fine-scale spatial genetic structure, i.e., the distribution of genetic diversity at short distances, is important in evolutionary research and in practical applications such as conservation and breeding programs. In trees, related individuals often grow close to each other due to limited seed and/or pollen dispersal. The extent of seed dispersal also limits the speed at which a tree species can spread to new areas. We studied the fine-scale spatial genetic structure of Scots pine (Pinus sylvestris) in two naturally regenerated sites located 20 km from each other in continuous south-eastern Finnish forest. We genotyped almost 500 adult trees for 150k SNPs using a custom made Affymetrix array. We detected some pairwise relatedness at short distances, but the average relatedness was low and decreased with increasing distance, as expected. Despite the clustering of related individuals, the sampling sites were not differentiated (FST = 0.0005). According to our results, Scots pine has a large neighborhood size (Nb = 1680- 3210), but a relatively short gene dispersal distance ({sigma}g = 36.5-71.3 m). Knowledge of Scots pine fine-scale spatial genetic structure can be used to define suitable sampling distances for evolutionary studies and practical applications. Detailed empirical estimates of dispersal are necessary both in studying post-glacial recolonization and predicting the response of forest trees to climate change.

evolutionary biology↗

bioRxiv2022

Ultra Low Power, Event-Driven Data Compression of Multi-Unit Activity

Recent years have demonstrated the feasibility of using intracortical Brain-Machine Interfaces (iBMIs), by decoding thoughts, for communication and cursor control tasks. iBMIs are increasingly becoming wireless due to the risk of infection and mechanical failure, typically associated with percutaneous connections. The wireless communication itself, however, increases the power consumption further; with the total dissipation being strictly limited due to safety heating limits of cortical tissue. Since wireless power is typically proportional to the communication bandwidth, the output Bit Rate (BR) must be minimised. Whilst most iBMIs utilise Multi-Unit activity (MUA), i.e. spike events, and this in itself significantly reduces the output BR (compared to raw data), it still limits the scalability (number of channels) that can be achieved. As such, additional compression for MUA signals are essential for fully-implantable, high-information-bandwidth systems. To meet this need, this work proposes various hardware-efficient, ultra-low power MUA compression schemes. We investigate them in terms of their BRs and hardware requirements as a function of various on-implant conditions such as MUA Binning Period (BP) and number of channels. It was found that for BPs [≤] 10 ms, the delta-asynchronous method had the lowest total power and reduced the BR by almost an order of magnitude relative to classical methods (e.g. to approx. 151 bps/channel for a BP of 1 ms and 1000 channels on-implant.). However, at larger BPs the synchronous method performed best (e.g. approx. 29 bps/channel for a BP of 50 ms, independent of channel count). As such, this work can guide the choice of MUA data compression scheme for BMI applications, where the BR can be significantly reduced in hardware efficient ways. This enables the next generation of wireless iBMIs, with small implant sizes, high channel counts, low-power, and small hardware footprint. All code and results have been made publicly available.

bioengineering↗

bioRxiv2022

Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders

Brain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.

bioengineering↗

bioRxiv2022

Environmental response in gene expression and DNA methylation reveals factors influencing the adaptive potential of Arabidopsis lyrata

Understanding what factors influence plastic and genetic variation is valuable for predicting how organisms respond to changes in the selective environment. Here, using gene expression and DNA methylation as molecular phenotypes, we study environmentally induced variation among Arabidopsis lyrata plants grown at lowland and alpine field sites. Our results show that gene expression is highly plastic, as many more genes are differentially expressed between the field sites than between populations. These environmentally responsive genes evolve under strong selective constraint - the strength of purifying selection on the coding sequence is high, while the rate of adaptive evolution is low. We find, however, that positive selection on cis-regulatory variants has likely contributed to the maintenance of genetically variable environmental responses, but such variants segregate only between distantly related populations. In contrast to gene expression, DNA methylation at genic regions is largely insensitive to the environment, and plastic methylation changes are not associated with differential gene expression. Besides genes, we detect environmental effects at transposable elements (TEs): TEs at the high-altitude field site have higher expression and methylation levels, suggestive of a broad-scale TE activation. Compared to the lowland population, plants native to the alpine environment harbor an excess of recent TE insertions, and we observe that specific TE families are enriched within environmentally responsive genes. Together, our findings provide insight into selective forces shaping plastic and genetic variation. We also highlight how plastic responses at TEs can rapidly create novel heritable variation in stressful conditions.

evolutionary biology↗

bioRxiv2022

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

This study details the development of a novel, approx. {pound}20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 {+/-} 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

bioengineering↗

bioRxiv2021

Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research

Scots pine (Pinus sylvestris) is the most widespread coniferous tree in the boreal forests of Eurasia and has major economic and ecological importance. However, its large and repetitive genome presents a challenge for conducting genome-wide analyses such as association studies and genomic selection. We present a new 50K SNP genotyping array for Scots pine research, breeding programs, and other applications. To select the SNP set, we first genotyped 480 Scots pine samples on a 407 540 SNP screening array, and identified 47 712 high-quality SNPs for the final array (called PiSy50k). Here, we provide details of the design and testing, as well as allele frequency estimates from the discovery panel, functional annotation, tissue-specific expression patterns, and expression level information for the SNPs or corresponding genes, when available. We validated the performance of the PiSy50k array using samples from breeding populations from Finland and Scotland. Overall, 39 678 (83.2%) SNPs showed low error rates (mean = 0.92%). Relatedness estimates based on array genotypes were consistent with the expected pedigrees, and the amount of Mendelian error was negligible. In addition, array genotypes successfully discriminate Scots pine populations from different geographic origins. The PiSy50k array will be a valuable tool for future genetic studies and forestry applications. Significance statementScots pine is an evolutionary, economically and ecologically impressive coniferous species but its gigantic genome has limited studying e.g. the genetic basis of its functional trait variation. We have developed a genotyping array that facilitates Scots pine genetic research and linking its trait variation to genetic polymorphisms and gene expression levels across the genome.

genomics↗

bioRxiv2020

Investigating the Effects of Macaque Primary Motor Cortex Multi-Unit Activity Binning Period on Behavioural Decoding Performance

This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). The Neural data originates from intracortical recordings from Macaque Primary Motor cortex [1]. The BDP is measured by the Pearson correlation r between the observed and predicted velocity of the subjects X-Y hand coordinates in reaching tasks [1]. The results suggest a statistically significant but slight linear relationship between increasing MUA BP and decreasing BDP. For example, when using a 100 ms moving average window, increasing the BP by 10 ms on average reduces the BDP r by approximately 0.85%. This relationship may be due to the reduced number of training examples, or due to the loss of Behavioral information because of reduced MUA temporal resolution.

bioinformatics↗

bioRxiv2019

Expanding functional protein sequence space using generative adversarial networks

De novo protein design for catalysis of any desired chemical reaction is a long standing goal in protein engineering, due to the broad spectrum of technological, scientific and medical applications. Currently, mapping protein sequence to protein function is, however, neither computationionally nor experimentally tangible 1,2. Here we developed ProteinGAN, a specialised variant of the generative adversarial network 3 that is able to learn natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase as a template enzyme, we show that 24% of the ProteinGAN-generated and experimentally tested sequences are soluble and display wild-type level catalytic activity in the tested conditions in vitro, even in highly mutated (>100 mutations) sequences. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse novel functional proteins within the allowed biological constraints of the sequence space.

synthetic biology↗

bioRxiv2019

Maintenance of adaptive dynamics in a bottlenecked range-edge population that retained outcrossing

During range expansion, edge populations are expected to face increased genetic drift, which in turn can alter and potentially compromise adaptive dynamics, preventing the removal of deleterious mutations and slowing down adaptation. Here, we contrast populations of the European sub-species Arabidopsis lyrata ssp petraea, which expanded its Northern range after the last glaciation. We document a sharp decline in effective population size in the range-edge population and observe that non-synonymous variants segregate at higher frequencies. We detect a 4.9% excess of derived non-synonymous variants per individual in the range-edge population, suggesting an increase of the genomic burden of deleterious mutations. Inference of the fitness effects of mutations and modeling of allele frequencies under the explicit demographic history of each population predicts a depletion of rare deleterious variants in the range-edge population, but an enrichment for fixed ones, consistent with the bottleneck effect. However, the demographic history of the range-edge population predicts a small net decrease in per-individual fitness. Consistent with this prediction, the range-edge population is not impaired in its growth and survival measured in a common garden experiment. We further observe that the allelic diversity at the self-incompatibility locus, which ensures strict outcrossing and evolves under negative frequency-dependent selection, has remained unchanged. Genomic footprints indicative of selective sweeps are broader in the Northern population but not less frequent. We conclude that the outcrossing species A. lyrata ssp petraea shows a strong resilience to the effect of range expansion.

evolutionary biology↗

bioRxiv2019

Genomics of clinal local adaptation in Pinus sylvestris under continuous environmental and spatial genetic setting

Understanding the consequences of local adaptation at the genomic diversity is a central goal in evolutionary genetics of natural populations. In species with large continuous geographical distributions the phenotypic signal of local adaptation is frequently clear, but the genetic background often remains elusive. We examined the patterns of genetic diversity in Pinus sylvestris, a keystone species in many Eurasian ecosystems with a huge distribution range and decades of forestry research showing that it is locally adapted to the vast range of environmental conditions. Making P. sylvestris an even more attractive subject of local adaptation study, population structure has been shown to be weak previously and in this study. However, little is known about the molecular genetic basis of adaptation, as the massive size of gymnosperm genomes has prevented large scale genomic surveys. We generated a both geographically and genomically extensive dataset using a targeted sequencing approach. By applying divergence-based and landscape genomics methods we found that several coding loci contribute to local adaptation. We also discovered a very large (ca. 300 Mbp) putative inversion with a signal of local adaptation, which to our knowledge is the first such discovery in conifers. Our results call for more detailed analysis of structural variation in relation to genomic basis of local adaptation, emphasize the lack of large effect loci contributing to local adaptation in the coding regions and thus point out to the need for more attention towards multi-locus analysis of polygenic adaptation.

genomics↗

bioRxiv2018

Local adaptation under gene flow: Recombination, conditional neutrality and genetic trade-offs shape genomic patterns in Arabidopsis lyrata

Short-scale local adaptation is a complex process involving selection, migration and drift. The expected effects on the genome are well grounded in theory, but examining these on an empirical level has proven difficult, as it requires information about local selection, demographic history and recombination rate variation. Here, we use locally adapted and phenotypically differentiated Arabidopsis lyrata populations from two altitudinal gradients in Norway to test these expectations at the whole-genome level. Demography modelling indicates that populations within the gradients diverged less than 2000 years ago and that the sites are connected by gene flow. The gene flow estimates are, however, highly asymmetric with migration from high to low altitudes being several times more frequent than vice versa. To detect signatures of selection for local adaptation, we estimate patterns of lineage specific differentiation among these populations. Theory predicts that gene flow leads to concentration of adaptive loci in areas of low recombination; a pattern we observe in both lowland-alpine comparisons. Although most selected loci display patterns of conditional neutrality, we found indications of genetic trade-offs, with one locus particularly showing high divergence and signs of selection in both populations. Our results further suggest that resistance to solar radiation is an important adaptation to alpine environments, while vegetative growth and bacterial defense are indicated as selected traits in the lowland habitats. These results provide insights into genetic architectures and evolutionary processes driving local adaptation under gene flow. We also contribute to understanding of traits and biological processes underlying alpine adaptation in northern latitudes.

evolutionary biology↗