Biology subjects

Vehtari, A.

Publications and source records attributed to Vehtari, A..

4 recordsLinked to original sources

bioRxiv2024

Raw signal segmentation for estimating RNA modifications and structures from Nanopore direct RNA sequencing data

Estimating RNA modifications from Nanopore direct RNA sequencing data is a critical task for the RNA research community. However, current computational methods often fail to deliver satisfactory results due to inaccurate segmentation of the raw signal. We have developed a new method, SegPore, which leverages a molecular jiggling translocation hypothesis to improve raw signal segmentation. SegPore is a pure white-box model with enhanced interpretability, significantly reducing structured noise in the raw signal. We demonstrate that SegPore outperforms state-of-the-art methods, such as Nanopolish and Tombo, in raw signal segmentation across three large benchmark datasets. Moreover, the improved signal segmentation achieved by SegPore enables SegPore+m6Anet to deliver state-of-the-art performance in site-level m6A identification. Additionally, SegPore surpasses baseline methods like CHEUI in single-molecule level m6A identification.

bioinformatics↗

bioRxiv2021

Age and sex dependent variability of type 2 dopamine receptors in the human brain: A large-scale PET cohort

BACKGROUNDThe dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved. METHODSIn this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018. RESULTSStriatal D2R availability decreased through age for both sexes and was higher in females versus males throughout age. BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135). CONCLUSIONSD2R density is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.

neuroscience↗

bioRxiv2019

Interindividual variability and lateralization of μ-opioid receptors in the human brain

The brains {micro}-opioid receptors (MORs) are involved in analgesia, reward and mood regulation. Several neuropsychiatric diseases have been associated with dysfunctional MOR system, and there is also considerable variation in receptor density among healthy individuals. Sex, age, body mass and smoking have been proposed to influence the MOR system, but due to small sample sizes the magnitude of their influence remains inconclusive. Here we quantified in vivo MOR availability in the brains of 204 individuals with no neurologic or psychiatric disorders using positron emission tomography (PET) with tracer [11C]carfentanil. We then used Bayesian hierarchical modeling to estimate the effects of sex, age, body mass index (BMI) and smoking on [11C]carfentanil binding potential. We also examined hemispheric lateralization of MOR availability. Age had regionally specific effects on MOR availability, with age-dependent increase in frontotemporal areas but decrease in amygdala, thalamus, and nucleus accumbens. The age-dependent increase was stronger in males. MOR availability was globally lowered in smokers but independent of BMI. Finally, MOR availability was higher in the right versus the left hemisphere. The presently observed variation in MOR availability may explain why some individuals are prone to develop MOR-linked pathological states, such as chronic pain or psychiatric disorders.

neuroscience↗

bioRxiv2018

LonGP: an additive Gaussian process regression model for longitudinal study designs

MotivationBiomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models have become the standard workhorse for statistical analysis of data from longitudinal study designs. However, analysis of longitudinal data can be complicated for both practical and theoretical reasons, including difficulties in modelling, correlated outcome values, functional (time-varying) covariates, nonlinear effects, and model inference.\n\nResultsWe present LonGP, an additive Gaussian process regression model for analysis of experimental data from longitudinal study designs. LonGP implements a flexible, non-parametric modelling framework that solves commonly faced challenges in longitudinal data analysis. In addition to inheriting all standard features of Gaussian processes, LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We develop an accurate Bayesian inference and model selection method, and implement an efficient model search algorithm for our additive Gaussian process model. We demonstrate LonGPs performance and accuracy by analysing various simulated and real longitudinal -omics datasets. Our work is accompanied by a versatile software implementation.\n\nAvailabilityLonGP software tool is available at http://research.cs.aalto.fi/csb/software/longp/.\n\nContactlu.cheng.ac@gmail.com, harri.lahdesmaki@aalto.fi

bioinformatics↗