Splicing accuracy varies across human introns, tissues, age and disease.

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigate splicing accuracy using RNA-sequencing data from >14k control samples and 40 human body sites, focusing on split reads partially mapping to known transcripts in annotation.

Human longevity and Alzheimer's disease variants act via microglia and oligodendrocyte gene networks.

Ageing underlies functional decline of the brain and is the primary risk factor for several neurodegenerative conditions, including Alzheimer's disease (AD). However, the molecular mechanisms that cause functional decline of the brain during ageing, and how these contribute to AD pathogenesis, are not well understood. The objective of this study was to identify biological processes that are altered during ageing in the hippocampus and that modify Ad risk and lifespan, and then to identify putative gene drivers of these programmes.

Circulating blood circular RNA in Parkinson's Disease; from involvement in pathology to diagnostic tools in at-risk individuals.

To identify circRNAs associated with Parkinson's disease (PD) we leveraged two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. We performed a cross-sectional study utilizing the last visit of each participant (N = 1848), and a longitudinal analysis that included 1166 participants with at least two time points. We identified 192 differentially expressed circRNAs, with effects that were sustained during disease, in mutation carriers, and diverse ancestry.

Plasma acellular transcriptome contains Parkinson's disease signatures that can inform clinical diagnosis.

We aimed to identify plasma cell-free transcripts (cfRNA) associated with Parkinson's disease (PD) that also have a high predictive value to differentiate PD from healthy controls. Leveraging two independent populations from two different movement disorder centers we identified 2,188 differentially expressed cfRNAs after meta-analysis. The identified transcripts were enriched in PD relevant pathways, such as PD (p=9.

Targeted co-expression networks for the study of traits.

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease.

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

Biallelic NAA60 variants with impaired n-terminal acetylation capacity cause autosomal recessive primary familial brain calcifications.

Primary familial brain calcification (PFBC) is characterized by calcium deposition in the brain, causing progressive movement disorders, psychiatric symptoms, and cognitive decline. PFBC is a heterogeneous disorder currently linked to variants in six different genes, but most patients remain genetically undiagnosed. Here, we identify biallelic NAA60 variants in ten individuals from seven families with autosomal recessive PFBC.

Circulating blood circular RNA in Parkinson's Disease; a systematic study.

We aimed to identify circRNAs associated with Parkinson's disease (PD) by leveraging 1,848 participants and 1,789 circRNA from two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. To comprehensively understand changes in circRNAs we performed a cross-sectional study utilizing the last visit of each participant, and a longitudinal (mix model) analysis that included 1,166 participants with at least two time points. We identified 192 circRNAs differentially expressed in PD participants compared to healthy controls, with effects that were consistent in the mixed models, mutation carriers, and diverse ancestry.

GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers.

Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping.

Cell-free RNA signatures predict Alzheimer's disease.

There is a need for affordable, scalable, and specific blood-based biomarkers for Alzheimer's disease that can be applied to a population level. We have developed and validated disease-specific cell-free transcriptomic blood-based biomarkers composed by a scalable number of transcripts that capture AD pathobiology even in the presymptomatic stages of the disease. Accuracies are in the range of the current CSF and plasma biomarkers, and specificities are high against other neurodegenerative diseases.

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts.

Splicing accuracy varies across human introns, tissues and age.

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigated mis-splicing using RNA-sequencing data from ~14K control samples and 42 human body sites, focusing on split reads partially mapping to known transcripts in annotation.

PhenoExam: gene set analyses through integration of different phenotype databases.

Background: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.

Results: PhenoExam generates sensitive and accurate phenotype enrichment analyses.

IntroVerse: a comprehensive database of introns across human tissues.

Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed IntroVerse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes.

Bioinformatic approach for the discovery of cis-eQTL signals during fruit ripening of a woody species as grape (Vitis vinifera L.).

Expression quantitative trait loci (eQTLs) are associations between genetic variants, such as Single Nucleotide Polymorphisms (SNPs), and gene expression. eQTLs are an important tool to understand the genetic variance of gene expression of complex phenotypes. eQTLs analyses are common in biomedical models but are scarce in woody crop species such as fruit trees or grapes.

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes.

There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs.

Multi-modality machine learning predicting Parkinson's disease.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort.