Biology subjects

Pavlidis, P.

Publications and source records attributed to Pavlidis, P..

48 records · Page 3Linked to original sources

bioRxiv2019

VariCarta: a comprehensive database of harmonized genomic variants found in ASD sequencing studies

BackgroundRecent years has seen a boom in the application of the next-generation sequencing technology to the study of human diseases, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation.\n\nDescriptionHere we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling and consistently cataloguing literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps and documents data provenance. The harmonization aspect is especially important since it prevents the potential double-counting of variants which can lead to inflation of gene-based evidence for ASD-association.\n\nConclusionVariCarta is the largest collection of systematically curated, harmonized and comprehensively annotated literature-derived ASD-associated variants. The database currently contains 35,615 variant events from 8,044 subjects, collected across 50 publications, and reconciles 6,057 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca.

genomics↗

bioRxiv2019

Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types

In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were \"class-driven,\" meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from Patch-seq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.

neuroscience↗

bioRxiv2019

Draft Genome Sequence and intraspecific diversification of the wild crop relative Brassica cretica Lam. using demographic model selection

Crop wild relatives contain great levels of genetic diversity, representing an invaluable resource for crop improvement. Many of their traits have the potential to help crops become more resistant and resilient, and adapt to the new conditions that they will experience due to climate change. An impressive global effort occurs for the conservation of various wild crop relatives and facilitates their use in crop breeding for food security.\n\nThe genus Brassica is listed in Annex I of the International Treaty on Plant Genetic Resources for Food and Agriculture. Brassica oleracea (or wild cabbage) is a species native to coastal southern and western Europe that has become established as an important human food crop plant because of its large reserves stored over the winter in its leaves.\n\nBrassica cretica Lam. is a wild relative crop in the brassica group and B. cretica subsp. nivea has been suggested as a separate subspecies. The species B. cretica has been proposed as a potential gene donor to a number of crops in the brassica group, including broccoli, Brussels sprout, cabbage, cauliflower, kale, swede, turnip and oilseed rape.\n\nHere, we present the draft de novo genome assemblies of four B. cretica individuals, including two B. cretica subsp. nivea and two B. cretica.\n\nDe novo assembly of Illumina MiSeq genomic shotgun sequencing data yielded 243,461 contigs totalling 412.5 Mb in length, corresponding to 122 % of the estimated genome size of B. cretica (339 Mb). According to synteny mapping and phylogenetic analysis of conserved genes, B. cretica genome based on our sequence data reveals approximately 30.360 proteins.\n\nFurthermore, our demographic analysis based on whole genome data, suggests that distinct populations of B. cretica are not isolated. Our findings suggest that the classification of the B. cretica in distinct subspecies is not supported from the genome sequence data we analyzed.

genomics↗

bioRxiv2018

Evolutionary Models of Amino Acid Substitutions Based on the Tertiary Structure of their Neighborhoods

Intra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for -helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.

evolutionary biology↗

bioRxiv2018

Evolution of gene regulatory networks by means of selection and random genetic drift

The evolution of a population by means of genetic drift and natural selection operating on a gene regulatory network (GRN) of an individual has not been scrutinized in depth. Thus, the relative importance of various evolutionary forces and processes on shaping genetic variability in GRNs is understudied. Furthermore, it is not known if existing tools that identify recent and strong positive selection from genomic sequences, in simple models of evolution, can detect recent positive selection when it operates on GRNs. Here, we propose a simulation framework, called EvoNET, that simulates forward-in-time the evolution of GRNs in a population. Since the population size is finite, random genetic drift is explicitly applied. The fitness of a mutation is not constant, but we evaluate the fitness of each individual by measuring its genetic distance from an optimal genotype. Mutations and recombination may take place from generation to generation, modifying the genotypic composition of the population. Each individual goes through a maturation period, where its GRN reaches equilibrium. At the next step, individuals compete to produce the next generation. As time progresses, the beneficial genotypes push the population higher in the fitness landscape. We examine properties of the GRN evolution such as robustness against the deleterious effect of mutations and the role of genetic drift. We confirm classical results from Andreas Wagners work that GRNs show robustness against mutations and we provide new results regarding the interplay between random genetic drift and natural selection.

bioinformatics↗

bioRxiv2018

Early split between African and European populations of Drosophila melanogaster

Natural populations of the fruit fly Drosophila melanogaster have been used extensively as a model system to investigate the effect of neutral and selective processes on genetic variation. The species expanded outside its Afrotropical ancestral range during the last glacial period and numerous studies have focused on identifying molecular adaptations associated with the colonization of northern habitats. The sequencing of many genomes from African and non-African natural populations has facilitated the analysis of the interplay between adaptive and demographic processes. However, most of the non-African sequenced material has been sampled from American and Australian populations that have been introduced within the last hundred years following recent human dispersal and are also affected by recent genetic admixture with African populations. Northern European populations, at the contrary, are expected to be older and less affected by complex admixture patterns and are therefore more appropriate to investigate neutral and adaptive processes. Here we present a new dataset consisting of 14 fully sequenced haploid genomes sampled from a natural population in Ume[a], Sweden. We co-analyzed this new data with an African population to compare the likelihood of several competing demographic scenarios for European and African populations. We show that allowing for gene flow between populations in neutral demographic models leads to a significantly better fit to the data and strongly affects estimates of the divergence time and of the size of the bottleneck in the European population. Our results indicate that the time of divergence between cosmopolitan and ancestral populations is 30,000 years older than reported by previous studies.

evolutionary biology↗

bioRxiv2018

Amylase copy number analysis in several mammalian lineages reveals convergent adaptive bursts shaped by diet

The amylase gene (AMY), which codes for a starch-digesting enzyme in animals, underwent several gene copy number gains in humans1, dogs2, and mice3, presumably along with increased starch consumption during the evolution of these species. Here we present evidence for additional AMY copy number expansions in several mammalian species, most of which also consume starch-rich diets. We also show that these independent AMY copy number gains are often accompanied by a gain in enzymatic activity of amylase in saliva. We used multi-species coalescent modeling to provide further evidence that these recurrent AMY gene copy number expansions were adaptive. Our findings underscore the overall importance of gene copy number amplification as a flexible and fast adaptive mechanism in evolution that can independently occur in different branches of the phylogeny.

evolutionary biology↗

bioRxiv2018

Monitoring changes in the Gene Ontology and their impact on genomic data analysis

The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of \"enrichment analysis\". To facilitate informed use of GO, we present GOTrack (https://gotrack.msl.ubc.ca), which provides access to historical records and trends in the Gene Ontology and GO annotations (GOA). GOTrack gives users access to gene- and term-level information on annotations for nine model organisms as well as an interactive tool that measures the stability of enrichment results over time for user-provided \"hit lists\" of genes. To document the effects of GO evolution on enrichment, we analyzed over 2500 published hit lists of human genes (most over 9 years old). 53% of hit lists were considered to yield significantly stable enrichment results. Because stability is far from assured for any individual hit list, GOTrack can lead to more informed and cautious application of GO to genomics research.

bioinformatics↗

bioRxiv2018

Systematic evaluation of isoform function in literature reports of alternative splicing

Although most mammalian genes have multiple isoforms, an ongoing debate is whether these isoforms are all functional as well as the extent to which they increase the genomes functional repertoire. To ground this debate in data, we established a curation framework for evaluating experimental evidence of functionally distinct splice isoforms (FDSIs) and analyzed splice isoform function for over 700 human and mouse genes. Despite our bias towards prominently studied genes, we found experimental evidence meeting the classical definition for functionally distinct isoforms for only ~5% of the curated genes. If we relax our criteria, the fraction of genes with support for FDSIs remains low (~13%). We provide evidence that this picture will not change substantially with further curation. Furthermore, many FDSIs did not trace to a specific isoform in Ensembl. Our work has implications for computational analyses of alternative splicing and should help shape research around the role of splicing on gene function from presuming large general effects to acknowledging the need for stronger experimental evidence.

molecular biology↗

bioRxiv2018

Data-driven approaches for improving the interpretability of patch-seq data

Patch-seq, enabling simultaneous measurement of a transcriptomic, electrophysiological, and morphological features, has recently emerged as a powerful tool for neuronal characterization. However, we show the method is susceptible to technical artifacts, including the presence of mRNA contaminants from multiple cells, that limit the interpretability of the data. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features.

neuroscience↗

bioRxiv2017

Transcriptomic Correlates Of Neuron Electrophysiological Diversity

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating transcriptomics with intracellular electrophysiology. Using a brain-wide dataset of 34 neuron types, we identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 electrophysiological parameters. The majority of these correlations were consistent in an independent sample of 12 visual cortex cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. These results suggest that despite the complexity linking gene expression to electrophysiology, there are likely some general principles that govern how individual genes establish phenotypic diversity across very different cell types.

neuroscience↗

bioRxiv2016

NeuroExpresso: A cross-laboratory database of brain cell-type expression profiles with applications to marker gene identification and bulk brain tissue transcriptome interpretation

The identification of cell type marker genes, genes highly enriched in specific cell types, plays an important role in the study of the nervous system. In particular, marker genes can be used to identify cell types to enable studies of their properties. Marker genes can also aid the interpretation of bulk tissue expression profiles by revealing cell type specific changes.\n\nWe assembled a database, NeuroExpresso, of publicly available mouse brain cell type-specific gene expression datasets. We then used stringent criteria to select marker genes highly expressed in individual cell types. We found a substantial number of novel markers previously unknown in the literature and validated a subset of them using in silico analyses and in situ hybridization. We next demonstrate the use of marker genes in analysis of whole tissue data by summarizing their expression into \"cell type profiles\" that can be thought of as surrogates for the relative abundance of the cell types across the samples studied.\n\nFurther analysis of our cell type-specific expression database confirms some recent findings about brain cell types along with revealing novel properties, such as Ddc expression in oligodendrocytes. To facilitate further use of this expanding database, we provide a user-friendly web interface for the visualization of expression data.\n\nSignificance StatementCell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.

neuroscience↗