Biology subjects

Chikina, M.

Publications and source records attributed to Chikina, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and non-physical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes as compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than non-physical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

evolutionary biology↗

bioRxiv2023

Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction

MotivationMost models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Pythons scikit-learn package, using two different optimization approaches (coordinate descent and stochastic gradient descent), to predict driver mutation presence or absence from gene expression across 84 pan-cancer driver genes. Across varying levels of regularization, we compared performance and model sparsity between optimizers. ResultsAfter model selection and tuning, we found that coordinate descent (implemented in the liblinear library) and SGD tended to perform comparably. liblinear models required more extensive tuning of regularization strength, performing best for high model sparsities (more nonzero coefficients), but did not require selection of a learning rate parameter. SGD models required tuning of the learning rate to perform well, but generally performed more robustly across different model sparsities as regularization strength decreased. Given these tradeoffs, we believe that the choice of optimizers should be clearly reported as a part of the model selection and validation process, to allow readers and reviewers to better understand the context in which results have been generated. Availability and implementationThe code used to carry out the analyses in this study is available at https://github.com/greenelab/pancancer-evaluation/tree/master/01_stratified_classification. Performance/regularization strength curves for all genes in the Vogelstein et al. 2013 dataset are available at https://doi.org/10.6084/m9.figshare.22728644.

bioinformatics↗

bioRxiv2023

How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Introductory Paragraph Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

bioinformatics↗

bioRxiv2023

InstaPrism: an R package for fast implementation of BayesPrism

Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies. However, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism algorithm which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling steps in BayesPrism with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed advantage. InstaPrism is implemented as a standalone R package with C++ backend and can be accessed from GitHub at https://github.com/humengying0907/InstaPrism.

bioinformatics↗

bioRxiv2023

L0 segmentation enables data-driven concise representations of diverse epigenomic data

MotivationEpigenetic assays using next-generation sequencing (NGS) have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data represented by nucleotide resolution signal tracks. The signal strength at a given nucleotide is subject to numerous sources of technical and biological noise and thus conveys limited information about the underlying biological state. In order to draw biological conclusions, data is typically summarized into higher order patterns. Numerous specialized algorithms for summarizing epigenetic signal have been proposed and include methods for peak calling or finding differentially methylated regions. A key unifying principle underlying these approaches is that they all leverage the strong prior that signal must be locally consistent. ResultsWe propose L0 segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources. L0 serves to both compress and smooth the input signal by approximating it as piece-wise constant. We implement a highly scalable L0 segmentation with additional loss functions designed for NGS epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that the L0 segmentation approach retains the salient features of the data yet can identify subtle features, such as transcription end sites, missed by other analytic approaches. AvailabilityOur approach is implemented as an R package "l01segmentation" with a C++ backend. Available at https://github.com/boooooogey/l01segmentation.

bioinformatics↗

bioRxiv2023

An intrinsically interpretable neural network architecture for sequence to function learning

MotivationSequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called tiSFM (totally interpretable sequence to function model). tiSFM improves upon the performance of standard multi-layer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multi-layer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. ResultsWe analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFMs model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. Availability and implementationThe source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python. Contactatb44@pitt.edu

bioinformatics↗

bioRxiv2023

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

Computational cell type deconvolution enables estimation of cell type abundance from bulk tissues and is important for understanding cell-cell interactions, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudbulk datasets by adding individual cells-types in controlled proportions. In our work we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. Our heterogeneously simulated samples show realistic variance across hallmark gene-sets when comparing with real bulk samples from the TCGA dataset of the same tumor type. Using this new simulation pipeline to benchmark deconvolution methods we show that introducing biological heterogeneity has a notable effect on the results. Evaluating the robustness of different deconvolution approaches to heterogeneous simulation we find that reference-free methods that rely on simplex estimation perform poorly, marker-based methods and BayesPrism are most robust, while regress-based approaches fall in between. Importantly, we find that under the heterogeneous scenario marker based methods and BayesPrism outperform state of the art reference methods. Our findings highlight how different conceptual approaches can negate unmodeled heterogeneity and suggest that there is room for further methodological development.

bioinformatics↗

bioRxiv2022

Prediction of local convergent shifts in evolutionary rates with phyloConverge characterizes the phenotypic associations and modularity of regulatory elements

Physiological and morphological adaptations to extreme environments arise from the molecular evolution of protein-coding regions and regulatory elements (REs) that regulate gene expression. Comparative genomics methods can characterize genetic elements that underlie the organism-level adaptations, but convergence analyses of REs are often limited by their evolutionary properties. A RE can be modularly composed of multiple transcription factor binding sites (TFBS) that may each experience different evolutionary pressures. The modular composition and rapid turnover of TFBS also enables a compensatory mechanism among nearby TFBS that allows for weaker sequence conservation/divergence than intuitively expected. Here, we introduce phyloConverge, a comparative genomics method that can perform fast, fine-grained local convergence analysis of genetic elements. phyloConverge calibrates for local shifts in evolutionary rates using a combination of maximum likelihood-based estimation of nucleotide substitution rates and phylogenetic permutation tests. Using the classical convergence case of mammalian adaptation to subterranean environments, we validate that phyloConverge identifies rate-accelerated conserved non-coding elements (CNEs) that are strongly correlated with ocular tissues, with improved specificity compared to competing methods. We use phyloConverge to perform TFBS-scale and nucleotide-scale scoring to dissect each CNE into subregions with uneven convergence signals and demonstrate its utility for understanding the modularity and pleiotropy of REs. Subterranean-accelerated regions are also enriched for molecular pathways and TFBS motifs associated with neuronal phenotypes, suggesting that subterranean eye degeneration may coincide with a remodeling of the nervous system. phyloConverge offers a rapid and accurate approach for understanding the evolution and modularity of regulatory elements underlying phenotypic adaptation.

evolutionary biology↗

bioRxiv2021

A cautionary tale on proper use of branch-site models to detect convergent positive selection

Comparative genomics has become a powerful tool to elucidate genotype-phenotype relationships, particularly through the study of convergently acquired phenotypes. By identifying genes under positive selection specifically on branches with the convergent phenotype we can potentially link genes to that phenotype. Such gene scans are often done using branch-site codon models. However, we have observed a recent troubling trend of misinterpretation of branch-site models in which phylogeny-wide positive selection is not distinguished from positive selection specific to convergent branches. Here, we use simulations and real data to demonstrate how failing to discern between these two cases leads to false inferences of positive selection associated with a convergent trait. We then present a " drop-out" test solution to distinguish the two cases and thereby truly capture positive selection events associated with convergent phenotypes, thus allowing for further insights into both genetic and phenotypic evolution.

evolutionary biology↗

bioRxiv2021

Prediction of cancer mutation states using multiple data modalities reveals the utility and consistency of gene expression and DNA methylation

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal may be unclear. In this study, we consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem. Since functional signatures of cancer mutation have been identified across many data types, this problem presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. The TCGA Pan-Cancer Atlas contains genetic alteration data including somatic mutations and copy number variants (CNVs), as well as several -omics data types. From TCGA, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. Surprisingly, we found that for many of the genes we considered, one or more other data types are approximately equally effective predictors. Performance was more variable between mutations than it was between data types for the same mutation, and there was often little difference between the top data types. We also found that combining data types into a single multi-omics model provided little or no improvement in predictive ability over the best individual data type. Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

bioinformatics↗

bioRxiv2021

Evolutionary rate covariation identifies SLC30A9 (ZnT9) as a mitochondrial zinc transporter.

Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using evolutionary rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence evolutionary rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) uniquely and prominently coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.

evolutionary biology↗

bioRxiv2021

Complementary evolution of coding and noncoding sequence underlies mammalian hairlessness

Body hair is a defining mammalian characteristic, but several mammals, such as whales, naked mole-rats, and humans, have notably less hair than others. To find the genetic basis of reduced hair quantity, we used our evolutionary-rates-based method, RERconverge, to identify coding and noncoding sequences that evolve at significantly different rates in so-called hairless mammals compared to hairy mammals. Using RERconverge, we performed an unbiased, genome-wide scan over 62 mammal species using 19,149 genes and 343,598 conserved noncoding regions to find genetic elements that evolve at significantly different rates in hairless mammals compared to hairy mammals. We show that these rate shifts resulted from relaxation of evolutionary constraint on hair-related sequences in hairless species. In addition to detecting known and potential novel hair-related genes, we also discovered hundreds of putative hair-related regulatory elements. Computational investigation revealed that genes and their associated noncoding regions show different evolutionary patterns and influence different aspects of hair growth and development. Many genes under accelerated evolution are associated with the structure of the hair shaft itself, while evolutionary rate shifts in noncoding regions also included the dermal papilla and matrix regions of the hair follicle that contribute to hair growth and cycling. Genes that were top-ranked for coding sequence acceleration included known hair and skin genes KRT2, KRT35, PKP1, and PTPRM that surprisingly showed no signals of evolutionary rate shifts in nearby noncoding regions. Conversely, accelerated noncoding regions are most strongly enriched near regulatory hair-related genes and microRNAs, such as mir205, ELF3, and FOXC1, that themselves do not show rate shifts in their protein-coding sequences. Such dichotomy highlights the interplay between the evolution of protein sequence and regulatory sequence to contribute to the emergence of a convergent phenotype.

evolutionary biology↗

bioRxiv2020

Phylogenetic Permulations: a statistically rigorous approach to measure confidence in associations between phenotypes and genetic elements in a phylogenetic context

The wealth of high-quality genomes for numerous species has motivated many investigations into the genetic underpinnings of phenotypes. Comparative genomics methods approach this task by identifying convergent shifts at the genetic level that are associated with traits evolving convergently across independent lineages. However, these methods have complex statistical behaviors that are influenced by non-trivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased p-values from phylogenetic methods. Permulations construct the null expectation for p-values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical p-values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for non-independence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.

evolutionary biology↗

bioRxiv2020

Non-negative Independent Factor Analysis for single cell RNA-seq

MotivationSingle-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, dimensionality reduction should preserve the biological information while discarding the noise. However if the dimensionality reduction is to be used directly to gain biological insight it must also be interpretable - that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. ResultsWe present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AvailabilityNFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). Contactmchikina@pitt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Causal network perturbations for instance-specific analysis of single cell and disease samples

Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.\n\nssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNAseq data, where the correct cell timing is recovered. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches.

bioinformatics↗

bioRxiv2019

Cancer control is a key functionality underlying evolution of extended lifespan in mammals

The biological origin of life expectancy remains a fundamental and unanswered scientific question with important ramifications for human health, especially as the bulk of burden of human healthcare shifts from infectious to age-related diseases. The striking variability in life-span among animals occupying similar ecological niches1 and the numerous mutations that have been shown to increase lifespan in model organisms2-5 point to a considerable genetic contribution. Using mammalian comparative genomics, we correlate lifespan phenotypes with relative evolutionary rates, a measure of evolutionary selective pressure6. Our analysis demonstrates that many genes and pathways are under increased evolutionary constraint in both Long-Lived Large-bodied mammals (3L) and mammals Exceptionally Long-Lived given their size (ELL), suggesting that these genes and pathways contribute to the maintenance of both traits. For 3L species, we find strong evolutionary constraint on multiple pathways involved in controlling carcinogenesis, including cell cycle, apoptosis, and immune pathways. These findings provide additional perspective on the well-known Petos Paradox that large animals with large numbers of cells do not get cancer at higher rates than smaller animals with fewer cells7. For the ELL phenotype, our analysis strongly implicates pathways involved in DNA repair, further supporting the importance of DNA repair processes in aging8-12. Moreover, these correlations with lifespan phenotypes are consistent across the entire mammalian phylogeny, suggesting that additional constraint on these pathways is a universal requirement for long lifespan.

evolutionary biology↗

bioRxiv2018

Robust methods for detecting convergent shifts in evolutionary rates

Identifying genomic elements underlying phenotypic adaptations is an important problem in evolutionary biology. Comparative analyses learning from convergent evolution of traits are gaining momentum in accurately detecting such elements. We previously developed a method for predicting phenotypic associations of genetic elements by contrasting patterns of sequence evolution in species showing a phenotype with those that do not. Using this method, we successfully demonstrated convergent evolutionary rate shifts in genetic elements associated with two phenotypic adaptations, namely the independent subterranean and marine transitions of terrestrial mammalian lineages. Our method calculates gene-specific rates of evolution on branches of phylogenetic trees using linear regression. These rates represent the extent of sequence divergence on a branch after removing the expected divergence on the branch due to background factors. The rates calculated using this regression analysis exhibit an important statistical limitation, namely heteroscedasticity. We observe that the rates on branches that are longer on average show higher variance, and describe how this problem adversely affects the confidence with which we can make inferences about rate shifts. Using a combination of data transformation and weighted regression, we have developed an updated method that corrects this heteroscedasticity in the rates. We additionally illustrate the improved performance offered by the updated method at robust detection of convergent rate shifts in phylogenetic trees of protein-coding genes across mammals, as well as using simulated tree datasets. Overall, we present an important extension to our evolutionary-rates-based method that performs more robustly and consistently at detecting convergent shifts in evolutionary rates.

evolutionary biology↗

bioRxiv2018

RERconverge: an R package for associating evolutionary rates with convergent traits

Motivation: When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits.\n\nResults: Here we present the R package RERconverge, which tests for association between relative evolutionary rates of genes and the evolution of traits across a phylogeny. RERconverge can perform associations with binary and continuous traits, and it contains tools for visualization and enrichment analyses of association results.\n\nAvailability: RERconverge source code, documentation, and a detailed usage walk-through are freely available at https://github.com/nclark-lab/RERconverge. Datasets for mammals, Drosophila, and yeast are available at https://bit.ly/2J2QBnj.\n\nContact: mchikina@pitt.edu\n\nSupplementary information: Supplementary information, containing detailed vignettes for usage of RERconverge, are available at Bioinformatics online.

evolutionary biology↗