Biology subjects

Ochoa, A.

Publications and source records attributed to Ochoa, A..

4 recordsLinked to original sources

bioRxiv2017

Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

bioinformatics↗

bioRxiv2016

Domain prediction with probabilistic directional context

MotivationProtein domain prediction is one of the most powerful approaches for sequence-based function prediction. While domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.\n\nResultsWe introduce a probabilistic approach for domain prediction that models \"directional\" domain context. Our method is the first to score all domain pairs within a sequence while taking their order into account, even for non-sequential domains. We show that our approach extends a previous Markov model-based approach to additionally score all pairwise terms, and that it can be interpreted within the context of Markov random fields. We formulate our underlying combinatorial optimization problem as an integer linear program, and demonstrate that it can be solved quickly in practice. Finally, we perform extensive evaluation of domain context methods and demonstrate that incorporating context increases the number of domain predictions by [~]15%, with our approach dPUC2 (Domain Prediction Using Context) outperforming all competing approaches.\n\nAvailabilitydPUC2 is available at http://github.com/alexviiia/dpuc2.

bioinformatics↗

bioRxiv2016

FST and kinship for arbitrary population structures I: Generalized definitions

FST is a fundamental measure of genetic differentiation and population structure, currently defined for subdivided populations. FST in practice typically assumes independent, non-overlapping subpopulations, which all split simultaneously from their last common ancestral population so that genetic drift in each subpopulation is probabilistically independent of the other subpopulations. We introduce a generalized FST definition for arbitrary population structures, where individuals may be related in arbitrary ways, allowing for arbitrary probabilistic dependence among individuals. Our definitions are built on identity-by-descent (IBD) probabilities that relate individuals through inbreeding and kinship coefficients. We generalize FST as the mean inbreeding coefficient of the individuals local populations relative to their last common ancestral population. We show that the generalized definition agrees with Wrights original and the independent subpopulation definitions as special cases. We define a novel coancestry model based on \"individual-specific allele frequencies\" and prove that its parameters correspond to probabilistic kinship coefficients. Lastly, we extend the Pritchard-Stephens-Donnelly admixture model in the context of our coancestry model and calculate its FST. To motivate this work, we include a summary of analyses we have carried out in follow-up papers, where our new approach has been applied to simulations and global human data, showcasing the complexity of human population structure, demonstrating our success in estimating kinship and FST, and the shortcomings of existing approaches. The probabilistic framework we introduce here provides a theoretical foundation that extends FST in terms of inbreeding and kinship coefficients to arbitrary population structures, paving the way for new estimators and novel analyses.\n\nNote: This article is Part I of two-part manuscripts. We refer to these in the text as Part I and Part II, respectively.\n\nPart I: Alejandro Ochoa and John D. Storey. \"FST and kinship for arbitrary population structures I: Generalized definitions\". bioRxiv (10.1101/083915) (2019). https://doi.org/10.1101/083915. First published 2016-10-27.\n\nPart II: Alejandro Ochoa and John D. Storey. \"FST and kinship for arbitrary population structures II: Method of moments estimators\". bioRxiv (10.1101/083923) (2019). https://doi.org/10.1101/083923. First published 2016-10-27.

genetics↗

bioRxiv2016

FST and kinship for arbitrary population structures II: Method of moments estimators

FST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently used estimators of FST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we provide new results on the behavior of these estimators in the presence of arbitrarily complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing FST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally assumed models of structure. We then present our new approach, which consistently estimates kinship and FST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and FST estimates.

genetics↗