Pattern-based phylogenetic distance estimation and tree reconstruction

We have developed an alignment-free method that calculates phylogenetic distances using a maximum likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a dataset of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods on this dataset. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.Comment: 21 pages, 3 figures, 2 table

Transcription Factor-DNA Binding Via Machine Learning Ensembles

We present ensemble methods in a machine learning (ML) framework combining predictions from five known motif/binding site exploration algorithms. For a given TF the ensemble starts with position weight matrices (PWM's) for the motif, collected from the component algorithms. Using dimension reduction, we identify significant PWM-based subspaces for analysis. Within each subspace a machine classifier is built for identifying the TF's gene (promoter) targets (Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool. Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string) feature PWM-based subspaces that stand out in identifying gene targets. We approach Problem 3 (binding sites) with a novel machine learning approach that uses promoter string features and ML importance scores in a classification algorithm locating binding sites across the genome. For target gene identification this method improves performance (measured by the F1 score) by about 10 percentage points over the (a) motif scanning method and (b) the coexpression-based association method. Top motif outperformed 5 component algorithms as well as two other common algorithms (BEST and DEME). For identifying individual binding sites on a benchmark cross species database (Tompa et al., 2005) we match the best performer without much human intervention. It also improved the performance on mammalian TFs. The ensemble can integrate orthogonal information from different weak learners (potentially using entirely different types of features) into a machine learner that can perform consistently better for more TFs. The TF gene target identification component (problem 1 above) is useful in constructing a transcriptional regulatory network from known TF-target associations. The ensemble is easily extendable to include more tools as well as future PWM-based information.Comment: 33 page

DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies

Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP–chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies

RNA-Seq optimization with eQTL gold standards.

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments

Non-coding genome contributions to the development and evolution of mammalian organs

Protein-coding sequences only cover 1-2% of a typical mammalian genome. The remaining non-coding space hides thousands of genomic elements, some of which act via their DNA sequence while others are transcribed into non-coding RNAs. Many well-characterized non-coding elements are involved in the regulation of other genes, a process essential for the emergence of different cell types and organs during development. Changes in the expression of conserved genes during development are in turn thought to facilitate evolutionary innovation in form and function. Thus, non-coding genomic elements are hypothesized to play important roles in developmental and evolutionary processes. However, challenges related to the identification and characterization of these elements, in particular in non-model organisms, has limited the study of their overall contributions to mammalian organ development and evolution. During my dissertation work, I addressed this gap by studying two major classes of non-coding elements, long non-coding RNAs (lncRNAs) and cis-regulatory elements (CREs). In the first part of my thesis, I analyzed the expression profiles of lncRNAs during the development of seven major organs in six mammals and a bird. I showed that, unlike protein-coding genes, only a small fraction of lncRNAs is expressed in reproducibly dynamic patterns during organ development. These lncRNAs are enriched for a series of features associated with functional relevance, including increased evolutionary conservation and regulatory complexity, highlighting them as candidates for further molecular characterization. I then associated these lncRNAs with specific genes and functions based on their spatiotemporal expression profiles. My analyses also revealed differences in lncRNA contributions across organs and developmental stages, identifying a developmental transition from broadly expressed and conserved lncRNAs towards an increasing number of lineage- and organ-specific lncRNAs. Following up on these global analyses, I then focused on a newly-identified lncRNA in the marsupial opossum, Female Specific on chromosome X (FSX). The broad and likely autonomous female-specific expression of FSX suggests a role in marsupial X-chromosome inactivation (XCI). I showed that FSX shares many expression and sequence features with another lncRNA, RSX — a known regulator of XCI in marsupials. Comparisons to other marsupials revealed that both RSX and FSX emerged in the common marsupial ancestor and have since been preserved in marsupial genomes, while their broad and female-specific expression has been retained for at least 76 million years of evolution. Taken together, my analyses highlighted FSX as a novel candidate for regulating marsupial XCI. In the third part of this work, I shifted my focus to CREs and their cell type-specific activities in the developing mouse cerebellum. After annotating cerebellar cell types and states based on single-cell chromatin accessibility data, I identified putative CREs and characterized their spatiotemporal activity across cell types and developmental stages. Focusing on progenitor cells, I described temporal changes in CRE activity that are shared between early germinal zones, supporting a model of cell fate induction through common developmental cues. By examining chromatin accessibility dynamics during neuronal differentiation, I revealed a gradual divergence in the regulatory programs of major cerebellar neuron types. In the final part, I explored the evolutionary histories of CREs and their potential contributions to gene expression changes between species. By comparing mouse CREs to vertebrate genomes and chromatin accessibility profiles from the marsupial opossum, I identified a temporal decrease in CRE conservation, which is shared across cerebellar cell types. However, I also found differences in constraint between cell types, with microglia having the fastest evolving CREs in the mouse cerebellum. Finally, I used deep learning models to study the regulatory grammar of cerebellar cell types in human and mouse, showing that the sequence rules determining CRE activity are conserved across mammals. I then used these models to retrace the evolutionary changes leading to divergent CRE activity between species. Collectively, my PhD work provides insights into the evolutionary dynamics of non-coding genes and regulatory elements, the processes associated with their conservation, and their contributions to the development and evolution of mammalian cell types and organs

ATTACK OF THE CLONES: ELUCIDATING THE ROLE OF CLONALITY IN THE INVASION SUCCESS OF CARPOBROTUS EDULIS

Transcriptomics is a modern technique in genomics that utilizes RNA sequences to get a snapshot of genetic expression. This is a powerful tool in non-model species lacking a reference genome. Thus, the application of comparative transcriptomics has the potential to help us elucidate the evolutionary mechanisms that facilitate species invasion. Carpobrotus edulis is a prolific and widespread invasive succulent plant belonging to the Aizoaceae family. A native to South Africa, this species has become a dominant invader of many Mediterranean coastal areas. In this study, we leveraged the use of RNAseq to investigate evolutionary changes among invasive populations. RNA-seq data was gathered from experimental native and invasive populations tested under stress conditions and used to assemble a de novo transcriptome and perform a differential expression analysis. We provide the first annotated transcriptome for the plant genus Carpobrotus and the Aizoaceae family. Differential expression analysis identified several differentially expressed transcripts between native and invasive populations. Invasive populations showed no increases in putative genes related to flowering and decreased expression in genes related to protection and defense. Supporting the hypothesis that C. edulis has undergone significant genetic modification in the invaded range. These results are expected under the Evolution of Increased Competitive Ability hypothesis and Genetic Accommodation hypothesis, wherein novel metabolic processes and changes in gene regulatory networks evolve when introduced to ecologies