iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

TranSNPs: A class of functional SNPs affecting mRNA translation potential revealed by fraction-based allelic imbalance

Few studies have explored the association between SNPs and alterations in mRNA translation potential. We developed an approach to identify SNPs that can mark allele-specific protein expression levels and could represent sources of inter-individual variation in disease risk. Using MCF7 cells under different treatments, we performed polysomal profiling followed by RNA sequencing of total or polysome-associated mRNA fractions and designed a computational approach to identify SNPs showing a significant change in the allelic balance between total and polysomal mRNA fractions. We identified 147 SNPs, 39 of which located in UTRs. Allele-specific differences at the translation level were confirmed in transfected MCF7 cells by reporter assays. Exploiting breast cancer data from TCGA we identified UTR SNPs demonstrating distinct prognosis features and altering binding sites of RNA-binding proteins. Our approach produced a catalog of tranSNPs, a class of functional SNPs associated with allele-specific translation and potentially endowed with prognostic value for disease risk.

Landscape of allele-specific transcription factor binding in the human genome

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data

Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.

Association analysis using somatic mutations

Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively.

Cancer is a genetic disease that is driven by the accumulation of somatic mutations. Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features. One challenge for such tasks is the non-ignorable somatic mutation calling errors. We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types. Our results show that some somatic mutations affect gene expression in several cancer types. In particular, we show that the associations between gene expression traits and TP53 gene level mutation reveal some similarities across a few cancer types.

An empirical Bayes test for allelic-imbalance detection in ChIP-seq

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has enabled discovery of genomic regions enriched with biological signals such as transcription factor binding and histone modifications. Allelic-imbalance (ALI) detection is a complementary analysis of ChIP-seq data for associating biological signals with single nucleotide polymorphisms (SNPs). It has been successfully used in elucidating functional roles of non-coding SNPs. Commonly used statistical approaches for ALI detection are often based on binomial testing and mixture models, both of which rely on strong assumptions on the distribution of the unobserved allelic probability, and have significant practical shortcomings. We propose Non-Parametric Binomial (NPBin) test for ALI detection and for modeling Binomial data in general. NPBin models the density of the unobserved allelic probability non-parametrically, and estimates its empirical null distribution via curve fitting. We demonstrate the advantages of NPBin in terms of interpretability of the estimated density and the accuracy in ALI detection using simulations and analysis of several ChIP-seq data sets. We also illustrate the generality of our modeling framework beyond ALI detection by an application to a baseball batting average prediction problem. This article has supplementary material available at Biostatistics online. The code and the sample input data have been also deposited to github https://github.com/QiZhangStat/ALIdetection.

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets

Current analytic approaches for querying large collections of chromatin immunoprecipitation followed by sequencing (ChIP-seq) data from multiple cell types rely on individual analysis of each data set (i.e., peak calling) independently. This approach discards the fact that functional elements are frequently shared among related cell types and leads to overestimation of the extent of divergence between different ChIP-seq samples. Methods geared toward multisample investigations have limited applicability in settings that aim to integrate 100s to 1000s of ChIP-seq data sets for query loci (e.g., thousands of genomic loci with a specific binding site). Recently, Zuo et al. developed a hierarchical framework for state-space matrix inference and clustering, named MBASIC, to enable joint analysis of user-specified loci across multiple ChIP-seq data sets. Although this versatile framework estimates both the underlying state-space (e.g., bound vs. unbound) and also groups loci with similar patterns together, its Expectation-Maximization-based estimation structure hinders its applicability with large number of loci and samples. We address this limitation by developing MAP-based asymptotic derivations from Bayes (MAD-Bayes) framework for MBASIC. This results in a K-means-like optimization algorithm that converges rapidly and hence enables exploring multiple initialization schemes and flexibility in tuning. Comparison with MBASIC indicates that this speed comes at a relatively insignificant loss in estimation accuracy. Although MAD-Bayes MBASIC is specifically designed for the analysis of user-specified loci, it is able to capture overall patterns of histone marks from multiple ChIP-seq data sets similar to those identified by genome-wide segmentation methods such as ChromHMM and Spectacle.

A Hierarchical Framework for State-Space Matrix Inference and Clustering

In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden state of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred states. Although there are a number of methods tailored for specific datasets, there is currently no state-of-the-art modeling framework for this general class of problems. In this paper, we develop the MBASIC (Matrix Based Analysis for State-space Inference and Clustering) framework. MBASIC consists of two parts: state-space mapping and state-space clustering. In state-space mapping, it maps observations onto a finite state-space, representing the activation states of units across conditions. In state-space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred state-space profiles across all conditions. Both the state-space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. It allows for imposing structural assumptions on each cluster, and enables model selection using information criterion. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying state-space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. The first application grouped genes based on transcription factor occupancy profiles of their promoter regions in two different cell types. The second application focused on identifying groups of loci that are similar to a GATA2 binding site that is functional at its endogenous locus by utilizing transcription factor occupancy data and illustrated applicability of MBASIC in a wide variety of problems. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.

Allele-specific transcription factor binding in liver and cervix cells unveils many likely drivers of GWAS signals

Genome-wide association studies (GWAS) point to regions with associated genetic variants but rarely to a specific gene and therefore detailed knowledge regarding the genes contributing to complex traits and diseases remains elusive. The functional role of GWAS-SNPs is also affected by linkage disequilibrium with many variants on the same haplotype and sometimes in the same regulatory element almost equally likely to mediate the effect. Using ChIP-seq data on many transcription factors, we pinpointed genetic variants in HepG2 and HeLa-S3 cell lines which show a genome-wide significant difference in binding between alleles. We identified a collection of 3713 candidate functional regulatory variants many of which are likely drivers of GWAS signals or genetic difference in expression. A recent study investigated many variants before finding the functional ones at the GALNT2 locus, which we found in our genome-wide screen in HepG2. This illustrates the efficiency of our approach.

Integrative Analysis of "-Omics" Data Using Penalty Functions

In the analysis of omics data, integrative analysis provides an effective way of pooling information across multiple datasets or multiple correlated responses, and can be more effective than single-dataset (response) analysis. Multiple families of integrative analysis methods have been proposed in the literature. The current review focuses on the penalization methods. Special attention is paid to sparse meta-analysis methods that pool summary statistics across datasets, and integrative analysis methods that pool raw data across datasets. We discuss their formulation and rationale. Beyond "standard" penalized selection, we also review contrasted penalization and Laplacian penalization which accommodate finer data structures. The computational aspects, including computational algorithms and tuning parameter selection, are examined. This review concludes with possible limitations and extensions.

SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles

MOTIVATION

Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-Seq) measures the genome-wide occupancy of transcription factors in vivo. Different combinations of DNA-binding protein occupancies may result in a gene being expressed in different tissues or at different developmental stages. To fully understand the functions of genes, it is essential to develop probabilistic models on multiple ChIP-Seq profiles to decipher the combinatorial regulatory mechanisms by multiple transcription factors.

RESULTS

In this work, we describe a probabilistic model (SignalSpider) to decipher the combinatorial binding events of multiple transcription factors. Comparing with similar existing methods, we found SignalSpider performs better in clustering promoter and enhancer regions. Notably, SignalSpider can learn higher-order combinatorial patterns from multiple ChIP-Seq profiles. We have applied SignalSpider on the normalized ChIP-Seq profiles from the ENCODE consortium and learned model instances. We observed different higher-order enrichment and depletion patterns across sets of proteins. Those clustering patterns are supported by Gene Ontology (GO) enrichment, evolutionary conservation and chromatin interaction enrichment, offering biological insights for further focused studies. We also proposed a specific enrichment map visualization method to reveal the genome-wide transcription factor combinatorial patterns from the models built, which extend our existing fine-scale knowledge on gene regulation to a genome-wide level.

AVAILABILITY AND IMPLEMENTATION

The matrix-algebra-optimized executables and source codes are available at the authors' websites: http://www.cs.toronto.edu/∼wkc/SignalSpider.

ALEA: a toolbox for allele-specific epigenomics analysis

The assessment of expression and epigenomic status using sequencing based methods provides an unprecedented opportunity to identify and correlate allelic differences with epigenomic status. We present ALEA, a computational toolbox for allele-specific epigenomics analysis, which incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. ALEA provides a customizable pipeline of command line tools for allele-specific analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. The pipeline has been validated using human and hybrid mouse ChIP-seq and RNA-seq data.

AVAILABILITY

The package, test data and usage instructions are available online at http://www.bcgsc.ca/platform/bioinfo/software/alea CONTACT: : mkarimi1@interchange.ubc.ca or sjones@bcgsc.ca Supplementary information: Supplementary data are available at Bioinformatics online.