Removing batch effects for prediction problems with frozen surrogate variable analysis

Batch effects are responsible for the failure of promising genomic prognos- tic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to re- move these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where sam- ples are analyzed one at a time for diagnostic, prognostic, and predictive applica- tions. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA) that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction ac- curacy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package

Epiviz: a view inside the design of an integrated visual analysis software for genomics

Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows. In this paper we expand on the design decisions behind Epiviz, and introduce a series of new advanced features that further support the type of interactive exploratory workflow we have targeted. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a genome browser. In our discussion section, we present security implications of the current design, as well as a series of limitations and future research steps. Since many of the design choices of Epiviz are novel in genomics data analysis, this paper serves both as a document of our own approaches with lessons learned, as well as a start point for future efforts in the same direction for the genomics community.https://doi.org/10.1186/1471-2105-16-S11-S

Distinct genomic and epigenomic features demarcate hypomethylated blocks in colon cancer

Large mega base-pair genomic regions show robust alterations in DNA methylation levels in multiple cancers. A vast majority of these regions are hypomethylated in cancers. These regions are generally enriched for CpG islands, Lamin Associated Domains and Large organized chromatin lysine modification domains, and are associated with stochastic variability in gene expression. Given the size and consistency of hypomethylated blocks (HMB) across cancer types, we hypothesized that the immediate causes of methylation instability are likely to be encoded in the genomic region near HMB boundaries, in terms of specific genomic or epigenomic signatures. However, a detailed characterization of the HMB boundaries has not been reported. Here, we focused on ~13 k HMBs, encompassing approximately half of the genome, identified in colon cancer. We modeled the genomic features of HMB boundaries by Random Forest to identify their salient features, in terms of transcription factor (TF) binding motifs. Additionally we analyzed various epigenomic marks, and chromatin structural features of HMB boundaries relative to the non-HMB genomic regions. We found that the classical promoter epigenomic mark – H3K4me3, is highly enriched at HMB boundaries, as are CTCF bound sites. HMB boundaries harbor distinct combinations of TF motifs. Our Random Forest model based on TF motifs can accurately distinguish boundaries not only from regions inside and outside HMBs, but surprisingly, from active promoters as well. Interestingly, the distinguishing TFs and their interacting proteins are involved in chromatin modification. Finally, HMB boundaries significantly coincide with the boundaries of Topologically Associating Domains of the chromatin. Our analyses suggest that the overall architecture of HMBs is guided by pre-existing chromatin architecture, and are associated with aberrant activity of promoter-like sequences at the boundary.https://doi.org/10.1186/s12885-016-2128-

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.https://doi.org/10.1186/s12859-019-2947-

Effective detection of rare variants in pooled DNA samples using Cross-pool tailcurve analysis

Sequencing targeted DNA regions in large samples is necessary to discover the full spectrum of rare variants. We report an effective Illumina sequencing strategy utilizing pooled samples with novel quality (Srfim) and filtering (SERVIC4E) algorithms. We sequenced 24 exons in two cohorts of 480 samples each, identifying 47 coding variants, including 30 present once per cohort. Validation by Sanger sequencing revealed an excellent combination of sensitivity and specificity for variant detection in pooled samples of both cohorts as compared to publicly available algorithms

Gene expression anti-profiles as a basis for accurate universal cancer signatures

Background: Early screening for cancer is arguably one of the greatest public health advances over the last fifty years. However, many cancer screening tests are invasive (digital rectal exams), expensive (mammograms, imaging) or both (colonoscopies). This has spurred growing interest in developing genomic signatures that can be used for cancer diagnosis and prognosis. However, progress has been slowed by heterogeneity in cancer profiles and the lack of effective computational prediction tools for this type of data. Results: We developed anti-profiles as a first step towards translating experimental findings suggesting that stochastic across-sample hyper-variability in the expression of specific genes is a stable and general property of cancer into predictive and diagnostic signatures. Using single-chip microarray normalization and quality assessment methods, we developed an anti-profile for colon cancer in tissue biopsy samples. To demonstrate the translational potential of our findings, we applied the signature developed in the tissue samples, without any further retraining or normalization, to screen patients for colon cancer based on genomic measurements from peripheral blood in an independent study (AUC of 0.89). This method achieved higher accuracy than the signature underlying commercially available peripheral blood screening tests for colon cancer (AUC of 0.81). We also confirmed the existence of hyper-variable genes across a range of cancer types and found that a significant proportion of tissue-specific genes are hyper-variable in cancer. Based on these observations, we developed a universal cancer anti-profile that accurately distinguishes cancer from normal regardless of tissue type (ten-fold cross-validation AUC > 0.92). Conclusions: We have introduced anti-profiles as a new approach for developing cancer genomic signatures that specifically takes advantage of gene expression heterogeneity. We have demonstrated that anti-profiles can be successfully applied to develop peripheral-blood based diagnostics for cancer and used anti-profiles to develop a highly accurate universal cancer signature. By using single-chip normalization and quality assessment methods, no further retraining of signatures developed by the anti-profile approach would be required before their application in clinical settings. Our results suggest that anti-profiles may be used to develop inexpensive and non-invasive universal cancer screening tests.https://doi.org/10.1186/1471-2105-13-27

Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment

Acknowledgements The authors wish to thank Mark Stares, Richard Rance, and other members of the Wellcome Trust Sanger Institute’s 454 sequencing team for generating the 16S rRNA gene data. Lili Fox Vélez provided editorial support. Funding IA, JNP, and MP were partly supported by the NIH, grants R01-AI-100947 to MP, and R21-GM-107683 to Matthias Chung, subcontract to MP. JNP was partly supported by an NSF graduate fellowship number DGE750616. IA, JNP, BRL, OCS and MP were supported in part by the Bill and Melinda Gates Foundation, award number 42917 to OCS. JP and AWW received core funding support from The Wellcome Trust (grant number 098051). AWW, and the Rowett Institute of Nutrition and Health, University of Aberdeen, receive core funding support from the Scottish Government Rural and Environmental Science and Analysis Service (RESAS).Peer reviewedPublisher PD

A Phylogenetic Mixture Model for the Evolution of Gene Expression

Microarray platforms are used increasingly to make comparative inferences through genome-wide surveys of gene expression. Although recent studies focus on describing the evidence for natural selection using estimates of the within- and between-taxa mutational variances, these methods do not explicitly or flexibly account for predicted nonindependence due to phylogenetic associations between measurements. In the interest of parsing the effects of selection: we introduce a mixture model for the comparative analysis of variation in gene expression across multiple taxa. This class of models isolates the phylogenetic signal from the nonphylogenetic and the heritable signal from the nonheritable while measuring the proper amount of correction. As a result, the mixture model resolves outstanding differences between existing models, relates different ways to estimate the across taxa variance, and induces a likelihood ratio test for selection. We investigate by simulation and application the feasibility and utility of estimation of the required parameters and the power of the proposed test. We illustrate analysis under this mixture model with a gene duplication family data set