Response to: the nature of evidence for and against epigenetic inheritance

We thank Dr. Nadeau for his interest in our work. Dr. Nadeau has raised concerns about the experimental approach (mouse strains, route of administration, lack of phenotypic assessment) and about the validity of our conclusions. We will respond to each of these concerns point-by point.Please see related article: www.dx.doi.org/10.1186/s13059-015-0709-y     

Predicting the spatial organization of chromosomes using epigenetic data

Chromosome folding can reinforce the demarcation between euchromatin and heterochromatin. Two new studies show how epigenetic data, including DNA methylation, can accurately predict chromosome folding in three dimensions. Such computational approaches reinforce the idea of a linkage between epigenetically marked chromatin domains and their segregation into distinct compartments at the megabase scale or topological domains at a higher resolution.Please see related articles: http://dx.doi.org/10.1186/s13059-015-0741-y and http://dx.doi.org/10.1186/s13059-015-0740-z     

Our unique microbial identity

A recent article examines the extent of individual variation in microbial identities and how this might determine disease susceptibility, therapeutic responses and recovery from clinical interventions.Please see related article: http://dx.doi.org/10.1186/s13059-015-0646-9     

Assembling allopolyploid genomes: no longer formidable

A combined approach of whole genome shotgun sequencing and ultra-high density linkage mapping using skim sequencing of a segregating population is effective for assembling allopolyploid genomes.See related Research, http://dx.doi.org/10.1186/s13059-015-0582-8     

Integrating host gene expression and the microbiome to explore disease pathogenesis

In a recent study, rich clinical assessment and longitudinal study design are combined with host gene expression and microbial sequencing analyses to develop a framework for exploring disease etiology and outcomes in the context of human inflammatory disease.See related article: http://dx.doi.org/10.1186/s13059-015-0637-x     

Marrying microfluidics and microwells for parallel,high-throughput single-cell genomics

An innovative, microwell-based platform for single-cell RNA sequencing (RNA-seq) combines cost efficiency, scalability and parallelizability, and will enable many new avenues of biological inquiry.See related Research article: http://dx.doi.org/10.1186/s13059-015-0684-3Over the past several years, it has become increasingly clear that there can be substantial variability in the behaviors of cells once deemed to be identical. This realization has brought about a renewed interest in defining cellular phenotypes and their variation, and in examining how these change under different biological contexts. To achieve this, approaches are needed that can deeply profile individual cells across diverse experimental conditions. Now, by marrying recent advances in molecular biology with microfluidics and microwells, Bose and colleagues present a new platform for achieving this goal [1], opening up exciting new opportunities to explore cells and their heterogeneity.     

quantro: a data-driven approach to guide the choice of an appropriate normalization method

Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html.

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CPAG: software for leveraging pleiotropy in GWAS to reveal similarity between human traits links plasma fatty acids and intestinal inflammation

Meta-analyses of genome-wide association studies (GWAS) have demonstrated that the same genetic variants can be associated with multiple diseases and other complex traits. We present software called CPAG (Cross-Phenotype Analysis of GWAS) to look for similarities between 700 traits, build trees with informative clusters, and highlight underlying pathways. Clusters are consistent with pre-defined groups and literature-based validation but also reveal novel connections. We report similarity between plasma palmitoleic acid and Crohn''s disease and find that specific fatty acids exacerbate enterocolitis in zebrafish. CPAG will become increasingly powerful as more genetic variants are uncovered, leading to a deeper understanding of complex traits. CPAG is freely available at www.sourceforge.net/projects/CPAG/.

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MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome

Functional metagenomic analyses commonly involve a normalization step, where measured levels of genes or pathways are converted into relative abundances. Here, we demonstrate that this normalization scheme introduces marked biases both across and within human microbiome samples, and identify sample- and gene-specific properties that contribute to these biases. We introduce an alternative normalization paradigm, MUSiCC, which combines universal single-copy genes with machine learning methods to correct these biases and to obtain an accurate and biologically meaningful measure of gene abundances. Finally, we demonstrate that MUSiCC significantly improves downstream discovery of functional shifts in the microbiome.MUSiCC is available at http://elbo.gs.washington.edu/software.html.

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High-resolution genomic analysis: the tumor-immune interface comes into focus

A genomic analysis of heterogeneous colorectal tumor samples has uncovered interactions between immunophenotype and various aspects of tumor biology, with implications for informing the choice of immunotherapies for specific patients and guiding the design of personalized neoantigen-based vaccines.Please see related article: http://dx.doi.org/10.1186/s13059-015-0620-6Immunotherapy is a promising new approach for treating human malignancies. Approximately 20% of melanoma and lung cancer patients receiving immune checkpoint inhibitors show responses [1,2]. Current major challenges include identification of patients most likely to respond to specific therapies and elucidation of novel targets to treat those who do not. To address these problems, a detailed understanding of the dynamic interactions between tumors and the immune system is required. In a new study, Zlatko Trajanoski and colleagues [3] describe a powerful approach to dissecting these issues through high-resolution analysis of patient genomic data. This study represents a significant advance over previous work from this group, which defined 28 immune-cell-type gene expression signatures and identified specific cell types as prognostic indicators in colorectal cancer (CRC) patients [4]. Here, the authors [3] integrate genomic analyses of CRC tumor molecular phenotypes, predicted antigenicity (called the ‘antigenome’), and immune-cell infiltration derived from multiple independent cohorts to gain refined insights into tumor-immune system interactions.     

PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

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Ultra-large alignments using phylogeny-aware profiles

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.

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DGEclust: differential expression analysis of clustered count data

We present a statistical methodology, DGEclust, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supported by the data and uncertainty in parameter estimation. DGEclust successfully identifies differentially expressed genes under a number of different scenarios, maintaining a low error rate and an excellent control of its false discovery rate with reasonable computational requirements. It is formulated to perform particularly well on low-replicated data and be applicable to multi-group data. DGEclust is available at http://dvav.github.io/dgeclust/.

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Spectacle: fast chromatin state annotation using spectral learning

Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle.

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Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg

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MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework

We present MUSIC, a signal processing approach for identification of enriched regions in ChIP-Seq data, available at music.gersteinlab.org. MUSIC first filters the ChIP-Seq read-depth signal for systematic noise from non-uniform mappability, which fragments enriched regions. Then it performs a multiscale decomposition, using median filtering, identifying enriched regions at multiple length scales. This is useful given the wide range of scales probed in ChIP-Seq assays. MUSIC performs favorably in terms of accuracy and reproducibility compared with other methods. In particular, analysis of RNA polymerase II data reveals a clear distinction between the stalled and elongating forms of the polymerase.

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