A machine learning approach for somatic mutation discovery.

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

p53 binding sites in normal and cancer cells are characterized by distinct chromatin context.

The tumor suppressor protein p53 interacts with DNA in a sequence-dependent manner. Thousands of p53 binding sites have been mapped genome-wide in normal and cancer cells. However, the way p53 selectively binds its cognate sites in different types of cells is not fully understood. Here, we performed a comprehensive analysis of 25 published p53 cistromes and identified 3,551 and 6,039 'high-confidence' binding sites in normal and cancer cells, respectively. Our analysis revealed 2 distinct epigenetic features underlying p53-DNA interactions in vivo. First, p53 binding sites are associated with transcriptionally active histone marks (H3K4me3 and H3K36me3) in normal-cell chromatin, but with repressive histone marks (H3K27me3) in cancer-cell chromatin. Second, p53 binding sites in cancer cells are characterized by a lower level of DNA methylation than their counterparts in normal cells, probably related to global hypomethylation in cancers. Intriguingly, regardless of the cell type, p53 sites are highly enriched in the endogenous retroviral elements of the ERV1 family, highlighting the importance of this repeat family in shaping the transcriptional network of p53. Moreover, the p53 sites exhibit an unusual combination of chromatin patterns: high nucleosome occupancy and, at the same time, high sensitivity to DNase I. Our results suggest that p53 can access its target sites in a chromatin environment that is non-permissive to most DNA-binding transcription factors, which may allow p53 to act as a pioneer transcription factor in the context of chromatin.

Cell type-specific transcriptome profiling in mammalian brains.

A mammalian brain contains numerous types of cells. Advances in neuroscience in the past decade allow us to identify and isolate neural cells of interest from mammalian brains. Recent developments in high-throughput technologies, such as microarrays and next-generation sequencing (NGS), provide detailed information on gene expression in pooled cells on a genomic scale. As a result, many novel genes have been found critical in cell type-specific transcriptional regulation. These differentially expressed genes can be used as molecular signatures, unique to a particular class of neural cells. Use of this gene expression-based approach can further differentiate neural cell types into subtypes, potentially linking some of them with neurological diseases. In this article, experimental techniques used to purify neural cells are described, followed by a review on recent microarray- or NGS-based transcriptomic studies of common neural cell types. The future prospects of cell type-specific research are also discussed.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor.

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Cross-species Transcriptomic Comparison of In Vitro and In Vivo Mammalian Neural Cells.

The mammalian brain is characterized by distinct classes of cells that differ in morphology, structure, signaling, and function. Dysregulation of gene expression in these cell populations leads to various neurological disorders. Neural cells often need to be acutely purified from animal brains for research, which requires complicated procedure and specific expertise. Primary culture of these cells in vitro is a viable alternative, but the differences in gene expression of cells grown in vitro and in vivo remain unclear. Here, we cultured three major neural cell classes of rat brain (ie, neurons, astrocytes, and oligodendrocyte precursor cells [OPCs]) obtained from commercial sources. We measured transcript abundance of these cell types by RNA sequencing (RNA-seq) and compared with their counterparts acutely purified from mouse brains. Cross-species RNA-seq data analysis revealed hundreds of genes that are differentially expressed between the cultured and acutely purified cells. Astrocytes have more such genes compared to neurons and OPCs, indicating that signaling pathways are greatly perturbed in cultured astrocytes. This dataset provides a powerful resource to demonstrate the similarities and differences of biological processes in mammalian neural cells grown in vitro and in vivo at the molecular level.

Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy.

BACKGROUND: An organism's DNA sequence is one of the key factors guiding the positioning of nucleosomes within a cell's nucleus. Sequence-dependent bending anisotropy dictates how DNA is wrapped around a histone octamer. One of the best established sequence patterns consistent with this anisotropy is the periodic occurrence of AT-containing dinucleotides (WW) and GC-containing dinucleotides (SS) in the nucleosomal locations where DNA is bent in the minor and major grooves, respectively. Although this simple pattern has been observed in nucleosomes across eukaryotic genomes, its use for prediction of nucleosome positioning was not systematically tested. RESULTS: We present a simple computational model, termed the W/S scheme, implementing this pattern, without using any training data. This model accurately predicts the rotational positioning of nucleosomes both in vitro and in vivo, in yeast and human genomes. About 65 - 75% of the experimentally observed nucleosome positions are predicted with the precision of one to two base pairs. The program is freely available at http://people.rit.edu/fxcsbi/WS_scheme/. We also introduce a simple and efficient way to compare the performance of different models predicting the rotational positioning of nucleosomes. CONCLUSIONS: This paper presents the W/S scheme to achieve accurate prediction of rotational positioning of nucleosomes, solely based on the sequence-dependent anisotropic bending of nucleosomal DNA. This method successfully captures DNA features critical for the rotational positioning of nucleosomes, and can be further improved by incorporating additional terms related to the translational positioning of nucleosomes in a species-specific manner.