Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles.
The analysis of mammalian transcriptomes could provide new insights into human biology. Here the authors carry out RNA sequencing in a large collection of mouse tissues and compare these data to human transcriptome profiles, identifying a set of constrained genes that carry out basic cellular functions with remarkably constant expression levels across tissues and species.
The era of genome sequencing has produced long lists of the molecular parts from which cellular machines are constructed. A fundamental goal in systems biology is to understand how cellular behavior emerges from the interaction in time and space of genetically encoded molecular parts, as well as non-genetically encoded small molecules. Networks provide a natural framework for the organization and quantitative representation of all the available data about molecular interactions. The structural and dynamic properties of molecular networks have been the subject of intense research. Despite major advances, bridging network structure to dynamics – and therefore to behavior – remains challenging. A key concept of modern engineering that recurs in the functional analysis of biological networks is modularity. Most approaches to molecular network analysis rely to some extent on the assumption that molecular networks are modular – that is, they are separable and can be studied to some degree in isolation. We describe recent advances in the analysis of modularity in biological networks, focusing on the increasing realization that a dynamic perspective is essential to grouping molecules into modules and determining their collective function.
The anatomical and functional architecture of the human brain is largely determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and postmitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and human-expanded outer subventricular zones. Both germinal and postmitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in frontal lobe. Finally, many neurodevelopmental disorder and human evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.
Human brain; Transcriptome; Microarray; Development; Gene expression; Evolution
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.
Electronic supplementary material
The online version of this article (doi:10.1186/s13059-014-0474-3) contains supplementary material, which is available to authorized users.
Identification of noncoding drivers from thousands of somatic alterations in a typical tumor is a difficult and unsolved problem. We report a computational framework, FunSeq2, to annotate and prioritize these mutations. The framework combines an adjustable data context integrating large-scale genomics and cancer resources with a streamlined variant-prioritization pipeline. The pipeline has a weighted scoring system combining: inter- and intra-species conservation; loss- and gain-of-function events for transcription-factor binding; enhancer-gene linkages and network centrality; and per-element recurrence across samples. We further highlight putative drivers with information specific to a particular sample, such as differential expression. FunSeq2 is available from funseq2.gersteinlab.org.
Electronic supplementary material
The online version of this article (doi:10.1186/s13059-014-0480-5) contains supplementary material, which is available to authorized users.
Transcription factors (TFs) bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 TFs in 458 ChIP-Seq experiments. We found the combinatorial, co-association of TFs to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the TF binding into a hierarchy and integrated it with other genomic information (e.g. miRNA regulation), forming a dense meta-network. Factors at different levels have different properties: for instance, top-level TFs more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs -- e.g. noise-buffering feed-forward loops. Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (i.e., differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.
Whole-genome sequencing is becoming commonplace, but the accuracy and completeness of variant calling by the most widely used platforms from Illumina and Complete Genomics have not been reported. Here we sequenced the genome of an individual with both technologies to a high average coverage of ~76×, and compared their performance with respect to sequence coverage and calling of single-nucleotide variants (SNVs), insertions and deletions (indels). Although 88.1% of the ~3.7 million unique SNVs were concordant between platforms, there were tens of thousands of platform-specific calls located in genes and other genomic regions. In contrast, 26.5% of indels were concordant between platforms. Target enrichment validated 92.7% of the concordant SNVs, whereas validation by genotyping array revealed a sensitivity of 99.3%. The validation experiments also suggested that >60% of the platform-specific variants were indeed present in the genome. Our results have important implications for understanding the accuracy and completeness of the genome sequencing platforms.
By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.
Sixty years after Watson and Crick published the double helix model of DNA's structure, thirteen members of Genome Biology's Editorial Board select key advances in the field of genome biology subsequent to that discovery.
Interpreting variants, especially noncoding ones, in the increasing
number of personal genomes is challenging. We used patterns of polymorphisms in
functionally annotated regions in 1092 humans to identify deleterious variants;
then we experimentally validated candidates. We analyzed both coding and
noncoding regions, with the former corroborating the latter. We found regions
particularly sensitive to mutations (“ultrasensitive”) and
variants that are disruptive because of mechanistic effects on
transcription-factor binding (that is, “motif-breakers”). We also
found variants in regions with higher network centrality tend to be deleterious.
Insertions and deletions followed a similar pattern to single-nucleotide
variants, with some notable exceptions (e.g., certain deletions and enhancers).
On the basis of these patterns, we developed a computational tool (FunSeq),
whose application to ~90 cancer genomes reveals nearly a hundred
candidate noncoding drivers.
Androgen receptor (AR) signaling plays a critical role in prostate cancer (PCA) pathogenesis. Yet, the regulation of AR signaling remains elusive. Even with stringent androgen deprivation therapy, AR signaling persists. Here, our data suggest that there is a complex interaction between the expression of the tumor suppressor miRNA, miR-31 and AR signaling. We examined primary and metastatic PCA and found that miR-31 expression was reduced as a result of promoter hypermethylation and importantly, the levels of miR-31 expression was inversely correlated with the aggressiveness of the disease. As the expression of AR and miR-31 was inversely correlated in the cell lines, our study further suggested that miR-31 and AR could mutually repress each other. Upregulation of miR-31 effectively suppressed AR expression through multiple mechanisms and inhibited PCA growth in vivo. Notably, we found that miR-31 targeted AR directly at a site located in the coding region, which was commonly mutated in PCA. Additionally, miR-31 suppressed cell cycle regulators, including E2F1, E2F2, EXO1, FOXM1, and MCM2. Together, our findings suggest a novel AR regulatory mechanism mediated through miR-31 expression. The downregulation of miR-31 may disrupt cellular homeostasis and contribute to the evolution and progression of PCA. We provide implications for epigenetic treatment and support clinical development of detecting miR-31 promoter methylation as a novel biomarker.
prostate cancer; androgen receptor; miR-31; DNA hypermethylation; biomarker
Eukaryotic protein kinases are generally classified as being either tyrosine or serine-threonine specific. Though not evident from inspection of their primary sequences, many serine-threonine kinases display a significant preference for serine or threonine as the phosphoacceptor residue. Here we show that a residue located in the kinase activation segment, which we term the “DFG+1” residue, acts as a major determinant for serine-threonine phosphorylation site specificity. Mutation of this residue was sufficient to switch the phosphorylation site preference for multiple kinases, including the serine-specific kinase PAK4 and the threonine-specific kinase MST4. Kinetic analysis of peptide substrate phosphorylation and crystal structures of PAK4-peptide complexes suggested that phosphoacceptor residue preference is not mediated by stronger binding of the favored substrate. Rather, favored kinase-phosphoacceptor combinations likely promote a conformation optimal for catalysis. Understanding the rules governing kinase phosphoacceptor preference allows kinases to be classified as serine or threonine specific based on their sequence.
•A single active site residue can determine kinase phosphoacceptor specificity•Favored and disfavored substrates promote distinct kinase-bound conformations•A simple rule predicts kinase phosphoacceptor preference from its DFG+1 residue
Summary: The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment.
Availability and Implementation: VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org.
Contact: firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic features – transcription factor binding and motif profiles.
Through integrating gene-expression data with ChIP-chip binding and putative binding sites of transcription factors, our method shows high accuracy in discriminating yeast cell cycle regulated genes from non-cell cycle regulated ones. We predict 211 novel cell cycle regulated genes. Our model rediscovers the main cell cycle transcription factors and provides new insights into the regulatory mechanisms. The model also reveals a regulatory circuit mediated by a number of key cell cycle regulators.
Our model suggests that the periodical pattern of cell cycle genes is largely coded in their promoter regions, which can be captured by motif and transcription factor binding data. Cell cycle is controlled by a relatively small number of master transcription factors. The concept of genomic feature based method can be readily extended to human cell cycle process and other transcriptionally regulated processes, such as tissue-specific expression.
Cell cycle regulated genes; Genomic features; Prediction
Next generation exome sequencing (ES) and whole genome sequencing (WGS) are new powerful tools for discovering the gene(s) that underlie Mendelian disorders. To accelerate these discoveries, the National Institutes of Health has established three Centers for Mendelian Genomics (CMGs): the Center for Mendelian Genomics at the University of Washington; the Center for Mendelian Disorders at Yale University; and the Baylor-Johns Hopkins Center for Mendelian Genomics at Baylor College of Medicine and Johns Hopkins University. The CMGs will provide ES/WGS and extensive analysis expertise at no cost to collaborating investigators where the causal gene(s) for a Mendelian phenotype has yet to be uncovered. Over the next few years and in collaboration with the global human genetics community, the CMGs hope to facilitate the identification of the genes underlying a very large fraction of all Mendelian disorders see http://mendelian.org.
mendelian; exome sequencing; commentary
The genetic network involved in the bacterial cell cycle is poorly understood even though it underpins the remarkable ability of bacteria to proliferate. How such network evolves is even less clear. The major aims of this work were to identify and examine the genes and pathways that are differentially expressed during the Caulobacter crescentus cell cycle, and to analyze the evolutionary features of the cell cycle network.
We used deep RNA sequencing to obtain high coverage RNA-Seq data of five C. crescentus cell cycle stages, each with three biological replicates. We found that 1,586 genes (over a third of the genome) display significant differential expression between stages. This gene list, which contains many genes previously unknown for their cell cycle regulation, includes almost half of the genes involved in primary metabolism, suggesting that these “house-keeping” genes are not constitutively transcribed during the cell cycle, as often assumed. Gene and module co-expression clustering reveal co-regulated pathways and suggest functionally coupled genes. In addition, an evolutionary analysis of the cell cycle network shows a high correlation between co-expression and co-evolution. Most co-expression modules have strong phylogenetic signals, with broadly conserved genes and clade-specific genes predominating different substructures of the cell cycle co-expression network. We also found that conserved genes tend to determine the expression profile of their module.
We describe the first phylogenetic and single-nucleotide-resolution transcriptomic analysis of a bacterial cell cycle network. In addition, the study suggests how evolution has shaped this network and provides direct biological network support that selective pressure is not on individual genes but rather on the relationship between genes, which highlights the importance of integrating phylogenetic analysis into biological network studies.
Cell cycle phylogenomics; Caulobacter crescentus; Co-expression network; Functional modules; Selective pressure
The West Nile virus (WNV) is an emerging infection of biodefense concern and there are no available treatments or vaccines. Here we used a high-throughput method based on a novel gene expression analysis, RNA-Seq, to give a global picture of differential gene expression by primary human macrophages of 10 healthy donors infected in vitro with WNV. From a total of 28 million reads per sample, we identified 1,514 transcripts that were differentially expressed after infection. Both predicted and novel gene changes were detected, as were gene isoforms, and while many of the genes were expressed by all donors, some were unique. Knock-down of genes not previously known to be associated with WNV resistance identified their critical role in control of viral infection. Our study distinguishes both common gene pathways as well as novel cellular responses. Such analyses will be valuable for translational studies of susceptible and resistant individuals—and for targeting therapeutics—in multiple biological settings.
anti-viral gene expression; immune response; macrophage; RNA-Seq; West Nile virus
Reprogramming human somatic cells into induced pluripotent stem cells (iPSCs) has been suspected of causing de novo copy number variations (CNVs)1-4. To explore this issue, we performed a whole-genome and transcriptome analysis of 20 human iPSC lines derived from primary skin fibroblasts of 7 individuals using next-generation sequencing. We find that, on average, an iPSC line manifests two CNVs not apparent in the fibroblasts from which the iPSC was derived. Using qPCR, PCR, and digital droplet PCR (ddPCR), we show that at least 50% of those CNVs are present as low frequency somatic genomic variants in parental fibroblasts (i.e. the fibroblasts from which each corresponding hiPSC line is derived) and are manifested in iPSC colonies due to the colonies’ clonal origin. Hence, reprogramming does not necessarily lead to de novo CNVs in iPSC, since most of line-manifested CNVs reflect somatic mosaicism in the human skin. Moreover, our findings demonstrate that clonal expansion, and iPSC lines in particular, can be used as a discovery tool to reliably detect low frequency CNVs in the tissue of origin. Overall, we estimate that approximately 30% of the fibroblast cells have somatic CNVs in their genomes, suggesting widespread somatic mosaicism in the human body. Our study paves the way to understanding the fundamental question of the extent to which cells of the human body normally acquire structural alterations in their DNA post-zygotically.
Eukaryotic cells make many types of primary and processed RNAs that are found either in specific sub-cellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic sub-cellular localizations are also poorly understood. Since RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell’s regulatory capabilities are focused on its synthesis, processing, transport, modifications and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations taken together prompt to a redefinition of the concept of a gene.
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here we present an integrative Personal Omics Profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14-month period. Our iPOP analysis revealed various medical risks, including Type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high coverage genomic and transcriptomic data, which provide the basis of our iPOP, discovered extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and disease states by connecting genomic information with additional dynamic omics activity.
The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called ‘loss-of-function tolerant’, is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified ‘Multinet’ correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.
The number of personal genomes sequenced has grown rapidly over the last few years and is likely to grow further. In order to use the DNA sequence variants amongst individuals for personalized medicine, we need to understand the functional impact of these variants. Deleterious variants in genes can have a wide spectrum of global effects, ranging from fatal for essential genes to no obvious damaging effect for loss-of-function tolerant genes. The global effect of a gene mutation is largely governed by the diverse biological networks in which the gene participates. Since genes participate in many networks, no singular network captures the global picture of gene interactions. Here we integrate the diverse modes of gene interactions (regulatory, genetic, phosphorylation, signaling, metabolic and physical protein-protein interactions) to create a unified biological network. We then exploit the unique properties of loss-of-function tolerant and essential genes in this unified network to build a computational model that can predict global perturbation caused by deleterious mutations in all genes. Our model can distinguish between these two gene sets with high accuracy and we further show that it can be used for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.
Precise identification of RNA-coding regions and transcriptomes of eukaryotes is a significant problem in biology. Currently, eukaryote transcriptomes are analyzed using deep short-read sequencing experiments of complementary DNAs. The resulting short-reads are then aligned against a genome and annotated junctions to infer biological meaning. Here we use long-read complementary DNA datasets for the analysis of a eukaryotic transcriptome and generate two large datasets in the human K562 and HeLa S3 cell lines. Both data sets comprised at least 4 million reads and had median read lengths greater than 500 bp. We show that annotation-independent alignments of these reads provide partial gene structures that are very much in-line with annotated gene structures, 15% of which have not been obtained in a previous de novo analysis of short reads. For long-noncoding RNAs (i.e., lncRNA) genes, however, we find an increased fraction of novel gene structures among our alignments. Other important aspects of transcriptome analysis, such as the description of cell type-specific splicing, can be performed in an accurate, reliable and completely annotation-free manner, making it ideal for the analysis of transcriptomes of newly sequenced genomes. Furthermore, we demonstrate that long read sequence can be assembled into full-length transcripts with considerable success. Our method is applicable to all long read sequencing technologies.
RNA; Roche sequencing; human; splicing; transcriptome
The tumor suppressor Rb/E2F regulates gene expression to control differentiation in multiple tissues during development, although how it directs tissue-specific gene regulation in vivo is poorly understood.
We determined the genome-wide binding profiles for Caenorhabditis elegans Rb/E2F-like components in the germline, in the intestine and broadly throughout the soma, and uncovered highly tissue-specific binding patterns and target genes. Chromatin association by LIN-35, the C. elegans ortholog of Rb, is impaired in the germline but robust in the soma, a characteristic that might govern differential effects on gene expression in the two cell types. In the intestine, LIN-35 and the heterochromatin protein HPL-2, the ortholog of Hp1, coordinately bind at many sites lacking E2F. Finally, selected direct target genes contribute to the soma-to-germline transformation of lin-35 mutants, including mes-4, a soma-specific target that promotes H3K36 methylation, and csr-1, a germline-specific target that functions in a 22G small RNA pathway.
In sum, identification of tissue-specific binding profiles and effector target genes reveals important insights into the mechanisms by which Rb/E2F controls distinct cell fates in vivo.
Higher-order chromosomal organization for transcription regulation is poorly understood in eukaryotes. Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET), we mapped long-range chromatin interactions associated with RNA polymerase II in human cells and uncovered widespread promoter-centered intra-genic, extra-genic and inter-genic interactions. These interactions further aggregated into higher-order clusters, wherein proximal and distal genes were engaged through promoter-promoter interactions. Most genes with promoter-promoter interactions were active and transcribed cooperatively, and some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls. Comparative analyses of different cell lines showed that cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription, and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions. Furthermore, genetically-identified disease-associated non-coding elements were found to be spatially engaged with corresponding genes through long-range interactions. Overall, our study provides insights into the transcription regulation by three-dimensional chromatin interactions for both housekeeping and cell-specific genes in human cells.