Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression.

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.

VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications.

Summary: VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant.

Decoding neuroproteomics: integrating the genome, translatome and functional anatomy.

The immense intercellular and intracellular heterogeneity of the CNS presents major challenges for high-throughput omic analyses. Transcriptional, translational and post-translational regulatory events are localized to specific neuronal cell types or subcellular compartments, resulting in discrete patterns of protein expression and activity. A spatial and quantitative knowledge of the neuroproteome is therefore critical to understanding both normal and pathological aspects of the functional genomics and anatomy of the CNS.

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 atmusic.gersteinlab.org.

FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer.

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.

Characterization of stress-responsive lncRNAs in Arabidopsis thaliana by integrating expression, epigenetic and structural features.

Recently, in addition to poly(A)+ long non-coding RNAs (lncRNAs), many lncRNAs without poly(A) tails, have been characterized in mammals. However, the non-polyA lncRNAs and their conserved motifs, especially those associated with environmental stresses, have not been fully investigated in plant genomes. We performed poly(A)- RNA-seq for seedlings of Arabidopsis thaliana under four stress conditions, and predicted lncRNA transcripts.

OrthoClust: an orthology-based network framework for clustering data across multiple species.

Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species.

Comparative analysis of regulatory information and circuits across distant species.

Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.

Comparative analysis of the transcriptome across distant species.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly.

Comparative analysis of pseudogenes across three phyla.

Pseudogenes are degraded fossil copies of genes. Here, we report a comparison of pseudogenes spanning three phyla, leveraging the completed annotations of the human, worm, and fly genomes, which we make available as an online resource. We find that pseudogenes are lineage specific, much more so than protein-coding genes, reflecting the different remodeling processes marking each organism's genome evolution.

Defining functional DNA elements in the human genome.

With the completion of the human genome sequence, attention turned to identifying and annotating its functional DNA elements. As a complement to genetic and comparative genomics approaches, the Encyclopedia of DNA Elements Project was launched to contribute maps of RNA transcripts, transcriptional regulator binding sites, and chromatin states in many cell types. The resulting genome-wide data reveal sites of biochemical activity with high positional resolution and cell type specificity that facilitate studies of gene regulation and interpretation of noncoding variants associated with human disease.

Transcriptional landscape of the prenatal human brain.

The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the 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 post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes.

Identification of a major determinant for serine-threonine kinase phosphoacceptor specificity.

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.

Integrative annotation of variants from 1092 humans: application to cancer genomics.

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.

Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division.

In primates and other animals, reverse transcription of mRNA followed by genomic integration creates retroduplications. Expressed retroduplications are either "retrogenes" coding for functioning proteins, or expressed "processed pseudogenes," which can function as noncoding RNAs. To date, little is known about the variation in retroduplications in terms of their presence or absence across individuals in the human population.

Identification of yeast cell cycle regulated genes based on genomic features.

Background: 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.

Identification of genes critical for resistance to infection by West Nile virus using RNA-Seq analysis.

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.

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution.

Background: 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.

Machine learning and genome annotation: a match meant to be?

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 of genome biology.

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.

Interpretation of genomic variants using a unified biological network approach.

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.

The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes.

Short insertions and deletions (indels) are the second most abundant form of human genetic variation, but our understanding of their origins and functional effects lags behind that of other types of variants. Using population-scale sequencing, we have identified a high-quality set of 1.6 million indels from 179 individuals representing three diverse human populations.

Accurate identification and analysis of human mRNA isoforms using deep long read sequencing.

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.