Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq.

Cellular control of gene expression is a complex process that is subject to multiple levels of regulation, but ultimately it is the protein produced that determines the biosynthetic state of the cell. One way that a cell can regulate the protein output from each gene is by expressing alternate isoforms with distinct amino acid sequences. These isoforms may exhibit differences in localization and binding interactions that can have profound functional implications.

Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies.

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks.

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.

The reality of pervasive transcription.

Despite recent controversies, the evidence that the majority of the human genome is transcribed into RNA remains strong.

FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data.

We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions.

Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification.

A generic DNA microarray design applicable to any species would greatly benefit comparative genomics. We have addressed the feasibility of such a design by leveraging the great feature densities and relatively unbiased nature of genomic tiling microarrays. Specifically, we first divided each Homo sapiens Refseq-derived gene's spliced nucleotide sequence into all of its possible contiguous 25 nt subsequences.

Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies.

Recent progress in mapping transcription factor (TF) binding regions can largely be credited to chromatin immunoprecipitation (ChIP) technologies. We compared strategies for mapping TF binding regions in mammalian cells using two different ChIP schemes: ChIP with DNA microarray analysis (ChIP-chip) and ChIP with DNA sequencing (ChIP-PET). We first investigated parameters central to obtaining robust ChIP-chip data sets by analyzing STAT1 targets in the ENCODE regions of the human genome, and then compared ChIP-chip to ChIP-PET.

The DART classification of unannotated transcription within the ENCODE regions: associating transcription with known and novel loci.

For the approximately 1% of the human genome in the ENCODE regions, only about half of the transcriptionally active regions (TARs) identified with tiling microarrays correspond to annotated exons. Here we categorize this large amount of "unannotated transcription." We use a number of disparate features to classify the 6988 novel TARs-array expression profiles across cell lines and conditions, sequence composition, phylogenetic profiles (presence/absence of syntenic conservation across 17 species), and locations relative to genes.

What is a gene, post-ENCODE? History and updated definition.

While sequencing of the human genome surprised us with how many protein-coding genes there are, it did not fundamentally change our perspective on what a gene is. In contrast, the complex patterns of dispersed regulation and pervasive transcription uncovered by the ENCODE project, together with non-genic conservation and the abundance of noncoding RNA genes, have challenged the notion of the gene. To illustrate this, we review the evolution of operational definitions of a gene over the past century--from the abstract elements of heredity of Mendel and Morgan to the present-day ORFs enumerated in the sequence databanks.

Assessing the need for sequence-based normalization in tiling microarray experiments.

Motivation: Increases in microarray feature density allow the construction of so-called tiling microarrays. These arrays, or sets of arrays, contain probes targeting regions of sequenced genomes at regular genomic intervals. The unbiased nature of this approach allows for the identification of novel transcribed sequences, the localization of transcription factor binding sites (ChIP-chip), and high resolution comparative genomic hybridization, among other uses.

Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome.

Genomic tiling microarrays have become a popular tool for interrogating the transcriptional activity of large regions of the genome in an unbiased fashion. There are several key parameters associated with each tiling experiment (e.g.

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge.

Motivation: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g.

Extrapolating traditional DNA microarray statistics to tiling and protein microarray technologies.

A credit to microarray technology is its broad application. Two experiments--the tiling microarray experiment and the protein microarray experiment--are exemplars of the versatility of the microarrays. With the technology's expanding list of uses, the corresponding bioinformatics must evolve in step.

Design optimization methods for genomic DNA tiling arrays.

A recent development in microarray research entails the unbiased coverage, or tiling, of genomic DNA for the large-scale identification of transcribed sequences and regulatory elements. A central issue in designing tiling arrays is that of arriving at a single-copy tile path, as significant sequence cross-hybridization can result from the presence of non-unique probes on the array. Due to the fragmentation of genomic DNA caused by the widespread distribution of repetitive elements, the problem of obtaining adequate sequence coverage increases with the sizes of subsequence tiles that are to be included in the design.

Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping.

Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment.

Global identification of human transcribed sequences with genome tiling arrays.

Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue.

Major molecular differences between mammalian sexes are involved in drug metabolism and renal function.

Many anatomical differences exist between males and females; these are manifested on a molecular level by different hormonal environments. Although several molecular differences in adult tissues have been identified, a comprehensive investigation of the gene expression differences between males and females has not been performed. We surveyed the expression patterns of 13,977 mouse genes in male and female hypothalamus, kidney, liver, and reproductive tissues.