Studying chromosome-wide transcriptional networks: new insights into disease?

A large amount of experimental data collected over the last decade has shown that genomic organization is very complex and has highlighted the fact that the current set of gene annotations does not fully capture this complexity. Much of the RNA detected in a cell is found to originate from outside the exons of annotated genes. Exons of annotated and unannotated transcripts separated by large genomic distances can be joined together in chimeric transcripts. Any given base-pair in a genome could be traversed by many protein-coding and non-coding RNAs. We discuss the implications of these effects for our understanding of disease

From transcription start site to cell biology

The regulation of transcription is a complex process. Recent novel insights concerning the in vivo regulation and expression of protein-coding and non-coding RNAs have added previously unimagined levels of complexity to these processes

Dark Matter RNA: Existence, Function, and Controversy

The mysteries surrounding the ∼97–98% of the human genome that does not encode proteins have long captivated imagination of scientists. Does the protein-coding, 2–3% of the genome carry the 97–98% as a mere passenger and neutral “cargo” on the evolutionary path, or does the latter have biological function? On one side of the debate, many commentaries have referred to the non-coding portion of the genome as “selfish” or “junk” DNA (Orgel and Crick, 1980), while on the other side, authors have argued that it contains the real blueprint for organismal development (Penman, 1995; Mattick, 2003), and the mechanisms of developmental complexity. Thus, this question could be referred to without much exaggeration as the most important issue in genetics today

Dark matter RNA: an intelligent scaffold for the dynamic regulation of the nuclear information landscape

Perhaps no other topic in contemporary genomics has inspired such diverse viewpoints as the 95+% of the genome, previously known as “junk DNA,” that does not code for proteins. Here, we present a theory in which dark matter RNA plays a role in the generation of a landscape of spatial micro-domains coupled to the information signaling matrix of the nuclear landscape. Within and between these micro-domains, dark matter RNAs additionally function to tether RNA interacting proteins and complexes of many different types, and by doing so, allow for a higher performance of the various processes requiring them at ultra-fast rates. This improves signal to noise characteristics of RNA processing, trafficking, and epigenetic signaling, where competition and differential RNA binding among proteins drives the computational decisions inherent in regulatory events

Differential analysis for high density tiling microarray data

Background: High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. Results: We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. Conclusion: The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003 it is most significant at the 5' end of genes, at a p-value < 10^{-13}. The prototype R code has been made available as supplementary material [see Additional file 1]

High resolution transcriptome maps for wild-type and nonsense-mediated decay-defective Caenorhabditis elegans

The high-resolution transcriptome of wild-type and nonsense-mediated decay (NMD) defective C. elegans during development reveals insights into the NMD pathway and it’s role in development

Microarray-based DNA methylation profiling: technology and applications

This work is dedicated to the development of a technology for unbiased, high-throughput DNA methylation profiling of large genomic regions. In this method, unmethylated and methylated DNA fractions are enriched using a series of treatments with methylation sensitive restriction enzymes, and interrogated on microarrays. We have investigated various aspects of the technology including its replicability, informativeness, sensitivity and optimal PCR conditions using microarrays containing oligonucleotides representing 100 kb of genomic DNA derived from the chromosome 22 COMT region in addition to 12 192 element CpG island microarrays. Several new aspects of methylation profiling are provided, including the parallel identification of confounding effects of DNA sequence variation, the description of the principles of microarray design for epigenomic studies and the optimal choice of methylation sensitive restriction enzymes. We also demonstrate the advantages of using the unmethylated DNA fraction versus the methylated one, which substantially improve the chances of detecting DNA methylation differences. We applied this methodology for fine-mapping of methylation patterns of chromosomes 21 and 22 in eight individuals using tiling microarrays consisting of over 340 000 oligonucleotide probe pairs. The principles developed in this work will help to make epigenetic profiling of the entire human genome a routine procedure

Intronic RNAs constitute the major fraction of the non-coding RNA in mammalian cells

Background The function of RNA from the non-coding (the so called “dark matter”) regions of the genome has been a subject of considerable recent debate. Perhaps the most controversy is regarding the function of RNAs found in introns of annotated transcripts, where most of the reads that map outside of exons are usually found. However, it has been reported that the levels of RNA in introns are minor relative to those of the corresponding exons, and that changes in the levels of intronic RNAs correlate tightly with that of adjacent exons. This would suggest that RNAs produced from the vast expanse of intronic space are just pieces of pre-mRNAs or excised introns en route to degradation. Results We present data that challenges the notion that intronic RNAs are mere by-standers in the cell. By performing a highly quantitative RNAseq analysis of transcriptome changes during an inflammation time course, we show that intronic RNAs have a number of features that would be expected from functional, standalone RNA species. We show that there are thousands of introns in the mouse genome that generate RNAs whose overall abundance, which changes throughout the inflammation timecourse, and other properties suggest that they function in yet unknown ways. Conclusions So far, the focus of non-coding RNA discovery has shied away from intronic regions as those were believed to simply encode parts of pre-mRNAs. Results presented here suggest a very different situation – the sequences encoded in the introns appear to harbor a yet unexplored reservoir of novel, functional RNAs. As such, they should not be ignored in surveys of functional transcripts or other genomic studies