Absolute expression values for mouse transcripts: re-annotation of the READ expression database by the use of CAGE and EST sequence tags

Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians

Coral reefs provide habitats for a disproportionate number of marine species relative to the small area of the oceans that they occupy. The mutualism between the cnidarian animal hosts and their intracellular dinoflagellate symbionts provides the nutritional foundation for coral growth and formation of reef structures, because algal photosynthesis can provide >90% of the total energy of the host. Disruption of this symbiosis (“coral bleaching”) is occurring on a large scale due primarily to anthropogenic factors and poses a major threat to the future of coral reefs. Despite the importance of this symbiosis, the cellular mechanisms involved in its establishment, maintenance, and breakdown remain largely unknown. We report our continued development of genomic tools to study these mechanisms in Aiptasia, a small sea anemone with great promise as a model system for studies of cnidarian–dinoflagellate symbiosis. Specifically, we have generated de novo assemblies of the transcriptomes of both a clonal line of symbiotic anemones and their endogenous dinoflagellate symbionts. We then compared transcript abundances in animals with and without dinoflagellates. This analysis identified >900 differentially expressed genes and allowed us to generate testable hypotheses about the cellular functions affected by symbiosis establishment. The differentially regulated transcripts include >60 encoding proteins that may play roles in transporting various nutrients between the symbiotic partners; many more encoding proteins functioning in several metabolic pathways, providing clues regarding how the transported nutrients may be used by the partners; and several encoding proteins that may be involved in host recognition and tolerance of the dinoflagellate.

Detecting expressed genes using CAGE

Cap analysis of gene expression (CAGE) provides accurate high-throughput measurement of RNA expression. By the large-scale analysis of 5' end of transcripts using CAGE method, it enables not only determination of the transcription start site but also prediction of promoter region. Here we provide a protocol for the construction of no-amplification non-tagging CAGE libraries for Illumina next-generation sequencers (nAnT-iCAGE). We have excluded the commonly used PCR amplification and cleavage of restriction enzyme to eliminate any potential biases. As a result, we achieved less biased simple preparation process.

Unamplified cap analysis of gene expression on a single-molecule sequencer

We report the development of a simplified cap analysis of gene expression (CAGE) protocol adapted for single-molecule sequencers that avoids second strand synthesis, ligation, digestion, and PCR. HeliScopeCAGE directly sequences the 3' end of cap trapped first-strand cDNAs. As with previous versions of CAGE, we better define transcription start sites (TSS) than known models, identify novel regions of transcription and alternative promoters, and find two major classes of TSS signal, sharp peaks and broad regions. However, using this protocol, we observe reproducible evidence of regulation at the much finer level of individual TSS positions. The libraries are quantitative over 5 orders of magnitude and highly reproducible (Pearson's correlation coefficient of 0.987). We have also scaled down the sample requirement to 5 μg of total RNA for a standard HeliScopeCAGE library and 100 ng for a low-quantity version. When the same RNA was run as 5-μg and 100-ng versions, the 100 ng was still able to detect expression for ∼60% of the 13,468 loci detected by a 5-μg library using the same threshold, allowing comparative analysis of even rare cell populations. Testing the protocol for differential gene expression measurements on triplicate HeLa and THP-1 samples, we find that the log fold change compared to Illumina microarray measurements is highly correlated (0.871). In addition, HeliScopeCAGE finds differential expression for thousands more loci including those with probes on the array. Finally, although the majority of tags are 5' associated, we also observe a low level of signal on exons that is useful for defining gene structures.

Tissue-specific regulatory network extractor (TS-REX): a database and software resource for the tissue and cell type-specific investigation of transcription factor-gene networks

The prediction of transcription factor binding sites in genomic sequences is in principle very useful to identify upstream regulatory factors. However, when applying this concept to genomes of multicellular organisms such as mammals, one has to deal with a large number of false positive predictions since many transcription factor genes are only expressed in specific tissues or cell types. We developed TS-REX, a database/software system that supports the analysis of tissue and cell type-specific transcription factor-gene networks based on expressed sequence tag abundance of transcription factor-encoding genes in UniGene EST libraries. The use of expression levels of transcription factor-encoding genes according to hierarchical anatomical classifications covering different tissues and cell types makes it possible to filter out irrelevant binding site predictions and to identify candidates of potential functional importance for further experimental testing. TS-REX covers ESTs from H. sapiens and M. musculus, and allows the characterization of both presence and specificity of transcription factors in user-specified tissues or cell types. The software allows users to interactively visualize transcription factor-gene networks, as well as to export data for further processing. TS-REX was applied to predict regulators of Polycomb group genes in six human tumor tissues and in human embryonic stem cells.

Calculation of absolute expression values for DNA microarray data

INTRODUCTIONIn terms of cost per measurement, the use of DNA microarrays for comprehensive and quantitative expression measurements is vastly superior to other methods such as Northern blotting or quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). However, the output values of DNA microarrays are not always highly reliable or accurate compared with other techniques, and the output data sometimes consist of measurements of relative expression (treated sample vs. untreated) rather than absolute expression values as desired. In effect, some measurements from some laboratories do not represent absolute expression values (such as the number of transcripts) and as such are experimentally deficient. To address the problem that some microarray data sets fail to reflect the number of mRNA molecules sufficiently in a given sample (i.e., fail to provide absolute expression levels), additional methods are required. The procedure described here provides a new method for converting microarray data to absolute expression values with the use of external data such as expressed sequence tags (ESTs) and cap analysis of gene expression (CAGE) tags.

Discovering sequence motifs

Sequence motif discovery algorithms are an important part of the computational biologist's toolkit. The purpose of motif discovery is to discover patterns in biopolymer (nucleotide or protein) sequences in order to better understand the structure and function of the molecules the sequences represent. This chapter provides an overview of the use of sequence motif discovery in biology and a general guide to the use of motif discovery algorithms. The chapter discusses the types of biological features that DNA and protein motifs can represent and their usefulness. It also defines what sequence motifs are, how they are represented, and general techniques for discovering them. The primary focus is on one aspect of motif discovery: discovering motifs in a set of unaligned DNA or protein sequences. Also presented are steps useful for checking the biological validity and investigating the function of sequence motifs using methods such as motif scanning--searching for matches to motifs in a given sequence or a database of sequences. A discussion of some limitations of motif discovery concludes the chapter.

Tissue-specific functions based on information content of gene ontology using cap analysis gene expression

Gene expressions differ depending on tissue types and developmental stages. Analyzing how each gene is expressed is thus important. One way of analyzing gene expression patterns is to identify tissue-specific functions. This is useful for understanding how vital activities are performed. DNA microarray has been widely used to observe gene expressions exhaustively. However, comparing the expression value of a gene to that of other genes is impossible, as the gene expression value of a condition is measured as a proportion of that for the same gene under a control condition. We therefore could not determine whether one gene is more expressed than other genes. Cap analysis gene expression (CAGE) allows high-throughput analysis of gene expressions by counting the number of cDNAs of expressed genes. CAGE enables comparison of the expression value of the gene to that of other genes in the same tissue. In this study, we propose a method for exploring tissue-specific functions using data from CAGE. To identify tissue-specificity, one of the simplest ways is to assume that the function of the most expressed gene is regarded as the most tissue-specific. However, the most expressed gene in a tissue might highly express in all tissues, as seen with housekeeping genes. Functions of such genes cannot be tissue-specific. To remove these from consideration, we propose measuring tissue specificity of functions based on information content of gene ontology terms. We applied our method to data from 16 human tissues and 22 mouse tissues. The results from liver and prostate gland indicated that well-known functions of these tissues, such as functions related to signaling and muscle in prostate gland and immune function in liver, displayed high rank.

Large-scale clustering of CAGE tag expression data

BACKGROUND

Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed.

RESULTS

We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups.

CONCLUSION

Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data.

Discovering sequence motifs

Sequence motif discovery algorithms are an important part of the computational biologist's toolkit. The purpose of motif discovery is to discover patterns in biopolymer (nucleotide or protein) sequences to better understand the structure and function of the molecules the sequences represent. This chapter provides an overview of the use of sequence motif discovery in biology and a general guide to the use of motif discovery algorithms. This chapter examines the types of biological features that DNA and protein motifs can represent and their usefulness. This chapter also defines what sequence motifs are, how they are represented, and general techniques for discovering them. The primary focus of the chapter is on one aspect of motif discovery: discovering motifs in a set of unaligned DNA or protein sequences. This chapter also provides the steps useful for checking the biological validity and investigating the function of sequence motifs using methods such as motif scanning-searching for matches to motifs in a given sequence or a database of sequences. A discussion of some limitations of motif discovery concludes the chapter.

Transcriptional network dynamics in macrophage activation

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Genome-wide analysis of mammalian promoter architecture and evolution

Mammalian promoters can be separated into two classes, conserved TATA box-enriched promoters, which initiate at a well-defined site, and more plastic, broad and evolvable CpG-rich promoters. We have sequenced tags corresponding to several hundred thousand transcription start sites (TSSs) in the mouse and human genomes, allowing precise analysis of the sequence architecture and evolution of distinct promoter classes. Different tissues and families of genes differentially use distinct types of promoters. Our tagging methods allow quantitative analysis of promoter usage in different tissues and show that differentially regulated alternative TSSs are a common feature in protein-coding genes and commonly generate alternative N termini. Among the TSSs, we identified new start sites associated with the majority of exons and with 3' UTRs. These data permit genome-scale identification of tissue-specific promoters and analysis of the cis-acting elements associated with them.

A method for similarity search of genomic positional expression using CAGE

With the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. Moreover, in several cases, their transcripts, which control epigenetic transcription via processes such as transcriptional interference and genomic imprinting, occur in clusters. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns and that the characteristics of expression in the same genomic regions differ depending on tissue type and developmental stage. In this study, we analyzed gene expression patterns using the cap analysis gene expression (CAGE) method for exploring systematic views of the mouse transcriptome. Counting the number of mapped CAGE tags for fixed-length regions allowed us to determine genomic expression levels. These expression levels were normalized, quantified, and converted into four types of descriptors, allowing the expression patterns along the genome to be represented by character strings. We analyzed them using dynamic programming in the same manner as for sequence analysis. We have developed a novel algorithm that provides a novel view of the genome from the perspective of genomic positional expression. In a similarity search of expression patterns across chromosomes and tissues, we found regions that had clusters of genes that showed expression patterns similar to each other depending on tissue type. Our results suggest the possibility that the regions that have sense–antisense transcription show similar expression patterns between forward and reverse strands.

Through the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns. In this study, the authors analyzed gene expression patterns using the computational algorithm of similarity search for exploring systematic views of the mouse transcriptome. They found regions that had clusters of highly expressed genes in certain tissue types whose expression patterns showed strong similarity to each other. This work aims to provide additional insight into genome-wide mechanisms of transcription.

Tag-based approaches for transcriptome research and genome annotation

With the increasing number of whole genome sequences available, genomic research has shifted toward the annotation of functional elements and transcribed regions. Thus, the related field of transcriptome research requires accurate methods for the profiling of genes that are not biased by known sequence information, and that also allow for the identification of promoter regions. Starting with serial analysis of gene expression (SAGE), methods making use of short sequencing tags have greatly contributed to transcriptome studies. Here we review recent developments in the use of short sequencing tags in expression profiling, gene discovery and genome annotation. These tags are obtained from the 5' end of mRNAs, both terminal ends of mRNAs, or genomic regions. The 5' end-specific tags, with their ability to identify transcripts along with their transcriptional start sites, will be of particular interest for gene network studies and may become one of the most important approaches in systems biology.

Development of a spot reliability evaluation score for DNA microarrays

We developed a reliability index named SRED (Spot Reliability Evaluation Score for DNA microarrays) that represents the probability that the calibrated gene expression level from a DNA microarray would be less than a factor of 2 different from that of quantitative real-time polymerase chain reaction assays whose dynamic quantification range is treated statistically to be similar to that of the DNA microarray. To define the SRED score, two parameters, the reproducibility of measurement value and the relative expression value were selected from nine candidate parameters. The SRED score supplies the probability that the expression level in each spot of a microarray is less than a certain-fold different compared to other expression profiling data, such as QRT-PCR. This score was applied to approximately 1,500,000 points of the expression profile in the RIKEN Expression Array Database.