The hippocampal expression profiles of wild-type mice and mice transgenic for C-doublecortin-like kinase were compared with Solexa/Illumina deep sequencing technology and five different microarray platforms. With Illumina's digital gene expression assay, we obtained 2.4 million sequence tags per sample, their abundance spanning four orders of magnitude. Results were highly reproducible, even across laboratories. With a dedicated Bayesian model, we found differential expression of 3179 transcripts with an estimated false-discovery rate of 8.5%. This is a much higher figure than found for microarrays. The overlap in differentially expressed transcripts found with deep sequencing and microarrays was most significant for Affymetrix. The changes in expression observed by deep sequencing were larger than observed by microarrays or quantitative PCR. Relevant processes such as calmodulin-dependent protein kinase activity and vesicle transport along microtubules were found affected by deep sequencing but not by microarrays. While undetectable by microarrays, antisense transcription was found for 51% of all genes and alternative polyadenylation for 47%. We conclude that deep sequencing provides a major advance in robustness, comparability and richness of expression profiling data and is expected to boost collaborative, comparative and integrative genomics studies.

Motivation: Next generation sequencing technologies open exciting new possibilities for genome and transcriptome sequencing. While reads produced by these technologies are relatively short and error prone compared to the Sanger method their throughput is several magnitudes higher. To utilize such reads for transcriptome sequencing and gene structure identification, one needs to be able to accurately align the sequence reads over intron boundaries. This represents a significant challenge given their short length and inherent high error rate. Results: We present a novel approach, called QPALMA, for computing accurate spliced alignments which takes advantage of the read's quality information as well as computational splice site predictions. Our method uses a training set of spliced reads with quality information and known alignments. It uses a large margin approach similar to support vector machines to estimate its parameters to maximize alignment accuracy. In computational experiments, we illustrate that the quality information as well as the splice site predictions help to improve the alignment quality. Finally, to facilitate mapping of massive amounts of sequencing data typically generated by the new technologies, we have combined our method with a fast mapping pipeline based on enhanced suffix arrays. Our algorithms were optimized and tested using reads produced with the Illumina Genome Analyzer for the model plant Arabidopsis thaliana. Availability: Datasets for training and evaluation, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/qpalma.

Background Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects. Results XplorSeq is a software package that facilitates the compilation, management and phylogenetic analysis of DNA sequences. XplorSeq was developed for high-throughput analysis of environmental rRNA gene sequences, but can be used for any sequencing project. XplorSeq integrates and extends several commonly used UNIX-based analysis tools by use of a Macintosh OS-X-based graphical user interface (GUI). Through this GUI, users may perform basic sequence import and assembly steps (base-calling, vector/primer trimming, contig assembly), perform BLAST (Basic Local Alignment and Search Tool) searches of NCBI and local databases, create multiple sequence alignments, build phylogenetic trees, assemble Operational Taxonomic Units, estimate biodiversity indices, and summarize data in a variety of formats. Furthermore, sequences may be annotated with user-specified meta-data, which then can be used to sort data and organize analyses and reports. A document-based architecture permits parallel analysis of sequence data from multiple clones, with sequences and other data stored in a single file. Conclusions XplorSeq will benefit researchers who are engaged in analyses of environmental sequence data. Although XplorSeq was developed for management of rDNA sequence data, it can be applied to any sequencing project. The application is available free of charge for non-commercial use at http://vent.colorado.edu/phyloware.

Differences in gene expression underlie central questions in plant biology extending from gene function to evolutionary mechanisms and quantitative traits. However, resolving expression of closely related genes (e.g. alleles and gene family members) is challenging on a genome-wide scale due to extensive sequence similarity and frequently incomplete genome sequence data. We present a new expression-profiling strategy that utilizes long-read, high-throughput sequencing to capture the information-rich 3'-untranslated region (UTR) of messenger RNAs (mRNAs). Resulting sequences resolve gene-specific transcripts independent of a sequenced genome. Analysis of approximately 229,000 3'-anchored sequences from maize (Zea mays) ovaries identified 14,822 unique transcripts represented by at least two sequence reads. Total RNA from ovaries of drought-stressed wild-type and viviparous-1 mutant plants was used to construct a multiplex cDNA library. Each sample was labeled by incorporating one of 16 unique three-base key codes into the 3'-cDNA fragments, and combined samples were sequenced using a GS 20 454 instrument. Transcript abundance was quantified by frequency of sequences identifying each unique mRNA. At least 202 unique transcripts showed highly significant differences in abundance between wild-type and mutant samples. For a subset of mRNAs, quantitative differences were validated by real-time reverse transcription-polymerase chain reaction. The 3'-UTR profile resolved 12 unique cellulose synthase (CesA) transcripts in maize ovaries and identified previously uncharacterized members of a histone H1 gene family. In addition, this method resolved nearly identical paralogs, as illustrated by two auxin-repressed, dormancy-associated (Arda) transcripts, which showed reciprocal mRNA abundance in wild-type and mutant samples. Our results demonstrate the potential of 3'-UTR profiling for resolving gene- and allele-specific transcripts.

Massively parallel sequencing of DNA by pyrosequencing technology offers much higher throughput and lower cost than conventional Sanger sequencing. Although extensively used already for sequencing of genomes, relatively few applications of massively parallel pyrosequencing to transcriptome analysis have been reported. To test the ability of this technology to provide unbiased representation of transcripts, we analyzed mRNA from Arabidopsis (Arabidopsis thaliana) seedlings. Two sequencing runs yielded 541,852 expressed sequence tags (ESTs) after quality control. Mapping of the ESTs to the Arabidopsis genome and to The Arabidopsis Information Resource 7.0 cDNA models indicated: (1) massively parallel pyrosequencing detected transcription of 17,449 gene loci providing very deep coverage of the transcriptome. Performing a second sequencing run only increased the number of genes identified by 10%, but increased the overall sequence coverage by 50%. (2) Mapping of the ESTs to their predicted full-length transcripts indicated that all regions of the transcript were well represented regardless of transcript length or expression level. Furthermore, short, medium, and long transcripts were equally represented. (3) Over 16,000 of the ESTs that mapped to the genome were not represented in the existing dbEST database. In some cases, the ESTs provide the first experimental evidence for transcripts derived from predicted genes, and, for at least 60 locations in the genome, pyrosequencing identified likely protein-coding sequences that are not now annotated as genes. Together, the results indicate massively parallel pyrosequencing provides novel information helpful to improve the annotation of the Arabidopsis genome. Furthermore, the unbiased representation of transcripts will be particularly useful for gene discovery and gene expression analysis of nonmodel plants with less complete genomic information.

Massively parallel sequencing holds great promise for expression profiling, as it combines the high throughput of SAGE with the accuracy of EST sequencing. Nevertheless, until now only very limited information had been available on the suitability of the current technology to meet the requirements. Here, we evaluate the potential of 454 sequencing technology for expression profiling using Drosophila melanogaster. We show that short (< 80 bp) and long (> 300–400 bp) cDNA fragments are under-represented in 454 sequence reads. Nevertheless, sequencing of 3' cDNA fragments generated by nebulization could be used to overcome the length bias of the 454 sequencing technology. Gene expression measurements generated by restriction analysis and nebulization for fragments within the 80- to 300-bp range showed correlations similar to those reported for replicated microarray experiments (0.83–0.91); 97% of the cDNA fragments could be unambiguously mapped to the genomic DNA, demonstrating the advantage of longer sequence reads. Our analyses suggest that the 454 technology has a large potential for expression profiling, and the high mapping accuracy indicates that it should be possible to compare expression profiles across species.

Massively parallel sequencing instruments enable rapid and inexpensive DNA sequence data production. Because these instruments are new, their data require characterization with respect to accuracy and utility. To address this, we sequenced a Caernohabditis elegans N2 Bristol strain isolate using the Solexa Sequence Analyzer, and compared the reads to the reference genome to characterize the data and to evaluate coverage and representation. Massively parallel sequencing facilitates strain-to-reference comparison for genome-wide sequence variant discovery. Owing to the short-read-length sequences produced, we developed a revised approach to determine the regions of the genome to which short reads could be uniquely mapped. We then aligned Solexa reads from C. elegans strain CB4858 to the reference, and screened for single-nucleotide polymorphisms (SNPs) and small indels. This study demonstrates the utility of massively parallel short read sequencing for whole genome resequencing and for accurate discovery of genome-wide polymorphisms.

We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 105 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.

Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.

Background "Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression), LongSAGE and MPSS (Massively Parallel Signature Sequencing) are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered. Results In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method. Conclusion We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.