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The present study focused upon comprehensive analysis of the expression of stress-responsive genes at different time points in two indica rice varieties, IR-64 (moderately salt-sensitive) and Nonabokra (salt-tolerant), under salinity, dehydration and cold, and exogenous ABA treatment. The massively parallel signature sequencing and publicly-available microarray data indicated the physiological role of the concerned genes in different tissues. Gene expression through semi-quantitative reverse transcriptase-polymerase chain reaction followed by factorial analyses of variance identified that members of the antioxidant genes (OsCAT, OsSOD, OsGR2 and OsAPX8) showed early response and higher expression under different abiotic stresses in IR-64. The TFs and LEA family (TRAB1 and Osem), osmolyte (OsBADH1, OsP5CS) and polyamine (SAMDC) metabolic genes were more up regulated in the salt-tolerant cultivar, whereas the proline (ProDH) and polyamine (DAO and PAO) catabolic genes were significantly up regulated in the salt-sensitive one. The higher accumulation of TRAB1 transcript and protein in the tolerant cultivar during various stresses suggested it as an important component in stress signaling pathway, which participates in mediating cross-talk signaling between diverse metabolic pathways to withstand various forms of abiotic stress. Heat map data further corroborated differential transcript abundance in the two varieties, coinciding with the transcript profiling data. We envisage that the identified genes in this study, linked with tolerance against various abiotic stress factors, are the chief candidates involved in regulating the complexity of multiple stress responses in rice. The finding of the candidate genes in this study would be helpful in generating multiple abiotic stress tolerance in rice.

Rice growth and development is highly affected by the excessive concentration of the heavy metal cadmium (Cd). To elucidate the molecular basis of Cd tolerance, we carried out comprehensive transcriptome profiling of diverse groups of genes like those encoding antioxidative enzymes (APX, CAT, SOD, and GR), osmolyte-regulatory enzymes (P5CS, PDH, and BADH1), polyamine-regulatory enzymes (SAMDC, SPDS, SPMS, and DAO), transcription factors (TRAB-1 and WRKY-71), Osem and RbcS from leaf and root tissues under cadmium chloride (1.5 mM) stress at different time points (6, 12, and 24 h) of exposure in two indica rice varieties, that is, IR-64 (sensitive) and Nonabokra (tolerant). Analyses of massively parallel signature sequencing (MPSS) and publicly available microarray databases indicated the physiological role of the concerned genes and possible cross-talk between Cd and other abiotic stresses. Moreover, the pattern of expression, for a particular gene in leaves, varied in most cases from that in roots, suggesting a tissue-specific response. The higher accumulation of TRAB-1 transcript and protein in the tolerant cultivar Nonabokra in response to Cd stress suggested a role for TRAB-1 as a nodal component in Cd signaling pathway. Overall, the transcriptome analyses undertaken in the present study indicated that the genes, belonging to different functional classes and conferring Cd tolerance, were differentially regulated and mostly overrepresented under stress situations, thus providing novel insight into the functional basis of Cd tolerance in rice varieties and the importance of cross-talk of different signaling pathways. The present work will also pave the way in the future to select gene(s) for overexpression, so that rice can better tolerate Cd stress.

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp, while it failed for datasets with

Single nucleus RNA-seq (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-seq, massively parallel sNuc-Seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Analyses of gene expressions in single cells are important for understanding detailed biological phenomena. Here, a highly sensitive and accurate method by sequencing (called “bead-seq”) to obtain a whole gene expression profile for a single cell is proposed. A key feature of the method is to use a complementary DNA (cDNA) library on magnetic beads, which enables adding washing steps to remove residual reagents in a sample preparation process. By adding the washing steps, the next steps can be carried out under the optimal conditions without losing cDNAs. Error sources were carefully evaluated to conclude that the first several steps were the key steps. It is demonstrated that bead-seq is superior to the conventional methods for single-cell gene expression analyses in terms of reproducibility, quantitative accuracy, and biases caused during sample preparation and sequencing processes.

14-3-3 isoforms were relatively less conserved at the C-terminal region across plant groups. Both Os 14-3-3f and Os 14-3-3g were inducible with differential gene expression levels under different abiotic stress and developmental stages in sensitive and tolerant indica rice cultivars as confirmed both at transcript and protein level. Plant 14-3-3s has been well characterized to function in several signaling pathways, biotic as well as abiotic stress and nutrient metabolism. We attempted comprehensive analysis of 14-3-3 genes in different plant lineages such as green algae (Chlamydomonas reinhardtii), moss (Physcomitrella patens) and lycophyte (Selaginella moellendorffii), dicot Arabidopsis thaliana and monocot Oryza sativa sub sp. japonica at the gene and protein level. Sequence alignment results revealed that 14-3-3 isoforms were evolutionarily conserved across all taxa with variable C-terminal end. Phylogenetic analysis indicated that the majority of 14-3-3 isoforms in rice belong to the non-epsilon group that clustered separately from the dicot group. Segmental duplication event played a significant role in the expansion of both, Arabidopsis and rice, 14-3-3 isoforms as revealed by synteny studies. In silico gene expression using Massive Parallel Signature Sequencing and microarray analysis revealed that 14-3-3 isoforms have variable expression in different tissue types and under different abiotic stress regime in Arabidopsis and japonica rice. Both, semi-quantitative and qPCR results, confirmed that Os14-3-3f and Os14-3-3g were inducible under abiotic stress in lamina and roots of indica rice and relatively higher under salinity and cold stress in Nonabokra, under dehydration stress in N-22 and under exogenous ABA in IR-29 usually after 3–6 h of treatment. Both, 14-3-3f and 14-3-3g, were highly expressed in flag leaves, stems and panicles and mature roots. These results were further confirmed by immunoblot analysis of rice cultivars using Os14-3-3f antibody generated from recombinant Os14-3-3f protein. The results provide the first comprehensive report of Os14-3-3 gene expression in indica rice cultivars which differ in tolerance to abiotic stress that might be useful for further research.

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

Motivation: RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. Results: We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies. Availability and implementation: The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403. Contact: kpwhite@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.

MicroRNA (miRNA) are highly conserved, noncoding small RNA involved in post-transcriptional gene regulation in a variety of biological processes. To elucidate roles of miRNA in bovine muscle type specification and maintenance, we sought to determine differentially expressed miRNA between semitendinosus (STD) and masseter (MS) muscles from 3 Japanese black cattle by massively parallel sequencing. Differential gene expression of myosin heavy chain (MyHC) isoforms confirmed that STD and MS were MyHC-2x- and MyHC-1-abundant muscles, respectively. In total, 192 known miRNA and 20 potential new bovine miRNA were obtained from the sequencing. The differentially expressed miRNA with more than 2-fold difference in each muscle were identified. In particular, miR-196a and miR-885 were exclusively expressed in STD muscle, which was validated by quantitative reverse transcription-PCR (P=0.045 and P

The calcium (Ca(2+)) transporters, like Ca(2+) channels, Ca(2+) ATPases, and Ca(2+) exchangers, are instrumental for signaling and transport. However, the mechanism by which they orchestrate the accumulation of Ca(2+) in grain filling has not yet been investigated. Hence the present study was designed to identify the potential calcium transporter genes that may be responsible for the spatial accumulation of calcium during grain filling. In silico expression analyses were performed to identify Ca(2+) transporters that predominantly express during the different developmental stages of Oryza sativa. A total of 13 unique calcium transporters (7 from massively parallel signature sequencing [MPSS] data analysis, and 9 from microarray analysis) were identified. Analysis of variance (ANOVA) revealed differential expression of the transporters across tissues, and principal component analysis (PCA) exhibited their seed-specific distinctive expression profile. Interestingly, Ca(2+) exchanger genes are highly expressed in the initial stages, whereas some Ca(2+) ATPase genes are highly expressed throughout seed development. Furthermore, analysis of the cis-elements located in the promoter region of the subset of 13 genes suggested that D of proteins play essential roles in regulating the expression of Ca(2+) transporter genes during rice seed development. Based on these results, we developed a hypothetical model explaining the transport and tissue specific distribution of calcium in developing cereal seeds. The model may be extrapolated to understand the mechanism behind the exceptionally high level of calcium accumulation seen in grains like finger millet.

QuantSeq provides an easy protocol to generate highly strand-specific next-generation sequencing (NGS) libraries close to the 3′ end of polyadenylated RNAs within 4.5 h. Only one fragment per transcript is generated, directly linking the number of reads mapping to a gene to its expression. QuantSeq reduces data analysis time and enables a higher level of multiplexing per run. QuantSeq is the RNA sample preparation method for accurate and affordable gene expression measurement.

Differential gene expression analysis has been conventionally performed by microarray techniques; however with the recent advent of next-generation sequencing (NGS) approaches, it has become easier to analyze the coding as well as the noncoding components. Additionally, NGS data analysis also provides information regarding the expression changes of specific isoforms. There are several bioinformatics tools available to analyze NGS data but with different parameters. This chapter provides a comparative insight into these tools by utilizing NGS datasets available from Wt1 knockout and embryonic stem cell line model.

RNA sequencing has increasingly become an indispensable tool for biological research. While sequencing costs have fallen dramatically in recent years, the current cost of RNA sequencing, nonetheless, remains a barrier to even more widespread adoption. Here, we present a simple RNA sequencing protocol with substantially reduced costs. This protocol uses as little as 10 ng of total RNA, allows multiplex sequencing of up to 96 samples per lane and is strand specific. Extensive validation using human embryonic stem cells showed high consistency between technical replicates at various multiplexing levels.

We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.

Large-scale sequencing of short mRNA-derived tags can establish the qualitative and quantitative characteristics of a complex transcriptome. We sequenced 12,304,362 tags from five diverse libraries of Arabidopsis thaliana using massively parallel signature sequencing (MPSS). A total of 48,572 distinct signatures, each representing a different transcript, were expressed at significant levels. These signatures were compared to the annotation of the A. thaliana genomic sequence; in the five libraries, this comparison yielded between 17,353 and 18,361 genes with sense expression, and between 5,487 and 8,729 genes with antisense expression. An additional 6,691 MPSS signatures mapped to unannotated regions of the genome. Expression was demonstrated for 1,168 genes for which expression data were previously unknown. Alternative polyadenylation was observed for more than 25% of A. thaliana genes transcribed in these libraries. The MPSS expression data suggest that the A. thaliana transcriptome is complex and contains many as-yet uncharacterized variants of normal coding transcripts.

Whereas information is rapidly accumulating about the structure and position of genes encoded in the human genome, less is known about the complexity and relative abundance of their expression in individual human cells and tissues. Here, we describe the characteristics of the transcriptomes of two cultured cell lines, HB4a (normal breast epithelium) and HCT-116 (colon adenocarcinoma), using massively parallel signature sequencing (MPSS). We generated in excess of 10 7 short signature sequences per cell line, thus providing a comprehensive snapshot of gene expression, within the technical limitations of the method. The number of genes expressed at one copy per cell or more in either of the lines was estimated to be between 10,000 and 15,000. The vast majority of the transcripts found in these cells can be mapped to known genes and their polyadenylation variants. Among the genes that could be identified from their signature sequences, ≈8,500 were expressed by both cell lines, whereas 6,000 showed cellular specificity. Taking into account sequence tags that map uniquely to the genome but not to known transcripts, overall the data are consistent with an upper limit of 17,000 for the total number of genes expressed at more than one copy per cell in one or both of the two cell lines examined.

Over the past 15 years, global analysis of mRNA expression has emerged as a powerful strategy for biological discovery. Using the power of parallel processing, robotics, and computer-based informatics, a number of high-throughput methods have been devised. These include DNA microarrays, serial analysis of gene expression, quantitative RT-PCR, differential-display RT-PCR, and massively parallel signature sequencing. Each of these methods has inherent advantages and disadvantages, often related to expense, technical difficulty, specificity, and reliability. Further, the ability to generate large data sets of gene expression has led to new challenges in bioinformatics. Nonetheless, this technological revolution is transforming disease classification, gene discovery, and our understanding of regulatory gene networks.

Background Xylogen, a chimeric arabinogalactan protein containing a non-specific lipid transfer protein domain, can promote xylem cell differentiation. No comprehensive study has been carried out on the XYLP gene family in rice. As a first step in research on this gene family and as a useful strategy in general, a genome-wide analysis of the OsXYLP gene family is thus needed. Results In this study, we identified 21 XYLP genes from the rice genome and comprehensively analyzed their protein structures, phylogenetic relationships, chromosomal locations, and gene duplication status. Our results indicate that gene duplication has played major roles in the expansion of the OsXYLP gene family. We used expressed sequence tag, microarray, massively parallel signature sequencing, and quantitative real-time PCR data to analyze OsXYLP gene expression during various developmental stages and under abiotic stress conditions. We found that many OsXYLP genes are abundantly expressed in vascular tissues and seeds, with some genes regulated under hormonal or abiotic stresses. In addition, we identified knockout mutants of OsXYLP7 and OsXYLP16 and discovered that the mutant xylp7 has a defect in stem height. Conclusions We analyzed expression profiles of 21 XYLP genes and characterized the structures and evolutionary relationships of their proteins. Our results demonstrate that the rice XYLP gene family may play roles in plant vascular system development and hormone signaling. Among the 21 detected OsXYLPs, 19 are newly identified genes encoding arabinogalactan proteins. Our results provide comprehensive insights that will assist future research on the biological functions of the rice XYLP gene family. Electronic supplementary material The online version of this article (doi:10.1186/s12870-014-0299-y) contains supplementary material, which is available to authorized users.

Motivation: It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA–mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. Results: The objective of this study was to modify our widely recognized Web server for the integrated mRNA–miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy. Availability and implementation: http://epigenomics.snu.ac.kr/biovlab_mmia_ngs/ Contact: sunkim.bioinfo@snu.ac.kr , heechae@cs.indiana.edu

To identify the genes responsible for yield related traits, and heterosis, massively parallel signature sequencing (MPSS) libraries were constructed from leaves, roots and meristem tissues from the two parents, 'Nipponbare' and '93-11', and their F1 hybrid. From the MPSS libraries, 1-3 million signatures were obtained. Using cluster analysis, commonly and specifically expressed genes in the parents and their F1 hybrid were identified. To understand heterosis in the F1 hybrid, the differentially expressed genes in the F1 hybrid were mapped to yield related quantitative trait loci (QTL) regions using a linkage map constructed from 131 polymorphic simple sequence repeat markers with 266 recombinant inbred lines derived from a cross between Nipponbare and 93-11. QTLs were identified for yield related traits including days to heading, plant height, plant type, number of tillers, main panicle length, number of primary branches per main panicle, number of kernels per main panicle, total kernel weight per main panicle, 1000 grain weight and total grain yield per plant. Seventy one QTLs for these traits were mapped, of which 3 QTLs were novel. Many highly expressed chromatin-related genes in the F1 hybrid encoding histone demethylases, histone deacetylases, argonaute-like proteins and polycomb proteins were located in these yield QTL regions. A total of 336 highly expressed transcription factor (TF) genes belonging to 50 TF families were identified in the yield QTL intervals. These findings provide the starting genomic materials to elucidate the molecular basis of yield related traits and heterosis in rice.

An integrative analysis of transcriptome and proteome was performed to identify differential genes/proteins of a red-flesh sweet orange Cara Cara in comparison with a common cultivar Newhall at ripening stages. At the transcript level, gene expression was measured with Massively Parallel Signature Sequencing (MPSS), and 629 genes of these two sweet orange cultivars differed by two fold or more (FDR

Background Human disease genes can be distinguished from essential (embryonically lethal) and non-disease genes using gene attributes. Such attributes include gene age, tissue specificity of expression, regulatory capacity, sequence length, rate of sequence variation and capacity for interaction. The resulting information has been used to inform data mining approaches seeking to identify novel disease genes. Given the dynamic nature of this field and the rapid rise in relevant information, we have chosen to perform a single integrated mining approach to explore relationships among gene attributes and thereby characterise evolutionary trends associated with disease genes. Results All against all cross comparison of 2,522 disease gene attributes revealed significant relationships existed between the age, disease-association and expression pattern of genes and the tissues within which they are expressed. We found that the over-representation of disease genes among old genes holds for tissue-specific genes, but the correlation between age and disease association vanished when conditioning on tissue-specificity. Of the 32 tissues studied, the genes expressed in pancreas are on average older than the genes expressed in any other tissue, while the testis expressed the lowest proportion of old genes. Following a focussed analysis on the impact of regulatory apparatus on evolution of disease genes, we show that regulators, comprising transcription factors and post-translation modified proteins, are over-represented among ancient disease genes. In addition, we show that the proportion of regulator genes is affected by gene age among disease genes and by tissue-specificity among non-disease genes. Finally, using 55,606 true positive gene interaction data, we find that old disease genes interacts with other old disease genes and interacting new genes interacts with genes originating from higher phylostrata. Conclusion This study supports the non-random nature of the human diseasome. We have identified a variety of distinct features and correlations to other molecular attributes that can be used to distinguish the set of disease causing genes. This was achieved by harnessing the power of mining large scale datasets from OMIM and other databases. Ultimately such knowledge may contribute to the identification of novel human disease genes and an enhanced understanding of human biology.

Background Although the complete genome sequence and annotation of Arabidopsis were released at the end of year 2000, it is still a great challenge to understand the function of each gene in the Arabidopsis genome. One way to understand the function of genes on a genome-wide scale is expression profiling by microarrays. However, the expression level of many genes in Arabidopsis genome cannot be detected by microarray experiments. In addition, there are many more novel genes that have been discovered by experiments or predicted by new gene prediction programs. Another way to understand the function of individual genes is to investigate their in vivo expression patterns by reporter constructs in transgenic plants which can provide basic information on the patterns of gene expression. Results A high throughput pipeline was developed to generate promoter-reporter (GFP) transgenic lines for Arabidopsis genes expressed at very low levels and to examine their expression patterns in vivo. The promoter region from a total of 627 non- or low-expressed genes in Arabidopsis based on Arabidopsis annotation release 5 were amplified and cloned into a Gateway vector. A total of 353 promoter-reporter (GFP) constructs were successfully transferred into Agrobacterium (GV3101) by triparental mating and subsequently used for Arabidopsis transformation. Kanamycin-resistant transgenic lines were obtained from 266 constructs and among them positive GFP expression was detected from 150 constructs. Of these 150 constructs, multiple transgenic lines exhibiting consistent expression patterns were obtained for 112 constructs. A total 81 different regions of expression were discovered during our screening of positive transgenic plants and assigned Plant Ontology (PO) codes. Conclusions Many of the genes tested for which expression data were lacking previously are indeed expressed in Arabidopsis during the developmental stages screened. More importantly, our study provides plant researchers with another resource of gene expression information in Arabidopsis. The results of this study are captured in a MySQL database and can be searched at http://www.jcvi.org/arabidopsis/qpcr/index.shtml. Transgenic seeds and constructs are also available for the research community.

Background RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci. Results We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t Conclusions By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Background A comprehensive transcriptome survey, or gene atlas, provides information essential for a complete understanding of the genomic biology of an organism. We present an atlas of RNA abundance for 92 adult, juvenile and fetal cattle tissues and three cattle cell lines. Results The Bovine Gene Atlas was generated from 7.2 million unique digital gene expression tag sequences (300.2 million total raw tag sequences), from which 1.59 million unique tag sequences were identified that mapped to the draft bovine genome accounting for 85% of the total raw tag abundance. Filtering these tags yielded 87,764 unique tag sequences that unambiguously mapped to 16,517 annotated protein-coding loci in the draft genome accounting for 45% of the total raw tag abundance. Clustering of tissues based on tag abundance profiles generally confirmed ontology classification based on anatomy. There were 5,429 constitutively expressed loci and 3,445 constitutively expressed unique tag sequences mapping outside annotated gene boundaries that represent a resource for enhancing current gene models. Physical measures such as inferred transcript length or antisense tag abundance identified tissues with atypical transcriptional tag profiles. We report for the first time the tissue-specific variation in the proportion of mitochondrial transcriptional tag abundance. Conclusions The Bovine Gene Atlas is the deepest and broadest transcriptome survey of any livestock genome to date. Commonalities and variation in sense and antisense transcript tag profiles identified in different tissues facilitate the examination of the relationship between gene expression, tissue, and gene function.

As the product of transcription and the blueprint for translation, mRNA is the main intermediate product of the gene expression pathway. The ability to accurately determine mRNA levels is, therefore, a major requirement when studying gene expression. mRNA is also a target of different regulatory steps, occurring in different subcellular compartments. To understand the different steps of gene expression regulation, it is therefore essential to analyze mRNA in the context of a single cell, maintaining spatial information. Here, we describe a stepwise protocol for fluorescent in situ hybridization (FISH) that allows detection of individual mRNAs in single yeast cells. This method allows quantitative analysis of mRNA expression in single cells, permitting "absolute" quantification by simply counting mRNAs. It further allows us to study many aspects of mRNA metabolism, from transcription to processing, localization, and mRNA degradation.

The human genome contains tens of thousands of gene loci which code for an even greater number of different protein and RNA products. The highly complex temporal and spatial expression of these genes makes possible all the biological processes of life from development and differentiation to homeostasis, aging, and programmed cell death. Therefore, it is not surprising that altered gene expression by mutation or deregulation is fundamental for the development of many human diseases including cancer. In some cases, cancers can be linked to large changes such as deletions or duplications of entire gene(s). In other cases, more subtle changes in expression levels of larger numbers of genes are involved. Until recently, cancer diagnosis was dependent on pathological methods and imaging studies that often require a tumor of significant size and suffer from poor sensitivity (Ahmed 2002). The recent development of gene expression profiling tools, promises to identify new and improved diagnostic tools at the molecular level.

Background Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression. Results We developed PanGEA, a tool which enables a fast and user-friendly analysis of allele specific gene expression using the 454 technology. PanGEA allows mapping of 454-ESTs to genes or whole genomes, displaying gene expression profiles, identification of SNPs and the quantification of allele specific gene expression. The intuitive GUI of PanGEA facilitates a flexible and interactive analysis of the data. PanGEA additionally implements a modification of the Smith-Waterman algorithm which deals with incorrect estimates of homopolymer length as occuring in the 454 technology Conclusion To our knowledge, PanGEA is the first tool which facilitates the identification of allele specific gene expression. PanGEA is distributed under the Mozilla Public License and available at: http://www.kofler.or.at/bioinformatics/PanGEA

Udgivelsesdato: 2009-Nov Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification.

Background The recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios. Results Presented here is a novel study of two porcine tissues based on integrative analysis of data from expression profiling of identical samples using cDNA microarray, 454-sequencing and iTRAQ-based proteomics. Sequence homology identified 2.541 unique transcripts that are detectable by both microarray hybridizations and 454-sequencing of 1.2 million cDNA tags. Both transcript-based technologies showed high reproducibility between sample replicates of the same tissue, but the correlation across these two technologies was modest. Thousands of genes being differentially expressed were identified with microarray. Out of the 306 differentially expressed genes, identified by 454-sequencing, 198 (65%) were also found by microarray. The relationship between the regulation of transcript and protein levels was analyzed by integrating iTRAQ-based proteomics data. Protein expression ratios were determined for 354 genes, of which 148 could be mapped to both microarray and 454-sequencing data. A comparison of the expression ratios from the three technologies revealed that differences in transcript and protein levels across heart and muscle tissues are positively correlated. Conclusion We show that the reproducibility within cDNA microarray and 454-sequencing is high, but that the agreement across these two technologies is modest. We demonstrate that the regulation of transcript and protein levels across identical tissue samples is positively correlated when the tissue expression ratios are used for comparison. The results presented are of interest in systems biology research in terms of integration and analysis of high-throughput expression data from mammalian tissues.

Background High-throughput cDNA synthesis and sequencing of poly(A)-enriched RNA is rapidly emerging as a technology competing to replace microarrays as a quantitative platform for measuring gene expression. Results Consequently, we compared full length cDNA sequencing to 2-channel gene expression microarrays in the context of measuring differential gene expression. Because of its comparable cost to a gene expression microarray, our study focused on the data obtainable from a single lane of an Illumina 1 G sequencer. We compared sequencing data to a highly replicated microarray experiment profiling two divergent strains of S. cerevisiae. Conclusion Using a large number of quantitative PCR (qPCR) assays, more than previous studies, we found that neither technology is decisively better at measuring differential gene expression. Further, we report sequencing results from a diploid hybrid of two strains of S. cerevisiae that indicate full length cDNA sequencing can discover heterozygosity and measure quantitative allele-specific expression simultaneously.

Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.

The Apicomplexa have some of the most comprehensive and integrated proteome datasets of all pathogenic micro-organisms. Coverage is currently at a level where these data can be used to help predict the potential biological function of proteins in these parasites, without having to defer to measurement of mRNA levels. Transcriptomic data for the Apicomplexa (microarrays, expressed sequence tag (EST) collections, serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS) tags) are also copious, enabling us to investigate the extent to which global mRNA levels correlate with proteomic data. Here, we present a proteomic and transcriptomic perspective of gene expression in key apicomplexan parasites, including Plasmodium spp., Toxoplasma gondii , Cryptosporidium parvum , Neospora caninum and Theileria spp., and discuss the alternative views of gene expression that they provide. Although proteomic evidence does not exist for every gene, many examples of readily detected proteins whose corresponding genes display little or no detectable transcription, are seen across the Apicomplexa. These examples are not easily explained by the “guilt by association”, or “stock and go” hypotheses of gene transcription. With the advent of ultra-high-throughput sequencing technologies there will be a quantum shift in transcriptional analysis which, combined with improving quantitative proteome datasets, will provide a core component of a systems-wide approach to studying the Apicomplexa.

Novel biomarker development requires a significant resource commitment to translate candidate markers into clinical assays. Consequently, it is imperative high quality candidates are selected early in a biomarker development program. High throughput gene expression data are routinely used to identify transcripts differentially expressed in diseased versus normal samples. Data-mining Expressed Sequence Tag, Serial Analysis of Gene Expression, Massively Parallel Signature Sequencing, and microarray expression databases can provide additional information on the expression of candidate biomarkers across multiple tissues, organs, and disease states. From this information, quantitative measures of tissue-specific gene specificity are computed and used to guide candidate biomarker selection.

Serial Analysis of Gene Expression (SAGE) is a powerful technique for genome wide analysis of gene expression. The SAGE technique quantifies a ‘tag’ which represents the transcriptome product of a gene. A tag for the purpose of SAGE, is a nucleotide sequence of a defined length, directly adjacent to the 3’-most restriction site for a particular restriction enzyme. Thus, data product of SAGE is a list of tags with their count values, providing a digital representation of cellular gene expression. Several technical modifications have been made to the original SAGE protocol to improve its efficiency, reducing the amount of input RNA, increasing the length of SAGE tags, and allowing further use of SAGE results. This chapter deals with the methodology of SAGE, problems associated with SAGE, various SAGE modifications attempted, comparison with other contemporary high-throughput methods like microarrays, and current applications of SAGE in plant transcript profiling. In plants, SAGE has been used to analyse: (1) host-pathogen interactions, (2) plant responses to various environmental and nutritional stresses, (3) metabolism of toxic compounds, and (4) transcript profiling of a particular tissue/organ. Although majority of these studies have confined to model plants i.e. rice and Arabidopsis thaliana, a few recent efforts have extended the use of SAGE to other plant species also.

Transcription factors play a key role in integrating and modulating biological information. In this study, we comprehensively measured the changing abundances of mRNAs over a time course of activation of human peripheral-blood-derived mononuclear cells (“macrophages”) with lipopolysaccharide. Global and dynamic analysis of transcription factors in response to a physiological stimulus has yet to be achieved in a human system, and our efforts significantly advanced this goal. We used multiple global high-throughput technologies for measuring mRNA levels, including massively parallel signature sequencing and GeneChip microarrays. We identified 92 of 1,288 known human transcription factors as having significantly measurable changes during our 24-h time course. At least 42 of these changes were previously unidentified in this system. Our data demonstrate that some transcription factors operate in a functional range below 10 transcripts per cell, whereas others operate in a range three orders of magnitude greater. The highly reproducible response of many mRNAs indicates feedback control. A broad range of activation kinetics was observed; thus, combinatorial regulation by small subsets of transcription factors would permit almost any timing input to cis -regulatory elements controlling gene transcription.

Small RNAs play pivotal roles in regulating gene expression in higher eukaryotes. Among them, trans-acting siRNAs (ta-siRNAs) are a class of small RNAs that regulate plant development. The biogenesis of ta-siRNA depends on microRNA-targeted cleavage followed by the DCL4-mediated production of small RNAs phased in 21-nt increments relative to the cleavage site on both strands. To find TAS genes, we have used these characteristics to develop the first computational algorithm that allows for a comprehensive search and statistical evaluation of putative TAS genes from any given small RNA database. A search in Arabidopsis small RNA massively parallel signature sequencing (MPSS) databases with this algorithm revealed both known and previously unknown ta-siRNA-producing loci. We experimentally validated the biogenesis of ta-siRNAs from two PPR genes and the trans-acting activity of one of the ta-siRNAs. The production of ta-siRNAs from the identified PPR genes was directed by the cleavage of a TAS2 -derived ta-siRNA instead of by microRNAs as was reported previously for TAS1a , - b , - c , TAS2 , and TAS3 genes. Our results indicate the existence of a small RNA regulatory cascade initiated by miR173-directed cleavage and followed by the consecutive production of ta-siRNAs from two TAS genes.

Massively parallel signature sequencing (MPSS) is a technology capable of sequencing simultaneously almost all the DNA molecules in a sample. This technology is well suited for deep profiling of mRNA and small RNA by the sequencing of cDNA tags. A series of mRNA MPSS databases has been created from various libraries derived from four different species (Arabidopsis, rice, grape, and Magnaporthe grisea, the rice blast fungus). Our mRNA MPSS databases measure the absolute expression level of most genes in the sample and provide information about potentially novel transcripts (antisense transcripts, alternative splice isoforms, and regulatory intergenic transcripts). In addition to these data, we have recently built an extensive database from MPSS-derived Arabidopsis small RNA samples. All the databases are accessible thorough our Web interface (http://mpss.udel.edu), and the individual pages are equipped with various graphical and analytical tools. Here, we focus on a subset of these tools (e.g., Gene Analysis [GA], Chromosome Viewer [CV], and Library Analysis [LIBAN]) and describe how the users can analyze and interpret our MPSS expression data.

Publisher Summary The most fundamental questions in human embryonic stem cell (hESC) research concern how pluripotence and differentiation are controlled. One hope is that large-scale studies comparing the gene expression of hESCs to their differentiated progeny will lead to insights about these processes. Gene expression of undifferentiated hESCs has been investigated by a variety of techniques including MPSS (massively parallel signature sequencing), SAGE (serial analysis of gene expression), EST (expressed sequence tag) scans, and hybridization-based technologies such as focused cDNA arrays and genome-wide microarray platforms. Microarray technology offers a unique combination of features that makes it well suited for most expression studies. It is comprehensive, allowing the monitoring of every annotated transcript in the human (or mouse) genome and it requires relatively little sample, approximately 100 ng (-10 000 cells) of total ribonucleic acid (RNA) for the most sensitive arrays.

Large-scale systematic gene expression analyses of early embryos and stem cells provide useful information to identify genes expressed differentially or uniquely in these cells. We review the current status of various approaches applied to preimplantation embryos and stem cells: expressed sequence tag, serial analysis of gene expression, differential display, massively parallel signature sequencing, DNA microarray (DNA chip) analysis, and chromatin-immunoprecipitation microarrays. We also discuss the biological questions that can only be addressed by the analysis of global gene expression patterns, such as so-called stemness and developmental potency. As the emphasis now shifts from expression profiling to functional studies, we review the genome-scale functional studies of genes: expression cloning, gene trapping, RNA interference, and gene disruptions. Finally, we discuss the future clinical application of such methodologies.