Project description:Genome-wide characterization of the retinal transcriptome is central to understanding development, physiology and disorders of the visual system. Massively parallel, short-read sequencing of mRNA libraries was used to generate an extensive map of the transcriptome of the adult, murine neural retina. RNA-seq data strongly corroborates prior transcriptome studies by microarray and SAGE. However, several novel features of the retinal transcriptome were discovered. For example, retinal disease genes were discovered to be among the most highly expressed in the transcriptome. We also demonstrate other interesting features of the retinal transcriptome, for example, that the retina appears to employ a very specific and restricted set of synaptic vesicle genes, and also that there is persistence of expression of a majority of "neurodevelopmental" genes into adulthood. Retina transcriptome studies utilizing novel sequencing methods have been highly informative and these data may also serve as a resource for the community of researchers.
Project description:To study the transcriptome profiles in the blood of recurrent implantation failure (RIF), recurrent miscarriage (RM) and fertile women during the window of implantation, and further analysis the correlation of transcriptome profiles between blood and endometrium.This is an observational prospective study. In total 9 subjects were recruited, 3 RIF, 3 RM, and 3 controls. Paired samples (endometrium and peripheral blood) from the same subjects were precisely timed on the 7th days after luteal hormone surge (LH+7). RNA sequencing was applied to investigate the transcriptome profiles.The results of transcriptome in peripheral blood cannot be used to characterize women with RIF and unexplained RM. There was a medium level correlation between transcriptome in peripheral blood and endometrium during the window of implantation.The differential transcriptome patterns in blood are not representative of those in endometrium, and the blood transcriptome cannot differentiate among the women with RIF, RM or fertile.
Project description:BACKGROUND:Wood is a valuable natural resource and a major carbon sink. Wood formation is an important developmental process in vascular plants which played a crucial role in plant evolution. Although genes involved in xylem formation have been investigated, the molecular mechanisms of xylem evolution are not well understood. We use comparative genomics to examine evolution of the xylem transcriptome to gain insights into xylem evolution. RESULTS:The xylem transcriptome is highly conserved in conifers, but considerably divergent in angiosperms. The functional domains of genes in the xylem transcriptome are moderately to highly conserved in vascular plants, suggesting the existence of a common ancestral xylem transcriptome. Compared to the total transcriptome derived from a range of tissues, the xylem transcriptome is relatively conserved in vascular plants. Of the xylem transcriptome, cell wall genes, ancestral xylem genes, known proteins and transcription factors are relatively more conserved in vascular plants. A total of 527 putative xylem orthologs were identified, which are unevenly distributed across the Arabidopsis chromosomes with eight hot spots observed. Phylogenetic analysis revealed that evolution of the xylem transcriptome has paralleled plant evolution. We also identified 274 conifer-specific xylem unigenes, all of which are of unknown function. These xylem orthologs and conifer-specific unigenes are likely to have played a crucial role in xylem evolution. CONCLUSIONS:Conifers have highly conserved xylem transcriptomes, while angiosperm xylem transcriptomes are relatively diversified. Vascular plants share a common ancestral xylem transcriptome. The xylem transcriptomes of vascular plants are more conserved than the total transcriptomes. Evolution of the xylem transcriptome has largely followed the trend of plant evolution.
Project description:The transcriptome is the mRNA transcript pool in a cell, organ or tissue with the liver transcriptome being amongst the most complex of any organ. Functional genomics methodologies are now being widely utilized to study transcriptomes including the hepatic transcriptome. This review outlines commonly used methods of transcriptome analysis, especially gene array analysis, focusing on publications utilizing these methods to understand human liver disease. Additionally, we have outlined the relationship between transcript and protein expressions as well as summarizing what is known about the variability of the transcriptome in non-diseased liver tissue. The approaches covered include gene array analysis, serial analysis of gene expression, subtractive hybridization and differential display. The discussion focuses on primate whole organ studies and in-vitro cell culture systems utilized. It is now clear that there are a vast number research opportunities for transcriptome analysis of human liver disease as we attempt to better understand both non-diseased and disease hepatic mRNA expression. We conclude that hepatic transcriptome analysis has already made significant contributions to the understanding of human liver pathobiology.
Project description:An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.
Project description:RNA-Seq has become increasingly popular in transcriptome profiling. The major challenge in RNA-Seq data analysis is the accurate mapping of junction reads to their genomic origins. To detect splicing sites in short reads, many RNA-Seq aligners use reference transcriptome to inform placement of junction reads. However, no systematic evaluation has been performed to assess or quantify the benefits of incorporating reference transcriptome in mapping RNA-Seq reads. In this paper, we have studied the impact of reference transcriptome on mapping RNA-Seq reads, especially on junction ones. The same dataset were analysed with and without RefGene transcriptome, respectively. Then a Perl script was developed to analyse and compare the mapping results. It was found that about 50-55% junction reads can be mapped to the same genomic regions regardless of the usage of RefGene model. More than one-third of reads fail to be mapped without the help of a reference transcriptome. For "Alternatively" mapped reads, i.e., those reads mapped differently with and without RefGene model, the mappings without RefGene model are usually worse than their corresponding alignments with RefGene model. For junction reads that span more than two exons, it is less likely to align them correctly without the assistance of reference transcriptome. As the sequencing technology evolves, the read length is becoming longer and longer. When reads become longer, they are more likely to span multiple exons, and thus the mapping of long junction reads is actually becoming more and more challenging without the assistance of reference transcriptome. Therefore, the advantages of using reference transcriptome in the mapping demonstrated in this study are becoming more evident for longer reads. In addition, the effect of the completeness of reference transcriptome on mapping of RNA-Seq reads is discussed.
Project description:De novo assembled transcriptomes, in combination with RNA-Seq, are powerful tools to explore gene sequence and expression level in organisms without reference genomes. Investigators must first choose which high throughput sequencing platforms will provide data most suitable for their experimental goals. In this study, we explore the utility of 454 and Illumina sequences for de novo transcriptome assembly and downstream RNA-Seq applications in a reproductive gland from a non-model bird species, the Japanese quail (Coturnix japonica). Four transcriptomes composed of either pure 454 or Illumina reads or mixtures of read types were assembled and evaluated for the same cost. Illumina assemblies performed best for de novo transcriptome characterization in terms of contig length, transcriptome coverage, and complete assembly of gene transcripts. Improvements over the Hybrid assembly were marginal, with the exception that the addition of 454 data significantly increased the number of genes annotated. The Illumina assembly provided the best reference to align an independent set of RNA-Seq data as ?84% of reads mapped to single genes in the transcriptome. Contigs constructed solely from 454 data may impose problems for RNA-Seq as our 454 transcriptome revealed a high number of indels and many ambiguously mapped reads. Correcting the 454 transcriptome with Illumina reads was an effective strategy to deal with indel and frameshift errors inherent to the 454 transcriptome, but at the cost of transcriptome coverage. In the absence of a reference genome, we find that Illumina reads alone produced a high quality transcriptome appropriate for RNA-Seq gene expression analyses.
Project description:The Schizothoracinae fishes, endemic species in the Tibetan Plateau, are considered as ideal models for highland adaptation and speciation investigation. Despite several transcriptome studies for highland fishes have been reported before, the transcriptome information of Schizothoracinae is still lacking. To obtain comprehensive transcriptome data for Schizothoracinae, the transcriptome of a total of 183 samples from 14 representative Schizothoracinae species, were sequenced and de novo assembled. As a result, about 1,363?Gb transcriptome clean data was obtained. After the assembly, we obtain 76,602-154,860 unigenes for each species with sequence N50 length of 1,564-2,143?bp. More than half of the unigenes were functionally annotated by public databases. The Schizothoracinae fishes in this work exhibited diversified ecological distributions, phenotype characters and feeding habits; therefore, the comprehensive transcriptome data of those species provided valuable information for the environmental adaptation and speciation of Schizothoracinae in the Tibetan Plateau.
Project description:The eukaryotic cytosol contains multiple RNP granules, including P-bodies and stress granules. Three different methods have been used to describe the transcriptome of stress granules or P-bodies, but how these methods compare and how RNA partitioning occurs between P-bodies and stress granules have not been addressed. Here, we compare the analysis of the stress granule transcriptome based on differential centrifugation with and without subsequent stress granule immunopurification. We find that while differential centrifugation alone gives a first approximation of the stress granule transcriptome, this methodology contains nonspecific transcripts that play a confounding role in the interpretation of results. We also immunopurify and compare the RNAs in stress granules and P-bodies under arsenite stress and compare those results to those for the P-body transcriptome described under nonstress conditions. We find that the P-body transcriptome is dominated by poorly translated mRNAs under nonstress conditions, but during arsenite stress, when translation is globally repressed, the P-body transcriptome is very similar to the stress granule transcriptome. This suggests that translation is a dominant factor in targeting mRNAs into both P-bodies and stress granules, and during stress, when most mRNAs are untranslated, the composition of P-bodies reflects this broader translation repression.
Project description:Gene expression profiles potentially hold valuable information for the prediction of breeding values and phenotypes. In this study, the utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of Drosophila melanogaster for nine traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms (SNPs) and transcriptome data. The genotypic data was used to construct the common additive genomic relationship, which was used in genomic best linear unbiased prediction (GBLUP) or jointly in a linear mixed model with a transcriptome-based linear kernel (GTBLUP), or with a transcriptome-based Gaussian kernel (GRBLUP). We studied the predictive ability of the models and discuss a concept of "omics-augmented broad sense heritability" for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception to Ethyl Butyrate in females) in which the predictive ability of GRBLUP (0.23) was significantly higher than the predictive ability of GBLUP (0.21). Our results suggest that accounting for transcriptome data has the potential to improve genomic predictions if transcriptome data can be included on a larger scale.