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      Proteomics. Tissue-based map of the human proteome.

      1 , 2 , 3 , 4 , 2 , 5 , 2 , 4 , 6 , 4 , 4 , 7 , 2 , 8 , 9 , 2 , 4 , 9 , 9 , 2 , 4 , 9 , 9 , 10 , 9 , 2 , 2 , 9 , 2 , 2 , 2 , 2 , 2 , 11 , 12 , 4
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          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

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          Activities at the Universal Protein Resource (UniProt)

          The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.
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            Is Open Access

            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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              An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data

              Introduction A fundamental question in molecular biology is how cells and tissues differ in gene expression and how those differences specify biological function. A related question is what part of the cellular machinery represents housekeeping functions needed by all cells and how many genes encode such functions. The transcriptomes of mammalian tissues have been extensively studied using methods such as reassociation kinetics (Rot) [1], serial analysis of gene expression (SAGE) [2], microarrays [3],[4], and sequencing of expressed sequence tags (ESTs) and full length transcripts [5]. Reassociation kinetics was used early on to study and compare global properties of tissue transcriptomes [1],[6]. From those studies it was concluded that ∼20,000 mRNAs are expressed in each cell or tissue, and that roughly 90% of all mRNAs are common between two tissues, drawing the first conclusions on tissue transcriptome compositions [7]. Later studies of tissue transcriptomes using SAGE [8] identified ∼1,000 ubiquitously expressed genes (i.e. expressed in all cell types examined) and concluded that tissue-specific transcripts make up roughly 1% of mRNA mass of cells. Focusing on colorectal cancer cell lines, for which the deepest coverage was available, it was estimated that half of all mRNA transcripts in these cells came from the 623 most highly expressed genes. Comparing mRNA expression levels across panels of human and mouse tissues by microarrays, Su and coworkers identified tissue-specific genes for each tissue, and estimated that ∼6% of genes were ubiquitously expressed, and that individual tissues express 30–40% of all genes [9]. Using additional microarray data, expression of ∼8,000 genes was detected in each tissue but as few as 1–3% of these were detected in all tissues [10]. Similar conclusions were drawn from a second mouse tissue atlas [11] that identified ∼1,800 genes as ubiquitously expressed. Altogether, microarrays and SAGE have been quite successful in identifying tissue and cell specific genes [8]–[12]. However, the discrepancy between estimates of the composition and characteristics of tissue transcriptomes obtained by microarray and SAGE methods on the one hand and reassociation kinetics studies on the other has not been explained. Deep sequencing of RNAs (RNA-Seq) has recently been used to quantify gene and alternative isoform expression levels [13]–[17]. In RNA-Seq, all RNAs of a sample (or, more often, polyA+ RNAs) are randomly fragmented, reverse transcribed, ligated to adapters and then these fragments are sequenced. Gene expression levels can then be estimated from the number of sequence reads deriving from each gene [15]. Expression estimates from RNA-Seq are quantitative over five orders of magnitude and replicates of mouse tissues are highly reproducible [13]. Compared to microarrays, RNA-Seq is more sensitive, both in terms of detection of lowly expressed and differentially expressed genes [15],[18], and expression values from RNA-Seq correlate better with protein levels [19]. The greater accuracy and coverage of the expressed transcriptome makes this method suitable for addressing global features of transcriptomes. We recently studied alternative isoform expressions across tissues using RNA-Seq and found both a very high frequency of alternative splicing and extensive tissue regulation of the expression of alternative mRNA isoforms [14]. Here we instead focused on a gene-centric analysis of transcript composition and complexity. The highly quantitative nature of RNA-Seq has motivated us to revisit the longstanding questions regarding the composition of tissue transcriptomes, as well as the expression of long non-coding RNAs, the variability in 3′UTR length, and the association between these features and gene function. Results Excluding 3′ UTR reads yields more accurate gene expression estimates We investigated the transcriptomes of a diverse collection of human and mouse tissues and five breast and breast cancer cell lines that were recently sequenced at a depth of roughly 20 million short reads per sample using RNA-Seq protocols (Table S1). Gene expression was initially estimated by calculating read density as ‘reads per kilobase of exon model per million mapped reads’ (RPKM) [13]. These estimates are typically performed using common gene annotations (e.g., RefSeq) with the entire annotated transcript representing the ‘exon model’. These expression level estimates may however be confounded by the expression of shorter isoforms due to alternative cleavage and polyadenylation (Figure S1A and S1B). We found that excluding annotated 3′UTRs – which will sometimes vary between mRNA isoforms as a result of alternative cleavage and polyadenylation – enabled estimation of expression levels that correspond more closely with quantitative RT-PCR measurements (Figure S1C). We noted that removing the 3′UTR from calculation of gene expression yields a >2-fold change for over one thousand genes (Figure S1D), and that the effect of 3′UTRs on expression estimates does not seem to be a technical issue caused by secondary structure in the 3′UTR (Figure S2). We therefore advocate excluding UTRs from such estimates, and all subsequent gene expression estimates described here excluded 3′UTR regions. Ubiquitous expression of ∼8,000 human genes We next sought to answer how many genes are expressed in a tissue or cell type. A comparison between the expression levels of exons and intergenic regions was used to first find a threshold for detectable expression above background (Figure 1A, algorithm in Figure S3), yielding a threshold RPKM value of 0.3 which balances the numbers of false positives and false negatives. For individual samples, we obtained threshold values between 0.2 and 0.8. As it is difficult to identify untranscribed DNA regions with confidence [20],[21], it is very possible that the background was overestimated. Applying the threshold 0.3 RPKM, the number of genes expressed in most human and mouse tissues varied from 11,000 to 13,000, corresponding to roughly 60–70% of RefSeq protein-coding genes (Table 1). These gene number estimates were stable across different sequencing depths (Figure 1B) and therefore represent bona fide tissue differences. Testis was a clear outlier, expressing more than 15,000 different genes (84% of RefSeq genes). As many as 7,897 genes (42%) were observed to be expressed in all tissues and cell lines (Dataset S1). The corresponding number for Ensembl annotation was 8,214, or 38% of protein-coding genes (Ensembl is an automated gene annotation system, whereas RefSeq is manually curated). Each ubiquitous gene was typically expressed at roughly the same order of magnitude in all tissues, suggesting that there were few problems with genes being considered ubiquitous when they were really specific to one or a few tissues but had a leaky, non-functional expression elsewhere (Figure S4). While we observed small numbers of reads for 8 genes known to have leaky transcription [22],[23] in several tissues, these genes were all too weakly or narrowly transcribed outside their main tissue to be detected as ubiquitous. The estimated number of ubiquitously expressed genes appeared to plateau as the number of samples used was increased to the full set of 24 (Figure 1C). The detection threshold used affects the number of genes detected (Table 1), and the number of detected ubiquitous genes can vary by up to ∼2,000 genes depending on threshold used. The number of samples is large enough that background is unlikely to cause relatively tissue-specific genes to be detected in every sample. These differences between thresholds therefore most likely reflect the presence of low-abundance RNA species. The number of ubiquitous genes we detected is much greater than the ∼1,000 shared genes identified by SAGE [8] and the 1–6% of genes from microarrays [9]–[11], but is in relatively good agreement with the ∼10,000 shared genes estimated by reassociation kinetics [6] and the 3,140 to 6,909 estimated from ESTs [24] (the higher number came from a cutoff of presence in 16 out of 18 tissues, used to remedy uneven EST sequencing across tissues). The increased number of ubiquitously expressed genes compared to SAGE and microarrays most likely results from the increased depth of mRNA-Seq data and improved detection of lowly expressed genes [22]. The number of genes expressed in a tissue ranged from 11,199 to 15,518 genes (Table 2), so a majority of the genes expressed in a specific tissue or cell type are ubiquitously expressed genes. These genes contribute ∼75% of the polyA+ RNA molecules in most tissues (Table 3), although this fraction was higher in the cancer cell lines, perhaps as a result of their elevated metabolic rate. 10.1371/journal.pcbi.1000598.g001 Figure 1 Functions of ubiquitous genes. (A) False discovery and negative rate for the detection of genes as a function of detection threshold used, demonstrating how a threshold of 0.3 RPKM was chosen. (B) The number of genes detected (>0.3 RPKM) at different sequencing depths. Each curve represents a sample. Above 3 million reads the sequence depth matters little for how many genes are detected as expressed. (C) The number of ubiquitous genes (expressed >0.3 RPKM in all samples) as a function of the number of samples used. Error bars show the standard variation, black line the mean. (D) The fraction of genes among ubiquitous and other genes with CpG-poor (purple), intermediate (yellow) or CpG-rich (green) promoters. (E) Illustration of subcellular localizations aligned to protein functional and localization categories for significant categories enriched in ubiquitously expressed genes (blue) and genes that were only expressed in one or a few tissues (red). For each category we have plotted the fraction of all genes that were not ubiquitous (the overall fraction of non-ubiquitous genes are shown as a vertical dashed line). Extracellular functions and membrane functions were highly enriched for non-ubiquitous genes while intracellular functions were dominated by ubiquitous genes. The categories shown are a subset of all significant categories listed in Dataset S2 and S3. 10.1371/journal.pcbi.1000598.t001 Table 1 Number of expressed and ubiquitous genes for various minimum expression thresholds. Threshold RPKM In all 24 samples On average per sample 0.01 10,233 14,885 0.1 9,205 14,011 0.2 8,466 13,327 0.3 7,897 12,859 0.4 7,388 12,489 0.5 6,946 12,170 0.6 6,535 11,887 0.7 6,176 11,633 0.8 5,898 11,401 0.9 5,618 11,189 1 5,361 10,989 2 3,510 9,432 3 2,513 8,340 4 1,931 7,493 5 1,548 6,804 10.1371/journal.pcbi.1000598.t002 Table 2 Number of human genes expressed per tissue. Tissue/Cell Number of genes* Fraction of genes* Ensembl genes† Skeletal muscle1 11,276 0.61 11,953 Liver1 , 3 11,392 0.61 12,191 BT4744 11,844 0.64 12,808 MB4354 11,847 0.64 12,726 HME5 12,084 0.65 12,920 T47D4 12,205 0.66 12,983 Heart 12,209 0.66 13,159 MCF74 12,281 0.66 13,216 Adipose tissue 12,553 0.68 13,503 Colon 13,016 0.70 14,052 Cerebellum2 , 3 13,132 0.70 14,043 Kidney 13,235 0.71 14,177 Brain1 13,298 0.71 14,107 Breast 13,406 0.72 14,537 Lymph node 13,534 0.73 14,686 Testes 15,518 0.84 16,869 *annotations from RefSeq, protein-coding genes. †number of protein-coding genes, annotations from Ensembl. 1 number of genes detected in mouse: skeletal muscle 11,799; liver 11,201; brain 13,626. 2 standard deviation for samples from different individuals: 106. 3 mean number for different individuals. 4 breast cancer cell line. 5 human mammary epithelial cell line. 10.1371/journal.pcbi.1000598.t003 Table 3 Fraction of mRNA pool by copy number from ubiquitous human genes. Tissue/Cell Fraction ubiquitous Liver2 0.31 Heart 0.66 Brain 0.74 HME4 0.75 Breast 0.75 Skeletal muscle 0.76 Cerebellum1 , 2 0.76 Testes 0.77 Kidney 0.78 Adipose tissue 0.81 Colon 0.82 Lymph node 0.84 T47D3 0.87 MB4353 0.89 MCF73 0.89 BT4743 0.90 1 standard deviation for samples from different individuals: 0.01. 2 mean number for different individuals. 3 breast cancer cell line. 4 human mammary epithelial cell line. Functions of ubiquitous and non-ubiquitous genes To characterize the set of ubiquitously expressed genes we had identified, we looked for functional enrichment compared to genes expressed only in a subset of the tissues analyzed (hereafter called non-ubiquitous). The protein products of human ubiquitously expressed genes were more likely to have intracellular localization and to be involved in metabolism and other core cellular functions such as macromolecule synthesis, general transcription and vesicles (Figure 1E). Genes that were expressed in only one or a few tissues were more often secreted or membrane-bound (Figure 1E; Dataset S2 and S3), suggesting that cellular contacts and communication are mediated more often by specialized tissue-specific components. Interestingly, an exception to this inside-outside rule was sequence-specific DNA binding proteins, which are nuclear yet seldom ubiquitously expressed. Among these transcription factors we found that POU, homeobox and forkhead genes had the fewest ubiquitously expressed members, consistent with roles in specifying cell and tissue identity [25], whereas e.g. basic-leucine zipper factors were more often ubiquitous (Table 4). Functional characterization of housekeeping genes has been done in the past [26],[27] (and indirectly by [28]), with comparable results, although transporters were found to be relatively tissue-specific in one study [26]. Rather than looking at ubiquitous expression, that study compared the mean number of tissues where the genes were expressed, which could explain the difference. Ubiquitous genes often had CpG islands near their promoters (Figure 1D), as has been observed previously for ubiquitous and developmental genes [29]. The set of ubiquitous genes with CpG-poor promoters were not enriched for any GO category compared to all ubiquitous genes, nor were those with CpG-rich promoters. These observations suggest that ubiquitous expression is a better indicator of housekeeping functions than promoter CpG content. Together, these analyses suggest that much of the internal cytoplasmic machinery and most nuclear functions are common to most or all tissues, and that a large portion of the differences between tissues lie primarily in expression of receptors and ligands that mediate communication, and in a subset of sequence-specific DNA binding transcription factors. 10.1371/journal.pcbi.1000598.t004 Table 4 Expression of sequence-specific transcription factors. Transcription factor classification Number of genes Fraction non-ubiquitous POU 14 0.93 Homedomain 239 0.89 Forkhead 41 0.78 ETS 28 0.71 Helix-loop-helix 86 0.67 p53 family 42 0.67 Other 152 0.66 Nuclear hormone receptor 47 0.66 Zinc finger, C2H2 623 0.61 High mobility group 39 0.59 IPT/TIG1 17 0.47 Basic-leucine zipper 53 0.42 1 IPT: Immunoglobin-like fold shared by Plexins and Transcription factors; TIG: Transcription factor ImmunoGlobin. Estimating the fraction of the transcriptome devoted to specific functions As RNA-Seq expression measurements are highly quantitative, we also explored tissue transcriptome composition in terms of mRNA abundance classes [1] and the extent to which mRNA populations are dominated by a few highly expressed genes. Genes were sorted according to their expression and the fraction of the total cellular polyA+ RNA pool devoted to the most highly expressed genes was determined. This analysis showed that mRNA expression in both tissues (Figure 2A) and cell lines (Figure 2B) followed a continuous distribution rather than separating into distinct abundance classes as reported in previous studies (e.g. [1],[6]). 10.1371/journal.pcbi.1000598.g002 Figure 2 Complexity of tissue transcriptomes. (A) The fraction of all mRNAs derived from the most highly expressed genes for a number of mouse and human tissues. For example, the 10 most expressed genes in mouse liver contribute 25% of all mRNAs in that tissue. (B) Same as A, but with cell lines from breast. HME is a transformed cell line from normal mammary epithelium, breast is the normal tissue, the others are breast cancer cell lines from invasive ductal carcinoma. Gray lines are the tissues in A. (C) Same as B, but with 2 human livers and 6 human cerebellar samples from different individuals, to illustrate the degree of reproducibility in this type of plot and little inter-individual variation. (D) Same as B, but with three tissues from mouse. In muscle and liver transcriptomes, a small number of genes contributed a large fraction of the total mRNA pool, e.g. the ten most highly expressed genes in liver and muscle made up roughly 20–40% of the mRNA population. Other tissue transcriptomes were more complex, with the ten most highly expressed genes contributing only 5–10% of the mRNAs in brain, kidney and testis. The remaining tissues had intermediate levels of complexity (Figure 2A). The breast cancer cell lines had similar or greater complexity than normal breast tissue (Figure 2B). Biological replicates in both human and mouse tended to have highly similar complexity distributions (Figure 2C, 2D). Mouse tissues had somewhat similar profiles to corresponding human tissues (Figure 2D), although a much higher expression of several acute-phase genes in both human liver samples shifted their curves toward lower complexity compared to mouse liver. We conclude that kidney, testes and brain tissues have more complex transcriptomes due to the expression of more genes and with less dominance of a few highly expressed genes, whereas liver and muscle tissues are the least complex and express fewer genes, with more dramatic contributions of highly expressed genes. We next asked what fractions of total cellular mRNA are allocated to genes involved in different biological processes across the different tissues and cell lines. For this purpose, we developed a tool called FRACT (Functional Relative Allocation of Transcripts) that assesses relative gene expression from RNA-Seq read density for arbitrary sets of genes or broad gene ontology (GO) categories (results for a subset of tissues are shown in Figure 3A). This analysis provided a perspective on the functional priorities of cells in each tissue, since allocating a large fraction of the polyA+ RNA content in a cell (and likely of translational capacity) to one functional category represents a major investment of cellular resources. For some categories, including ‘metabolic process’, ‘transport’, and also ‘regulation of cell proliferation’, FRACT allocation varied relatively little across the tissues and cell lines (as measured by the coefficient of variation, CV, of the transcriptome fraction), consistent with the expected ‘housekeeping’ functions of these gene categories. Other categories had a far higher fraction of transcripts allocated to them in one tissue than in others, e.g. immune response (high in lymph node), muscle contraction, heart development and electron transport (all high in heart), and signal transduction and G protein-coupled receptor signaling (both high in brain). These examples, representing more specialized activities expected to be of increased importance in the corresponding tissues, provided a molecular-level validation of the integrity of the tissue samples and protocol used. In some cases, differences not readily apparent from the broad GO categorization shown in Figure 3A, could be detected by finer sub-classification of categories – an example is shown in Figure 3B. 10.1371/journal.pcbi.1000598.g003 Figure 3 FRACT analysis of tissue transcriptomes. (A) Pie graphs show estimated fraction of cellular transcripts deriving from genes belonging to a set of top-level Gene Ontology Biological Process categories for 7 human tissues and 1 cell line. Fractions were estimated from read density (RPKM) of Ensembl transcripts for each gene. Names of categories, distribution of transcriptome fraction across the samples (each line is a sample), and the coefficients of variation are shown at right. Biological processes with significantly higher or lower densities in individual tissues and cell lines are denoted by arrows. (B) FRACT analysis of sub-categories of the top-level ‘Development’ category in brain and testes. We also investigated the expression of thousands of large non-coding RNAs (ncRNAs). These genes were found to contribute a small fraction of transcripts to polyA+ transcriptomes compared to mRNAs (Figure 4A) as a result of their considerably lower expression levels (Figure 4B). These levels are lower than for mRNAs for all degrees of tissue-specificity (Figure 4C). 10.1371/journal.pcbi.1000598.g004 Figure 4 Non-coding RNA expression. (A) Relative fractions of polyA+ transcripts from protein-coding RNA (mRNA), curated non-coding RNA (ncRNA) and lincRNA, presented as the mean across human tissues. (B) The number of genes above a particular RPKM threshold (in one or more tissues) as a function of the threshold. (C) The maximum tissue expression level of mRNAs, curated ncRNAs and lincRNAs as a function of the number of tissues with detected expression. The average and standard deviations of the max expression levels in each group of genes are shown. Tissue-specific gene expression is fairly well conserved Muscle and brain tissues from human and mouse were observed to have similar expression and FRACT distributions (Figure 2D and data not shown), raising the question of the extent of conservation of tissue-specific expression patterns. We compared global gene expression levels between human and mouse tissues and observed high correlations between expression of orthologous genes between human and mouse (Pearson correlation 0.76 for muscle, 0.77 for liver and brain). When different tissues were compared (e.g. human brain vs. mouse muscle) substantially weaker correlations were observed (Pearson correlations in the range 0.47 to 0.61). These observations indicate a fairly strong overall conservation of gene expression levels between mouse and man, consistent with previous studies based on microarrays [30]. 3′ UTR length varies 3-fold between different functional groups of genes The lengths of mRNAs were studied by mapping the reads to coding and untranslated regions. Using RefSeq annotations, the density of reads in untranslated regions was lower than in coding regions (Figure 5A), suggesting that expression of mRNAs with UTRs shorter than or distinct from those annotated in RefSeq is common. We therefore estimated the lengths of the UTRs as their relative number of reads to coding regions using the annotated coding region length. Mouse data from [13] was chosen for this analysis as this dataset had little 3′ bias (Figure S5). In all three mouse tissues studied, significant negative correlations were observed between expression level and transcript length (−0.31 in liver and muscle, −0.16 in brain; all tissues p 2 exons and only one annotated isoform in RefSeq whose expressions had been measured by the MicroArray Quality Control project [50] in the same two samples, UHR (universal human reference) RNA and brain. Cleavage and polyadenylation sites are from [51],[52]. RefSeq or Ensembl gene annotations without 3′UTRs were then used for all gene expression estimates. For genes with multiple splice variants, we fitted an RPKM value to each variant by least square regression and used the sum of the expression of all isoforms (Figure S6). Isoforms that did not overlap directly but were grouped only through overlap with a third isoform were not considered to represent the same gene. All Pearson correlations were calculated based on log-transformed expression. False discovery and false negative rates were estimated using the algorithm presented in Figure S3, which seeks to correct for the presence of spurious reads mapping to non-expressed genes. The extent of leaky ubiquitous transcription by comparison of the ubiquitous set of genes to shuffled controls (Figure S4). Gene ontology and CpG content For three mouse tissues, we calculated RPKM values in the same way as had been done for the human ones. Mouse genes were matched to human orthologs using Entrez Gene. A list of acute-phase genes was taken from http://www.informatics.jax.org. DAVID [53] was used for finding enriched gene ontology categories. Categorization of promoters by CpG content was performed as described in [29]. Transcription factor annotations are from [54]. Non-coding RNA RefSeq gene annotation was used for protein-coding RNA (i.e. accessions starting with NM_) and curated non-coding RNA (NR_). We used the liftOver tool from the UCSC genome browser to obtain human positions for lincRNA regions from [21]. Transcriptome analysis with FRACT GO annotations for Ensembl transcripts were downloaded from Ensembl (BioMart). The read density for each transcript in each tissue was distributed among its annotated GO categories (total transcript density/no. GO categories for the transcript). GO categories were sorted by the total transcriptome density across tissues and cell lines, and the 400 categories with greatest density (accounting for 94% of total density) were aggregated into 17 broad classes; the remaining categories (6% of total transcriptome density) were aggregated into an “other” class (see Dataset S4 for mappings). The total density of transcripts devoted to each class in each tissue was tabulated. The coefficient of variation in the fraction of each transcriptome devoted to different classes was computed, and a Z-score for each class was computed to identify particular tissues which devote a significantly different fraction of the transcriptome to particular classes (|Z-score|>2). Length of the untranslated regions The UTR lengths were calculated as the number of reads in a UTR divided by the number of reads in CDS multiplied by the CDS length. For the expression weighted average gene lengths, we used the CDS length from Refseq gene annotation, but weighted according to the expression of each gene. To see the correlation between mRNA length and abundance, we took the CDS length from RefSeq annotation for gene isoforms and added UTR length according to the distribution of reads in the three regions. Only those expressed above 0.3 RPKM were included, in order to exclude genes with few reads that could drive an artificial correlation. To compare 3′ bias between samples, i.e. to what extent genes get more reads as you go in the 3′ direction, we plotted the average read density for all genes (weighted so that each gene contributed equally) across the coding region and fit a line y = kx+m where y = read density, x = location along coding region, and k/m is a measure of 3′ bias. Supporting Information Table S1 Tissue transcriptome data used (0.25 MB PDF) Click here for additional data file. Figure S1 Gene expression estimates using different gene models (0.66 MB PDF) Click here for additional data file. Figure S2 Folding of 3′UTR and expression level estimates (0.19 MB PDF) Click here for additional data file. Figure S3 Estimation of false discovery and negative rates at different expression levels (0.38 MB PDF) Click here for additional data file. Figure S4 Estimation of false discovery and negative rates at different expression levels (0.24 MB PDF) Click here for additional data file. Figure S5 Read density across genes (0.17 MB PDF) Click here for additional data file. Figure S6 Gene expression for genes with multiple mRNA isoforms (0.19 MB PDF) Click here for additional data file. Dataset S1 Ubiquitously expressed human genes (0.45 MB XLS) Click here for additional data file. Dataset S2 Enriched gene ontology categories among ubiquitous genes (0.16 MB XLS) Click here for additional data file. Dataset S3 Enriched gene ontology categories among non-ubiquitous genes (0.14 MB XLS) Click here for additional data file. Dataset S4 Functional Relative Allocation of Transcripts (0.17 MB XLS) Click here for additional data file.

                Author and article information

                Science (New York, N.Y.)
                Jan 23 2015
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                [1 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark. mathias.uhlen@scilifelab.se.
                [2 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
                [3 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
                [4 ] Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
                [5 ] Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
                [6 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
                [7 ] Leibniz Research Centre for Working Environment and Human Factors (IfADo) at Dortmund TU, D-44139 Dortmund, Germany.
                [8 ] Lab Surgpath, Mumbai, India.
                [9 ] Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
                [10 ] Science for Life Laboratory, Department of Neuroscience, Karolinska Institute, SE-171 77 Stockholm, Sweden.
                [11 ] Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
                [12 ] Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark. Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
                Copyright © 2015, American Association for the Advancement of Science.


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