Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention.

BACKGROUND AND AIMS: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. METHODS: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. RESULTS: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. CONCLUSIONS: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood.

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

iNetModels 2.0: an interactive visualization and database of multi-omics data.

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Genome-wide annotation of protein-coding genes in pig.

BACKGROUND: There is a need for functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Here, a new annotation strategy is introduced based on dimensionality reduction and density-based clustering of whole-body co-expression patterns. This strategy has been used to explore the gene expression landscape in pig, and we present a whole-body map of all protein-coding genes in all major pig tissues and organs. RESULTS: An open-access pig expression map ( www.rnaatlas.org ) is presented based on the expression of 350 samples across 98 well-defined pig tissues divided into 44 tissue groups. A new UMAP-based classification scheme is introduced, in which all protein-coding genes are stratified into tissue expression clusters based on body-wide expression profiles. The distribution and tissue specificity of all 22,342 protein-coding pig genes are presented. CONCLUSIONS: Here, we present a new genome-wide annotation strategy based on dimensionality reduction and density-based clustering. A genome-wide resource of the transcriptome map across all major tissues and organs in pig is presented, and the data is available as an open-access resource ( www.rnaatlas.org ), including a comparison to the expression of human orthologs.

Blood protein profiles related to preterm birth and retinopathy of prematurity.

BACKGROUND: Nearly one in ten children is born preterm. The degree of immaturity is a determinant of the infant's health. Extremely preterm infants have higher morbidity and mortality than term infants. One disease affecting extremely preterm infants is retinopathy of prematurity (ROP), a multifactorial neurovascular disease that can lead to retinal detachment and blindness. The advances in omics technology have opened up possibilities to study protein expressions thoroughly with clinical accuracy, here used to increase the understanding of protein expression in relation to immaturity and ROP. METHODS: Longitudinal serum protein profiles the first months after birth in 14 extremely preterm infants were integrated with perinatal and ROP data. In total, 448 unique protein targets were analyzed using Proximity Extension Assays. RESULTS: We found 20 serum proteins associated with gestational age and/or ROP functioning within mainly angiogenesis, hematopoiesis, bone regulation, immune function, and lipid metabolism. Infants with severe ROP had persistent lower levels of several identified proteins during the first postnatal months. CONCLUSIONS: The study contributes to the understanding of the relationship between longitudinal serum protein levels and immaturity and abnormal retinal neurovascular development. This is essential for understanding pathophysiological mechanisms and to optimize diagnosis, treatment and prevention for ROP. IMPACT: Longitudinal protein profiles of 14 extremely preterm infants were analyzed using a novel multiplex protein analysis platform combined with perinatal data. Proteins associated with gestational age at birth and the neurovascular disease ROP were identified. Among infants with ROP, longitudinal levels of the identified proteins remained largely unchanged during the first postnatal months. The main functions of the proteins identified were angiogenesis, hematopoiesis, immune function, bone regulation, lipid metabolism, and central nervous system development. The study contributes to the understanding of longitudinal serum protein patterns related to gestational age and their association with abnormal retinal neuro-vascular development.

Dramatic changes in blood protein levels during the first week of life in extremely preterm infants.

BACKGROUND: Preterm birth and its complications are the primary cause of death among children under the age of 5. Among the survivors, morbidity both perinatally and later in life is common. The dawn of novel technical platforms for comprehensive and sensitive analysis of protein profiles in blood has opened up new possibilities to study both health and disease with significant clinical accuracy, here used to study the preterm infant and the physiological changes of the transition from intrauterine to extrauterine life. METHODS: We have performed in-depth analysis of the protein profiles of 14 extremely preterm infants using longitudinal sampling. Medical variables were integrated with extensive profiling of 448 unique protein targets. RESULTS: The preterm infants have a distinct unified protein profile in blood directly at birth regardless of clinical background; however, the pattern changed profoundly postnatally, expressing more diverse profiles only 1 week later and further on up to term-equivalent age. Clusters of proteins depending on temporal trend were identified. CONCLUSION: The protein profiles and the temporal trends here described will contribute to the understanding of the physiological changes in the intrauterine-extrauterine transition, which is essential to adjust early-in-life interventions to prone a normal development in the vulnerable preterm infants. IMPACT: We have performed longitudinal and in-depth analysis of the protein profiles of 14 extremely preterm infants using a novel multiplex protein analysis platform. The preterm infants had a distinct unified protein profile in blood directly at birth regardless of clinical background. The pattern changed dramatically postnatally, expressing more diverse profiles only 1 week later and further on up to term-equivalent age. Certain clusters of proteins were identified depending on their temporal trend, including several liver and immune proteins. The study contributes to the understanding of the physiological changes in the intrauterine-extrauterine transition.

Longitudinal Plasma Protein Profiling Using Targeted Proteomics and Recombinant Protein Standards.

Spike-in of standards of known concentrations used in proteomics-based workflows is an attractive approach for both accurate and precise multiplexed protein quantification. Here, a quantitative method based on targeted proteomics analysis of plasma proteins using isotope-labeled recombinant standards originating from the Human Protein Atlas project has been established. The standards were individually quantified prior to being employed in the final multiplex assay. The assays are mainly directed toward actively secreted proteins produced in the liver, but may also originate from other parts of the human body. This study included 21 proteins classified by the FDA as either drug targets or approved clinical protein biomarkers. We describe the use of this multiplex assay for profiling a well-defined human cohort with sample collection spanning over a one-year period. Samples were collected at four different time points, which allowed for a longitudinal analysis to assess the variable plasma proteome within individuals. Two assays toward APOA1 and APOB had available clinical data, and the two assays were benchmarked against each other. The clinical assay is based on antibodies and shows high correlation between the two orthogonal methods, suggesting that targeted proteomics with highly parallel, multiplex analysis is an attractive alternative to antibody-based protein assays.

Systematic Development of Sandwich Immunoassays for the Plasma Secretome.

The plasma proteome offers a clinically useful window into human health. Recent advances from highly multiplexed assays now call for appropriate pipelines to validate individual candidates. Here, a workflow is developed to build dual binder sandwich immunoassays (SIA) and for proteins predicted to be secreted into plasma. Utilizing suspension bead arrays, ≈1800 unique antibody pairs are first screened against 209 proteins with recombinant proteins as well as EDTA plasma. Employing 624 unique antibodies, dilution-dependent curves in plasma and concentration-dependent curves of full-length proteins for 102 (49%) of the targets are obtained. For 22 protein assays, the longitudinal, interindividual, and technical performance is determined in a set of plasma samples collected from 18 healthy subjects every third month over 1 year. Finally, 14 of these assays are compared with with SIAs composed of other binders, proximity extension assays, and affinity-free targeted mass spectrometry. The workflow provides a multiplexed approach to screen for SIA pairs that suggests using at least three antibodies per target. This design is applicable for a wider range of targets of the plasma proteome, and the assays can be applied for discovery but also to validate emerging candidates derived from other platforms.

Cell Type-Specific Expression of Testis Elevated Genes Based on Transcriptomics and Antibody-Based Proteomics.

One of the most complex organs in the human body is the testis, where spermatogenesis takes place. This physiological process involves thousands of genes and proteins that are activated and repressed, making testis the organ with the highest number of tissue-specific genes. However, the function of a large proportion of the corresponding proteins remains unknown and testis harbors many missing proteins (MPs), defined as products of protein-coding genes that lack experimental mass spectrometry evidence. Here, an integrated omics approach was used for exploring the cell type-specific protein expression of genes with an elevated expression in testis. By combining genome-wide transcriptomics analysis with immunohistochemistry, more than 500 proteins with distinct testicular protein expression patterns were identified, and these were selected for in-depth characterization of their in situ expression in eight different testicular cell types. The cell type-specific protein expression patterns allowed us to identify six distinct clusters of expression at different stages of spermatogenesis. The analysis highlighted numerous poorly characterized proteins in each of these clusters whose expression overlapped with that of known proteins involved in spermatogenesis, including 85 proteins with an unknown function and 60 proteins that previously have been classified as MPs. Furthermore, we were able to characterize the in situ distribution of several proteins that previously lacked spatial information and cell type-specific expression within the testis. The testis elevated expression levels both at the RNA and protein levels suggest that these proteins are related to testis-specific functions. In summary, the study demonstrates the power of combining genome-wide transcriptomics analysis with antibody-based protein profiling to explore the cell type-specific expression of both well-known proteins and MPs. The analyzed proteins constitute important targets for further testis-specific research in male reproductive disorders.

Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.

Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.

Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics.

Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease. Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody-based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to ∼80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body.

The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling.

BACKGROUND: To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level. RESULTS: Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction. CONCLUSIONS: Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.

The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling.

The combined action of multiple cell types is essential for the physiological function of the lung, and increased awareness of the molecular constituents characterizing each cell type is likely to advance the understanding of lung biology and disease. In the current study, we used genome-wide RNA sequencing of normal lung parenchyma and 26 additional tissue types, combined with antibody-based protein profiling, to localize the expression to specific cell types. Altogether, 221 genes were found to be elevated in the lung compared with their expression in other analyzed tissues. Among the gene products were several well-known markers, but also several proteins previously not described in the context of the lung. To link the lung-specific molecular repertoire to human disease, survival associations of pneumocyte-specific genes were assessed by using transcriptomics data from 7 non-small-cell lung cancer (NSCLC) cohorts. Transcript levels of 10 genes (SFTPB, SFTPC, SFTPD, SLC34A2, LAMP3, CACNA2D2, AGER, EMP2, NKX2-1, and NAPSA) were significantly associated with survival in the adenocarcinoma subgroup, thus qualifying as promising biomarker candidates. In summary, based on an integrated omics approach, we identified genes with elevated expression in lung and localized corresponding protein expression to different cell types. As biomarker candidates, these proteins may represent intriguing starting points for further exploration in health and disease.

The human liver-specific proteome defined by transcriptomics and antibody-based profiling.

Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.-Kampf, C., Mardinoglu, A., Fagerberg, L., Hallström, B. M., Edlund, K., Lundberg, E., Pontén, F., Nielsen, J., Uhlen, M. The human liver-specific proteome defined by transcriptomics and antibody-based profiling.

Defining the human gallbladder proteome by transcriptomics and affinity proteomics.

Global protein analysis of human gallbladder tissue is vital for identification of molecular regulators and effectors of its physiological activity. Here, we employed a genome-wide deep RNA sequencing analysis in 28 human tissues to identify the genes overrepresented in the gallbladder and complemented it with antibody-based immunohistochemistry in 48 human tissues. We characterized human gallbladder proteins and identified 140 gallbladder-specific proteins with an elevated expression in the gallbladder as compared to the other analyzed tissues. Five genes were categorized as enriched, with at least fivefold higher levels in gallbladder, 60 genes were categorized as group enriched with elevated transcript levels in gallbladder shared with at least one other tissue and 75 genes were categorized as enhanced with higher expression than the average expression in other tissues. We explored the localization of the genes within the gallbladder through cell-type specific antibody-based protein profiling and the subcellular localization of the genes through immunofluorescent-based profiling. Finally, we revealed the biological processes and metabolic functions carried out by these genes through the use of GO, KEGG Pathway, and HMR2.0 that is compilation of the human metabolic reactions. We demonstrated the results of the combined analysis of the transcriptomics and affinity proteomics.

RNA- and antibody-based profiling of the human proteome with focus on chromosome 19.

An important part of the Human Proteome Project is to characterize the protein complement of the genome with antibody-based profiling. Within the framework of this effort, a new version 12 of the Human Protein Atlas ( www.proteinatlas.org ) has been launched, including transcriptomics data for 27 tissues and 44 cell lines to complement the protein expression data from antibody-based profiling. Besides the extensive addition of transcriptomics data, the Human Protein Atlas now contains antibody-based protein profiles for 82% of the 20 329 putative protein-coding genes. The comprehensive data resulting from RNA-seq analysis and antibody-based profiling performed within the Human Protein Atlas as well as information from UniProt were used to generate evidence summary scores for each of the 20 329 genes, of which 94% now have experimental evidence at least at transcript level. The evidence scores for all individual genes are displayed with regards to both RNA- and antibody-based protein profiles, including chromosome-centric visualizations. An analysis of the human chromosome 19 shows that ∼43% of the genes are expressed at the transcript level in all 27 tissues analyzed, suggesting a "house-keeping" function, while 12% of the genes show a more tissue-specific pattern with enriched expression in one of the analyzed tissues only.

Defining the human adipose tissue proteome to reveal metabolic alterations in obesity.

White adipose tissue (WAT) has a major role in the progression of obesity. Here, we combined data from RNA-Seq and antibody-based immunohistochemistry to describe the normal physiology of human WAT obtained from three female subjects and explored WAT-specific genes by comparing WAT to 26 other major human tissues. Using the protein evidence in WAT, we validated the content of a genome-scale metabolic model for adipocytes. We employed this high-quality model for the analysis of subcutaneous adipose tissue (SAT) gene expression data obtained from subjects included in the Swedish Obese Subjects Sib Pair study to reveal molecular differences between lean and obese individuals. We integrated SAT gene expression and plasma metabolomics data, investigated the contribution of the metabolic differences in the mitochondria of SAT to the occurrence of obesity, and eventually identified cytosolic branched-chain amino acid (BCAA) transaminase 1 as a potential target that can be used for drug development. We observed decreased glutaminolysis and alterations in the BCAAs metabolism in SAT of obese subjects compared to lean subjects. We also provided mechanistic explanations for the changes in the plasma level of BCAAs, glutamate, pyruvate, and α-ketoglutarate in obese subjects. Finally, we validated a subset of our model-based predictions in 20 SAT samples obtained from 10 lean and 10 obese male and female subjects.

Improved production of a heterologous amylase in Saccharomyces cerevisiae by inverse metabolic engineering.

The increasing demand for industrial enzymes and biopharmaceutical proteins relies on robust production hosts with high protein yield and productivity. Being one of the best-studied model organisms and capable of performing posttranslational modifications, the yeast Saccharomyces cerevisiae is widely used as a cell factory for recombinant protein production. However, many recombinant proteins are produced at only 1% (or less) of the theoretical capacity due to the complexity of the secretory pathway, which has not been fully exploited. In this study, we applied the concept of inverse metabolic engineering to identify novel targets for improving protein secretion. Screening that combined UV-random mutagenesis and selection for growth on starch was performed to find mutant strains producing heterologous amylase 5-fold above the level produced by the reference strain. Genomic mutations that could be associated with higher amylase secretion were identified through whole-genome sequencing. Several single-point mutations, including an S196I point mutation in the VTA1 gene coding for a protein involved in vacuolar sorting, were evaluated by introducing these to the starting strain. By applying this modification alone, the amylase secretion could be improved by 35%. As a complement to the identification of genomic variants, transcriptome analysis was also performed in order to understand on a global level the transcriptional changes associated with the improved amylase production caused by UV mutagenesis.

Antibody-based protein profiling of the human chromosome 21.

The Human Proteome Project has been proposed to create a knowledge-based resource based on a systematical mapping of all human proteins, chromosome by chromosome, in a gene-centric manner. With this background, we here describe the systematic analysis of chromosome 21 using an antibody-based approach for protein profiling using both confocal microscopy and immunohistochemistry, complemented with transcript profiling using next generation sequencing data. We also describe a new approach for protein isoform analysis using a combination of antibody-based probing and isoelectric focusing. The analysis has identified several genes on chromosome 21 with no previous evidence on the protein level, and the isoform analysis indicates that a large fraction of human proteins have multiple isoforms. A chromosome-wide matrix is presented with status for all chromosome 21 genes regarding subcellular localization, tissue distribution, and molecular characterization of the corresponding proteins. The path to generate a chromosome-specific resource, including integrated data from complementary assay platforms, such as mass spectrometry and gene tagging analysis, is discussed.

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae.

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.