Normalization of RNA-sequencing data from samples with varying mRNA levels

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge-A Transcriptomics Perspective

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides invaluable insights into allopolyploid plants by elucidating the fate of duplicated genes, revealing evolutionary novelties and uncovering their environmental adaptations. By examining gene expression profiles, scientists can discern how duplicated genes have evolved to acquire new functions or regulatory roles. This process often leads to the development of novel traits and adaptive strategies that allopolyploid plants leverage to thrive in diverse ecological niches. Understanding these molecular mechanisms not only enhances our appreciation of the genetic complexity underlying allopolyploidy but also underscores their importance in agriculture and ecosystem resilience. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid gene expression, focusing on critical steps like read mapping and normalization in differential gene expression analysis. This approach effectively captures gene expression from both parental genomes, facilitating a comprehensive analysis of their combined profiles. Its sensitivity in detecting low-abundance transcripts allows for subtle differences between parental genomes to be identified, crucial for understanding regulatory dynamics and gene expression balance in allopolyploids.

Niche differentiation within bacterial key-taxa in stratified surface waters of the Southern Pacific Gyre

One of the most hostile marine habitats on Earth is the surface of the South Pacific Gyre (SPG), characterized by high solar radiation, extreme nutrient depletion, and low productivity. During the SO-245 "UltraPac" cruise through the center of the ultra-oligotrophic SPG, the marine alphaproteobacterial group AEGEAN169 was detected by fluorescence in situ hybridization at relative abundances up to 6% of the total microbial community in the uppermost water layer, with two distinct populations (Candidatus Nemonibacter and Ca. Indicimonas). The high frequency of dividing cells combined with high transcript levels suggests that both clades may be highly metabolically active. Comparative metagenomic and metatranscriptomic analyses of AEGEAN169 revealed that they encoded subtle but distinct metabolic adaptions to this extreme environment in comparison to their competitors SAR11, SAR86, SAR116, and Prochlorococcus. Both AEGEAN169 clades had the highest percentage of transporters per predicted proteins (9.5% and 10.6%, respectively). In particular, the high expression of ABC transporters in combination with proteorhodopsins and the catabolic pathways detected suggest a potential scavenging lifestyle for both AEGEAN169 clades. Although both AEGEAN169 clades may share the genomic potential to utilize phosphonates as a phosphorus source, they differ in their metabolic pathways for carbon and nitrogen. Ca. Nemonibacter potentially use glycine-betaine, whereas Ca. Indicimonas may catabolize urea, creatine, and fucose. In conclusion, the different potential metabolic strategies of both clades suggest that both are well adapted to thrive resource-limited conditions and compete well with other dominant microbial clades in the uppermost layers of SPG surface waters.

Acquiring of photosensitivity by Mycobacterium tuberculosis in vitro and inside infected macrophages is associated with accumulation of endogenous Zn-porphyrins

Mycobacterium tuberculosis (Mtb) is able to transition into a dormant state, causing the latent state of tuberculosis. Dormant mycobacteria acquire resistance to all known antibacterial drugs and can survive in the human body for decades before becoming active. In the dormant forms of M. tuberculosis, the synthesis of porphyrins and its Zn-complexes significantly increased when 5-aminolevulinic acid (ALA) was added to the growth medium. Transcriptome analysis revealed an activation of 8 genes involved in the metabolism of tetrapyrroles during the Mtb transition into a dormant state, which may lead to the observed accumulation of free porphyrins. Dormant Mtb viability was reduced by more than 99.99% under illumination for 30 min (300 J/cm2) with 565 nm light that correspond for Zn-porphyrin and coproporphyrin absorptions. We did not observe any PDI effect in vitro using active bacteria grown without ALA. However, after accumulation of active cells in lung macrophages and their persistence within macrophages for several days in the presence of ALA, a significant sensitivity of active Mtb cells (ca. 99.99%) to light exposure was developed. These findings create a perspective for the treatment of latent and multidrug-resistant tuberculosis by the eradication of the pathogen in order to prevent recurrence of this disease.

A Camptothetin Analog, Topotecan, Promotes HIV Latency via Interference with HIV Transcription and RNA Splicing

Low level HIV transcription during modern antiretroviral therapy (ART) in persons with HIV is linked to residual inflammation and associated diseases, like cardiovascular disease and cancer. The "block and lock" approach to hold HIV in a state of deep latency may help decrease residual inflammation in a person with HIV on ART and thus improve health. A camptothecin analog topotecan (TPT) was previously implicated as an inhibitor of active HIV replication. Using an in vitro primary T cell model of HIV latency, we demonstrated that (i) TPT reduces HIV transcriptional activity in latently infected cells; (ii) downregulation of HIV RNA by TPT cannot be reversed by latency reversing agents; (iii) several primary and secondary mechanism of action of TPT may be involved in control of HIV replication; (iv) regulation of HIV RNA by TPT is dependent on splicing complexity; (v) increase in proportion of unspliced HIV transcripts was facilitated by intron retention and upregulation of splicing factors, specifically SRSF6, by TPT. Although high TPT dosing (10 μM) was needed to achieve the observed effects, viability of primary CD4+ T cells was not greatly affected. Because toxicity can be observed with TPT in persons with cancer, TPT is unlikely to be used as an anti-HIV agent in clinic, but our study provides proof that camptothetin has "block and lock" activity. Other camptothetin analogs, which are less toxic than TPT, should be designed and tested as HIV "block and lock" agents. IMPORTANCE HIV survives in a state of very low activity, called latency, for long periods in persons with HIV on antiretroviral therapy. This low activity of HIV is linked to residual inflammation and associated diseases, such as heart disease and cancer. New strategies are being explored to further silence the HIV provirus and suppress residual inflammation. This study provides strong evidence that the camptothetin analog, Topotecan, can reduce residual activity of HIV in an experimental model of HIV latency. While Topotecan itself is likely not suitable for use in the clinic due to its toxicity, other camptothetin analogs should be designed and investigated as "block and lock" agents.

Beyond Transcript Concentrations: Quantifying Polyploid Expression Responses per Biomass, per Genome, and per Cell with RNA-Seq

RNA-seq has been used extensively to study expression responses to polyploidy. Most current methods for normalizing RNA-seq data yield estimates of transcript concentrations (transcripts per transcriptome). The implicit assumption of these normalization methods is that transcriptome size is equivalent between the samples being compared such that transcript concentrations are equivalent to transcripts per cell. In recent years, however, evidence has mounted that transcriptome size can vary dramatically in response to a range of factors including polyploidy and that such variation is ubiquitous. Where such variation exists, transcript concentration is often a poor or even misleading proxy for expression responses at other biologically relevant scales (e.g., expression per cell). Thus, it is important that transcriptomic studies of polyploids move beyond simply comparing transcript concentrations if we are to gain a complete understanding of how genome multiplication affects gene expression. I discuss this issue in more detail and summarize a suite of approaches that can leverage RNA-seq to quantify expression responses per genome, per cell, and per unit of biomass.

Extracellular Vesicles from Animal Milk: Great Potentialities and Critical Issues

Other than representing the main source of nutrition for newborn mammals, milk delivers a sophisticated signaling system from mother to child that promotes postnatal health. The bioactive components transferred through the milk intake are important for the development of the newborn immune system and include oligosaccharides, lactoferrin, lysozyme, α-La, and immunoglobulins. In the last 15 years, a pivotal role in this mother-to-child exchange has been attributed to extracellular vesicles (EVs). EVs are micro- and nanosized structures enclosed in a phospholipidic double-layer membrane that are produced by all cell types and released in the extracellular environment, reaching both close and distant cells. EVs mediate the intercellular cross-talk from the producing to the receiving cell through the transfer of molecules contained within them such as proteins, antigens, lipids, metabolites, RNAs, and DNA fragments. The complex cargo can induce a wide range of functional modulations in the recipient cell (i.e., anti-inflammatory, immunomodulating, angiogenetic, and pro-regenerative modulations) depending on the type of producing cells and the stimuli that these cells receive. EVs can be recovered from every biological fluid, including blood, urine, bronchoalveolar lavage fluid, saliva, bile, and milk, which is one of the most promising scalable vesicle sources. This review aimed to present the state-of-the-art of animal-milk-derived EV (mEV) studies due to the exponential growth of this field. A focus on the beneficial potentialities for human health and the issues of studying vesicles from milk, particularly for the analytical methodologies applied, is reported.

Transcriptome Profiling of Starvation in the Peripheral Chemosensory Organs of the Crop Pest Spodoptera littoralis Caterpillars

Simple Summary

Starvation increases olfactory sensitivity in a manner that enhances the search for food in animals, including insects. However, the molecular mechanisms via which starvation modulates olfactory receptor neuron function are poorly understood. In this study, we sequenced and compared the whole transcriptomes of the main olfactory organs (antennae and palps) of fed and starved caterpillars from the species Spodoptera littoralis. We revealed that transcripts involved in several biological processes are regulated upon starvation. These processes include glucose metabolism, immune defense, foraging activity, and olfaction. In this last process, we evidenced regulation of chemosensory proteins and odorant-degrading enzymes, known to play a role in the dynamics and the sensitivity of the olfactory receptor neuron response. Our results identify new elements in the cascade of olfactory neuron modulation, in addition to insulin, GABA, and short neuropeptide F signaling.

Abstract

Starvation is frequently encountered by animals under fluctuating food conditions in nature, and response to it is vital for life span. Many studies have investigated the behavioral and physiological responses to starvation. In particular, starvation is known to induce changes in olfactory behaviors and olfactory sensitivity to food odorants, but the underlying mechanisms are not well understood. Here, we investigated the transcriptional changes induced by starvation in the chemosensory tissues of the caterpillar Spodoptera littoralis, using Illumina RNA sequencing. Gene expression profiling revealed 81 regulated transcripts associated with several biological processes, such as glucose metabolism, immune defense, response to stress, foraging activity, and olfaction. Focusing on the olfactory process, we observed changes in transcripts encoding proteins putatively involved in the peri-receptor events, namely, chemosensory proteins and odorant-degrading enzymes. Such modulation of their expression may drive fluctuations in the dynamics and the sensitivity of the olfactory receptor neuron response. In combination with the enhanced presynaptic activity mediated via the short neuropeptide F expressed during fasting periods, this could explain an enhanced olfactory detection process. Our observations suggest that a coordinated transcriptional response of peripheral chemosensory organs participates in the regulation of olfactory signal reception and olfactory-driven behaviors upon starvation.

Dissecting regulatory pathways for transcription recovery following DNA damage reveals a non-canonical function of the histone chaperone HIRA

Transcription restart after a genotoxic challenge is a fundamental yet poorly understood process. Here, we dissect the interplay between transcription and chromatin restoration after DNA damage by focusing on the human histone chaperone complex HIRA, which is required for transcription recovery post UV. We demonstrate that HIRA is recruited to UV-damaged chromatin via the ubiquitin-dependent segregase VCP to deposit new H3.3 histones. However, this local activity of HIRA is dispensable for transcription recovery. Instead, we reveal a genome-wide function of HIRA in transcription restart that is independent of new H3.3 and not restricted to UV-damaged loci. HIRA coordinates with ASF1B to control transcription restart by two independent pathways: by stabilising the associated subunit UBN2 and by reducing the expression of the transcription repressor ATF3. Thus, HIRA primes UV-damaged chromatin for transcription restart at least in part by relieving transcription inhibition rather than by depositing new H3.3 as an activating bookmark.

Gene expression profile of human follicle dermal papilla cells in response to Camellia japonica phytoplacenta extract

Camellia japonica L. is a flowering tree with several medicinal and cosmetic applications. Here, we investigated the efficacy of C. japonica placenta extract (CJPE) as a potential therapeutic agent for promotion of hair growth and scalp health by using various in vitro and in vivo assays. Moreover, we performed transcriptome analysis to examine the relative expression of human follicle dermal papilla cells (HFDPC) in response to CJPE by RNA-sequencing (RNA-seq). In vitro assays revealed upregulation of the expression of hair growth marker genes in HFDPC after CJPE treatment. Moreover, in vivo clinical tests with 42 adult female participants showed that a solution containing 0.5% CJPE increased the moisture content of the scalp and decreased the scalp's sebum content, dead scalp keratin, and erythema. Furthermore, RNA-seq analysis revealed key genes in HFDPC which are associated with CJPE. Interestingly, genes associated with lipid metabolism and cholesterol efflux were upregulated. Genes upregulated by CJPE are associated with several hormones, including parathyroid, adrenocorticotropic hormone, α-melanocyte-stimulating hormone (alpha-MSH), and norepinephrine, which are involved in hair follicle biology. Furthermore, some upregulated genes are associated with the regulation of axon guidance. In contrast, many genes downregulated by CJPE are associated with structural components of the cytoskeleton. In addition, CJPE suppressed genes associated with muscle structure and development. Taken together, this study provides extensive evidence that CJPE may have potential as a therapeutic agent for scalp treatment and hair growth promotion.

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Intron retention and its impact on gene expression and protein diversity: A review and a practical guide

Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers

The early detection of cancer holds the key to combat and control the increasing global burden of cancer morbidity and mortality. Blood-based screenings using circulating DNAs (ctDNAs), circulating RNA (ctRNAs), circulating tumor cells (CTCs) and extracellular vesicles (EVs) have shown promising prospects in the early detection of cancer. Recent high-throughput gene expression profiling of blood samples from cancer patients has provided a valuable resource for developing new biomarkers for the early detection of cancer. However, a well-organized online repository for these blood-based high-throughput gene expression data is still not available. Here, we present BBCancer (http://bbcancer.renlab.org/), a web-accessible and comprehensive open resource for providing the expression landscape of six types of RNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), tRNA-derived fragments (tRFRNAs) and Piwi-interacting RNAs (piRNAs) in blood samples, including plasma, CTCs and EVs, from cancer patients with various cancer types. Currently, BBCancer contains expression data of the six RNA types from 5040 normal and tumor blood samples across 15 cancer types. We believe this database will serve as a powerful platform for developing blood biomarkers.

Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review

Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.

Isolating Male Meiocytes from Maize and Wheat for "-Omics" Analyses

Genome-wide gene expression studies have become a routine approach due to the advances in sequencing technologies, their ease of use, and increasing affordability. Simultaneous investigation of small RNA expression adds further valuable information but is not adopted as widely yet. Both RNA-seq and small RNA-seq benefit from the use of specific cell types. Here, we describe a protocol for the isolation of male meiotic cells from maize or wheat plants, along with the application of downstream RNA sequencing, extendable to other -omics approaches.

Unanchored ubiquitin chains do not lead to marked alterations in gene expression in Drosophila melanogaster

The small protein modifier ubiquitin regulates various aspects of cellular biology through its chemical conjugation onto proteins. Ubiquitination of proteins presents itself in numerous iterations, from a single mono-ubiquitination event to chains of poly-ubiquitin. Ubiquitin chains can be attached onto other proteins or can exist as unanchored species, i.e. free from another protein. Unanchored ubiquitin chains are thought to be deleterious to the cell and rapidly disassembled into mono-ubiquitin. We recently examined the toxicity and utilization of unanchored poly-ubiquitin in Drosophila melanogaster. We found that free poly-ubiquitin species are largely innocuous to flies and that free poly-ubiquitin can be controlled by being degraded by the proteasome or by being conjugated onto another protein as a single unit. Here, to explore whether an organismal defense is mounted against unanchored chains, we conducted RNA-Seq analyses to examine the transcriptomic impact of free poly-ubiquitin in the fly. We found ∼90 transcripts whose expression is altered in the presence of different types of unanchored poly-ubiquitin. The set of genes identified was essentially devoid of ubiquitin-, proteasome-, or autophagy-related components. The seeming absence of a large and multipronged response to unanchored poly-ubiquitin supports the conclusion that these species need not be toxic in vivo and underscores the need to re-examine the role of free ubiquitin chains in the cell.

Summary: Our Drosophila studies indicate the lack of a marked, coordinated response towards unanchored poly-ubiquitin in flies, suggesting that untethered ubiquitin chains are not necessarily problematic in intact organisms.

Prediction and Characterization of miRNA/Target Pairs in Non-Model Plants Using RNA-seq

Plant microRNAs (miRNAs) are ∼20- to 24-nucleotide small RNAs that post-transcriptionally regulate gene expression of mRNA targets. Here, we present a workflow to characterize the miRNA transcriptome of a non-model plant, focusing on miRNAs and targets that are differentially expressed under one experimental treatment. We cover RNA-seq experimental design to create paired small RNA and mRNA libraries and perform quality control of raw data, de novo mRNA transcriptome assembly and annotation, miRNA prediction, differential expression, target identification, and functional enrichment analysis. Additionally, we include validation of differential expression and miRNA-induced target cleavage using qRT-PCR and modified RNA ligase-mediated 5' rapid amplification of cDNA ends, respectively. Our procedure relies on freely available software and web resources. It is intended for users that lack programming skills but can navigate a command-line interface. To enable an understanding of formatting requirements and anticipated results, we provide sample RNA-seq data and key input/output files for each stage. © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Smooth quantile normalization

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

Discovery of 5-Signature Predicting Survival of Patients with Lower-Grade Glioma

OBJECTIVE

In the study, we aimed to identify key microRNAs (miRNAs) and clinical factors associated with survival time of lower-grade glioma (LGG) and develop an expression-based miRNA signature to provide survival risk prediction for patients with LGG.

METHODS

We obtained miRNA expression profiles and clinical information of patients with LGG from The Cancer Genome Atlas dataset. All 591 miRNAs were modeled using random Forest Survival, Regression, and Classification to construct a random forest model for survival analysis, and feature selection was performed. We used univariate and multivariate Cox regression analysis to screen differentially expressed miRNAs and clinical factors related to overall survival of patients with LGG.

RESULTS

A total of 591 differentially expressed miRNAs were obtained between LGG and normal tissues. After univariate and multivariate Cox regression analysis, 2 predictive miRNAs (hsa-miR-10b-5p and hsa-miR-15b-5p) and 3 clinical factors (grade, age, and cancer status) were finally screened out to construct a 5-signature, based on which patients in the training dataset were divided into high-risk and low-risk groups. The competitive performance of the 5-signature was revealed by receiver operating characteristic curve analysis. The prognostic value of the 5-signature was successfully validated in the testing and validation dataset.

CONCLUSIONS

Our study demonstrated the promising potential of the novel 5-signature as an independent biomarker for survival prediction of patients with LGG.

Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes

BACKGROUND

The amount of RNA per cell, namely the transcriptome size, may vary under many biological conditions including tumor. If the transcriptome size of two cells is different, direct comparison of the expression measurements on the same amount of total RNA for two samples can only identify genes with changes in the relative mRNA abundances, i.e., cellular mRNA concentration, rather than genes with changes in the absolute mRNA abundances.

RESULTS

Our recently proposed RankCompV2 algorithm identify differentially expressed genes (DEGs) through comparing the relative expression orderings (REOs) of disease samples with that of normal samples. We reasoned that both the mRNA concentration and the absolute abundances of these DEGs must have changes in disease samples. In simulation experiments, this method showed excellent performance for identifying DEGs between normal and disease samples with different transcriptome sizes. Through analyzing data for ten cancer types, we found that a significantly higher proportion of the DEGs with absolute mRNA abundance changes overlapped or directly interacted with known cancer driver genes and anti-cancer drug targets than that of the DEGs only with mRNA concentration changes alone identified by the traditional methods. The DEGs with increased absolute mRNA abundances were enriched in DNA damage-related pathways, while DEGs with decreased absolute mRNA abundances were enriched in immune and metabolism associated pathways.

CONCLUSIONS

Both the mRNA concentration and the absolute abundances of the DEGs identified through REOs comparison change in disease samples in comparison with normal samples. In cancers these genes might play more important upstream roles in carcinogenesis.

Concurrent Host-Pathogen Transcriptional Responses in a Clostridium perfringens Murine Myonecrosis Infection

To obtain an insight into host-pathogen interactions in clostridial myonecrosis, we carried out comparative transcriptome analysis of both the bacterium and the host in a murine Clostridium perfringens infection model, which is the first time that such an investigation has been conducted. Analysis of the host transcriptome from infected muscle tissues indicated that many genes were upregulated compared to the results seen with mock-infected mice. These genes were enriched for host defense pathways, including Toll-like receptor (TLR) and Nod-like receptor (NLR) signaling components. Real-time PCR confirmed that host TLR2 and NLRP3 inflammasome genes were induced in response to C. perfringens infection. Comparison of the transcriptome of C. perfringens cells from the infected tissues with that from broth cultures showed that host selective pressure induced a global change in C. perfringens gene expression. A total of 33% (923) of C. perfringens genes were differentially regulated, including 10 potential virulence genes that were upregulated relative to their expression in vitro These genes encoded putative proteins that may be involved in the synthesis of cell wall-associated macromolecules, in adhesion to host cells, or in protection from host cationic antimicrobial peptides. This report presents the first successful expression profiling of coregulated transcriptomes of bacterial and host genes during a clostridial myonecrosis infection and provides new insights into disease pathogenesis and host-pathogen interactions.IMPORTANCEClostridium perfringens is the causative agent of traumatic clostridial myonecrosis, or gas gangrene. In this study, we carried out transcriptional analysis of both the host and the bacterial pathogen in a mouse myonecrosis infection. The results showed that in comparison to mock-infected control tissues, muscle tissues from C. perfringens-infected mice had a significantly altered gene expression profile. In particular, the expression of many genes involved in the innate immune system was upregulated. Comparison of the expression profiles of C. perfringens cells isolated from the infected tissues with those from equivalent broth cultures identified many potential virulence genes that were significantly upregulated in vivo These studies have provided a new understanding of the range of factors involved in host-pathogen interactions in a myonecrosis infection.

Transcriptome profiling of sorted endoreduplicated nuclei from tomato fruits: how the global shift in expression ascribed to DNA ploidy influences RNA-Seq data normalization and interpretation

As part of normal development most eukaryotic organisms, ranging from insects and mammals to plants, display variations in nuclear ploidy levels resulting from somatic endopolyploidy. Endoreduplication is the major source of endopolyploidy in higher plants. Endoreduplication is a remarkable characteristic of the fleshy pericarp tissue of developing tomato fruits, where it establishes a highly integrated cellular system that acts as a morphogenetic factor supporting cell growth. However, the functional significance of endoreduplication is not fully understood. Although endoreduplication is thought to increase metabolic activity due to a global increase in transcription, the issue of gene-specific ploidy-regulated transcription remains open. To investigate the influence of endoreduplication on transcription in tomato fruit, we tested the feasibility of a RNA sequencing (RNA-Seq) approach using total nuclear RNA extracted from purified populations of flow cytometry-sorted nuclei based on their DNA content. Here we show that cell-based approaches to the study of RNA-Seq profiles need to take into account the putative global shift in expression between samples for correct analysis and interpretation of the data. From ploidy-specific expression profiles we found that the activity of cells inside the pericarp is related both to the ploidy level and their tissue location.

Aberrant expression of epithelial leucine-rich repeat containing G protein-coupled receptor 5-positive cells in the eutopic endometrium in endometriosis and implications in deep-infiltrating endometriosis

OBJECTIVE

To characterize leucine-rich repeat containing G protein-coupled receptor 5-positive (LGR5⁺) cells from the endometrium of women with endometriosis.

DESIGN

Prospective experimental study.

SETTING

University hospital/fertility clinic.

PATIENT(S)

Twenty-seven women with endometriosis who underwent surgery and 12 healthy egg donors, together comprising 39 endometrial samples.

INTERVENTION(S)

Obtaining of uterine aspirates by using a Cornier Pipelle.

MAIN OUTCOMES MEASURE(S)

Immunofluorescence in formalin-fixed paraffin-embedded tissue from mice and healthy and pathologic human endometrium using antibodies against LGR5, E-cadherin, and cytokeratin, and epithelial and stromal LGR5⁺ cells isolated from healthy and pathologic human eutopic endometrium by fluorescence-activated cell sorting and transcriptomic characterization by RNA high sequencing.

RESULT(S)

Immunofluorescence showed that LGR5⁺ cells colocalized with epithelial markers in the stroma of the endometrium only in endometriotic patients. The results from RNA high sequencing of LGR5⁺ cells from epithelium and stroma did not show any statistically significant differences between them. The LGR5⁺ versus LGR5^- cells in pathologic endometrium showed 394 differentially expressed genes. The LGR5⁺ cells in deep-infiltrating endometriosis expressed inflammatory markers not present in the other types of the disease.

CONCLUSION(S)

Our results revealed the presence of aberrantly located LGR5⁺ cells coexpressing epithelial markers in the stromal compartment of women with endometriosis. These cells have a statistically significantly different expression profile in deep-infiltrating endometriosis in comparison with other types of endometriosis, independent of the menstrual cycle phase. Further studies are needed to elucidate their role and influence in reproductive outcomes.

Comparison of alternative approaches for analysing multi-level RNA-seq data

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.

Serum MicroRNAs Reflect Injury Severity in a Large Animal Model of Thoracic Spinal Cord Injury

Therapeutic development for spinal cord injury is hindered by the difficulty in conducting clinical trials, which to date have relied solely on functional outcome measures for patient enrollment, stratification, and evaluation. Biological biomarkers that accurately classify injury severity and predict neurologic outcome would represent a paradigm shift in the way spinal cord injury clinical trials could be conducted. MicroRNAs have emerged as attractive biomarker candidates due to their stability in biological fluids, their phylogenetic similarities, and their tissue specificity. Here we characterized a porcine model of spinal cord injury using a combined behavioural, histological, and molecular approach. We performed next-generation sequencing on microRNAs in serum samples collected before injury and then at 1, 3, and 5 days post injury. We identified 58, 21, 9, and 7 altered miRNA after severe, moderate, and mild spinal cord injury, and SHAM surgery, respectively. These data were combined with behavioural and histological analysis. Overall miRNA expression at 1 and 3 days post injury strongly correlates with outcome measures at 12 weeks post injury. The data presented here indicate that serum miRNAs are promising candidates as biomarkers for the evaluation of injury severity for spinal cord injury or other forms of traumatic, acute, neurologic injury.

Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model

The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency in vivo are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of primary central memory CD4 T cells (TCM), full-length virus infection (HIVNL4-3) and ART to suppress virus replication, was used to investigate the establishment of HIV latency using RNA-Seq. Initially, an investigation of host and viral gene expression in the resting and activated states of this model indicated that the resting condition was reflective of a latent state. Then, a comparison of the host transcriptome between the uninfected and latently infected conditions of this model identified 826 differentially expressed genes, many of which were related to p53 signaling. Inhibition of the transcriptional activity of p53 by pifithrin-α during HIV-1 infection reduced the ability of HIV-1 to be reactivated from its latent state by an unknown mechanism. In conclusion, this model may be used to screen latency reversing agents utilized in shock and kill approaches to cure HIV, to search for cellular markers of latency, and to understand the mechanisms by which HIV-1 establishes latency.

Leucine-rich repeat-containing G-protein-coupled receptor 5-positive cells in the endometrial stem cell niche

OBJECTIVE

To study, isolate and characterize leucine-rich repeat-containing heterotrimeric guanine nucleotide-binding protein-coupled receptor 5 (LGR5)-positive cells from human endometrium to determine their functional relevance.

DESIGN

Prospective experimental animal study.

SETTING

University research laboratories.

ANIMAL(S)

Nonobese diabetic mice (NOD-SCID) (strain code 394; NOD.CB17-Prkdc^scid/NcrCrl).

INTERVENTION(S)

Human LGR5⁺ cells were labeled with superparamagnetic iron oxide nanoparticles (SPIOs) and injected under the kidney capsule in immunocompromised mice.

MAIN OUTCOME MEASURE(S)

Epithelial and stromal LGR5⁺ cells were isolated from human endometrium by means of fluorescence-activated cell sorting, and phenotypic characterization was performed by means of flow cytometry with the use of hematopoietic and mesenchymal markers. Engrafted SPIO-labeled LGR5⁺ cells were localized with the use of Prussian blue staining and immunohistochemistry against CD9 and Vimentin. Deep transcriptomic profiling of LGR5⁺ cells was performed with the use of microarrays and RNA sequencing.

RESULT(S)

The percentage of LGR5⁺ cells in human endometrium represented 1.08 ± 0.73% and 0.82 ± 0.76% of total cells in the epithelial and stromal compartments, respectively. LGR5⁺ cells were phenotypically characterized by abundant expression of CD45 hematopoietic marker and no expression of surface markers CD31, CD34, CD133, CD73, and CD90. Coexpression with the macrophage marker CD163 was detected. Xenotransplantation of labeled LGR5⁺ cells into the kidney capsules of immunocompromised mice resulted in a weak endometrial reconstitution from this cell of origin. Transcriptomic profiling revealed new attributes for LGR5⁺ cells related to their putative hematopoietic origin.

CONCLUSION(S)

These data suggest that endometrial LGR5 is not an endogenous stem cell marker. Instead, LGR5⁺ cells appear to be recruited from blood to be part of the stem cell niche at the perivascular microenvironment to activate the endogenous niche.

Tandem Duplication Events in the Expansion of the Small Heat Shock Protein Gene Family in Solanum lycopersicum (cv. Heinz 1706)

In plants, fruit maturation and oxidative stress can induce small heat shock protein (sHSP) synthesis to maintain cellular homeostasis. Although the tomato reference genome was published in 2012, the actual number and functionality of sHSP genes remain unknown. Using a transcriptomic (RNA-seq) and evolutionary genomic approach, putative sHSP genes in the Solanum lycopersicum (cv. Heinz 1706) genome were investigated. A sHSP gene family of 33 members was established. Remarkably, roughly half of the members of this family can be explained by nine independent tandem duplication events that determined, evolutionarily, their functional fates. Within a mitochondrial class subfamily, only one duplicated member, Solyc08g078700, retained its ancestral chaperone function, while the others, Solyc08g078710 and Solyc08g078720, likely degenerated under neutrality and lack ancestral chaperone function. Functional conservation occurred within a cytosolic class I subfamily, whose four members, Solyc06g076570, Solyc06g076560, Solyc06g076540, and Solyc06g076520, support ∼57% of the total sHSP RNAm in the red ripe fruit. Subfunctionalization occurred within a new subfamily, whose two members, Solyc04g082720 and Solyc04g082740, show heterogeneous differential expression profiles during fruit ripening. These findings, involving the birth/death of some genes or the preferential/plastic expression of some others during fruit ripening, highlight the importance of tandem duplication events in the expansion of the sHSP gene family in the tomato genome. Despite its evolutionary diversity, the sHSP gene family in the tomato genome seems to be endowed with a core set of four homeostasis genes: Solyc05g014280, Solyc03g082420, Solyc11g020330, and Solyc06g076560, which appear to provide a baseline protection during both fruit ripening and heat shock stress in different tomato tissues.

Circulating microbial RNA and health

Measurement of health indicators in the blood is a commonly performed diagnostic procedure. Two blood studies one involving extended observations on the health of an individual by integrative Personal Omics Profiling (iPOP), and the other tracking the impact of Left Ventricular Assist Device (LVAD) placement on nine heart failure patients were examined for the association of change in health status with change in microbial RNA species. Decrease in RNA expression ratios of human to bacteria and viruses accompanying deteriorated conditions was evident in both studies. Despite large between-subject variations in bacterial composition before LVAD implantation among all the patients, on day 180 after the implantation they manifested apparent between-subject bacterial similarity. In the iPOP study three periods, namely, pre-respiratory syncytial virus (RSV) infection with normal blood glucose level, RSV infection with normal blood glucose level, and post-RSV infection with high blood glucose level could be defined. The upsurge of Enterobacteria phage PhiX 174 sensu lato and Escherichia coli gene expression, in which membrane transporters, membrane receptors for environment signalling, carbohydrate catabolic genes and carbohydrate-active enzymes were enriched only throughout the second period, which suggests a potentially overlooked microbial response to or modulation of the host blood glucose level.

Dormant non-culturable Mycobacterium tuberculosis retains stable low-abundant mRNA

Background

Dormant Mycobacterium tuberculosis bacilli are believed to play an important role in latent tuberculosis infection. Previously, we have demonstrated that cultivation of M. tuberculosis in K⁺-deficient medium resulted in generation of dormant cells. These bacilli were non-culturable on solid media (a key feature of dormant M. tuberculosis in vivo) and characterized by low metabolism and tolerance to anti-tuberculosis drugs. The dormant bacteria demonstrated a high potential to reactivation after K⁺ reintroduction even after prolonged persistence under rifampicin. In this work, we studied the transcriptome and stability of transcripts in persisting dormant bacilli under arrest of mRNA de novo synthesis.

Results

RNA-seq-based analysis of the dormant non-culturable population obtained under rifampicin exposure revealed a 30–50-fold decrease of the total mRNA level, indicating global transcriptional repression. However, the analysis of persisting transcripts displayed a cohort of mRNA molecules coding for biosynthetic enzymes, proteins involved in adaptation and repair processes, detoxification, and control of transcription initiation. This ‘dormant transcriptome’ demonstrated considerable stability during M. tuberculosis persistence and mRNA de novo synthesis arrest. On the contrary, several small non-coding RNAs showed increased abundance on dormancy. Interestingly, M. tuberculosis entry into dormancy was accompanied by the cleavage of 23S ribosomal RNA at a specific point located outside the ribosome catalytic center.

Conclusions

Dormant non-culturable M. tuberculosis bacilli are characterized by a global transcriptional repression. At the same time, the dormant bacilli retain low-abundant mRNAs, which are considerably stable during in vitro persistence, reflecting their readiness for translation upon early resuscitation steps. Increased abundance of non-coding RNAs on dormancy may indicate their role in the entry into and maintenance of M. tuberculosis dormant non-culturable state.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2197-6) contains supplementary material, which is available to authorized users.

Using mixtures of biological samples as process controls for RNA-sequencing experiments

Background

Genome-scale “-omics” measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls.

Results

We apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the post-enrichment ‘target RNA’ content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine enriched RNA content of total RNA in samples.

Conclusions

Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The target RNA fraction accounts for differential selection of RNA out of variable total RNA samples. Spike-in controls can be utilized to measure this relationship between target RNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1912-7) contains supplementary material, which is available to authorized users.

Drosophila anti-nematode and antibacterial immune regulators revealed by RNA-Seq

Background

Drosophila melanogaster activates a variety of immune responses against microbial infections. However, information on the Drosophila immune response to entomopathogenic nematode infections is currently limited. The nematode Heterorhabditis bacteriophora is an insect parasite that forms a mutualistic relationship with the gram-negative bacteria Photorhabdus luminescens. Following infection, the nematodes release the bacteria that quickly multiply within the insect and produce several toxins that eventually kill the host. Although we currently know that the insect immune system interacts with Photorhabdus, information on interaction with the nematode vector is scarce.

Results

Here we have used next generation RNA-sequencing to analyze the transcriptional profile of wild-type adult flies infected by axenic Heterorhabditis nematodes (lacking Photorhabdus bacteria), symbiotic Heterorhabditis nematodes (carrying Photorhabdus bacteria), and Photorhabdus bacteria alone. We have obtained approximately 54 million reads from the different infection treatments. Bioinformatic analysis shows that infection with Photorhabdus alters the transcription of a large number of Drosophila genes involved in translational repression as well in response to stress. However, Heterorhabditis infection alters the transcription of several genes that participate in lipidhomeostasis and metabolism, stress responses, DNA/protein sythesis and neuronal functions. We have also identified genes in the fly with potential roles in nematode recognition, anti-nematode activity and nociception.

Conclusions

These findings provide fundamental information on the molecular events that take place in Drosophila upon infection with the two pathogens, either separately or together. Such large-scale transcriptomic analyses set the stage for future functional studies aimed at identifying the exact role of key factors in the Drosophila immune response against nematode-bacteria complexes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1690-2) contains supplementary material, which is available to authorized users.

FCMM: A comparative metagenomic approach for functional characterization of multiple metagenome samples

Next-generation sequencing (NGS) technologies make it possible to obtain the entire genomic content of microorganisms in metagenome samples. Thus, many studies have developed methods for the processing and analysis of metagenomic NGS reads, including analyses for predicting functions and their enrichments in environmental metagenome samples. Especially, comparative functional studies by using multi-metagenome samples are essential for identifying and comparing different characteristics of multiple environmental samples. In this paper, we introduce a pipeline for functional characterization of multiple metagenome samples to infer major functions as well as their quantitative scores in a comparative metagenomics manner. The pipeline performs the annotation of functions related to expected proteins in the metagenome samples, calculates their enrichment scores based on the reads per kilobase per million reads (RPKM) measure, and predicts the relative abundance of associated functions by a statistical test. The results from single sample analysis are then used to find common and sample-specific major functions. By applying the pipeline to six different environmental metagenome samples, including two ocean (Antarctica aquatic and Baltic Sea) and four terrestrial (Acid mine drainage, human gut microbiome, Amazon River, and Wasca soil) samples, we were able to predict common functions as well as environment-specific functions. Our pipeline is available at http://bioinfo.konkuk.ac.kr/FCMM/.

GeneFriends: a human RNA-seq-based gene and transcript co-expression database

Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts and genome-wide association studies often identify loci containing novel genes and there is a need to infer their functions and interaction partners. To facilitate this we have expanded GeneFriends, an online database that allows users to identify co-expressed genes with one or more user-defined genes. This expansion entails an RNA-seq-based co-expression map that includes genes and transcripts that are not present in the microarray-based co-expression maps, including over 10 000 non-coding RNAs. The results users obtain from GeneFriends include a co-expression network as well as a summary of the functional enrichment among the co-expressed genes. Novel insights can be gathered from this database for different splice variants and ncRNAs, such as microRNAs and lincRNAs. Furthermore, our updated tool allows candidate transcripts to be linked to diseases and processes using a guilt-by-association approach. GeneFriends is freely available from http://www.GeneFriends.org and can be used to quickly identify and rank candidate targets relevant to the process or disease under study.