Mural cell-derived chemokines provide a protective niche to safeguard vascular macrophages and limit chronic inflammation.

Maladaptive, non-resolving inflammation contributes to chronic inflammatory diseases such as atherosclerosis. Because macrophages remove necrotic cells, defective macrophage programs can promote chronic inflammation with persistent tissue injury. Here, we investigated the mechanisms sustaining vascular macrophages. Intravital imaging revealed a spatiotemporal macrophage niche across vascular beds alongside mural cells (MCs)-pericytes and smooth muscle cells. Single-cell transcriptomics, co-culture, and genetic deletion experiments revealed MC-derived expression of the chemokines CCL2 and MIF, which actively preserved macrophage survival and their homeostatic functions. In atherosclerosis, this positioned macrophages in viable plaque areas, away from the necrotic core, and maintained a homeostatic macrophage phenotype. Disruption of this MC-macrophage unit via MC-specific deletion of these chemokines triggered detrimental macrophage relocalizing, exacerbated plaque necrosis, inflammation, and atheroprogression. In line, CCL2 inhibition at advanced stages of atherosclerosis showed detrimental effects. This work presents a MC-driven safeguard toward maintaining the homeostatic vascular macrophage niche.

Human genetic defects in SRP19 and SRPRA cause severe congenital neutropenia with distinctive proteome changes.

The mechanisms of coordinated changes in proteome composition and their relevance for the differentiation of neutrophil granulocytes are not well studied. Here, we discover 2 novel human genetic defects in signal recognition particle receptor alpha (SRPRA) and SRP19, constituents of the mammalian cotranslational targeting machinery, and characterize their roles in neutrophil granulocyte differentiation. We systematically study the proteome of neutrophil granulocytes from patients with variants in the SRP genes, HAX1, and ELANE, and identify global as well as specific proteome aberrations. Using in vitro differentiation of human induced pluripotent stem cells and in vivo zebrafish models, we study the effects of SRP deficiency on neutrophil granulocyte development. In a heterologous cell-based inducible protein expression system, we validate the effects conferred by SRP dysfunction for selected proteins that we identified in our proteome screen. Thus, SRP-dependent protein processing, intracellular trafficking, and homeostasis are critically important for the differentiation of neutrophil granulocytes.

The Amino Acid Homoarginine Inhibits Atherogenesis by Modulating T-Cell Function.

BACKGROUND: Amino acid metabolism is crucial for inflammatory processes during atherogenesis. The endogenous amino acid homoarginine is a robust biomarker for cardiovascular outcome and mortality with high levels being protective. However, the underlying mechanisms remain elusive. We investigated the effect of homoarginine supplementation on atherosclerotic plaque development with a particular focus on inflammation. METHODS: Female ApoE-deficient mice were supplemented with homoarginine (14 mg/L) in drinking water starting 2 weeks before and continuing throughout a 6-week period of Western-type diet feeding. Control mice received normal drinking water. Immunohistochemistry and flow cytometry were used for plaque- and immunological phenotyping. T cells were characterized using mass spectrometry-based proteomics, by functional in vitro approaches, for example, proliferation and migration/chemotaxis assays as well as by super-resolution microscopy. RESULTS: Homoarginine supplementation led to a 2-fold increase in circulating homoarginine concentrations. Homoarginine-treated mice exhibited reduced atherosclerosis in the aortic root and brachiocephalic trunk. A substantial decrease in CD3+ T cells in the atherosclerotic lesions suggested a T-cell-related effect of homoarginine supplementation, which was mainly attributed to CD4+ T cells. Macrophages, dendritic cells, and B cells were not affected. CD4+ T-cell proteomics and subsequent pathway analysis together with in vitro studies demonstrated that homoarginine profoundly modulated the spatial organization of the T-cell actin cytoskeleton and increased filopodia formation via inhibition of Myh9 (myosin heavy chain 9). Further mechanistic studies revealed an inhibition of T-cell proliferation as well as a striking impairment of the migratory capacities of T cells in response to relevant chemokines by homoarginine, all of which likely contribute to its atheroprotective effects. CONCLUSIONS: Our study unravels a novel mechanism by which the amino acid homoarginine reduces atherosclerosis, establishing that homoarginine modulates the T-cell cytoskeleton and thereby mitigates T-cell functions important during atherogenesis. These findings provide a molecular explanation for the beneficial effects of homoarginine in atherosclerotic cardiovascular disease.

Toward a gold standard for benchmarking gene set enrichment analysis.

MOTIVATION: Although gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets. RESULTS: We develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. AVAILABILITY: http://bioconductor.org/packages/GSEABenchmarkeR. CONTACT: ludwig.geistlinger@sph.cuny.edu.

Analysis of proteome adaptation reveals a key role of the bacterial envelope in starvation survival.

Bacteria reorganize their physiology upon entry to stationary phase. What part of this reorganization improves starvation survival is a difficult question because the change in physiology includes a global reorganization of the proteome, envelope, and metabolism of the cell. In this work, we used several trade-offs between fast growth and long survival to statistically score over 2,000 Escherichia coli proteins for their global correlation with death rate. The combined ranking allowed us to narrow down the set of proteins that positively correlate with survival and validate the causal role of a subset of proteins. Remarkably, we found that important survival genes are related to the cell envelope, i.e., periplasm and outer membrane, because the maintenance of envelope integrity of E. coli plays a crucial role during starvation. Our results uncover a new protective feature of the outer membrane that adds to the growing evidence that the outer membrane is not only a barrier that prevents abiotic substances from reaching the cytoplasm but also essential for bacterial proliferation and survival.

iBRET Screen of the ABCD1 Peroxisomal Network and Mutation-Induced Network Perturbations.

Mapping the network of proteins provides a powerful means to investigate the function of disease genes and to unravel the molecular basis of phenotypes. We present an automated informatics-aided and bioluminescence resonance energy transfer-based approach (iBRET) enabling high-confidence detection of protein-protein interactions in living mammalian cells. A screen of the ABCD1 protein, which is affected in X-linked adrenoleukodystrophy (X-ALD), against an organelle library of peroxisomal proteins demonstrated applicability of iBRET for large-scale experiments. We identified novel protein-protein interactions for ABCD1 (with ALDH3A2, DAO, ECI2, FAR1, PEX10, PEX13, PEX5, PXMP2, and PIPOX), mapped its position within the peroxisomal protein-protein interaction network, and determined that pathogenic missense variants in ABCD1 alter the interaction with selected binding partners. These findings provide mechanistic insights into pathophysiology of X-ALD and may foster the identification of new disease modifiers.

Improved Ribo-seq enables identification of cryptic translation events.

Ribosome profiling has been used to predict thousands of short open reading frames (sORFs) in eukaryotic cells, but it suffers from substantial levels of noise. PRICE (https://github.com/erhard-lab/price) is a computational method that models experimental noise to enable researchers to accurately resolve overlapping sORFs and noncanonical translation initiation. We experimentally validated translation using major histocompatibility complex class I (MHC I) peptidomics and observed that sORF-derived peptides efficiently enter the MHC I presentation pathway and thus constitute a substantial fraction of the antigen repertoire.

CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes.

SUMMARY: Copy number variation (CNV) is a major type of structural genomic variation that is increasingly studied across different species for association with diseases and production traits. Established protocols for experimental detection and computational inference of CNVs from SNP array and next-generation sequencing data are available. We present the CNVRanger R/Bioconductor package which implements a comprehensive toolbox for structured downstream analysis of CNVs. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and genome-wide association analysis with gene expression and quantitative phenotypes. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/CNVRanger.

MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs).In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.

Comparison of six breast cancer classifiers using qPCR.

MOTIVATION: Several gene expression-based risk scores and subtype classifiers for breast cancer were developed to distinguish high- and low-risk patients. Evaluating the performance of these classifiers helps to decide which classifiers should be used in clinical practice for personal therapeutic recommendations. So far, studies that compared multiple classifiers in large independent patient cohorts mostly used microarray measurements. qPCR-based classifiers were not included in the comparison or had to be adapted to the different experimental platforms. RESULTS: We used a prospective study of 726 early breast cancer patients from seven certified German breast cancer centers. Patients were treated according to national guidelines and the expressions of 94 selected genes were measured by the mid-throughput qPCR platform Fluidigm. Clinical and pathological data including outcome over five years is available. Using these data, we could compare the performance of six classifiers (scmgene and research versions of PAM50, ROR-S, recurrence score, EndoPredict and GGI). Similar to other studies, we found a similar or even higher concordance between most of the classifiers and most were also able to differentiate high- and low-risk patients. The classifiers that were originally developed for microarray data still performed similarly using the Fluidigm data. Therefore, Fluidigm can be used to measure the gene expressions needed by several classifiers for a large cohort with little effort. In addition, we provide an interactive report of the results, which enables a transparent, in-depth comparison of classifiers and their prediction of individual patients. AVAILABILITY AND IMPLEMENTATION: https://services.bio.ifi.lmu.de/pia/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-Reference Spectral Library Yields Almost Complete Coverage of Heterogeneous LC-MS/MS Data Sets.

Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of a peptide in a particular charge state (peptide charge pair). However, we find that this is often not the case. We carry out a systematic evaluation of spectral variability over public repositories and in-house data sets. We show that spectral variability is widespread and partly occurs under fixed experimental conditions. Using clustering of preprocessed spectra, we derive a limited number of multiple characteristic intensity patterns (MCIPs) for each peptide charge pair, which allow almost complete coverage of our heterogeneous data set without affecting the false discovery rate. We show that a MCIP library derived from public repositories performs in most cases similar to a "custom-made" spectral library, which has been acquired under identical experimental conditions as the query spectra. We apply the MCIP approach to a DIA data set and observe a significant increase in peptide recognition. We propose the MCIP approach as an easy-to-implement addition to current spectral library search engines and as a new way to utilize the data stored in spectral repositories.

Dicer in Macrophages Prevents Atherosclerosis by Promoting Mitochondrial Oxidative Metabolism.

BACKGROUND: Alternative macrophage activation, which relies on mitochondrial oxidative metabolism, plays a central role in the resolution of inflammation and prevents atherosclerosis. Moreover, macrophages handle large amounts of cholesterol and triglycerides derived from the engulfed modified lipoproteins during atherosclerosis. Although several microRNAs regulate macrophage polarization, the role of the microRNA-generating enzyme Dicer in macrophage activation during atherosclerosis is unknown. METHODS: To evaluate the role of Dicer in atherosclerosis, Apoe-/- mice with or without macrophage-specific Dicer deletion were fed a high-fat diet for 12 weeks. Anti-argonaute 2 RNA immunoprecipitation chip and RNA deep sequencing combined with microRNA functional screening were performed in the Dicer wild-type and knockout bone marrow-derived macrophages to identify the individual microRNAs and the mRNA targets mediating the phenotypic effects of Dicer. The role of the identified individual microRNA and its target in atherosclerosis was determined by tail vein injection of the target site blockers in atherosclerotic Apoe-/- mice. RESULTS: We show that Dicer deletion in macrophages accelerated atherosclerosis in mice, along with enhanced inflammatory response and increased lipid accumulation in lesional macrophages. In vitro, alternative activation was limited whereas lipid-filled foam cell formation was exacerbated in Dicer-deficient macrophages as a result of impaired mitochondrial fatty acid oxidative metabolism. Rescue of microRNA (miR)-10a, let-7b, and miR-195a expression restored the oxidative metabolism in alternatively activated Dicer-deficient macrophages. Suppression of ligand-dependent nuclear receptor corepressor by miR-10a promoted fatty acid oxidation, which mediated the lipolytic and anti-inflammatory effect of Dicer. miR-10a expression was negatively correlated to the progression of atherosclerosis in humans. Blocking the interaction between ligand-dependent nuclear receptor corepressor and miR-10a by target site blockers aggravated atherosclerosis development in mice. CONCLUSIONS: Dicer plays an atheroprotective role by coordinately regulating the inflammatory response and lipid metabolism in macrophages through enhancing fatty acid-fueled mitochondrial respiration, suggesting that promoting Dicer/miR-10a-dependent metabolic reprogramming in macrophages has potential therapeutic implications to prevent atherosclerosis.

EBF1 binds to EBNA2 and promotes the assembly of EBNA2 chromatin complexes in B cells.

Epstein-Barr virus (EBV) infection converts resting human B cells into permanently proliferating lymphoblastoid cell lines (LCLs). The Epstein-Barr virus nuclear antigen 2 (EBNA2) plays a key role in this process. It preferentially binds to B cell enhancers and establishes a specific viral and cellular gene expression program in LCLs. The cellular DNA binding factor CBF1/CSL serves as a sequence specific chromatin anchor for EBNA2. The ubiquitous expression of this highly conserved protein raises the question whether additional cellular factors might determine EBNA2 chromatin binding selectively in B cells. Here we used CBF1 deficient B cells to identify cellular genes up or downregulated by EBNA2 as well as CBF1 independent EBNA2 chromatin binding sites. Apparently, CBF1 independent EBNA2 target genes and chromatin binding sites can be identified but are less frequent than CBF1 dependent EBNA2 functions. CBF1 independent EBNA2 binding sites are highly enriched for EBF1 binding motifs. We show that EBNA2 binds to EBF1 via its N-terminal domain. CBF1 proficient and deficient B cells require EBF1 to bind to CBF1 independent binding sites. Our results identify EBF1 as a co-factor of EBNA2 which conveys B cell specificity to EBNA2.

RelExplain-integrating data and networks to explain biological processes.

MOTIVATION: The goal of many genome-wide experiments is to explain the changes between the analyzed conditions. Typically, the analysis is started with a set of differential genes DG and the first step is to identify the set of relevant biological processes BP . Current enrichment methods identify the involved biological process via statistically significant overrepresentation of differential genes in predefined sets, but do not further explain how the differential genes interact with each other or which other genes might be important for the enriched process. Other network-based methods determine subnetworks of interacting genes containing many differential genes, but do not employ process knowledge for a more focused analysis. RESULTS: RelExplain is a method to analyze a given biological process bp (e.g. identified by enrichment) in more detail by computing an explanation using the measured DG and a given network. An explanation is a subnetwork that contains the differential genes in the process bp and connects them in the best way given the experimental data using also genes that are not differential or not in bp . RelExplain takes into account the functional annotations of nodes and the edge consistency of the measurements. Explanations are compact networks of the relevant part of the bp and additional nodes that might be important for the bp . Our evaluation showed that RelExplain is better suited to retrieve manually curated subnetworks from unspecific networks than other algorithms. The interactive RelExplain tool allows to compute and inspect sub-optimal and alternative optimal explanations. AVAILABILITY AND IMPLEMENTATION: A webserver is available at https://services.bio.ifi.lmu.de/relexplain . CONTACT: berchtold@bio.ifi.lmu.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Resolving Lipid Mediators Maresin 1 and Resolvin D2 Prevent Atheroprogression in Mice.

RATIONALE: Atheroprogression is a consequence of nonresolved inflammation, and currently a comprehensive overview of the mechanisms preventing resolution is missing. However, in acute inflammation, resolution is known to be orchestrated by a switch from inflammatory to resolving lipid mediators. Therefore, we hypothesized that lesional lipid mediator imbalance favors atheroprogression. OBJECTIVE: To understand the lipid mediator balance during atheroprogression and to establish an interventional strategy based on the delivery of resolving lipid mediators. METHODS AND RESULTS: Aortic lipid mediator profiling of aortas from Apoe-/- mice fed a high-fat diet for 4 weeks, 8 weeks, or 4 months revealed an expansion of inflammatory lipid mediators, Leukotriene B4 and Prostaglandin E2, and a concomitant decrease of resolving lipid mediators, Resolvin D2 (RvD2) and Maresin 1 (MaR1), during advanced atherosclerosis. Functionally, aortic Leukotriene B4 and Prostaglandin E2 levels correlated with traits of plaque instability, whereas RvD2 and MaR1 levels correlated with the signs of plaque stability. In a therapeutic context, repetitive RvD2 and MaR1 delivery prevented atheroprogression as characterized by halted expansion of the necrotic core and accumulation of macrophages along with increased fibrous cap thickness and smooth muscle cell numbers. Mechanistically, RvD2 and MaR1 induced a shift in macrophage profile toward a reparative phenotype, which secondarily stimulated collagen synthesis in smooth muscle cells. CONCLUSIONS: We present evidence for the imbalance between inflammatory and resolving lipid mediators during atheroprogression. Delivery of RvD2 and MaR1 successfully prevented atheroprogression, suggesting that resolving lipid mediators potentially represent an innovative strategy to resolve arterial inflammation.

Manananggal - a novel viewer for alternative splicing events.

BACKGROUND: Alternative splicing is an important cellular mechanism that can be analyzed by RNA sequencing. However, identification of splicing events in an automated fashion is error-prone. Thus, further validation is required to select reliable instances of alternative splicing events (ASEs). There are only few tools specifically designed for interactive inspection of ASEs and available visualization approaches can be significantly improved. RESULTS: Here, we present Manananggal, an application specifically designed for the identification of splicing events in next generation sequencing data. Manananggal includes a web application for visual inspection and a command line tool that allows for ASE detection. We compare the sashimi plots available in the IGV Viewer, the DEXSeq splicing plots and SpliceSeq to the Manananggal interface and discuss the advantages and drawbacks of these tools. We show that sashimi plots (such as those used by the IGV Viewer and SpliceSeq) offer a practical solution for simple ASEs, but also indicate short-comings for highly complex genes. CONCLUSION: Manananggal is an interactive web application that offers functions specifically tailored to the identification of alternative splicing events that other tools are lacking. The ability to select a subset of isoforms allows an easier interpretation of complex alternative splicing events. In contrast to SpliceSeq and the DEXSeq splicing plot, Manananggal does not obscure the gene structure by showing full transcript models that makes it easier to determine which isoforms are expressed and which are not.

Widespread context dependency of microRNA-mediated regulation.

Gene expression is regulated in a context-dependent, cell-type-specific manner. Condition-specific transcription is dependent on the presence of transcription factors (TFs) that can activate or inhibit its target genes (global context). Additional factors, such as chromatin structure, histone, or DNA modifications, also influence the activity of individual target genes (individual context). The role of the global and individual context for post-transcriptional regulation has not systematically been investigated on a large scale and is poorly understood. Here we show that global and individual context dependency is a pervasive feature of microRNA-mediated regulation. Our comprehensive and highly consistent data set from several high-throughput technologies (PAR-CLIP, RIP-chip, 4sU-tagging, and SILAC) provides strong evidence that context-dependent microRNA target sites (CDTS) are as frequent and functionally relevant as constitutive target sites (CTS). Furthermore, we found the global context to be insufficient to explain the CDTS, and that flanking sequence motifs provide individual context that is an equally important factor. Our results demonstrate that, similar to TF-mediated regulation, global and individual context dependency are prevalent in microRNA-mediated gene regulation, implying a much more complex post-transcriptional regulatory network than is currently known. The necessary tools to unravel post-transcriptional regulations and mechanisms need to be much more involved, and much more data will be needed for particular cell types and cellular conditions in order to understand microRNA-mediated regulation and the context-dependent post-transcriptional regulatory network.

p53-Regulated Networks of Protein, mRNA, miRNA, and lncRNA Expression Revealed by Integrated Pulsed Stable Isotope Labeling With Amino Acids in Cell Culture (pSILAC) and Next Generation Sequencing (NGS) Analyses.

We determined the effect of p53 activation on de novo protein synthesis using quantitative proteomics (pulsed stable isotope labeling with amino acids in cell culture/pSILAC) in the colorectal cancer cell line SW480. This was combined with mRNA and noncoding RNA expression analyses by next generation sequencing (RNA-, miR-Seq). Furthermore, genome-wide DNA binding of p53 was analyzed by chromatin-immunoprecipitation (ChIP-Seq). Thereby, we identified differentially regulated proteins (542 up, 569 down), mRNAs (1258 up, 415 down), miRNAs (111 up, 95 down) and lncRNAs (270 up, 123 down). Changes in protein and mRNA expression levels showed a positive correlation (r = 0.50, p < 0.0001). In total, we detected 133 direct p53 target genes that were differentially expressed and displayed p53 occupancy in the vicinity of their promoter. More transcriptionally induced genes displayed occupied p53 binding sites (4.3% mRNAs, 7.2% miRNAs, 6.3% lncRNAs, 5.9% proteins) than repressed genes (2.4% mRNAs, 3.2% miRNAs, 0.8% lncRNAs, 1.9% proteins), suggesting indirect mechanisms of repression. Around 50% of the down-regulated proteins displayed seed-matching sequences of p53-induced miRNAs in the corresponding 3'-UTRs. Moreover, proteins repressed by p53 significantly overlapped with those previously shown to be repressed by miR-34a. We confirmed up-regulation of the novel direct p53 target genes LINC01021, MDFI, ST14 and miR-486 and showed that ectopic LINC01021 expression inhibits proliferation in SW480 cells. Furthermore, KLF12, HMGB1 and CIT mRNAs were confirmed as direct targets of the p53-induced miR-34a, miR-205 and miR-486-5p, respectively. In line with the loss of p53 function during tumor progression, elevated expression of KLF12, HMGB1 and CIT was detected in advanced stages of cancer. In conclusion, the integration of multiple omics methods allowed the comprehensive identification of direct and indirect effectors of p53 that provide new insights and leads into the mechanisms of p53-mediated tumor suppression.

Count ratio model reveals bias affecting NGS fold changes.

Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, we show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are called differentially regulated in RNA-seq experiments with drastic effects on subsequent biological interpretation. Here, we propose a novel approach to estimate fold changes. Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data. Our software implementation is freely available from the project website http://www.bio.ifi.lmu.de/software/lfc.

Bioconductor's EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis.

BACKGROUND: Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation. RESULTS: We present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data preprocessing, differential expression analysis, and definition of suitable input gene sets and networks. CONCLUSION: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. It combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.