Complete mitogenome and phylogenetic analysis of Hyalinocerus flavoscutatus Cai and Shen 1998 (Hemiptera: Eurymelinae: Idiocerini).

This study presents the initial sequencing and characterization of the complete mitochondrial genome (mitogenome) of Hyalinocerus flavoscutatus, making the first comprehensive exploration of the mitogenome in the Hyalinocerus. Utilizing next-generation sequencing techniques, we identified a circular DNA molecule spanning 15,307 bp. The mitogenome comprises 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and a primary non-coding region. Maximum likelihood phylogenetic evaluation, based on 13 protein-coding genes and two ribosomal RNA genes, robustly supports H. flavoscutatus as the basal group within Idiocerini. This research unveils valuable insights into the mitogenome of H. flavoscutatus and enhances our understanding of phylogenetic placement within the broader context of related tribes.

Master Transcription Factor Reprogramming Unleashes Selective Translation Promoting Castration Resistance and Immune Evasion in Lethal Prostate Cancer.

Signaling rewiring allows tumors to survive therapy. Here we show that the decrease of the master regulator microphthalmia transcription factor (MITF) in lethal prostate cancer unleashes eukaryotic initiation factor 3B (eIF3B)-dependent translation reprogramming of key mRNAs conferring resistance to androgen deprivation therapy (ADT) and promoting immune evasion. Mechanistically, MITF represses through direct promoter binding eIF3B, which in turn regulates the translation of specific mRNAs. Genome-wide eIF3B enhanced cross-linking immunoprecipitation sequencing (eCLIP-seq) showed specialized binding to a UC-rich motif present in subsets of 5' untranslated regions. Indeed, translation of the androgen receptor and major histocompatibility complex I (MHC-I) through this motif is sensitive to eIF3B amount. Notably, pharmacologic targeting of eIF3B-dependent translation in preclinical models sensitizes prostate cancer to ADT and anti-PD-1 therapy. These findings uncover a hidden connection between transcriptional and translational rewiring promoting therapy-refractory lethal prostate cancer and provide a druggable mechanism that may transcend into effective combined therapeutic strategies. SIGNIFICANCE: Our study shows that specialized eIF3B-dependent translation of specific mRNAs released upon downregulation of the master transcription factor MITF confers castration resistance and immune evasion in lethal prostate cancer. Pharmacologic targeting of this mechanism delays castration resistance and increases immune-checkpoint efficacy. This article is featured in Selected Articles from This Issue, p. 2489.

Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease.

Somatic mutations in nonmalignant tissues accumulate with age and injury, but whether these mutations are adaptive on the cellular or organismal levels is unclear. To interrogate genes in human metabolic disease, we performed lineage tracing in mice harboring somatic mosaicism subjected to nonalcoholic steatohepatitis (NASH). Proof-of-concept studies with mosaic loss of Mboat7, a membrane lipid acyltransferase, showed that increased steatosis accelerated clonal disappearance. Next, we induced pooled mosaicism in 63 known NASH genes, allowing us to trace mutant clones side by side. This in vivo tracing platform, which we coined MOSAICS, selected for mutations that ameliorate lipotoxicity, including mutant genes identified in human NASH. To prioritize new genes, additional screening of 472 candidates identified 23 somatic perturbations that promoted clonal expansion. In validation studies, liver-wide deletion of Tbx3, Bcl6, or Smyd2 resulted in protection against hepatic steatosis. Selection for clonal fitness in mouse and human livers identifies pathways that regulate metabolic disease.

Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease.

Somatic mutations in non-malignant tissues accumulate with age and insult, but whether these mutations are adaptive on the cellular or organismal levels is unclear. To interrogate mutations found in human metabolic disease, we performed lineage tracing in mice harboring somatic mosaicism subjected to non-alcoholic steatohepatitis (NASH). Proof-of-concept studies with mosaic loss of Mboat7 , a membrane lipid acyltransferase, showed that increased steatosis accelerated clonal disappearance. Next, we induced pooled mosaicism in 63 known NASH genes, allowing us to trace mutant clones side-by-side. This in vivo tracing platform, which we coined MOSAICS, selected for mutations that ameliorate lipotoxicity, including mutant genes identified in human NASH. To prioritize new genes, additional screening of 472 candidates identified 23 somatic perturbations that promoted clonal expansion. In validation studies, liver-wide deletion of Bcl6, Tbx3, or Smyd2 resulted in protection against NASH. Selection for clonal fitness in mouse and human livers identifies pathways that regulate metabolic disease. Highlights: Mosaic Mboat7 mutations that increase lipotoxicity lead to clonal disappearance in NASH. In vivo screening can identify genes that alter hepatocyte fitness in NASH. Mosaic Gpam mutations are positively selected due to reduced lipogenesis. In vivo screening of transcription factors and epifactors identified new therapeutic targets in NASH.

Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology.

In spot-based spatial transcriptomics, spots that are of the same size and printed at the fixed location cannot precisely capture the actual randomly located single cells, therefore failing to profile the transcriptome at the single-cell level. The current studies primarily focused on enhancing the spot resolution in size via computational imputation or technical improvement, however, they largely overlooked that single-cell resolution, i.e., resolution in cellular or even smaller size, does not equal single-cell level. Using both real and simulated spatial transcriptomics data, we demonstrated that even the high-resolution spatial transcriptomics still has a large number of spots partially covering multiple cells simultaneously, revealing the intrinsic non-single-cell level of spot-based spatial transcriptomics regardless of spot size. To this end, we present STIE, an EM algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from up to ~70% gap area between spots via the nuclear morphological similarity and neighborhood information, thereby achieving the real single-cell level and whole-slide scale deconvolution/convolution and clustering for both low- and high-resolution spots. On both real and simulation spatial transcriptomics data, STIE characterizes the cell-type specific gene expression variation and demonstrates the outperforming concordance with the single-cell RNAseq-derived cell type transcriptomic signatures compared to the other spot- and subspot-level methods. Furthermore, STIE enabled us to gain novel insights that failed to be revealed by the existing methods due to the lack of single-cell level, for instance, lower actual spot resolution than its reported spot size, the additional contribution of cellular morphology to cell typing beyond transcriptome, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatially resolved cell-cell interactions at the single-cell level other than spot level. The STIE code is publicly available as an R package at https://github.com/zhushijia/STIE.

Hepatocellular carcinoma chemoprevention by targeting the angiotensin-converting enzyme and EGFR transactivation.

Hepatocellular carcinoma (HCC) is a leading cause of death among cirrhotic patients, for which chemopreventive strategies are lacking. Recently, we developed a simple human cell-based system modeling a clinical prognostic liver signature (PLS) predicting liver disease progression and HCC risk. In a previous study, we applied our cell-based system for drug discovery and identified captopril, an approved angiotensin converting enzyme (ACE) inhibitor, as a candidate compound for HCC chemoprevention. Here, we explored ACE as a therapeutic target for HCC chemoprevention. Captopril reduced liver fibrosis and effectively prevented liver disease progression toward HCC development in a diethylnitrosamine (DEN) rat cirrhosis model and a diet-based rat model for nonalcoholic steatohepatitis-induced (NASH-induced) hepatocarcinogenesis. RNA-Seq analysis of cirrhotic rat liver tissues uncovered that captopril suppressed the expression of pathways mediating fibrogenesis, inflammation, and carcinogenesis, including epidermal growth factor receptor (EGFR) signaling. Mechanistic data in liver disease models uncovered a cross-activation of the EGFR pathway by angiotensin. Corroborating the clinical translatability of the approach, captopril significantly reversed the HCC high-risk status of the PLS in liver tissues of patients with advanced fibrosis. Captopril effectively prevents fibrotic liver disease progression toward HCC development in preclinical models and is a generic and safe candidate drug for HCC chemoprevention.

Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease.

Prediction of hepatocellular carcinoma (HCC) risk is an urgent unmet need in patients with nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from multiple global regions, we defined and validated hepatic transcriptome and serum secretome signatures predictive of long-term HCC risk in patients with NAFLD. A 133-gene signature, prognostic liver signature (PLS)-NAFLD, predicted incident HCC over up to 15 years of longitudinal observation. High-risk PLS-NAFLD was associated with IDO1+ dendritic cells and dysfunctional CD8+ T cells in fibrotic portal tracts along with impaired metabolic regulators. PLS-NAFLD was validated in independent cohorts of patients with NAFLD who were HCC naïve (HCC incidence rates at 15 years were 22.7 and 0% in high- and low-risk patients, respectively) or HCC experienced (de novo HCC recurrence rates at 5 years were 71.8 and 42.9% in high- and low-risk patients, respectively). PLS-NAFLD was bioinformatically translated into a four-protein secretome signature, PLSec-NAFLD, which was validated in an independent cohort of HCC-naïve patients with NAFLD and cirrhosis (HCC incidence rates at 15 years were 37.6 and 0% in high- and low-risk patients, respectively). Combination of PLSec-NAFLD with our previously defined etiology-agnostic PLSec-AFP yielded improved HCC risk stratification. PLS-NAFLD was modified by bariatric surgery, lipophilic statin, and IDO1 inhibitor, suggesting that the signature can be used for drug discovery and as a surrogate end point in HCC chemoprevention clinical trials. Collectively, PLS/PLSec-NAFLD may enable NAFLD-specific HCC risk prediction and facilitate clinical translation of NAFLD-directed HCC chemoprevention.

Peroxidasin Deficiency Re-programs Macrophages Toward Pro-fibrolysis Function and Promotes Collagen Resolution in Liver.

BACKGROUND & AIMS: During liver fibrosis, tissue repair mechanisms replace necrotic tissue with highly stabilized extracellular matrix proteins. Extracellular matrix stabilization influences the speed of tissue recovery. Here, we studied the expression and function of peroxidasin (PXDN), a peroxidase that uses hydrogen peroxide to cross-link collagen IV during liver fibrosis progression and regression. METHODS: Mouse models of liver fibrosis and cirrhosis patients were analyzed for the expression of PXDN in liver and serum. Pxdn-/- and Pxdn+/+ mice were either treated with carbon tetrachloride for 6 weeks to generate toxin-induced fibrosis or fed with a choline-deficient L-amino acid-defined high-fat diet for 16 weeks to create nonalcoholic fatty liver disease fibrosis. Liver histology, quantitative real-time polymerase chain reaction, collagen content, flowcytometry and immunostaining of immune cells, RNA-sequencing, and liver function tests were analyzed. In vivo imaging of liver reactive oxygen species (ROS) was performed using a redox-active iron complex, Fe-PyC3A. RESULTS: In human and mouse cirrhotic tissue, PXDN is expressed by stellate cells and is secreted into fibrotic areas. In patients with nonalcoholic fatty liver disease, serum levels of PXDN increased significantly. In both mouse models of liver fibrosis, PXDN deficiency resulted in elevated monocyte and pro-fibrolysis macrophage recruitment into fibrotic bands and caused decreased accumulation of cross-linked collagens. In Pxdn-/- mice, collagen fibers were loosely organized, an atypical phenotype that is reversible upon macrophage depletion. Elevated ROS in Pxdn-/- livers was observed, which can result in activation of hypoxic signaling cascades and may affect signaling pathways involved in macrophage polarization such as TNF-a via NF-kB. Fibrosis resolution in Pxdn-/- mice was associated with significant decrease in collagen content and improved liver function. CONCLUSION: PXDN deficiency is associated with increased ROS levels and a hypoxic liver microenvironment that can regulate recruitment and programming of pro-resolution macrophages. Our data implicate the importance of the liver microenvironment in macrophage programming during liver fibrosis and suggest a novel pathway that is involved in the resolution of scar tissue.

Clinical Significance and Biological Function of miR-1274a in Non-small Cell Lung Cancer.

Since the discovery of microRNAs (miRNAs) as a class of important regulatory molecules, miRNAs are involved in the occurrence and development of tumors. In this paper, we aimed to identify the role of miR-1274a in non-small cell lung cancer (NSCLC). The miR-1274a expression levels in four NSCLC cells and tissues from 125 patients were determined by qRT-PCR assays. Kaplan-Meier survival curves and Cox regression analysis were used to examine the prognostic significance of miR-1274a in NSCLC patients. The CCK-8 and Transwell assays were performed to evaluate the cell proliferation, invasion, and migration ability of NSCLC cells. The miR-1274a expression levels were significantly higher in NSCLC tissues than in adjacent normal tissues, and overexpression of miR-1274a had a poor prognosis in NSCLC patients. Functional studies in two NSCLC cell lines have shown that overexpression of miR-1274a could promote cell proliferation, migration, and invasion. miR-1274a expression levels are upregulated in NSCLC tissues, and a high expression is associated with a poor prognosis in patients with NSCLC. Moreover, miR-1274a promotes cell proliferation, migration, and invasion. Based on our findings, miR-1274a may act as a tumor miRNA in the occurrence and development of NSCLC.

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models and patient-derived liver spheroids, we identify nizatidine, a histamine receptor H2 (HRH2) blocker, for treatment of advanced liver disease and HCC chemoprevention. Moreover, perturbation studies combined with single cell RNA-Seq analyses of patient liver tissues uncover hepatocytes and HRH2+, CLEC5Ahigh, MARCOlow liver macrophages as potential nizatidine targets. The PLS model combined with single cell RNA-Seq of patient tissues enables discovery of urgently needed targets and therapeutics for treatment of advanced liver disease and cancer prevention.

A blood-based prognostic liver secretome signature and long-term hepatocellular carcinoma risk in advanced liver fibrosis.

BACKGROUND: Accurate non-invasive prediction of long-term hepatocellular carcinoma (HCC) risk in advanced liver fibrosis is urgently needed for cost-effective HCC screening; however, this currently remains an unmet need. METHODS: A serum-protein-based prognostic liver secretome signature (PLSec) was bioinformatically derived from previously validated hepatic transcriptome signatures and optimized in 79 patients with advanced liver fibrosis. We independently validated PLSec for HCC risk in 331 cirrhosis patients with mixed etiologies (validation set 1 [V1]) and thereafter developed a score with clinical prognostic variables. The score was then validated in two independent cohorts: validation set 2 (V2): 164 patients with advanced liver fibrosis due to hepatitis C virus (HCV) infection cured after direct-acting antiviral therapy; validation set 3 (V3): 146 patients with advanced liver fibrosis with successfully-treated HCC and cured HCV infection. FINDINGS: An 8-protein blood-based PLSec recapitulated transcriptome-based hepatic HCC risk status. In V1, PLSec was significantly associated with incident HCC risk (adjusted hazard ratio [aHR], 2.35; 95% confidence interval [CI], 1.30-4.23). A composite score with serum alpha-fetoprotein (PLSec-AFP) was defined in V1, and validated in V2 (adjusted odds ratio, 3.80 [95%CI, 1.66-8.66]) and V3 (aHR, 3.08 [95%CI, 1.78-5.31]; c-index, 0.74). PLSec-AFP outperformed AFP alone (Brier score, 0.165 vs. 0.186 in V2; 0.196 vs. 0.206 in V3, respectively). CONCLUSIONS: The blood-based PLSec-AFP can accurately stratify patients with advanced liver fibrosis for long-term HCC risk and thereby guide risk-based tailored HCC screening.

Transcriptome-Guided Design of Physiological Multilineage Liver Organoids.

Despite several technical challenges, human induced pluripotent stem cell (hiPSC)-derived organoids enable biologically and clinically relevant functional study of physiology and disease. In a recent Cell Systems article, Velazquez et al. report a novel strategy to identify regulators of multilineage organoid maturation by reverse-engineering from the global transcriptome of human tissues.

A transcriptional landscape of 28 porcine tissues obtained by super deepSAGE sequencing.

BACKGROUND: Gene expression regulators identified in transcriptome profiling experiments may serve as ideal targets for genetic manipulations in farm animals. RESULTS: In this study, we developed a gene expression profile of 76,000+ unique transcripts for 224 porcine samples from 28 tissues collected from 32 animals using Super deepSAGE technology. Excellent sequencing depth was achieved for each multiplexed library, and replicated samples from the same tissues clustered together, demonstrating the high quality of Super deepSAGE data. Comparison with previous research indicated that our results not only have good reproducibility but also have greatly extended the coverage of the sample types as well as the number of genes. Clustering analysis revealed ten groups of genes showing distinct expression patterns among these samples. Our analysis of over-represented binding motifs identified 41 regulators, and we demonstrated a potential application of this dataset in infectious diseases and immune biology research by identifying an LPS-dependent transcription factor, runt-related transcription factor 1 (RUNX1), in peripheral blood mononuclear cells (PBMCs). The selected genes are specifically responsible for the transcription of toll-like receptor 2 (TLR2), lymphocyte-specific protein tyrosine kinase (LCK), and vav1 oncogene (VAV1), which belong to the T and B cell signaling pathways. CONCLUSIONS: The Super deepSAGE technology and tissue-differential expression profiles are valuable resources for investigating the porcine gene expression regulation. The identified RUNX1 target genes belong to the T and B cell signaling pathways, making them novel potential targets for the diagnosis and therapy of bacterial infections and other immune disorders.

Restricted immunological and cellular pathways are shared by murine models of chronic alcohol consumption.

Murine models of chronic alcohol consumption are frequently used to investigate alcoholic liver injury and define new therapeutic targets. Lieber-DeCarli diet (LD) and Meadows-Cook diet (MC) are the most accepted models of chronic alcohol consumption. It is unclear how similar these models are at the cellular, immunologic, and transcriptome levels. We investigated the common and specific pathways of LD and MC models. Livers from LD and MC mice were subjected to histologic changes, hepatic leukocyte population, hepatic transcripts level related to leukocyte recruitment, and hepatic RNA-seq analysis. Cross-species comparison was performed using the alcoholic liver disease (ALD) transcriptomic public dataset. Despite LD mice have increased liver injury and steatosis by alcohol exposure, the number of CD45+ cells were reduced. Opposite, MC mice have an increased number of monocytes/liver by alcohol. The pattern of chemokine gradient, adhesion molecules, and cytokine transcripts is highly specific for each model, not shared with advanced human alcoholic liver disease. Moreover, hepatic RNA-seq revealed a limited and restricted number of shared genes differentially changed by alcohol exposure in these 2 models. Thus, mechanisms involved in alcohol tissue injury are model-dependent at multiple levels and raise the consideration of significant pathophysiological diversity of human alcoholic liver injury.

Epigenomic characterization of Clostridioides difficile finds a conserved DNA methyltransferase that mediates sporulation and pathogenesis.

Clostridioides (formerly Clostridium) difficile is a leading cause of healthcare-associated infections. Although considerable progress has been made in the understanding of its genome, the epigenome of C. difficile and its functional impact has not been systematically explored. Here, we perform a comprehensive DNA methylome analysis of C. difficile using 36 human isolates and observe a high level of epigenomic diversity. We discovered an orphan DNA methyltransferase with a well-defined specificity, the corresponding gene of which is highly conserved across our dataset and in all of the approximately 300 global C. difficile genomes examined. Inactivation of the methyltransferase gene negatively impacts sporulation, a key step in C. difficile disease transmission, and these results are consistently supported by multiomics data, genetic experiments and a mouse colonization model. Further experimental and transcriptomic analyses suggest that epigenetic regulation is associated with cell length, biofilm formation and host colonization. These findings provide a unique epigenetic dimension to characterize medically relevant biological processes in this important pathogen. This study also provides a set of methods for comparative epigenomics and integrative analysis, which we expect to be broadly applicable to bacterial epigenomic studies.

Neuronal impact of patient-specific aberrant NRXN1α splicing.

NRXN1 undergoes extensive alternative splicing, and non-recurrent heterozygous deletions in NRXN1 are strongly associated with neuropsychiatric disorders. We establish that human induced pluripotent stem cell (hiPSC)-derived neurons well represent the diversity of NRXN1α alternative splicing observed in the human brain, cataloguing 123 high-confidence in-frame human NRXN1α isoforms. Patient-derived NRXN1+/- hiPSC-neurons show a greater than twofold reduction in half of the wild-type NRXN1α isoforms and express dozens of novel isoforms from the mutant allele. Reduced neuronal activity in patient-derived NRXN1+/- hiPSC-neurons is ameliorated by overexpression of individual control isoforms in a genotype-dependent manner, whereas individual mutant isoforms decrease neuronal activity levels in control hiPSC-neurons. In a genotype-dependent manner, the phenotypic impact of patient-specific NRXN1+/- mutations can occur through a reduction in wild-type NRXN1α isoform levels as well as the presence of mutant NRXN1α isoforms.

MPIC: Molecular Prognostic Indicators in Cirrhosis Database for Clinical Context-Specific in Silico Prognostic Biomarker Validation.

Prognostic biomarkers are vital in the management of progressive chronic diseases such as liver cirrhosis, affecting 1-2% of the global population and causing over 1 million deaths every year. Despite numerous candidate biomarkers in literature, the costly and lengthy process of validation hampers their clinical translation. Existing omics databases are not suitable for in silico validation due to the ignorance of critical factors, i.e., study design, clinical context of biomarker application, and statistical power. To address the unmet need, we have developed the Molecular Prognostic Indicators in Cirrhosis (MPIC) database as a representative example of an omics database tailored for prognostic biomarker validation. MPIC consists of (i) a molecular and clinical database structured by defined disease context and specific clinical outcome and annotated with employed study design and anticipated statistical power by disease domain experts, (ii) a bioinformatics analysis engine for user-provided gene-signature- or gene-based prognostic prediction, and (iii) a user interface for interactive exploration of relevant clinical cohort/scenario and assessment of significance and reliability of the result for prognostic prediction. MPIC assists cost-effective prognostic biomarker development by facilitating the process of validation, and will transform the care of chronic diseases such as cirrhosis. MPIC is freely available at www.mpic-app.org. The website is implemented in Java, Apache, and MySQL with all major browsers supported.

Use of big data in drug development for precision medicine: an update.

INTRODUCTION: Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. AREAS COVERED: Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. EXPERT OPINION: In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data.

Summary: level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set. Availability and implementation: GIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA. Supplementary information: Supplementary data are available at Bioinformatics online.