InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor

Construction and analysis of a reliable five-gene prognostic signature for colon adenocarcinoma associated with the wild-type allelic state of the COL6A6 gene

Background

Tumors emerge by acquiring a number of mutations over time. The first mutation provides a selective growth advantage compared to adjacent epithelial cells, allowing the cell to create a clone that can outgrow the cells that surround it. Subsequent mutations determine the risk of the tumor progressing to metastatic cancer. Some secondary mutations may inhibit the aggressiveness of the tumor while still increasing the survival of the clone. Meaningful mutations in genes may provide a strong molecular foundation for developing novel therapeutic strategies for cancer.

Methods

The somatic mutation and prognosis in colon adenocarcinoma (COAD) were analyzed. The copy number variation (CNV) and differentially expressed genes (DEGs) between the collagen type VI alpha 6 chain (COL6A6) mutation (COL6A6-MUT) and the COL6A6 wild-type (COL6A6-WT) subgroups were evaluated. The independent prognostic signatures based on COL6A6-allelic state were determined to construct a Cox model. The biological characteristics and the immune microenvironment between the two risk groups were compared.

Results

COL6A6 was found to be highly mutated in COAD at a frequency of 9%. Patients with COL6A6-MUT had a good overall survival (OS) compared to those with COL6A6-WT, who had a different CNV pattern. Significant differences in gene expression were established for 593 genes between the COL6A6-MUT and COL6A6-WT samples. Among them, MUC16, ASNSP1, PRR18, PEG10, and RPL26P8 were determined to be independent prognostic factors. The internally validated prognostic risk model, constructed using these five genes, demonstrated its value by revealing a significant difference in patient prognosis between the high-risk and low-risk groups. Specifically, patients in the high-risk group exhibited a considerably worse prognosis than did those in the low-risk group. The high-risk group had a significantly higher proportion of patients over 60 years of age and patients in stage III. Moreover, the tumor immune dysfunction and exclusion (TIDE) score and the expression of human leukocyte antigen (HLA) family genes were all higher in the high-risk group than that in the low-risk group.

Conclusions

The allelic state of COL6A6 and the five associated DEGs were identified as novel biomarkers for the diagnosis and prognosis of COAD and may be therapeutic targets in COAD.

Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) and metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed to reveal the connection between metabolism reprogramming and the stemness characteristics of HCC, established a new gene signature related to stemness and metabolism and utilized it to assess HCC prognosis and immunotherapy response. The clinical information and gene expression profiles (GEPs) of 478 HCC patients came from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The one-class logistic regression (OCLR) algorithm was employed to calculate the messenger ribonucleic acid expression-based stemness index (mRNAsi), a new stemness index quantifying stemness features. Differentially expressed analyses were done between high- and low-mRNAsi groups and 74 differentially expressed metabolism-related genes (DEMRGs) were identified with the help of metabolism-related gene sets from Molecular Signatures Database (MSigDB). After integrated analysis, a risk score model based on the three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) and UDP-glucuronosyltransferase 1A5 (UGT1A5) was constructed and HCC patients were divided into high-risk and low-risk groups. Significant differences were found in pathway enrichment, immune cell infiltration patterns, and gene alterations between the two groups. High-risk group patients tended to have worse clinical outcomes and were more likely to respond to immunotherapy. A stemness-metabolism-related model composed of gender, age, the risk score model and tumor-node-metastasis (TNM) staging was generated and showed great discrimination and strong ability in predicting HCC prognosis and immunotherapy response.

Development and validation of an endoplasmic reticulum stress-related molecular prognostic model for breast cancer

Background

Breast cancer is the most frequently diagnosed cancer and a leading cause of cancer-related death in women. Endoplasmic reticulum stress (ERS) plays a crucial role in the pathogenesis of several malignancies. However, the prognostic value of ERS-related genes in breast cancer has not been thoroughly investigated.

Methods

We downloaded and analyzed expression profiling data for breast invasive carcinoma samples in The Cancer Genome Atlas-Breast Invasive Carcinoma (TCGA-BRCA) and identified 23 ERS-related genes differentially expressed between the normal breast tissue and primary breast tumor tissues. We constructed and validated risk models using external test datasets. We assessed the differences in sensitivity to common antitumor drugs between high- and low-scoring groups using the Genomics of Drug Sensitivity in Cancer (GDSC) database, evaluated the sensitivity of patients in high- and low-scoring groups to immunotherapy using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and assessed immune and stromal cell infiltration in the tumor microenvironment (TME) using the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. We also analyzed the expression of independent factors in the prognostic model using the Western-blot analysis for correlation in relation to breast cancer.

Results

Using multivariate Cox analysis, FBXO6, PMAIP1, ERP27, and CHAC1 were identified as independent prognostic factors in patients with breast cancer. The risk score in our model was defined as the endoplasmic reticulum score (ERScore). ERScore had high predictive power for overall survival in patients with breast cancer. The high-ERScore group exhibited a worse prognosis, lower drug sensitivity, and lower immunotherapy response and immune infiltration than did the low-ERScore group. Conclusions based on ERScore were consistent with Western-blot results.

Conclusion

We constructed and validated for the first time an endoplasmic reticulum stress-related molecular prognostic model for breast cancer with reliable predictive properties and good sensitivity, as an important addition to the prognostic prediction model for breast cancer.

Construction of a CCL20-centered circadian-signature based prognostic model in cervical cancer

BACKGROUND

Rather low vaccination rates for Human papillomavirus (HPV) and pre-existing cervical cancer patients with limited therapeutic strategies ask for more precise prognostic model development. On the other side, the clinical significance of circadian clock signatures in cervical cancer lacks investigation.

METHODS

Subtypes classification based upon eight circadian clock core genes were implemented in TCGA-CESC through k-means clustering methods. Afterwards, KEGG, GO and GSEA analysis were conducted upon differentially expressed genes (DEGs) between high and low-risk groups, and tumor microenvironment (TME) investigation by CIBERSORT and ESTIMATE. Furthermore, a prognostic model was developed by cox and lasso regression methods, and verified in GSE44001 by time-dependent receiver-operating characteristic curve (ROC) analysis. Lastly, FISH and IHC were used for validation of CCL20 expression in patients' specimens and U14 subcutaneous tumor models were built for TME composition.

RESULTS

We successfully classified cervical patients into high-risk and low-risk groups based upon circadian-oscillation-signatures. Afterwards, we built a prognostic risk model composed of GJB2, CCL20 and KRT24 with excellent predictive value on patients' overall survival (OS). We then proposed metabolism unbalance, especially for glycolysis, and immune related pathways to be major enriched signatures between the high-risk and low-risk groups. Then, we proposed an 'immune-desert'-like suppressive myeloid cells infiltration pattern in high-risk group TME and verified its resistance to immunotherapies. Finally, CCL20 was proved positively correlated with real-world patients' stages and induced significant less CD8⁺ T cells and more M2 macrophages infiltration in mouse model.

CONCLUSIONS

We unraveled a prognostic risk model based upon circadian oscillation and verified its solidity. Specifically, we unveiled distinct TME immune signatures in high-risk groups.

Prognosis, immune microenvironment, and personalized treatment prediction in Rho GTPase-activating protein 4-mutant cervical cancer: Computer strategies for precision oncology

AIMS

Cervical cancer with different mutations is associated with specific genomic differences. We developed a new mutation prediction model of the ARHGAP4 gene for cervical cancer.

MAIN METHODS

We conducted a panoramic analysis of CESC mutations based on The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) database. We made copy number variation analysis and correlation analysis of somatic mutations and tumor mutation load fraction. Then we established a prediction model of ARHGAP4 mutation, screened related genes based on the risk scores, calculated the correlation between the risk score and immune microenvironment, and analyzed drug sensitivity.

KEY FINDINGS

The prediction model of ARHGAP4 mutation based on mRNA expression is closely related to the survival rate of cervical cancer patients and to the effect of immunotherapy. The prediction model is also related to the infiltration of immune cells and human leukocyte antigen family expression in the immune microenvironment. After computational analysis, three drugs (cytarabine, docetaxel, imatinib) were identified as potential agents for the ARHGAP4 mutation high-risk group, and two drugs (erlotinib, methotrexate) were shown to have therapeutic significance for patients in the low-risk group. The expression of ARHGAP4 was higher in cervical cancer tissues. The proliferation ability of HeLa and SiHa cells decreased after ARHGAP4 knockdown.

SIGNIFICANCE

This study provides not only a new approach for the prediction of the response of the cervical cancer patients to targeted drug therapy but also a new strategy for combining risk stratification with precision treatment.

MDN1 Mutation Is Associated With High Tumor Mutation Burden and Unfavorable Prognosis in Breast Cancer

Background: Breast cancer (BRCA) is the most common cancer worldwide and a serious threat to human health. MDN1 mutations have been observed in several cancers. However, the associations of MDN1 mutation with tumor mutation burden (TMB) and prognosis of BRCA have not been investigated.

Methods: Genomic, transcriptomic, and clinical data of 973 patients with BRCA from The Cancer Genome Atlas (TCGA) database were analyzed. The clinical attributes of BRCA based on the MDN1 mutation status were assessed by comparing TMB and tumor infiltrating immune cells. Gene ontology analysis and gene set enrichment analysis (GSEA) were conducted to identify the key signaling pathways associated with MDN1 mutation. Moreover, univariate and multivariate Cox regression analyses were performed to assess the association between prognostic factors and survival outcomes. Finally, nomograms were used to determine the predictive value of MDN1 mutation on clinical outcomes in patients with BRCA.

Results: MDN1 was found to have a relatively high mutation rate (2.77%). Compared to the MDN1 wild-type patients, the TMB value was significantly higher in MDN1 mutant patients (p < 0.001). Prognostic analysis revealed that MDN1 mutant patients had a worse survival probability than MDN1 wild-type patients (hazard ratio = 2.91; 95% CI:1.07–7.92; p = 0.036). GSEA revealed samples with MDN1 mutation enriched in retinol metabolism, drug metabolism cytochrome P450, glucuronidation, miscellaneous transport, and binding event pathways.

Conclusion: MDN1 mutation was found to be associated with high TMB and inferior prognosis, suggesting that MDN1 mutation may play a potential role in prognosis prediction and immunotherapy guidance in BRCA.

Engineering for an HPV 9-valent vaccine candidate using genomic constitutive over-expression and low lipopolysaccharide levels in Escherichia coli cells

Background

The various advantages associated with the growth properties of Escherichia coli have justified their use in the production of genetically engineered vaccines. However, endotoxin contamination, plasmid vector instability, and the requirement for antibiotic supplementation are frequent bottlenecks in the successful production of recombinant proteins that are safe for industrial-scaled applications. To overcome these drawbacks, we focused on interrupting the expression of several key genes involved in the synthesis of lipopolysaccharide (LPS), an endotoxin frequently responsible for toxicity in recombinant proteins, to eliminate endotoxin contamination and produce better recombinant proteins with E. coli.

Results

Of 8 potential target genes associated with LPS synthesis, we successfully constructed 7 LPS biosynthesis-defective recombinant strains to reduce the production of LPS. The endotoxin residue in the protein products from these modified E. coli strains were about two orders of magnitude lower than that produced by the wild-type strain. Further, we found that 6 loci—lpxM, lpxP, lpxL, eptA, gutQ and kdsD—were suitable for chromosomal integrated expression of HPV L1 protein. We found that a single copy of the expression cassette conferred stable expression during long-term antibiotic-free cultivation as compared with the more variable protein production from plasmid-based expression. In large-scale fermentation, we found that recombinant strains bearing 3 to 5 copies of the expression cassette had 1.5- to 2-fold higher overall expression along with lower endotoxin levels as compared with the parental ER2566 strain. Finally, we engineered and constructed 9 recombinant E. coli strains for the later production of an HPV 9-valent capsid protein with desirable purity, VLP morphology, and antigenicity.

Conclusions

Reengineering the LPS synthesis loci in the E. coli ER2566 strain through chromosomal integration of expression cassettes has potential uses for the production of a 9-valent HPV vaccine candidate, with markedly reduced residual endotoxin levels. Our results offer a new strategy for recombinant E. coli strain construction, engineering, and the development of suitable recombinant protein drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01719-8.

Genomic landscape and chronological reconstruction of driver events in multiple myeloma

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

Estrogen receptor-alpha isoforms are the main estrogen receptors expressed in non-small cell lung carcinoma

The expression profile of estrogen receptors (ER) in Non-Small Cell Lung Carcinoma (NSCLC) remains contradictory. Here we investigated protein and transcriptome expression of ERα wild type and variants. Tissue Micro-Arrays of 200 cases of NSCLC (paired tumor/non-tumor) were assayed by immunohistochemistry using a panel of ERα antibodies targeting different epitopes (HC20, 6F11, 1D5, ERα36 and ERα17p). ERβ epitopes were also examined for comparison. In parallel we conducted a probe-set mapping (Affymetrix HGU133 plus 2 chip) meta-analysis of 12 NSCLC tumor public transcriptomic studies (1418 cases) and 39 NSCLC cell lines. Finally, we have investigated early transcriptional effects of 17β-estradiol, 17β-estradiol-BSA, tamoxifen and their combination in two NSCLC cell lines (A549, H520). ERα transcript and protein detection in NSCLC specimens and cell lines suggests that extranuclear ERα variants, like ERα36, prevail, while wild-type ERα66 is minimally expressed. In non-tumor lung, the wild-type ERα66 is quasi-absent. The combined evaluation of ERα isoform staining intensity and subcellular localization with sex, can discriminate NSCLC subtypes and normal lung. Overall ERα transcription decreases in NSCLC. ERα expression is sex-related in non-tumor tissue, but in NSCLC it is exclusively correlating with tumor histologic subtype. ERα isoform protein expression is higher than ERβ. ERα isoforms are functional and display specific early transcriptional effects following steroid treatment. In conclusion, our data show a wide extranuclear ERα-variant expression in normal lung and NSCLC that is not reported by routine pathology ER evaluation criteria, limited in the nuclear wild type receptor.

A merged lung cancer transcriptome dataset for clinical predictive modeling

The Gene Expression Omnibus (GEO) database is an excellent public source of whole transcriptomic profiles of multiple cancers. The main challenge is the limited accessibility of such large-scale genomic data to people without a background in bioinformatics or computer science. This presents difficulties in data analysis, sharing and visualization. Here, we present an integrated bioinformatics pipeline and a normalized dataset that has been preprocessed using a robust statistical methodology; allowing others to perform large-scale meta-analysis, without having to conduct time-consuming data mining and statistical correction. Comprising 1,118 patient-derived samples, the normalized dataset includes primary non-small cell lung cancer (NSCLC) tumors and paired normal lung tissues from ten independent GEO datasets, facilitating differential expression analysis. The data has been merged, normalized, batch effect-corrected and filtered for genes with low variance via multiple open source R packages integrated into our workflow. Overall this dataset (with associated clinical metadata) better represents the diseased population and serves as a powerful tool for early predictive biomarker discovery.

Cancer transcriptome profiling at the juncture of clinical translation

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

Pronounced strain-specific chemosensory receptor gene expression in the mouse vomeronasal organ

Background

The chemosensory system plays an important role in orchestrating sexual behaviors in mammals. Pheromones trigger sexually dimorphic behaviors and different mouse strains exhibit differential responses to pheromone stimuli. It has been speculated that differential gene expression in the sensory organs that detect pheromones may underlie sexually-dimorphic and strain-specific responses to pheromone cues.

Results

We have performed transcriptome analyses of the mouse vomeronasal organ, a sensory organ recognizing pheromones and interspecies cues. We find little evidence of sexual dimorphism in gene expression except for Xist, an essential gene for X-linked gene inactivation. Variations in gene expression are found mainly among strains, with genes from immune response and chemosensory receptor classes dominating the list. Differentially expressed genes are concentrated in genomic hotspots enriched in these families of genes. Some chemosensory receptors show exclusive patterns of expression in different strains. We find high levels of single nucleotide polymorphism in chemosensory receptor pseudogenes, some of which lead to functionalized receptors. Moreover, we identify a number of differentially expressed long noncoding RNA species showing strong correlation or anti-correlation with chemoreceptor genes.

Conclusions

Our analyses provide little evidence supporting sexually dimorphic gene expression in the vomeronasal organ that may underlie dimorphic pheromone responses. In contrast, we find pronounced variations in the expression of immune response related genes, vomeronasal and G-protein coupled receptor genes among different mouse strains. These findings raised the possibility that diverse strains of mouse perceive pheromone cues differently and behavioral difference among strains in response to pheromone may first arise from differential detection of pheromones. On the other hand, sexually dimorphic responses to pheromones more likely originate from dimorphic neural circuits in the brain than from differential detection. Moreover, noncoding RNA may offer a potential regulatory mechanism controlling the differential expression patterns.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4364-4) contains supplementary material, which is available to authorized users.

An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer

The prognosis and prediction of adjuvant chemotherapy (ACT) response in early-stage non-small cell lung cancer (NSCLC) patients remain poor in this era of personalized medicine. We hypothesize that extracellular matrix (ECM)-associated components could be potential markers for better diagnosis and prognosis due to their differential expression in 1,943 primary NSCLC tumors as compared to 303 normal lung tissues. Here we develop a 29-gene ECM-related prognostic and predictive indicator (EPPI). We validate a robust performance of the EPPI risk scoring system in multiple independent data sets, comprising a total of 2,071 early-stage NSCLC tumors. Patients are stratified according to the universal cutoff score based on the EPPI when applied in the clinical setting; the low-risk group has significantly better survival outcome. The functional EPPI gene set represents a potential genomic tool to improve patient selection in early-stage NSCLC to further derive the best benefits of ACT and prevent unnecessary treatment or ACT-associated morbidity.

Prognosis and prediction of adjuvant chemotherapy response in non-small cell lung cancer can have significant clinical impact. Here, the authors show that differential expression of a 29 extracellular matrix gene indicator, EPPI, can predict patient outcome.

IFN-α-mediated Base Excision Repair Pathway Correlates with Antiviral Response Against Hepatitis B Virus Infection

Previous studies identified APOBEC deaminases as enzymes targeting hepatitis B virus (HBV) DNA in the nucleus thus affecting its persistence. Interferon (IFN)-α treated chimpanzees and hepatitis C patients showed elevated APOBEC expression. We thus hypothesized that the responses to IFN-α treatment of chronic hepatitis B (CHB) patients is influenced by IFN-induced base excision repair (BER). CHB-treatment naïve patients, patients treated with PEGylated IFN-α, and patients with sequential treatment of Entecavior and PEGylated IFN-α were recruited. Blood and liver biopsy samples were collected before treatment and at treatment endpoint. BER genes were assessed by quantitative RT-PCR. BER gene expression levels and IFN treatment responses were correlated in patient liver biopsies. APOBEC3A, -B, -C, -D/E, and-G mRNA levels were up-regulated in IFN-treated patients. APOBEC3A expression was significantly higher in IFN-responders than in non-responders. BER genes NEIL3 was down-regulated in IFN-treated patients. APOBEC3 and BER gene expression at treatment endpoints partially correlated with the corresponding absolute DNA level or degree of HBsAg and HBV DNA decline. Our study suggests that the expression of APOBEC3A positively correlates with IFN-treatment responses in CHB patients, while NEIL3 shows negative correlation. These genes may involve to IFN mediated viral suppression and serve as biomarkers for CHB disease management.

Role for the ATPase inhibitory factor 1 in the environmental carcinogen-induced Warburg phenotype

Most tumors undergo metabolic reprogramming towards glycolysis, the so-called Warburg effect, to support growth and survival. Overexpression of IF1, the physiological inhibitor of the F0F1ATPase, has been related to this phenomenon and appears to be a relevant marker in cancer. Environmental contributions to cancer development are now widely accepted but little is known about the underlying intracellular mechanisms. Among the environmental pollutants humans are commonly exposed to, benzo[a]pyrene (B[a]P), the prototype molecule of polycyclic aromatic hydrocarbons (PAHs), is a well-known human carcinogen. Besides apoptotic signals, B[a]P can also induce survival signals in liver cells, both likely involved in cancer promotion. Our previous works showed that B[a]P elicited a Warburg-like effect, thus favoring cell survival. The present study aimed at further elucidating the molecular mechanisms involved in the B[a]P-induced metabolic reprogramming, by testing the possible involvement of IF1. We presently demonstrate, both in vitro and in vivo, that PAHs, especially B[a]P, strongly increase IF1 expression. Such an increase, which might rely on β2-adrenergic receptor activation, notably participates to the B[a]P-induced glycolytic shift and cell survival in liver cells. By identifying IF1 as a target of PAHs, this study provides new insights about how environmental factors may contribute to related carcinogenesis.

Crowdsourced direct-to-consumer genomic analysis of a family quartet

Background

We describe the pioneering experience of a Spanish family pursuing the goal of understanding their own personal genetic data to the fullest possible extent using Direct to Consumer (DTC) tests. With full informed consent from the Corpas family, all genotype, exome and metagenome data from members of this family, are publicly available under a public domain Creative Commons 0 (CC0) license waiver. All scientists or companies analysing these data (“the Corpasome”) were invited to return results to the family.

Methods

We released 5 genotypes, 4 exomes, 1 metagenome from the Corpas family via a blog and figshare under a public domain license, inviting scientists to join the crowdsourcing efforts to analyse the genomes in return for coauthorship or acknowldgement in derived papers. Resulting analysis data were compiled via social media and direct email.

Results

Here we present the results of our investigations, combining the crowdsourced contributions and our own efforts. Four companies offering annotations for genomic variants were applied to four family exomes: BIOBASE, Ingenuity, Diploid, and GeneTalk. Starting from a common VCF file and after selecting for significant results from company reports, we find no overlap among described annotations. We additionally report on a gut microbiome analysis of a member of the Corpas family.

Conclusions

This study presents an analysis of a diverse set of tools and methods offered by four DTC companies. The striking discordance of the results mirrors previous findings with respect to DTC analysis of SNP chip data, and highlights the difficulties of using DTC data for preventive medical care. To our knowledge, the data and analysis results from our crowdsourced study represent the most comprehensive exome and analysis for a family quartet using solely DTC data generation to date.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1973-7) contains supplementary material, which is available to authorised users.

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article's supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer's disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html.

RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasets

Gene targeting is a protocol for introducing a mutation to a specific gene in an organism. Because of the importance of in vivo assessment of gene function and modeling of human diseases, this technique has been widely adopted to generate a large number of mutant mouse models. Due to the recent breakthroughs in high-throughput sequencing technologies, RNA-Seq experiments have been performed on many of these mouse models, leading to hundreds of publicly available datasets. To facilitate the reuse of these datasets, we collected the associated metadata and organized them in a database called RNASeqMetaDB. The metadata were manually curated to ensure annotation consistency. We developed a web server to allow easy database navigation and data querying. Users can search the database using multiple parameters like genes, diseases, tissue types, keywords and associated publications in order to find datasets that match their interests. Summary statistics of the metadata are also presented on the web server showing interesting global patterns of RNA-Seq studies.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://rnaseqmetadb.ece.tamu.edu.

NexGenEx-Tom: a gene expression platform to investigate the functionalities of the tomato genome

BACKGROUND

Next Generation Sequencing technologies (NGS) unexpectedly pushed forward the capability of solving genome organization and of widely depicting gene expression. However, although the flourishing of tools to process the NGS data, versatile and user-friendly computational environments for integrative and comparative analyses of the results from the increasing amount of collections are still required.

DESCRIPTION

Here we present the architecture and the facilities of NexGenEx-, a web based platform that offers processed NGS transcriptome collections and enables immediate analyses of the results. The platform allows gene expression investigations, profiling and comparisons, and exploits different resources.

CONCLUSION

In the current version, NexGenEx-Tom includes processed and normalized NGS expression data from three collections covering several tissue/stages from different genotypes. Beyond providing a user-friendly interface, the platform was designed with the aim to easily be expanded to include other NGS based transcriptome collections. It can also integrate different genome releases, possibly from different cultivars or genotypes, but even from different species. The platform is proposed as an example effort in tomato, and is described as a profitable approach for the exploitation of these challenging and precious datasets.

ID helix-loop-helix proteins as determinants of cell survival in B-cell chronic lymphocytic leukemia cells in vitro

BACKGROUND

Members of the inhibitor of DNA-binding (ID) family of helix-loop-helix proteins have been causally implicated in the pathogenesis of several types of B-cell lineage malignancy, either on the basis of mutation or by altered expression. B-cell chronic lymphocytic leukemia encompasses a heterogeneous group of disorders and is the commonest leukaemia type in the Western world. In this study, we have investigated the pathobiological functions of the ID2 and ID3 proteins in this disease with an emphasis on their role in regulating leukemic cell death/survival.

METHODS

Bioinformatics analysis of microarray gene expression data was used to investigate expression of ID2/ID3 in leukemic versus normal B cells, their association with clinical course of disease and molecular sub-type and to reconstruct a gene regulatory network using the 'maximum information coefficient' (MIC) for target gene inference. In vitro cultured primary leukemia cells, either in isolation or co-cultured with accessory vascular endothelial cells, were used to investigate ID2/ID3 protein expression by western blotting and to assess the cytotoxic response of different drugs (fludarabine, chlorambucil, ethacrynic acid) by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. ID2/ID3 protein levels in primary leukemia cells and in MEC1 cells were manipulated by transduction with siRNA reagents.

RESULTS

Datamining showed that the expression profiles of ID2 and ID3 are associated with distinct pathobiological features of disease and implicated both genes in regulating cell death/survival by targeting multiple non-overlapping sets of apoptosis effecter genes. Consistent with microarray data, the overall pattern of ID2/ID3 protein expression in relation to cell death/survival responses of primary leukemia cells was suggestive of a pro-survival function for both ID proteins. This was confirmed by siRNA knock-down experiments in MEC1 cells and in primary leukemia cells, but with variability in the dependence of leukemic cells from different patients on ID protein expression for cell survival. Vascular endothelial cells rescued leukemia cells from spontaneous and cytotoxic drug-induced cell death at least in part, via an ID protein-coupled redox-dependent mechanism.

CONCLUSIONS

Our study provides evidence for a pro-survival function of the ID2/ID3 proteins in chronic lymphocytic leukemia cells and also highlights these proteins as potential determinants of the pathobiology of this disorder.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation

MOTIVATION

Modern molecular technologies allow the collection of large amounts of high-throughput data on the functional attributes of genes. Often multiple technologies and study designs are used to address the same biological question such as which genes are overexpressed in a specific disease state. Consequently, there is considerable interest in methods that can integrate across datasets to present a unified set of predictions.

RESULTS

An important aspect of data integration is being able to account for the fact that datasets may differ in how accurately they capture the biological signal of interest. While many methods to address this problem exist, they always rely either on dataset internal statistics, which reflect data structure and not necessarily biological relevance, or external gold standards, which may not always be available. We present a new rank aggregation method for data integration that requires neither external standards nor internal statistics but relies on Bayesian reasoning to assess dataset relevance. We demonstrate that our method outperforms established techniques and significantly improves the predictive power of rank-based aggregations. We show that our method, which does not require an external gold standard, provides reliable estimates of dataset relevance and allows the same set of data to be integrated differently depending on the specific signal of interest.

AVAILABILITY

The method is implemented in R and is freely available at http://www.pitt.edu/~mchikina/BIRRA/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

iRegulon: from a gene list to a gene regulatory network using large motif and track collections

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.

Gene regulatory networks control developmental, homeostatic, and disease processes by governing precise levels and spatio-temporal patterns of gene expression. Determining their topology can provide mechanistic insight into these processes. Gene regulatory networks consist of interactions between transcription factors and their direct target genes. Each regulatory interaction represents the binding of the transcription factor to a specific DNA binding site near its target gene. Here we present a computational method, called iRegulon, to identify master regulators and direct target genes in a human gene signature, i.e. a set of co-expressed genes. iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10.000 TF motifs and 1000 ChIP-seq data sets or “tracks”. Next, it associates enriched motifs and tracks with candidate transcription factors and determines the optimal subset of direct target genes. We validate iRegulon on ENCODE data, and use it in combination with RNA-seq and ChIP-seq data to map a p53 downstream network with new predicted co-factors and targets. iRegulon is available as a Cytoscape plugin, supporting human, mouse, and Drosophila genes, and provides access to hundreds of cancer-related TF-target subnetworks or “regulons”.

Glycerol-3-phosphate acyltranferase-2 behaves as a cancer testis gene and promotes growth and tumorigenicity of the breast cancer MDA-MB-231 cell line

The de novo synthesis of glycerolipids in mammalian cells begins with the acylation of glycerol-3-phosphate, catalyzed by glycerol-3-phosphate acyltransferase (GPAT). GPAT2 is a mitochondrial isoform primarily expressed in testis under physiological conditions. Because it is aberrantly expressed in multiple myeloma, it has been proposed as a novel cancer testis gene. Using a bioinformatics approach, we found that GPAT2 is highly expressed in melanoma, lung, prostate and breast cancer, and we validated GPAT2 expression at the protein level in breast cancer by immunohistochemistry. In this case GPAT2 expression correlated with a higher histological grade. 5-Aza-2′ deoxycytidine treatment of human cells lines induced GPAT2 expression suggesting epigenetic regulation of gene expression. In order to evaluate the contribution of GPAT2 to the tumor phenotype, we silenced its expression in MDA-MB-231 cells. GPAT2 knockdown diminished cell proliferation, anchorage independent growth, migration and tumorigenicity, and increased staurosporine-induced apoptosis. In contrast, GPAT2 over-expression increased cell proliferation rate and resistance to staurosporine-induced apoptosis. To understand the functional role of GPAT2, we performed a co-expression analysis in mouse and human testis and found a significant association with semantic terms involved in cell cycle, DNA integrity maintenance, piRNA biogenesis and epigenetic regulation. Overall, these results indicate the GPAT2 would be directly associated with the control of cell proliferation. In conclusion, we confirm GPAT2 as a cancer testis gene and that its expression contributes to the tumor phenotype of MDA-MB-231 cells.

SOX2 controls tumour initiation and cancer stem-cell functions in squamous-cell carcinoma

Cancer stem cells (CSCs) have been reported in various cancers, including in skin squamous-cell carcinoma (SCC). The molecular mechanisms regulating tumour initiation and stemness are still poorly characterized. Here we find that Sox2, a transcription factor expressed in various types of embryonic and adult stem cells, was the most upregulated transcription factor in the CSCs of squamous skin tumours in mice. SOX2 is absent in normal epidermis but begins to be expressed in the vast majority of mouse and human pre-neoplastic skin tumours, and continues to be expressed in a heterogeneous manner in invasive mouse and human SCCs. In contrast to other SCCs, in which SOX2 is frequently genetically amplified, the expression of SOX2 in mouse and human skin SCCs is transcriptionally regulated. Conditional deletion of Sox2 in the mouse epidermis markedly decreases skin tumour formation after chemical-induced carcinogenesis. Using green fluorescent protein (GFP) as a reporter of Sox2 transcriptional expression (SOX2-GFP knock-in mice), we showed that SOX2-expressing cells in invasive SCC are greatly enriched in tumour-propagating cells, which further increase upon serial transplantations. Lineage ablation of SOX2-expressing cells within primary benign and malignant SCCs leads to tumour regression, consistent with the critical role of SOX2-expressing cells in tumour maintenance. Conditional Sox2 deletion in pre-existing skin papilloma and SCC leads to tumour regression and decreases the ability of cancer cells to be propagated upon transplantation into immunodeficient mice, supporting the essential role of SOX2 in regulating CSC functions. Transcriptional profiling of SOX2-GFP-expressing CSCs and of tumour epithelial cells upon Sox2 deletion uncovered a gene network regulated by SOX2 in primary tumour cells in vivo. Chromatin immunoprecipitation identified several direct SOX2 target genes controlling tumour stemness, survival, proliferation, adhesion, invasion and paraneoplastic syndrome. We demonstrate that SOX2, by marking and regulating the functions of skin tumour-initiating cells and CSCs, establishes a continuum between tumour initiation and progression in primary skin tumours.

Androgenic pathways in the progression of triple-negative breast carcinoma: a comparison between aggressive and non-aggressive subtypes

One of the active intracellular pathways/networks in triple-negative breast carcinoma (TNBC) is that of the androgen receptor (AR). In this study, we examined AR and androgen-metabolising enzyme immunoreactivity in subcategories of TNBC to further elucidate the roles of androgenic pathways in TNBC. We utilised formalin-fixed paraffin-embedded breast cancer samples from ductal carcinoma in situ (DCIS) and invasive ductal carcinoma patient cohorts. We then used immunohistochemistry to classify these samples into basal-like and non-basal samples and to assess interactions between AR, androgen-metabolising enzymes and proliferation. To further substantiate our hypothesis and provide mechanistic insights, we also looked at the expression and regulation of these factors in publically available microarray data and in a panel of TNBC AR-positive cell lines. DCIS was associated with higher levels of AR and enzymes (p < 0.02), although a similar difference was not noticed in basal and non-basal samples. AR and enzymes were correlated in all states. In TNBC cell lines (MDA-MD-453, MFM-223 and SUM185-PE), we found that DHT treatment up-regulated 5αR1 and 17βHSD5 suggesting a mechanistic explanation for the correlations observed in the histological samples. Publicly available microarray data in TNBC cases suggested similar patterns to those observed in histological samples. In the majority of settings, including publically available microarray data, an inverse association between AR and proliferation was detected. These findings suggest that decreases in AR and androgen-metabolising enzymes may be involved in the increased biological aggressiveness in TNBC development.

Comparison of merging and meta-analysis as alternative approaches for integrative gene expression analysis

An increasing amount of microarray gene expression data sets is available through public repositories. Their huge potential in making new findings is yet to be unlocked by making them available for large-scale analysis. In order to do so it is essential that independent studies designed for similar biological problems can be integrated, so that new insights can be obtained. These insights would remain undiscovered when analyzing the individual data sets because it is well known that the small number of biological samples used per experiment is a bottleneck in genomic analysis. By increasing the number of samples the statistical power is increased and more general and reliable conclusions can be drawn. In this work, two different approaches for conducting large-scale analysis of microarray gene expression data-meta-analysis and data merging-are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies. Within this study, we investigate the hypothesis that analyzing large cohorts of samples resulting in merging independent data sets designed to study the same biological problem results in lower false discovery rates than analyzing the same data sets within a more conservative meta-analysis approach.

The cancer gene WWOX behaves as an inhibitor of SMAD3 transcriptional activity via direct binding

BACKGROUND

The WW domain containing protein WWOX has been postulated to behave as a tumor suppressor in breast and other cancers. Expression of this protein is lost in over 70% of ER negative tumors. This prompted us to investigate the phenotypic and gene expression effects of loss of WWOX expression in breast cells.

METHODS

Gene expression microarrays and standard in vitro assays were performed on stably silenced WWOX (shRNA) normal breast cells. Bioinformatic analyses were used to identify gene networks and transcriptional regulators affected by WWOX silencing. Co-immunoprecipitations and GST-pulldowns were used to demonstrate a direct interaction between WWOX and SMAD3. Reporter assays, ChIP, confocal microscopy and in silico analyses were employed to determine the effect of WWOX silencing on TGFβ-signaling.

RESULTS

WWOX silencing affected cell proliferation, motility, attachment and deregulated expression of genes involved in cell cycle, motility and DNA damage. Interestingly, we detected an enrichment of targets activated by the SMAD3 transcription factor, including significant upregulation of ANGPTL4, FST, PTHLH and SERPINE1 transcripts. Importantly, we demonstrate that the WWOX protein physically interacts with SMAD3 via WW domain 1. Furthermore, WWOX expression dramatically decreases SMAD3 occupancy at the ANGPTL4 and SERPINE1 promoters and significantly quenches activation of a TGFβ responsive reporter. Additionally, WWOX expression leads to redistribution of SMAD3 from the nuclear to the cytoplasmic compartment. Since the TGFβ target ANGPTL4 plays a key role in lung metastasis development, we performed a meta-analysis of ANGPTL4 expression relative to WWOX in microarray datasets from breast carcinomas. We observed a significant inverse correlation between WWOX and ANGPTL4. Furthermore, the WWOX(lo)/ANGPTL4(hi) cluster of breast tumors is enriched in triple-negative and basal-like sub-types. Tumors with this gene expression signature could represent candidates for anti-TGFβ targeted therapies.

CONCLUSIONS

We show for the first time that WWOX modulates SMAD3 signaling in breast cells via direct WW-domain mediated binding and potential cytoplasmic sequestration of SMAD3 protein. Since loss of WWOX expression increases with breast cancer progression and it behaves as an inhibitor of SMAD3 transcriptional activity these observations may help explain, at least in part, the paradoxical pro-tumorigenic effects of TGFβ signaling in advanced breast cancer.

The Gene Expression Barcode 3.0: improved data processing and mining tools

The Gene Expression Barcode project, http://barcode.luhs.org, seeks to determine the genes expressed for every tissue and cell type in humans and mice. Understanding the absolute expression of genes across tissues and cell types has applications in basic cell biology, hypothesis generation for gene function and clinical predictions using gene expression signatures. In its current version, this project uses the abundant publicly available microarray data sets combined with a suite of single-array preprocessing, quality control and analysis methods. In this article, we present the improvements that have been made since the previous version of the Gene Expression Barcode in 2011. These include a variety of new data mining tools and summaries, estimated transcriptomes and curated annotations.

FGFR1 mutations cause Hartsfield syndrome, the unique association of holoprosencephaly and ectrodactyly

Background

Harstfield syndrome is the rare and unique association of holoprosencephaly (HPE) and ectrodactyly, with or without cleft lip and palate, and variable additional features. All the reported cases occurred sporadically. Although several causal genes of HPE and ectrodactyly have been identified, the genetic cause of Hartsfield syndrome remains unknown. We hypothesised that a single key developmental gene may underlie the co-occurrence of HPE and ectrodactyly.

Methods

We used whole exome sequencing in four isolated cases including one case-parents trio, and direct Sanger sequencing of three additional cases, to investigate the causative variants in Hartsfield syndrome.

Results

We identified a novel FGFR1 mutation in six out of seven patients. Affected residues are highly conserved and are located in the extracellular binding domain of the receptor (two homozygous mutations) or the intracellular tyrosine kinase domain (four heterozygous de novo variants). Strikingly, among the six novel mutations, three are located in close proximity to the ATP's phosphates or the coordinating magnesium, with one position required for kinase activity, and three are adjacent to known mutations involved in Kallmann syndrome plus other developmental anomalies.

Conclusions

Dominant or recessive FGFR1 mutations are responsible for Hartsfield syndrome, consistent with the known roles of FGFR1 in vertebrate ontogeny and conditional Fgfr1-deficient mice. Our study shows that, in humans, lack of accurate FGFR1 activation can disrupt both brain and hand/foot midline development, and that FGFR1 loss-of-function mutations are responsible for a wider spectrum of clinical anomalies than previously thought, ranging in severity from seemingly isolated hypogonadotropic hypogonadism, through Kallmann syndrome with or without additional features, to Hartsfield syndrome at its most severe end.

GENESHIFT: a nonparametric approach for integrating microarray gene expression data based on the inner product as a distance measure between the distributions of genes

The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experiments, which are designed to study the same biological problem. However, this process is hindered by batch effects that are study-dependent and result in random data distortion; therefore numerical transformations are needed to render the integration of different data sets accurate and meaningful. Our contribution in this paper is two-fold. First we propose GENESHIFT, a new nonparametric batch effect removal method based on two key elements from statistics: empirical density estimation and the inner product as a distance measure between two probability density functions; second we introduce a new validation index of batch effect removal methods based on the observation that samples from two independent studies drawn from a same population should exhibit similar probability density functions. We evaluated and compared the GENESHIFT method with four other state-of-the-art methods for batch effect removal: Batch-mean centering, empirical Bayes or COMBAT, distance-weighted discrimination, and cross-platform normalization. Several validation indices providing complementary information about the efficiency of batch effect removal methods have been employed in our validation framework. The results show that none of the methods clearly outperforms the others. More than that, most of the methods used for comparison perform very well with respect to some validation indices while performing very poor with respect to others. GENESHIFT exhibits robust performances and its average rank is the highest among the average ranks of all methods used for comparison.

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

Background

With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck.

Results

We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well.

Conclusions

By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].