A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance the performance of state-of-the-art models. Here, we introduced TEC-LncMir, a novel approach for predicting lncRNA-miRNA interaction using Transformer Encoder and convolutional neural networks (CNNs). TEC-LncMir treats lncRNA and miRNA sequences as natural languages, encodes them using the Transformer Encoder, and combines representations of a pair of microRNA and lncRNA into a contact tensor (a three-dimensional array). Afterward, TEC-LncMir treats the contact tensor as a multi-channel image, utilizes a four-layer CNN to extract the contact tensor's features, and then uses these features to predict the interaction between the pair of lncRNA and miRNA. We applied a series of comparative experiments to demonstrate that TEC-LncMir significantly improves lncRNA-miRNA interaction prediction, compared with existing state-of-the-art models. We also trained TEC-LncMir utilizing a large training dataset, and as expected, TEC-LncMir achieves unprecedented performance. Moreover, we integrated miRanda into TEC-LncMir to show the secondary structures of high-confidence interactions. Finally, we utilized TEC-LncMir to identify microRNAs interacting with lncRNA NEAT1, where NEAT1 performs as a competitive endogenous RNA of the microRNAs' targets (mRNAs) in brain cells. We also demonstrated the regulatory mechanism of NEAT1 in Alzheimer's disease via transcriptome analysis and sequence alignment analysis. Overall, our results demonstrate the effectivity of TEC-LncMir, suggest a potential regulation of miRNAs by NEAT1 in Alzheimer's disease, and take a significant step forward in lncRNA-miRNA interaction prediction.

Misspellings or "miscellings"-Non-verifiable and unknown cell lines in cancer research publications

Reproducible laboratory research relies on correctly identified reagents. We have previously described gene research papers with wrongly identified nucleotide sequence(s), including papers studying miR-145. Manually verifying reagent identities in 36 recent miR-145 papers found that 56% and 17% of papers described misidentified nucleotide sequences and cell lines, respectively. We also found 5 cell line identifiers in miR-145 papers with misidentified nucleotide sequences and cell lines, and 18 cell line identifiers published elsewhere, that did not represent indexed human cell lines. These 23 identifiers were described as non-verifiable (NV), as their identities were unclear. Studying 420 papers that mentioned 8 NV identifier(s) found 235 papers (56%) that referred to 7 identifiers (BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803, and MGC-823) as independent cell lines. We could not find any publications describing how these cell lines were established. Six cell lines were sourced from cell line repositories with externally accessible online catalogs, but these cell lines were not indexed as claimed. Some papers also stated that short tandem repeat (STR) profiles had been generated for three cell lines, yet no STR profiles could be identified. In summary, as NV cell lines represent new challenges to research integrity and reproducibility, further investigations are required to clarify their status and identities.

Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data

BACKGROUND

Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments.

METHODS

We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected.

RESULTS

Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84-0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality.

CONCLUSION

Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC.

Identification of human gene research articles with wrongly identified nucleotide sequences

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

Analysis of dynamic molecular networks: the progression from colorectal adenoma to cancer

BACKGROUND

Colorectal cancer (CRC) is one of the deadliest cancers worldwide. It is the fourth most deadly cancer in the world with nearly 900,000 people die every year, the progression of polyps into cancer as one of its most common developmental pathways.

METHODS

This study obtained gene chip data collections from the Gene Expression Omnibus for colorectal adenoma (GSE8671) and colorectal cancer (GSE32323). Differentially expressed genes (DEGs) in normal tissue and different stages of CRC were analyzed for clustering, comparison, and visualization using R software. The Cytoscape plugin DyNetViewer was used to construct a dynamic protein-protein interaction network. Subsequently, through the Database for Annotation, Visualization and Integrated Discovery, the DEGs were functionally annotated and path enriched.

RESULTS

Our study found that the matrix metalloprotein family and chemokines were the key regulatory genes that drove CRC progression. The Wnt signaling pathway, chemokine signaling pathway, and CRC pathway were the pathological pathways for CRC. Maintenance played an important role in this process. In addition, the related nodes and pathways at various stages may be potential mechanisms for promoting dynamic CRC progression.

CONCLUSIONS

Our study provides a better understanding of the dynamic pattern of molecular interaction networks during CRC progression and provides relevant markers for more accurate screening of cancer in polyps.

Higher content of microcystin-leucine-arginine promotes the survival of intrahepatic cholangiocarcinoma cells via regulating SET resulting in the poorer prognosis of patients

Background & Aims

Intrahepatic cholangiocarcinoma (ICC) has over the last 10 years become the focus of increasing concern largely due to its rising incidence and high mortality rates worldwide. Microcystin‐leucine‐arginine (MC‐LR) has been reported to be carcinogenic, but there are no data on the linkage between MC‐LR and ICC. This study aimed to explore whether the content levels of MC‐LR in the tumour tissues of ICC patients be associated with the prognosis and if so, to characterize the mechanism in ICC cells.

Methods

We conducted a retrospective study to evaluate the prognostic value of MC‐LR in ICC after resection. All patients were divided into two groups according to the content of MC‐LR in tumour via immunohistochemistry: low‐MC‐LR group (n = 28) and high‐MC‐LR group (n = 30).

Results

Multivariate analysis showed high‐MC‐LR level was the prognostic factor for OS and RFS after hepatectomy (P = .011 and .044). We demonstrated that MC‐LR could promote the survival of human ICC cell lines and SET was identified as an important mRNA in the progression via RNA array.

Conclusions

We provide evidence that MC‐LR was an independent prognostic factor for ICC in humans by modulating the expression of SET in human ICC cells.

Co-expression network analysis identified atypical chemokine receptor 1 (ACKR1) association with lymph node metastasis and prognosis in cervical cancer

Cervical cancer (CC) is one kind of female cancer. With the development of bioinformatics, targeted specific biomarkers therapy has become much more valuable. GSE26511 was obtained from gene expression omnibus (GEO). We utilized a package called "WGCNA" to build co-expression network and choose the hub module. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) information of those genes in the hub module. A Plug-in called MCODE was utilized to choose hub clusters of PPI network, which was visualized in Cytoscape. Clusterprofiler was used to do functional analysis. Univariate and multivariate cox proportional hazards regression analysis were both conducted to predict the risk score of CC patients. Kaplan-Meier curve analysis was done to show the overall survival. Receiver operating characteristic (ROC) curve analysis was utilized to evaluate the predictive value of the patient outcome. Validation of the hub gene in databases, Gene set enrichment analysis (GSEA) and GEPIA were completed. We built co-expression network based on GSE26511 and one CC-related module was identified. Functional analysis of this module showed that extracellular space and Signaling pathways regulating pluripotency of stem cells were most related pathways. PPI network screened GNG11 as the most valuable protein. Cox analysis showed that ACKR1 was negatively correlated with CC progression, which was validated in Gene Expression Profiling Interactive Analysis (GEPIA) and datasets. Survival analysis was performed and showed the consistent result. GSEA set enrichment analysis was also completed. This study showed hub functional terms and gene participated in CC and then speculated that ACKR1 might be tumor suppressor for CC.

Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study

Background

Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized.

Methods

GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan–Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed.

Results

Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle.

Conclusion

BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

Identification of Potential Biomarkers in Association With Progression and Prognosis in Epithelial Ovarian Cancer by Integrated Bioinformatics Analysis

Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein-protein interaction. The three most relevant modules were detected. In addition, according to degree ≥ 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan-Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.

Overexpression of SNORA21 suppresses tumorgenesis of gallbladder cancer in vitro and in vivo

BACKGROUND

Gallbladder cancer (GBC) ranks fifth in the most common malignancy of the gastrointestinal tract worldwide. It is reported many small nucleolar RNAs (SNORNs) could regulate the progression of GBC. To identify potential therapeutic targets for GBC, we conducted microarray analysis in GBC tissues and adjacent normal tissues. We found that SNORA21 was downregulated most in gallbladder tumor samples. Therefore, this research aimed to investigate the role of SNORA21 during the tumorigenesis of GBC.

METHODS

The differential expression of SNORNs between GBC tissues and para-carcinoma tissues were examined by microarray analysis and that were confirmed by qRT-PCR. Cell proliferation was tested by CCK-8 and immunofluorescence. Cell apoptosis and cell cycle in GBC were detected by flow cytometry. Expression of proteins in GBC cells was measured by Western-blot. Transwell assay was used for testing the cell migration and invasion. Xenograft tumor model was established to verify the effect of SNORA21 overexpression on GBC in vivo.

RESULTS

The results revealed that SNORA21 overexpression inhibited the proliferation, migration and invasion of GBC cells. Moreover, overexpression of SNORA21 significantly increased the expression of E-cadherin and decreased the levels of N-cadherin and vimentin. Meanwhile, overexpression of SNORA21 significantly induced apoptosis and G1 arrest of GBC cells. Finally, SNORA21 overexpression significantly suppressed the growth of gallbladder tumors in vivo.

CONCLUSION

Overexpression of SNORA21 significantly suppressed the tumorigenesis of GBC in vitro and in vivo, which may serve as a potential novel target for the treatment of GBC.

Validation of gene expression profiles of candidate genes using low density array in peripheral blood of tobacco consuming head and neck cancer patients and auto/taxi drivers with preneoplastic lesions

TaqMan Low-Density Array (TLDA) based Real-Time PCR (RT-PCR) of selected genes showed increased expression of polycyclic aromatic hydrocarbons (PAHs) metabolizing cytochrome P450s (CYPs), glutathione S-transferases (GSTs) and associated transcription factors in biopsy and peripheral blood samples isolated from head and neck squamous cell carcinoma (HNSCC) patients when compared to the controls. The genes involved in DNA repair, signal transduction pathway, EMT pathway, apoptosis, and cell adhesion/motility were found to be altered in both peripheral blood and biopsy samples of HNSCC patients. Transcription profiles in blood isolated from auto/taxi drivers, with pre-neoplastic lesions and history of tobacco use, also showed similar alterations. The present TLDA data thus demonstrates that low-density array of selected genes in peripheral blood has the potential to be used as a surrogate for providing insight into cancer progression pathways and possibly as an early biomarker for monitoring tobacco induced HNSCC.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Background

Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.

Materials and methods

Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.

Results

We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.

Conclusion

In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

ACTG1 and TLR3 are biomarkers for alcohol-associated hepatocellular carcinoma

Alcohol consumption is a risk factor for the development of hepatocellular carcinoma (HCC); however, the association between alcohol and HCC remains unknown. The present study aimed to identify key genes related to alcohol-associated HCC to improve the current understanding of the pathology of this disease. Alcohol-associated and non-alcohol-associated HCC samples in the GSE50579 dataset of the Gene Omnibus Database were analyzed to investigate altered gene expression. Integrated bioinformatics methods were employed to clarify the biological functions of the differentially expressed genes (DEGs), including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interactions (PPIs). The present study reported that candidate biomarker micro (mi)RNAs via TargetScan Human 7.1. DEGs and their associated miRNAs (according to bioinformatics analysis) were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Additionally, 284 EGs from the GSE50579 dataset were revealed. In GO term analysis, DEGs were closely associated with the ‘regulation of nucleic acid metabolism’. KEGG pathway analysis indicated that the DEGs were tightly engaged in the ‘VEGF and VEGF receptor signaling network’, ‘proteoglycan syndecan-mediated signaling events’, ‘erbB receptor signaling’ and ‘β1 integrin cell surface interactions’. According to the results of PPI and heat map analysis, the main hub genes were centrin 3 (CETN3), Toll-like receptor 3 (TLR3), receptor tyrosine-protein kinase (ERBB4), heat shock protein family member 8, actin γ1 (ACTG1) and α-smooth muscle actin. it was demonstrated that the ACTG1, TLR3, miR-6819-3p and miRΝΑ (miR)-6877-3P had undefined associations. Furthermore, RT-qPCR analysis revealed that miR-6819-3p and miR-6877-3P may enhance the expression levels of ACTG1 and inhibit the expression levels of TLR3 in alcohol-associated HCC tissues. TLR3 and ACTG1 were proposed as potential biomarkers of alcohol-associated HCC. Investigation into the regulatory functions of miR-6819-3p and miR-6877-3P may provide novel insights into the treatment of alcohol-associated HCC.

Framework for reanalysis of publicly available Affymetrix® GeneChip® data sets based on functional regions of interest

BACKGROUND

Since the introduction of microarrays in 1995, researchers world-wide have used both commercial and custom-designed microarrays for understanding differential expression of transcribed genes. Public databases such as ArrayExpress and the Gene Expression Omnibus (GEO) have made millions of samples readily available. One main drawback to microarray data analysis involves the selection of probes to represent a specific transcript of interest, particularly in light of the fact that transcript-specific knowledge (notably alternative splicing) is dynamic in nature.

RESULTS

We therefore developed a framework for reannotating and reassigning probe groups for Affymetrix® GeneChip® technology based on functional regions of interest. This framework addresses three issues of Affymetrix® GeneChip® data analyses: removing nonspecific probes, updating probe target mapping based on the latest genome knowledge and grouping probes into gene, transcript and region-based (UTR, individual exon, CDS) probe sets. Updated gene and transcript probe sets provide more specific analysis results based on current genomic and transcriptomic knowledge. The framework selects unique probes, aligns them to gene annotations and generates a custom Chip Description File (CDF). The analysis reveals only 87% of the Affymetrix® GeneChip® HG-U133 Plus 2 probes uniquely align to the current hg38 human assembly without mismatches. We also tested new mappings on the publicly available data series using rat and human data from GSE48611 and GSE72551 obtained from GEO, and illustrate that functional grouping allows for the subtle detection of regions of interest likely to have phenotypical consequences.

CONCLUSION

Through reanalysis of the publicly available data series GSE48611 and GSE72551, we profiled the contribution of UTR and CDS regions to the gene expression levels globally. The comparison between region and gene based results indicated that the detected expressed genes by gene-based and region-based CDFs show high consistency and regions based results allows us to detection of changes in transcript formation.

Differential protein-coding gene and long noncoding RNA expression in smoking-related lung squamous cell carcinoma

Background

Cigarette smoking is one of the greatest preventable risk factors for developing cancer, and most cases of lung squamous cell carcinoma (lung SCC) are associated with smoking. The pathogenesis mechanism of tumor progress is unclear. This study aimed to identify biomarkers in smoking‐related lung cancer, including protein‐coding gene, long noncoding RNA, and transcription factors.

Methods

We selected and obtained messenger RNA microarray datasets and clinical data from the Gene Expression Omnibus database to identify gene expression altered by cigarette smoking. Integrated bioinformatic analysis was used to clarify biological functions of the identified genes, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the construction of a protein–protein interaction network, transcription factor, and statistical analyses. Subsequent quantitative real‐time PCR was utilized to verify these bioinformatic analyses.

Results

Five hundred and ninety‐eight differentially expressed genes and 21 long noncoding RNA were identified in smoking‐related lung SCC. GO and KEGG pathway analysis showed that identified genes were enriched in the cancer‐related functions and pathways. The protein–protein interaction network revealed seven hub genes identified in lung SCC. Several transcription factors and their binding sites were predicted. The results of real‐time quantitative PCR revealed that AURKA and BIRC5 were significantly upregulated and LINC00094 was downregulated in the tumor tissues of smoking patients. Further statistical analysis indicated that dysregulation of AURKA, BIRC5, and LINC00094 indicated poor prognosis in lung SCC.

Conclusion

Protein‐coding genes AURKA, BIRC5, and LINC00094 could be biomarkers or therapeutic targets for smoking‐related lung SCC.

Screening of Tumor Suppressor Genes in Metastatic Colorectal Cancer

Most tumor suppressor genes are commonly inactivated in the development of colorectal cancer (CRC). The activation of tumor suppressor genes may be beneficial to suppress the development and metastasis of CRC. This study analyzed genes expression and methylation levels in different stages of CRC. Genes with downregulated mRNA expression and upregulated methylation level in advanced CRC were screened as the potential tumor suppressor genes. After comparing the methylation level of screened genes, we found that MBD1 gene had downregulated mRNA expression and upregulated methylation levels in advanced CRC and continuously upregulated methylation level in the progression of CRC. Enrichment analysis revealed that genes expression in accordance with the elevated expression of MBD1 mainly located on chromosomes 17p13 and 17p12 and 8 tumor suppressor genes located on chromosome 17p13. Further enrichment analysis of transcription factor binding site identified that SP1 binding site had higher enrichment and could bind with MBD1. In conclusion, MBD1 may be a tumor suppressor gene in advanced CRC and affect the development and metastasis of CRC by regulating 8 tumor suppressor genes through binding with SP1.

miR-150 inhibits terminal erythroid proliferation and differentiation

MicroRNAs (miRNAs), a class of small non-coding linear RNAs, have been shown to play a crucial role in erythropoiesis. To evaluate the indispensable role of constant suppression of miR-150 during terminal erythropoiesis, we performed miR-150 gain- and loss-of-function experiments on hemin-induced K562 cells and EPO-induced human CD34+ cells. We found that forced expression of miR-150 suppresses commitment of hemoglobinization and CD235a labeling in both cell types. Erythroid proliferation is also inhibited via inducing apoptosis and blocking the cell cycle when miR-150 is overexpressed. In contrast, miR-150 inhibition promotes terminal erythropoiesis. 4.1 R gene is a new target of miR-150 during terminal erythropoiesis, and its abundance ensures the mechanical stability and deformability of the membrane. However, knockdown of 4.1 R did not affect terminal erythropoiesis. Transcriptional profiling identified more molecules involved in terminal erythroid dysregulation derived from miR-150 overexpression. These results shed light on the role of miR-150 during human terminal erythropoiesis. This is the first report highlighting the relationship between miRNA and membrane protein and enhancing our understanding of how miRNA works in the hematopoietic system.

Cross-species identification of genomic drivers of squamous cell carcinoma development across preneoplastic intermediates

Cutaneous squamous cell carcinoma (cuSCC) comprises 15-20% of all skin cancers, accounting for over 700,000 cases in USA annually. Most cuSCC arise in association with a distinct precancerous lesion, the actinic keratosis (AK). To identify potential targets for molecularly targeted chemoprevention, here we perform integrated cross-species genomic analysis of cuSCC development through the preneoplastic AK stage using matched human samples and a solar ultraviolet radiation-driven Hairless mouse model. We identify the major transcriptional drivers of this progression sequence, showing that the key genomic changes in cuSCC development occur in the normal skin to AK transition. Our data validate the use of this ultraviolet radiation-driven mouse cuSCC model for cross-species analysis and demonstrate that cuSCC bears deep molecular similarities to multiple carcinogen-driven SCCs from diverse sites, suggesting that cuSCC may serve as an effective, accessible model for multiple SCC types and that common treatment and prevention strategies may be feasible.

Transcriptomic Analysis Identifies the Effect of Beta-Blocking Agents on a Molecular Pathway of Contraction in the Heart and Predicts Response to Therapy

Over the last decades, beta-blockers have been a key component of heart failure therapy. However, currently there is no method to identify patients who will benefit from beta-blocking therapy versus those who will be unresponsive or worsen. Furthermore, there is an unmet need to better understand molecular mechanisms through which heart failure therapies, such as beta-blockers, improve cardiac function, in order to design novel targeted therapies. Solving these issues is an important step towards personalized medicine. Here, we present a comprehensive transcriptomic analysis of molecular pathways that are affected by beta-blocking agents and a transcriptomic biomarker to predict therapy response.

RNA-Seq and microarray analysis of the Xenopus inner ear transcriptome discloses orthologous OMIM(®) genes for hereditary disorders of hearing and balance

Background

Auditory and vestibular disorders are prevalent sensory disabilities caused by genetic and environmental (noise, trauma, chemicals) factors that often damage mechanosensory hair cells of the inner ear. Development of treatments for inner ear disorders of hearing and balance relies on the use of animal models such as fish, amphibians, reptiles, birds, and non-human mammals. Here, we aimed to augment the utility of the genus Xenopus for uncovering genetic mechanisms essential for the maintenance of inner ear structure and function.

Results

Using Affymetrix GeneChip^®X. laevis Genome 2.0 Arrays and Illumina-Solexa sequencing methods, we determined that the transcriptional profile of the Xenopuslaevis inner ear comprises hundreds of genes that are orthologous to OMIM^® genes implicated in deafness and vestibular disorders in humans. Analysis of genes that mapped to both technologies demonstrated that, with our methods, a combination of microarray and RNA-Seq detected expression of more genes than either platform alone.

Conclusions

As part of this study we identified candidate scaffold regions of the Xenopus tropicalis genome that can be used to investigate hearing and balance using genetic and informatics procedures that are available through the National Xenopus Resource (NXR), and the open access data repository, Xenbase. The results and approaches presented here expand the viability of Xenopus as an animal model for inner ear research.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1485-1) contains supplementary material, which is available to authorized users.

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal.

Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

Background

Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA), in which activated synovial fibroblasts (SFBs) play a major role. Since the detailed mechanisms underlying this activation are still unclear, simultaneous investigation of multi-stimuli activation of SFBs offers the possibility to elucidate the regulatory effects of multiple mediators and to gain new insights into disease pathogenesis.

Methods

A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1 β, TNF- α, TGF- β, and PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133 Plus 2.0) were batch-corrected applying ‘ComBat’, analyzed for differentially expressed genes over time with ‘Limma’, and used for the inference of a robust GRN with NetGenerator V2.0, a heuristic ordinary differential equation-based method with soft integration of prior knowledge.

Results

Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting the genes belonging to the most significant gene ontology (GO) term, i.e., ‘cartilage development’, a dynamic, robust, moderately complex multi-stimuli GRN was generated with 24 genes and 57 edges in total, 31 of which were gene-to-gene edges. Prior literature-based knowledge derived from Pathway Studio or manual searches was reflected in the final network by 25/57 confirmed edges (44%). The model contained known network motifs crucial for dynamic cellular behavior, e.g., cross-talk among pathways, positive feed-back loops, and positive feed-forward motifs (including suppression of the transcriptional repressor OSR2 by all 4 stimuli.

Conclusion

A multi-stimuli GRN highly concordant with literature data was successfully generated by network inference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since 10 predicted edges were independently validated by literature findings post network inference. The selected GO term ‘cartilage development’ contained a number of differentiation markers, growth factors, and transcription factors with potential relevance for RA. Finally, the model provided new insight into the response of RA-SFBs to multiple stimuli implicated in the pathogenesis of RA, in particular to the ‘novel’ potent growth factor PDGF-D.

Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies

BACKGROUND

Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial.

RESULTS

Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment.

CONCLUSIONS

This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments.

Gene expression profiling of candidate genes in peripheral blood mononuclear cells for predicting toxicity of diesel exhaust particles

To validate gene expression profiling of peripheral blood mononuclear cells (PBMCs) as a surrogate for monitoring tissue expression, this study using RT-PCR-based TaqMan low-density array (TLDA) was initiated to investigate similarities in the mRNA expression of target genes altered by exposure to diesel exhaust particles (DEPs) in freshly prepared PBMCs and in lungs. Adult Wistar rats were treated transtracheally with a single dose of 7.5 or 15 or 30mg/kg DEPs and sacrificed 24h later. Blood and lungs were immediately taken out and processed for RT-PCR. DEP treatment induced similar patterns of increase in the expression of polycyclic aromatic hydrocarbon-responsive cytochrome P450s, the phase II enzymes, and their associated transcription factors in both lungs and PBMCs, at all doses. Similar to that seen in lungs, a dose-dependent increase was observed in the expression of genes involved in inflammation, such as cytokines, chemokines, and adhesion molecules, in PBMCs. The expression of various genes involved in DNA repair and apoptosis was also increased in a dose-dependent manner in PBMCs and lungs. The present TLDA data indicating similarities in the responsiveness of candidate genes involved in the toxicity of DEPs between PBMCs and lungs after exposure to DEPs demonstrate that expression profiles of genes in PBMCs could be used as a surrogate for monitoring the acute toxicity of fine and ultrafine particulate matter present in vehicular emissions.

TIPMaP: a web server to establish transcript isoform profiles from reliable microarray probes

Background

Standard 3′ Affymetrix gene expression arrays have contributed a significantly higher volume of existing gene expression data than other microarray platforms. These arrays were designed to identify differentially expressed genes, but not their alternatively spliced transcript forms. No resource can currently identify expression pattern of specific mRNA forms using these microarray data, even though it is possible to do this.

Results

We report a web server for expression profiling of alternatively spliced transcripts using microarray data sets from 31 standard 3′ Affymetrix arrays for human, mouse and rat species. The tool has been experimentally validated for mRNAs transcribed or not-detected in a human disease condition (non-obstructive azoospermia, a male infertility condition). About 4000 gene expression datasets were downloaded from a public repository. ‘Good probes’ with complete coverage and identity to latest reference transcript sequences were first identified. Using them, ‘Transcript specific probe-clusters’ were derived for each platform and used to identify expression status of possible transcripts. The web server can lead the user to datasets corresponding to specific tissues, conditions via identifiers of the microarray studies or hybridizations, keywords, official gene symbols or reference transcript identifiers. It can identify, in the tissues and conditions of interest, about 40% of known transcripts as ‘transcribed’, ‘not-detected’ or ‘differentially regulated’. Corresponding additional information for probes, genes, transcripts and proteins can be viewed too. We identified the expression of transcripts in a specific clinical condition and validated a few of these transcripts by experiments (using reverse transcription followed by polymerase chain reaction). The experimental observations indicated higher agreements with the web server results, than contradictions. The tool is accessible at http://resource.ibab.ac.in/TIPMaP.

Conclusion

The newly developed online tool forms a reliable means for identification of alternatively spliced transcript-isoforms that may be differentially expressed in various tissues, cell types or physiological conditions. Thus, by making better use of existing data, TIPMaP avoids the dependence on precious tissue-samples, in experiments with a goal to establish expression profiles of alternative splice forms – at least in some cases.

Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks

Background

The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties.

Findings

PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3’ IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html.

Conclusions

These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well adapted for searching 3’ alternative poly-adenylation events and can be also useful for studying the structure of transcriptional networks. The PSAWNpy, (in Python) and PSAWNml (in Matlab) programmes are freely available and can be downloaded at http://code.google.com/p/arraymatic. Tutorials and reference manuals are available at BMC Research Notes online (Additional file 1) or from http://bns.crbm.cnrs.fr/softwares.html.

Global gene expression profiling of individual human oocytes and embryos demonstrates heterogeneity in early development

Early development in humans is characterised by low and variable embryonic viability, reflected in low fecundity and high rates of miscarriage, relative to other mammals. Data from assisted reproduction programmes provides additional evidence that this is largely mediated at the level of embryonic competence and is highly heterogeneous among embryos. Understanding the basis of this heterogeneity has important implications in a number of areas including: the regulation of early human development, disorders of pregnancy, assisted reproduction programmes, the long term health of children which may be programmed in early development, and the molecular basis of pluripotency in human stem cell populations. We have therefore investigated global gene expression profiles using polyAPCR amplification and microarray technology applied to individual human oocytes and 4-cell and blastocyst stage embryos. In order to explore the basis of any variability in detail, each developmental stage is replicated in triplicate. Our data show that although transcript profiles are highly stage-specific, within each stage they are relatively variable. We describe expression of a number of gene families and pathways including apoptosis, cell cycle and amino acid metabolism, which are variably expressed and may be reflective of embryonic developmental competence. Overall, our data suggest that heterogeneity in human embryo developmental competence is reflected in global transcript profiles, and that the vast majority of existing human embryo gene expression data based on pooled oocytes and embryos need to be reinterpreted.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

Impact of experimental noise and annotation imprecision on data quality in microarray experiments

Data quality is intrinsically influenced by design, technical, and analytical parameters. Quality parameters have not yet been well defined for gene expression analysis by microarrays, though ad interim, following recommended good experimental practice guidelines should ensure generation of reliable and reproducible data. Here we summarize essential practical recommendations for experimental design, technical considerations, feature annotation issues, and standardization efforts.

AbsIDconvert: an absolute approach for converting genetic identifiers at different granularities

BACKGROUND

High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.

RESULTS

All annotations, no matter the granularity, can be aligned to genomic sequences and therefore annotated by genomic intervals. We have developed AbsIDconvert, a methodology for converting between genomic identifiers by first mapping them onto a common universal coordinate system using an interval tree which is subsequently queried for overlapping identifiers. AbsIDconvert has many potential uses, including gene identifier conversion, identification of features within a genomic region, and cross-species comparisons. The utility is demonstrated in three case studies: 1) comparative genomic study mapping plasmodium gene sequences to corresponding human and mosquito transcriptional regions; 2) cross-species study of Incyte clone sequences; and 3) analysis of human Ensembl transcripts mapped by Affymetrix®; and Agilent microarray probes. AbsIDconvert currently supports ID conversion of 53 species for a given list of input identifiers, genomic sequence, or genome intervals.

CONCLUSION

AbsIDconvert provides an efficient and reliable mechanism for conversion between identifier domains of interest. The flexibility of this tool allows for custom definition identifier domains contingent upon the availability and determination of a genomic mapping interval. As the genomes and the sequences for genetic elements are further refined, this tool will become increasingly useful and accurate. AbsIDconvert is freely available as a web application or downloadable as a virtual machine at: http://bioinformatics.louisville.edu/abid/.

Expression Profile of Drug and Nutrient Absorption Related Genes in Madin-Darby Canine Kidney (MDCK) Cells Grown under Differentiation Conditions

The expression levels of genes involved in drug and nutrient absorption were evaluated in the Madin-Darby Canine Kidney (MDCK) in vitro drug absorption model. MDCK cells were grown on plastic surfaces (for 3 days) or on Transwell^® membranes (for 3, 5, 7, and 9 days). The expression profile of genes including ABC transporters, SLC transporters, and cytochrome P450 (CYP) enzymes was determined using the Affymetrix^® Canine GeneChip^®. Expression of genes whose probe sets passed a stringent confirmation process was examined. Expression of a few transporter (MDR1, PEPT1 and PEPT2) genes in MDCK cells was confirmed by RT-PCR. The overall gene expression profile was strongly influenced by the type of support the cells were grown on. After 3 days of growth, expression of 28% of the genes was statistically different (1.5-fold cutoff, p < 0.05) between the cells grown on plastic and Transwell^® membranes. When cells were differentiated on Transwell^® membranes, large changes in gene expression profile were observed during the early stages, which then stabilized after 5–7 days. Only a small number of genes encoding drug absorption related SLC, ABC, and CYP were detected in MDCK cells, and most of them exhibited low hybridization signals. Results from this study provide valuable reference information on endogenous gene expression in MDCK cells that could assist in design of drug-transporter and/or drug-enzyme interaction studies, and help interpret the contributions of various transporters and metabolic enzymes in studies with MDCK cells.

SCOREM: statistical consolidation of redundant expression measures

Many platforms for genome-wide analysis of gene expression contain ‘redundant’ measures for the same gene. For example, the most highly utilized platforms for gene expression microarrays, Affymetrix GeneChip® arrays, have as many as ten or more probe sets for some genes. Occasionally, individual probe sets for the same gene report different trends in expression across experimental conditions, a situation that must be resolved in order to accurately interpret the data. We developed an algorithm, SCOREM, for determining the level of agreement between such probe sets, utilizing a statistical test of concordance, Kendall's W coefficient of concordance, and a graph-searching algorithm for the identification of concordant probe sets. We also present methods for consolidating concordant groups into a single value for its corresponding gene and for post hoc analysis of discordant groups. By combining statistical consolidation with sequence analysis, SCOREM possesses the unique ability to identify biologically meaningful discordant behaviors, including differing behaviors in alternate RNA isoforms and tissue-specific patterns of expression. When consolidating concordant behaviors, SCOREM outperforms other methods in detecting both differential expression and overrepresented functional categories.

Toluene effects on gene expression in the hippocampus of young adult, middle-age, and senescent Brown Norway Rats

Differential susceptibility to environmental exposures across life stages is an area of toxicology about which little is known. We examined the effects of toluene on transcriptomic changes and oxidative stress (OS) parameters (e.g., NQO1 and GPX) in the rat brain at different life stages to elucidate key molecular pathways responsible for toluene-induced neurotoxicity, as well as possible age-related interactions. Changes in assessed end points following acute oral toluene (0, 0.65, and 1.0 g/kg) were examined 4 h after exposure in hippocampi of Brown Norway Rats at 4, 12, and 24 months of age. Genomic data were analyzed by two-way ANOVA to identify the effects of age, toluene, and interactions between the two factors. Analysis by one-way ANOVA identified 183 genes whose expression changed ≥ 1.25-fold with age. The majority of the genes were upregulated between life stages (> 79%). Similar analysis for toluene-related genes found only two sequences to vary significantly with dose. Fifty-six genes were identified to have expression changes due to an age-toluene interaction. Expression of genes with roles in immune response, cytoskeleton, protein, and energy metabolism was changed with advancing life stage, indicating changes in basic cellular homeostasis. Toluene affected similar cell functions, enhancing the effects of aging. OS parameters also indicated age-related changes in response mechanisms, evidence of toluene damage, and supported an age-toluene interaction. The data indicate that life stage can alter the toxicity of acute toluene exposure in various and complex ways, highlighting the need for further investigation into the role of aging in susceptibility.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

Gene set enrichment analysis: performance evaluation and usage guidelines

A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.

ArrayInitiative - a tool that simplifies creating custom Affymetrix CDFs

Background

Probes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications.

Results

ArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms.

Conclusions

ArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor.

Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine).

Ontology-based meta-analysis of global collections of high-throughput public data

Background

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.

Methodology/Results

We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.

Conclusions

Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Consistent annotation of gene expression arrays

Background

Gene expression arrays are valuable and widely used tools for biomedical research. Today's commercial arrays attempt to measure the expression level of all of the genes in the genome. Effectively translating the results from the microarray into a biological interpretation requires an accurate mapping between the probesets on the array and the genes that they are targeting. Although major array manufacturers provide annotations of their gene expression arrays, the methods used by various manufacturers are different and the annotations are difficult to keep up to date in the rapidly changing world of biological sequence databases.

Results

We have created a consistent microarray annotation protocol applicable to all of the major array manufacturers. We constantly keep our annotations updated with the latest Ensembl Gene predictions, and thus cross-referenced with a large number of external biomedical sequence database identifiers. We show that these annotations are accurate and address in detail reasons for the minority of probesets that cannot be annotated. Annotations are publicly accessible through the Ensembl Genome Browser and programmatically through the Ensembl Application Programming Interface. They are also seamlessly integrated into the BioMart data-mining tool and the biomaRt package of BioConductor.

Conclusions

Consistent, accurate and updated gene expression array annotations remain critical for biological research. Our annotations facilitate accurate biological interpretation of gene expression profiles.

GATExplorer: genomic and transcriptomic explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs

BACKGROUND

Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.

RESULTS

GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).

CONCLUSIONS

GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments.

Standards affecting the consistency of gene expression arrays in clinical applications

The use of microarray technology to measure gene expression has created optimism for the feasibility of using molecular assessments of tumors routinely in the clinical management of cancer. Gene expression arrays have been pioneers in the development of standards; both for research use and now for clinical application. Some of the existing standards have been driven by the early perception that microarray technology was inconsistent and perhaps unreliable. More recent experimentation has shown that reproducible data can be achieved and clinical standards are beginning to emerge. For the transcriptional assessment of tumors, this means a system that correctly samples a tumor, isolates RNA and processes this for microarray analysis, evaluates the data, and communicates findings in a consistent and timely fashion. The most important standard is to show that a clinically important assessment can be made with microarray data. The standards emerging from work on various parts of the entire process could guide the development of a workable system. However, the final standard for each component of the process depends on the accuracy required when the assay becomes part of the clinical routine: a routine that now includes the molecular evaluation of tumors.

Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy

Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3'-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway-level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses.

METHODS

Liver RNA from B6.Alb/cre,Pdss2(loxP/loxP) mice having primary coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA.

RESULTS

Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with 'steroid biosynthesis' consistently identified as the most down-regulated metabolic pathway by probucol treatment.

CONCLUSIONS

This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

Microarray probes and probe sets

DNA microarrays have gained wide use in biomedical research by simultaneously monitoring the expression levels of a large number of genes. The successful implementation of DNA microarray technologies requires the development of methods and techniques for the fabrication of microarrays, the selection of probes to represent genes, the quantification of hybridization, and data analysis. In this paper, we concentrate on probes that are either spotted or synthesized on the glass slides through several aspects: sources of probes, the criteria for selecting probes, tools available for probe selections, and probes used in commercial microarray chips. We then provide a detailed review of one type of DNA microarray: Affymetrix GeneChips, discuss the need to re-annotate probes, review different methods for regrouping probes into probe sets, and compare various redefinitions through public available datasets.

A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data

Illumina BeadArrays are among the most popular and reliable platforms for gene expression profiling. However, little external scrutiny has been given to the design, selection and annotation of BeadArray probes, which is a fundamental issue in data quality and interpretation. Here we present a pipeline for the complete genomic and transcriptomic re-annotation of Illumina probe sequences, also applicable to other platforms, with its output available through a Web interface and incorporated into Bioconductor packages. We have identified several problems with the design of individual probes and we show the benefits of probe re-annotation on the analysis of BeadArray gene expression data sets. We discuss the importance of aspects such as probe coverage of individual transcripts, alternative messenger RNA splicing, single-nucleotide polymorphisms, repeat sequences, RNA degradation biases and probes targeting genomic regions with no known transcription. We conclude that many of the Illumina probes have unreliable original annotation and that our re-annotation allows analyses to focus on the good quality probes, which form the majority, and also to expand the scope of biological information that can be extracted.

PLANdbAffy: probe-level annotation database for Affymetrix expression microarrays

Standard Affymetrix technology evaluates gene expression by measuring the intensity of mRNA hybridization with a panel of the 25-mer oligonucleotide probes, and summarizing the probe signal intensities by a robust average method. However, in many cases, signal intensity of the probe does not correlate with gene expression. This could be due to the hybridization of the probe to a transcript of another gene, mapping of the probe to an intron, alternative splicing, single nucleotide polymorphisms and other reasons. We have developed a database, PLANdbAffy (available at http://affymetrix2.bioinf.fbb.msu.ru), that contains the results of the alignment of probe sequences from five Affymetrix expression microarrays to the human genome. We have determined the probes matching the transcript-coding regions in the correct orientation. For each such probe alignment region, we determined the mRNA and EST sequences that contain the probe sequence. In the textual part of the database interface we summarize the data on the sequences that cover the probe alignment region and SNPs that are located inside it. The graphical part of our database interface is implemented as custom tracks to the UCSC genome browser that allows one to utilize all the data that are offered by UCSC browser.

Evaluation of cDNA microarray data by multiple clones mapping to the same transcript

Although novel technologies are rapidly emerging, the cDNA microarray data accumulated is still and will be an important source for bioinformatics and biological studies. Thus, the reliability and applicability of the cDNA microarray data warrants further evaluation. In cDNA microarrays, multiple clones are measured for a transcript, which can be exploited to evaluate the consistency of microarray data. We show that even for pairs of RCs, the average Pearson correlation coefficient of their measurements is not high. However, this low consistency could largely be explained by random noise signals for a fraction of unexpressed genes and/or low signal-to-noise ratios for low abundance transcripts. Encouragingly, a large fraction of inconsistent data will be filtered out in the procedure of selecting differentially expressed genes (DEGs). Therefore, although cDNA microarray data are of low consistency, applications based on DEGs selections could still reach correct biological results, especially at the functional modules level.