CeDR Atlas: a knowledgebase of cellular drug response.

Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.

DiseaseMeth version 3.0: a major expansion and update of the human disease methylation database.

DNA methylation has a growing potential for use as a biomarker because of its involvement in disease. DNA methylation data have also substantially grown in volume during the past 5 years. To facilitate access to these fragmented data, we proposed DiseaseMeth version 3.0 based on DiseaseMeth version 2.0, in which the number of diseases including increased from 88 to 162 and High-throughput profiles samples increased from 32 701 to 49 949. Experimentally confirmed associations added 448 pairs obtained by manual literature mining from 1472 papers in PubMed. The search, analyze and tools sections were updated to increase performance. In particular, the FunctionSearch now provides for the functional enrichment of genes from localized GO and KEGG annotation. We have also developed a unified analysis pipeline for identifying differentially DNA methylated genes (DMGs) from the original data stored in the database. 22 718 DMGs were found in 99 diseases. These DMGs offer application in disease evaluation using two self-developed online tools, Methylation Disease Correlation and Cancer Prognosis & Co-Methylation. All query results can be downloaded and can also be displayed through a box plot, heatmap or network module according to whichever search section is used. DiseaseMeth version 3.0 is freely available at http://diseasemeth.edbc.org/.

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

Transcriptome profiling of individual rhesus macaque oocytes and preimplantation embryos.

Early mammalian embryonic transcriptomes are dynamic throughout the process of preimplantation development. Cataloging of primate transcriptomics during early development has been accomplished in humans, but global characterization of transcripts is lacking in the rhesus macaque: a key model for human reproductive processes. We report here the systematic classification of individual macaque transcriptomes using RNA-Seq technology from the germinal vesicle stage oocyte through the blastocyst stage embryo. Major differences in gene expression were found between sequential stages, with the 4- to 8-cell stages showing the highest level of differential gene expression. Analysis of putative transcription factor binding sites also revealed a striking increase in key regulatory factors in 8-cell embryos, indicating a strong likelihood of embryonic genome activation occurring at this stage. Furthermore, clustering analyses of gene co-expression throughout this period resulted in distinct groups of transcripts significantly associated to the different embryo stages assayed. The sequence data provided here along with characterizations of major regulatory transcript groups present a comprehensive atlas of polyadenylated transcripts that serves as a useful resource for comparative studies of preimplantation development in humans and other species.

PipX, the coactivator of NtcA, is a global regulator in cyanobacteria.

To modulate the expression of genes involved in nitrogen assimilation, the cyanobacterial PII-interacting protein X (PipX) interacts with the global transcriptional regulator NtcA and the signal transduction protein PII, a protein found in all three domains of life as an integrator of signals of the nitrogen and carbon balance. PipX can form alternate complexes with NtcA and PII, and these interactions are stimulated and inhibited, respectively, by 2-oxoglutarate, providing a mechanistic link between PII signaling and NtcA-regulated gene expression. Here, we demonstrate that PipX is involved in a much wider interaction network. The effect of pipX alleles on transcript levels was studied by RNA sequencing of S. elongatus strains grown in the presence of either nitrate or ammonium, followed by multivariate analyses of relevant mutant/control comparisons. As a result of this process, 222 genes were classified into six coherent groups of differentially regulated genes, two of which, containing either NtcA-activated or NtcA-repressed genes, provided further insights into the function of NtcA-PipX complexes. The remaining four groups suggest the involvement of PipX in at least three NtcA-independent regulatory pathways. Our results pave the way to uncover new regulatory interactions and mechanisms in the control of gene expression in cyanobacteria.

A Conserved Core of Programmed Cell Death Indicator Genes Discriminates Developmentally and Environmentally Induced Programmed Cell Death in Plants.

A plethora of diverse programmed cell death (PCD) processes has been described in living organisms. In animals and plants, different forms of PCD play crucial roles in development, immunity, and responses to the environment. While the molecular control of some animal PCD forms such as apoptosis is known in great detail, we still know comparatively little about the regulation of the diverse types of plant PCD. In part, this deficiency in molecular understanding is caused by the lack of reliable reporters to detect PCD processes. Here, we addressed this issue by using a combination of bioinformatics approaches to identify commonly regulated genes during diverse plant PCD processes in Arabidopsis (Arabidopsis thaliana). Our results indicate that the transcriptional signatures of developmentally controlled cell death are largely distinct from the ones associated with environmentally induced cell death. Moreover, different cases of developmental PCD share a set of cell death-associated genes. Most of these genes are evolutionary conserved within the green plant lineage, arguing for an evolutionary conserved core machinery of developmental PCD. Based on this information, we established an array of specific promoter-reporter lines for developmental PCD in Arabidopsis. These PCD indicators represent a powerful resource that can be used in addition to established morphological and biochemical methods to detect and analyze PCD processes in vivo and in planta.

Bioinformatic analysis of specific genes in diabetic nephropathy.

OBJECTIVE: We attempt to explore the pathogenesis and specific genes with aberrant expression in diabetic nephropathy (DN). METHODS: The gene expression profile of GSE1009 was downloaded from Gene Expression Omnibus database, including 3 normal function glomeruli and DN glomeruli from cadaveric donor kidneys. The differentially expressed genes (DEGs) were analyzed and the aberrant gene-related functions were predicted by informatics methods. The protein-protein interaction (PPI) networks for DEGs were constructed and the functional sub-network was screened. RESULTS: A total of 416 DEGs were found to be differentially expressed in DN samples comparing with normal controls, including 404 up-regulated genes and 12 down-regulated genes. DEGs were involved in the process of combination to saccharides and the decline of tissue repairing ability of the organisms. The genes of VEGFA, ACTG1, HSP90AA1 had high degree in the PPI network. The main biological process of genes in the sub-network was related with cell proliferation and signal transmitting of cell membrane receptor. CONCLUSION: Significant nodes in PPI network provide new insights to understand the mechanism of DN. VEGFA, ACTG1 and HSP90AA1 may be the potential targets in the DN treatment.

Evolutionary approach for relative gene expression algorithms.

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space.

Image-level and group-level models for Drosophila gene expression pattern annotation.

BACKGROUND: Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison. RESULTS: We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach. CONCLUSION: In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.

Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma.

UNLABELLED: Clinical application of the prognostic gene expression signature has been delayed due to the large number of genes and complexity of prediction algorithms. In the current study we aimed to develop an easy-to-use risk score with a limited number of genes that can robustly predict prognosis of patients with hepatocellular carcinoma (HCC). The risk score was developed using Cox coefficient values of 65 genes in the training set (n = 139) and its robustness was validated in test sets (n = 292). The risk score was a highly significant predictor of overall survival (OS) in the first test cohort (P = 5.6 × 10(-5), n = 100) and the second test cohort (P = 5.0 × 10(-5) , n = 192). In multivariate analysis, the risk score was a significant risk factor among clinical variables examined together (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.13-1.64; P = 0.001 for OS). CONCLUSION: The risk score classifier we have developed can identify two clinically distinct HCC subtypes at early and late stages of the disease in a simple and highly reproducible manner across multiple datasets.

ColoGuideEx: a robust gene classifier specific for stage II colorectal cancer prognosis.

BACKGROUND AND AIMS: Several clinical factors have an impact on prognosis in stage II colorectal cancer (CRC), but as yet they are inadequate for risk assessment. The present study aimed to develop a gene expression classifier for improved risk stratification of patients with stage II CRC. METHODS: 315 CRC samples were included in the study. Gene expression measurements from 207 CRC samples (stage I-IV) from two independent Norwegian clinical series were obtained using Affymetrix exon-level microarrays. Differentially expressed genes between stage I and stage IV samples from the test series were identified and used as input for L1 (lasso) penalised Cox proportional hazards analyses of patients with stage II CRC from the same series. A second validation was performed in 108 stage II CRC samples from other populations (USA and Australia). RESULTS: An optimal 13-gene expression classifier (PIGR, CXCL13, MMP3, TUBA1B, SESN1, AZGP1, KLK6, EPHA7, SEMA3A, DSC3, CXCL10, ENPP3, BNIP3) for prediction of relapse among patients with stage II CRC was developed using a consecutive Norwegian test series from patients treated according to current standard protocols (n=44, p<0.001, HR=18.2), and its predictive value was successfully validated for patients with stage II CRC in a second Norwegian CRC series collected two decades previously (n=52, p=0.02, HR=3.6). Further validation of the classifier was obtained in a recent external dataset of patients with stage II CRC from other populations (n=108, p=0.001, HR=6.5). Multivariate Cox regression analyses, including all three sample series and various clinicopathological variables, confirmed the independent prognostic value of the classifier (p≤0.004). The classifier was shown to be specific to stage II CRC and does not provide prognostic stratification of patients with stage III CRC. CONCLUSION: This study presents the development and validation of a 13-gene expression classifier, ColoGuideEx, for prognosis prediction specific to patients with stage II CRC. The robustness was shown across patient series, populations and different microarray versions.

Gingival tissue transcriptomes in experimental gingivitis.

AIMS: We investigated the sequential gene expression in the gingiva during the induction and resolution of experimental gingivitis. MATERIAL AND METHODS: Twenty periodontally and systemically healthy non-smoking volunteers participated in a 3-week experimental gingivitis protocol, followed by debridement and 2-week regular plaque control. We recorded clinical indices and harvested gingival tissue samples from four interproximal palatal sites in half of the participants at baseline, Day 7, Day 14 and Day 21 (the "induction phase"), and at Day 21, Day 25, Day 30 and Day 35 in the other half (the "resolution phase"). RNA was extracted, amplified, reversed transcribed, amplified, labelled and hybridized using Affymetrix Human Genome U133Plus2.0 microarrays. Paired t-tests compared gene expression changes between consecutive time points. Gene ontology analyses summarized the expression patterns into biologically relevant categories. RESULTS: The median gingival index was 0 at baseline, 2 at Day 21 and 1 at Day 35. Differential gene regulation peaked during the third week of induction and the first 4 days of resolution. Leucocyte transmigration, cell adhesion and antigen processing/presentation were the top differentially regulated pathways. CONCLUSIONS: Transcriptomic studies enhance our understanding of the pathobiology of the reversible inflammatory gingival lesion and provide a detailed account of the dynamic tissue responses during the induction and resolution of experimental gingivitis.

Combining P values to improve classification of differential gene expression in the HTself software.

HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression in low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. We developed an extension of HTself, originally released in 2005, by calculating P values instead of using a fixed acceptance level α. As before, the statistic used to compute single-spot P values is obtained from the Gaussian kernel density estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent P values that can be combined by well-known statistical methods. The combined P value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses P values combination. It is implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure.

[Sarcoma gene signatures]. / Sarkome: Gensignaturen.

This review reports the main gene signature specific for the diagnosis, prognosis or prediction of drug response in sarcomas. Almost half of sarcomas show a simple genetic lesion which is specific for the diagnosis: recurrent translocations in 10 to 15% of sarcomas, specific activating and inactivating mutations in GIST and rhabdoid tumor respectively, and MDM2 amplification in well-differentiated and dedifferentiated liposarcomas as well as in intimal sarcoma. A recent study reported a gene expression signature which is much better than histological grading for predicting metastasis outcome. This signature is composed of 67 genes all belonging to pathways involved in chromosome integrity suggesting an important role of these mechanisms in the development of metastases. On the other hand, and except for GIST with KIT and PDGFRA mutations, there is no validated predictive gene signature so far.

[A classification method of gene expression profile based on a locally linear embedding algorism with improved distance].

With its high dimensionalities, small samples and great noise, feature reduction of gene expression profile becomes quite necessary. The most common form of gene expression profile is nonlinear, and traditional dimensionality reduction methods can not project high dimensional data, whose initial dimensionalities are low, into low dimensional space. In this work, an improved distance locally linear embedding (LLE ) algorism was proposed to reduce the dimensionalities. LLE method is very sensitive to the closely-neighboring parameters. In order to enhance the robustness to the number of neighbors, in the paper we presented a novel distance to measure the distance between the samples for the purpose of reducing-the influence of distribution of samples. Experimental results demonstrated that the improved distance LLE can effectively extract information of classification features and greatly reduce the dimensionalities of data while maintaining a higher classification accuracy.

Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

Bayesian unsupervised learning with multiple data types.

We propose Bayesian generative models for unsupervised learning with two types of data and an assumed dependency of one type of data on the other. We consider two algorithmic approaches, based on a correspondence model, where latent variables are shared across datasets. These models indicate the appropriate number of clusters in addition to indicating relevant features in both types of data. We evaluate the model on artificially created data. We then apply the method to a breast cancer dataset consisting of gene expression and microRNA array data derived from the same patients. We assume partial dependence of gene expression on microRNA expression in this study. The method ranks genes within subtypes which have statistically significant abnormal expression and ranks associated abnormally expressing microRNA. We report a genetic signature for the basal-like subtype of breast cancer found across a number of previous gene expression array studies. Using the two algorithmic approaches we find that this signature also arises from clustering on the microRNA expression data and appears derivative from this data.

Active pathway identification and classification with probabilistic ensembles.

A popular means of modeling metabolic networks is through identifying frequently observed pathways. However the definition of what constitutes an observation of a pathway and how to evaluate the importance of identified pathways remains unclear. In this paper we investigate different methods for defining an observed pathway and evaluate their performance with pathway classification models. We use three methods for defining an observed pathway; a path in gene over-expression, a path in probable gene over-expression and a path of most accurate classification. The performance of each definition is evaluated with three classification models; a probabilistic pathway classifier - HME3M, logistic regression and SVM. The results show that defining pathways using the probability of gene over-expression creates stable and accurate classifiers. Conversely we also show defining pathways of most accurate classification finds a severely biased pathways that are unrepresentative of underlying microarray data structure.