Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

Comparative Research: Regulatory Mechanisms of Ribosomal Gene Transcription in Saccharomyces cerevisiae and Schizosaccharomyces pombe

Restricting ribosome biosynthesis and assembly in response to nutrient starvation is a universal phenomenon that enables cells to survive with limited intracellular resources. When cells experience starvation, nutrient signaling pathways, such as the target of rapamycin (TOR) and protein kinase A (PKA), become quiescent, leading to several transcription factors and histone modification enzymes cooperatively and rapidly repressing ribosomal genes. Fission yeast has factors for heterochromatin formation similar to mammalian cells, such as H3K9 methyltransferase and HP1 protein, which are absent in budding yeast. However, limited studies on heterochromatinization in ribosomal genes have been conducted on fission yeast. Herein, we shed light on and compare the regulatory mechanisms of ribosomal gene transcription in two species with the latest insights.

Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database ‘EcoPromDB’ has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

A comprehensive weighted gene co-expression network analysis uncovers potential targets in diabetic kidney disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman’s correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Fermented Minor Grain Foods: Classification, Functional Components, and Probiotic Potential

Fermented minor grain (MG) foods often have unique nutritional value and functional characteristics, which are important for developing dietary culture worldwide. As a kind of special raw material in fermented food, minor grains have special functional components, such as trace elements, dietary fiber, and polyphenols. Fermented MG foods have excellent nutrients, phytochemicals, and bioactive compounds and are consumed as a rich source of probiotic microbes. Thus, the purpose of this review is to introduce the latest progress in research related to the fermentation products of MGs. Specific discussion is focused on the classification of fermented MG foods and their nutritional and health implications, including studies of microbial diversity, functional components, and probiotic potential. Furthermore, this review discusses how mixed fermentation of grain mixtures is a better method for developing new functional foods to increase the nutritional value of meals based on cereals and legumes in terms of dietary protein and micronutrients.

Prediction of Transcription Factor Binding Sites Using a Combined Deep Learning Approach

In the process of regulating gene expression and evolution, such as DNA replication and mRNA transcription, the binding of transcription factors (TFs) to TF binding sites (TFBS) plays a vital role. Precisely modeling the specificity of genes and searching for TFBS are helpful to explore the mechanism of cell expression. In recent years, computational and deep learning methods searching for TFBS have become an active field of research. However, existing methods generally cannot meet high performance and interpretability simultaneously. Here, we develop an accurate and interpretable attention-based hybrid approach, DeepARC, that combines a convolutional neural network (CNN) and recurrent neural network (RNN) to predict TFBS. DeepARC employs a positional embedding method to extract the hidden embedding from DNA sequences, including the positional information from OneHot encoding and the distributed embedding from DNA2Vec. DeepARC feeds the positional embedding of the DNA sequence into a CNN-BiLSTM-Attention-based framework to complete the task of finding the motif. Taking advantage of the attention mechanism, DeepARC can gain greater access to valuable information about the motif and bring interpretability to the work of searching for motifs through the attention weight graph. Moreover, DeepARC achieves promising performances with an average area under the receiver operating characteristic curve (AUC) score of 0.908 on five cell lines (A549, GM12878, Hep-G2, H1-hESC, and Hela) in the benchmark dataset. We also compare the positional embedding with OneHot and DNA2Vec and gain a competitive advantage.

Identification of upstream transcription factor binding sites in orthologous genes using mixed Student’s t-test statistics

Background

Transcription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive.

Methodology/Principal findings

This study presents a computational tool, named “Grit”, for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student’s t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets.

Significance

Grit is a good alternative to current available motif scanning tools.

Locating transcription factor-binding (TF-binding) site in the genome and identification their function is fundamental in understanding various biological processes. Improve the performance of the prediction tools is important because accurate TF-binding site prediction can save cost and time for wet-lab experiments. Also, genome wide TF-binding site prediction can provide new insights for transcriptome regulation in system biology perspective. This study developed a new TF-binding site prediction tool based on mixed Student’s t-test statistical method. The tool is amongst the top-ranked TF-binding site predictors, as such, it can help the researchers in TF-binding site identification and transcriptional regulation mechanism interpretation of genes.

Prediction and Experimental Validation of a New Salinity-Responsive Cis-Regulatory Element (CRE) in a Tilapia Cell Line

Transcriptional regulation is a major mechanism by which organisms integrate gene x environment interactions. It can be achieved by coordinated interplay between cis-regulatory elements (CREs) and transcription factors (TFs). Euryhaline tilapia (Oreochromis mossambicus) tolerate a wide range of salinity and thus are an appropriate model to examine transcriptional regulatory mechanisms during salinity stress in fish. Quantitative proteomics in combination with the transcription inhibitor actinomycin D revealed 19 proteins that are transcriptionally upregulated by hyperosmolality in tilapia brain (OmB) cells. We searched the extended proximal promoter up to intron1 of each corresponding gene for common motifs using motif discovery tools. The top-ranked motif identified (STREME1) represents a binding site for the Forkhead box TF L1 (FoxL1). STREME1 function during hyperosmolality was experimentally validated by choosing two of the 19 genes, chloride intracellular channel 2 (clic2) and uridine phosphorylase 1 (upp1), that are enriched in STREME1 in their extended promoters. Transcriptional induction of these genes during hyperosmolality requires STREME1, as evidenced by motif mutagenesis. We conclude that STREME1 represents a new functional CRE that contributes to gene x environment interactions during salinity stress in tilapia. Moreover, our results indicate that FoxL1 family TFs are contribute to hyperosmotic induction of genes in euryhaline fish.

Molecular Characterization Reveals the Involvement of Calcium Dependent Protein Kinases in Abiotic Stress Signaling and Development in Chickpea (Cicer arietinum)

Calcium-dependent protein kinases (CDPKs) are a major group of calcium (Ca²⁺⁾ sensors in plants. CDPKs play a dual function of "Ca²⁺ sensor and responder." These sensors decode the "Ca²⁺ signatures" generated in response to adverse growth conditions such as drought, salinity, and cold and developmental processes. However, knowledge of the CDPK family in the legume crop chickpea is missing. Here, we have identified a total of 22 CDPK genes in the chickpea genome. The phylogenetic analysis of the chickpea CDPK family with other plants revealed their evolutionary conservation. Protein homology modeling described the three-dimensional structure of chickpea CDPKs. Defined arrangements of α-helix, β-strands, and transmembrane-helix represent important structures like kinase domain, inhibitory junction domain, N and C-lobes of EF-hand motifs. Subcellular localization analysis revealed that CaCDPK proteins are localized mainly at the cytoplasm and in the nucleus. Most of the CaCDPK promoters had abiotic stress and development-related cis-regulatory elements, suggesting the functional role of CaCDPKs in abiotic stress and development-related signaling. RNA sequencing (RNA-seq) expression analysis indicated the role of the CaCDPK family in various developmental stages, including vegetative, reproductive development, senescence stages, and during seed stages of early embryogenesis, late embryogenesis, mid and late seed maturity. The real-time quantitative PCR (qRT-PCR) analysis revealed that several CaCDPK genes are specifically as well as commonly induced by drought, salt, and Abscisic acid (ABA). Overall, these findings indicate that the CDPK family is probably involved in abiotic stress responses and development in chickpeas. This study provides crucial information on the CDPK family that will be utilized in generating abiotic stress-tolerant and high-yielding chickpea varieties.

A Review on Planted (l, d) Motif Discovery Algorithms for Medical Diagnose

Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called “Motif”. Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient’s conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses.

Freezing firefly algorithm for efficient planted (ℓ, d) motif search

The detection of inimitable patterns (motif) occurring in a set of biological sequences could elevate new biological discoveries. Its application in recognition of transcription factors and their binding sites have demonstrated the necessity to attain knowledge of gene function, human diseases, and drug design. The literature identifies (ℓ, d) motif search as the widely studied problem in PMS (Planted Motif Search). This paper proposes an efficient optimization algorithm named "Freezing FireFly (FFF)" to solve (ℓ, d) motif search problem. The new strategy freezing such as local and global was added to increase the performance of the basic Firefly algorithm. It freezes the best possible out coming positions even in the lesser brighter one. The performance of the proposed algorithm is experienced on simulated and real datasets. The experimental results show that the proposed algorithm resolves the instance (50, 21) within 1.47 min in the simulated dataset. For real (such as ChIP-seq (Chromatin Immunoprecipitation)) and synthetic datasets, the proposed algorithm runs much faster in comparison to existing state-of-the-art optimization algorithms, including Samselect, TraverStringRef, PMS8, qPMS9, AlignACE, FMGA, and GSGA.

Interaction of TOR and PKA Signaling in S. cerevisiae

TOR and PKA signaling are the major growth-regulatory nutrient-sensing pathways in S. cerevisiae. A number of experimental findings demonstrated a close relationship between these pathways: Both are responsive to glucose availability. Both regulate ribosome production on the transcriptional level and repress autophagy and the cellular stress response. Sch9, a major downstream effector of TORC1 presumably shares its kinase consensus motif with PKA, and genetic rescue and synthetic defects between PKA and Sch9 have been known for a long time. Further, studies in the first decade of this century have suggested direct regulation of PKA by TORC1. Nonetheless, the contribution of a potential direct cross-talk vs. potential sharing of targets between the pathways has still not been completely resolved. What is more, other findings have in contrast highlighted an antagonistic relationship between the two pathways. In this review, I explore the association between TOR and PKA signaling, mainly by focusing on proteins that are commonly referred to as shared TOR and PKA targets. Most of these proteins are transcription factors which to a large part explain the major transcriptional responses elicited by TOR and PKA upon nutrient shifts. I examine the evidence that these proteins are indeed direct targets of both pathways and which aspects of their regulation are targeted by TOR and PKA. I further explore if they are phosphorylated on shared sites by PKA and Sch9 or when experimental findings point towards regulation via the PP2ASit4/PP2A branch downstream of TORC1. Finally, I critically review data suggesting direct cross-talk between the pathways and its potential mechanism.

Transcriptional control of ribosome biogenesis in yeast: links to growth and stress signals

Ribosome biogenesis requires prodigious transcriptional output in rapidly growing yeast cells and is highly regulated in response to both growth and stress signals. This minireview focuses on recent developments in our understanding of this regulatory process, with an emphasis on the 138 ribosomal protein genes (RPGs) themselves and a group of >200 ribosome biogenesis (RiBi) genes whose products contribute to assembly but are not part of the ribosome. Expression of most RPGs depends upon Rap1, a pioneer transcription factor (TF) required for the binding of a pair of RPG-specific TFs called Fhl1 and Ifh1. RPG expression is correlated with Ifh1 promoter binding, whereas Rap1 and Fhl1 remain promoter-associated upon stress-induced down regulation. A TF called Sfp1 has also been implicated in RPG regulation, though recent work reveals that its primary function is in activation of RiBi and other growth-related genes. Sfp1 plays an important regulatory role at a small number of RPGs where Rap1–Fhl1–Ifh1 action is subsidiary or non-existent. In addition, nearly half of all RPGs are bound by Hmo1, which either stabilizes or re-configures Fhl1–Ifh1 binding. Recent studies identified the proline rotamase Fpr1, known primarily for its role in rapamycin-mediated inhibition of the TORC1 kinase, as an additional TF at RPG promoters. Fpr1 also affects Fhl1–Ifh1 binding, either independently or in cooperation with Hmo1. Finally, a major recent development was the discovery of a protein homeostasis mechanism driven by unassembled ribosomal proteins, referred to as the Ribosome Assembly Stress Response (RASTR), that controls RPG transcription through the reversible condensation of Ifh1.

Caveolin-1-driven membrane remodelling regulates hnRNPK-mediated exosomal microRNA sorting in cancer

BACKGROUND

Caveolae proteins play diverse roles in cancer development and progression. In prostate cancer, non-caveolar caveolin-1 (CAV1) promotes metastasis, while CAVIN1 attenuates CAV1-induced metastasis. Here, we unveil a novel mechanism linking CAV1 to selective loading of exosomes with metastasis-promoting microRNAs.

RESULTS

We identify hnRNPK as a CAV1-regulated microRNA binding protein. In the absence of CAVIN1, non-caveolar CAV1 drives localisation of hnRPNK to multi-vesicular bodies (MVBs), recruiting AsUGnA motif-containing miRNAs and causing their release within exosomes. This process is dependent on the lipid environment of membranes as shown by cholesterol depletion using methyl-β-cyclodextrin or by treatment with n-3 polyunsaturated fatty acids. Consistent with a role in bone metastasis, knockdown of hnRNPK in prostate cancer PC3 cells abolished the ability of PC3 extracellular vesicles (EV) to induce osteoclastogenesis, and biofluid EV hnRNPK is elevated in metastatic prostate and colorectal cancer.

CONCLUSIONS

Taken together, these results support a novel pan-cancer mechanism for CAV1-driven exosomal release of hnRNPK and associated miRNA in metastasis, which is modulated by the membrane lipid environment.

A simple yet powerful test for assessing goodness-of-fit of high-dimensional linear models

We evaluate the validity of a projection-based test checking linear models when the number of covariates tends to infinity, and analyze two gene expression datasets. We show that the test is still consistent and derive the asymptotic distributions under the null and alternative hypotheses. The asymptotic properties are almost the same as those when the number of covariates is fixed as long as p/n → 0 with additional mild assumptions. The test dramatically gains dimension reduction, and its numerical performance is remarkable.

Base-resolution models of transcription-factor binding reveal soft motif syntax

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

Unusual DNA-binding properties of the Arabidopsis thaliana WRKY50 transcription factor at target gene promoters

WRKY50 from A. thaliana requires WT-boxes at target gene promoters for activation and binding. Based on the genome-wide prediction of WRKY50 target genes and the similarity of a WRKY50 binding site to WT-boxes in microbe-associated molecular pattern (MAMP)-responsive cis-regulatory modules (CRM), four WT-box containing CRMs from the promoter region of three WRKY50 target genes were investigated for their interaction with WRKY50. These target genes are DJ1E, WRKY30 and ATBBE4. Two of the four CRMs, one from DJ1E and one from WRKY30, were able to activate reporter gene expression in the presence of WRKY50. Activation requires the WT-boxes GGACTTTT, GGACTTTG from DJ1E and GGACTTTC from WRKY30. WRKY50 does not activate a second CRM from WRKY30 and the CRM from ATBBE4, both containing the WT-box TGACTTTT. In vitro gel-shift assays demonstrate WT-box-specific binding of the WRKY50 DNA-binding domain to all four CRMs. This work shows a high flexibility of WRKY50 binding site recognition beyond the classic W-box TTGACC/T.

Genomic structure and transcript analysis of the Rapid Alkalinization Factor (RALF) gene family during host-pathogen crosstalk in Fragaria vesca and Fragaria x ananassa strawberry

Rapid Alkalinization Factors (RALFs) are cysteine-rich peptides ubiquitous within plant kingdom. They play multiple roles as hormonal signals in diverse processes, including root elongation, cell growth, pollen tube development, and fertilization. Their involvement in host-pathogen crosstalk as negative regulators of immunity in Arabidopsis has also been recognized. In addition, peptides homologous to RALF are secreted by different fungal pathogens as effectors during early stages of infection. Previous studies have identified nine RALF genes in the diploid strawberry (Fragaria vesca) genome. This work describes the genomic organization of the RALF gene families in commercial octoploid strawberry (Fragaria × ananassa) and the re-annotated genome of F. vesca, and then compares findings with orthologs in Arabidopsis thaliana. We reveal the presence of 15 RALF genes in F. vesca genotype Hawaii 4 and 50 in Fragaria x ananassa cv. Camarosa, showing a non-homogenous localization of genes among the different Fragaria x ananassa subgenomes. Expression analysis of Fragaria x ananassa RALF genes upon infection with Colletotrichum acutatum or Botrytis cinerea showed that FanRALF3-1 was the only fruit RALF gene upregulated after fungal infection. In silico analysis was used to identify distinct pathogen inducible elements upstream of the FanRALF3-1 gene. Agroinfiltration of strawberry fruit with deletion constructs of the FanRALF3-1 promoter identified a 5' region required for FanRALF3-1 expression in fruit, but failed to identify a region responsible for fungal induced expression.

Set cover-based methods for motif selection

Motivation

De novo motif discovery algorithms find statistically over-represented sequence motifs that may function as transcription factor binding sites. Current methods often report large numbers of motifs, making it difficult to perform further analyses and experimental validation. The motif selection problem seeks to identify a minimal set of putative regulatory motifs that characterize sequences of interest (e.g. ChIP-Seq binding regions).

Results

In this study, the motif selection problem is mapped to variants of the set cover problem that are solved via tabu search and by relaxed integer linear programing (RILP). The algorithms are employed to analyze 349 ChIP-Seq experiments from the ENCODE project, yielding a small number of high-quality motifs that represent putative binding sites of primary factors and cofactors. Specifically, when compared with the motifs reported by Kheradpour and Kellis, the set cover-based algorithms produced motif sets covering 35% more peaks for 11 TFs and identified 4 more putative cofactors for 6 TFs. Moreover, a systematic evaluation using nested cross-validation revealed that the RILP algorithm selected fewer motifs and was able to cover 6% more peaks and 3% fewer background regions, which reduced the error rate by 7%.

Availability and implementation

The source code of the algorithms and all the datasets are available at https://github.com/YichaoOU/Set_cover_tools.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated TORC1 and PKA signaling control the temporal activation of glucose-induced gene expression in yeast

The growth rate of a yeast cell is controlled by the target of rapamycin kinase complex I (TORC1) and cAMP-dependent protein kinase (PKA) pathways. To determine how TORC1 and PKA cooperate to regulate cell growth, we performed temporal analysis of gene expression in yeast switched from a non-fermentable substrate, to glucose, in the presence and absence of TORC1 and PKA inhibitors. Quantitative analysis of these data reveals that PKA drives the expression of key cell growth genes during transitions into, and out of, the rapid growth state in glucose, while TORC1 is important for the steady-state expression of the same genes. This circuit design may enable yeast to set an exact growth rate based on the abundance of internal metabolites such as amino acids, via TORC1, but also adapt rapidly to changes in external nutrients, such as glucose, via PKA.

Multiple Negative Regulators Restrict Recruitment of the SWI/SNF Chromatin Remodeler to the HO Promoter in Saccharomyces cerevisiae

Activation of the Saccharomyces cerevisiae HO promoter is highly regulated, requiring the ordered recruitment of activators and coactivators and allowing production of only a few transcripts in mother cells within a short cell cycle window. We conducted genetic screens to identify the negative regulators of HO expression necessary to limit HO transcription. Known repressors of HO (Ash1 and Rpd3) were identified, as well as several additional chromatin-associated factors including the Hda1 histone deacetylase, the Isw2 chromatin remodeler, and the corepressor Tup1 We also identified clusters of HO promoter mutations that suggested roles for the Dot6/Tod6 (PAC site) and Ume6 repression pathways. We used ChIP assays with synchronized cells to validate the involvement of these factors and map the association of Ash1, Dot6, and Ume6 with the HO promoter to a brief window in the cell cycle between binding of the initial activating transcription factor and initiation of transcription. We found that Ash1 and Ume6 each recruit the Rpd3 histone deacetylase to HO, and their effects are additive. In contrast, Rpd3 was not recruited significantly to the PAC site, suggesting this site has a distinct mechanism for repression. Increases in HO expression and SWI/SNF recruitment were all additive upon loss of Ash1, Ume6, and PAC site factors, indicating the convergence of independent pathways for repression. Our results demonstrate that multiple protein complexes are important for limiting the spread of SWI/SNF-mediated nucleosome eviction across the HO promoter, suggesting that regulation requires a delicate balance of activities that promote and repress transcription.

THiCweed: fast, sensitive detection of sequence features by clustering big datasets

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared toward data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30 000 peaks in 1–2 h, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs. THiCweed performs best with large ‘window’ sizes (≥50 bp), much longer than typical binding sites (7–15 bp). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs and secondary motifs even when they occur in <5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq datasets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif finding to give new insights into genomic transcription factor-binding complexity.

Large scale control and programming of gene expression using CRISPR

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

Sfp1 regulates transcriptional networks driving cell growth and division through multiple promoter-binding modes

In this study, Albert et al. investigated the mechanisms by which the yeast Sfp1 protein coordinates both cell division and growth. They demonstrate that Sfp1 directly controls genes required for ribosome production and many other growth-promoting processes.

The yeast Sfp1 protein regulates both cell division and growth but how it coordinates these processes is poorly understood. We demonstrate that Sfp1 directly controls genes required for ribosome production and many other growth-promoting processes. Remarkably, the complete set of Sfp1 target genes is revealed only by a combination of ChIP (chromatin immunoprecipitation) and ChEC (chromatin endogenous cleavage) methods, which uncover two promoter binding modes, one requiring a cofactor and the other a DNA-recognition motif. Glucose-regulated Sfp1 binding at cell cycle “START” genes suggests that Sfp1 controls cell size by coordinating expression of genes implicated in mass accumulation and cell division.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

Background:

Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery.

Methods:

Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach.

Results:

Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum.

Conclusion:

Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV).

DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets

The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising strategy for harnessing the power of large quantities of accumulated high-throughput experimental data. However, in order to achieve high speed, these algorithms have to sacrifice accuracy by employing simplified statistical models during the searching process. In this paper, we propose a novel approach named Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on high-throughput datasets. Unlike previous approaches, DiscMLA tries to optimize with a more comprehensive criterion (AUC) during motifs searching. In addition, based on an experimental observation of motif identification on large-scale datasets, some novel procedures are designed to accelerate DiscMLA. The experimental results on 52 real-world datasets demonstrate that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA' stability, discriminability, and validity will help to exploit high-throughput datasets and answer many fundamental biological questions.

THR1 mediates GCN4 and CDC4 to link morphogenesis with nutrient sensing and the stress response in Candida albicans

Candida albicans (C. albicans) CDC4 (CaCDC4), encoding the F-box protein for the substrate specificity of the Skp1-cullin-F-box E3 ubiquitin ligase complex, suppresses the yeast-to-filament transition in C. albicans. In our previous study, Thr1 was identified as a CaCdc4-associated protein using affinity purification. THR1 encodes a homoserine kinase, which is involved in the threonine biosynthesis pathway. The present study generated a strain with repressible CaCDC4 expression and continuous THR1 expression. Colony and cell morphology analyses, as well as immunoblotting, revealed that the Thr1 protein was detectable under conditions in which the expression of CaCDC4 was repressed and that the filaments resulting from the repressed expression of CaCDC4 were suppressed by the constitutive expression of THR1 in C. albicans. Additionally, by using the CaSAT1-flipper method, the present study produced null mutants of THR1, GCN4, and CaCDC4. The phenotypic consequences were evaluated by growth curves, spotting assays, microscopic analysis, reverse transcription-polymerase chain reaction and XTT-based biofilm formation ability. The results revealed that fewer cells lacking THR1 entered the stationary phase but had no apparent morphological alteration. It was observed that the expression of THR1 was upregulated concurrently with GCN4 during nutrient depletion and that cells lacking GCN4 rescued the lethality of cells in the absence of THR1 in conditions accumulating homoserine in the threonine biosynthesis pathway. Of note, it was found that cells with either CaCDC4 or THR1 loss were sensitive to oxidative stress and osmotic stress, with those with THR1 loss being more sensitive. In addition, it was observed that cells with loss of either CaCDC4 or THR1 exhibited the ability to increase biofilm formation, with those lacking CaCDC4 exhibiting a greater extent of enhancement. It was concluded that CaCDC4 is important in the coordination of morphogenesis, nutrient sensing, and the stress response through THR1 in C. albicans.

Distinct patterns of histone acetyltransferase and Mediator deployment at yeast protein-coding genes

Here, Bruzzone et al. explore the relative contributions of the transcriptional coactivators Mediator and two histone acetyltransferase (HAT) complexes, NuA4 and SAGA, to RNA polymerase II association at specific genes and gene classes by rapid nuclear depletion of key complex subunits. They show that the NuA4 HAT Esa1 differentially affects certain groups of genes, whereas the SAGA HAT Gcn5 has a weaker but more uniform effect, and their findings suggest that at least three distinct combinations of coactivator deployment are used to generate moderate or high transcription levels.

The transcriptional coactivators Mediator and two histone acetyltransferase (HAT) complexes, NuA4 and SAGA, play global roles in transcriptional activation. Here we explore the relative contributions of these factors to RNA polymerase II association at specific genes and gene classes by rapid nuclear depletion of key complex subunits. We show that the NuA4 HAT Esa1 differentially affects certain groups of genes, whereas the SAGA HAT Gcn5 has a weaker but more uniform effect. Relative dependence on Esa1 and Tra1, a shared component of NuA4 and SAGA, distinguishes two large groups of coregulated growth-promoting genes. In contrast, we show that the activity of Mediator is particularly important at a separate, small set of highly transcribed TATA-box-containing genes. Our analysis indicates that at least three distinct combinations of coactivator deployment are used to generate moderate or high transcription levels and suggests that each may be associated with distinct forms of regulation.

Argo_CUDA: Exhaustive GPU based approach for motif discovery in large DNA datasets

The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top "peak" ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.

GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif

Regulatory elements are responsible for regulating gene transcription. Therefore, identification of these elements is a tremendous challenge in the field of gene expression. Transcription factors (TFs) play a key role in gene regulation by binding to target promoter sequences. A set of conserved sequence patterns with a highly similar structure that is bound by a TF is called a motif. Motif discovery has been a difficult problem over the past decades. Meanwhile, it is a foundation stone in meeting this challenge. Recent advances in obtaining genomic sequences and high-throughput gene expression analysis techniques have enabled the rapid development of computational methods for motif discovery. As a result, a large number of motif-finding algorithms aiming at various motif models have sprung up in the past few years. However, most of them are not suitable for analysis of the large data sets generated by next-generation sequencing. To better handle large-scale ChIP-Seq data and achieve better performance in computational time and motif detection accuracy, we propose an excellent motif-finding algorithm known as GSMC (Combining Parallel Gibbs Sampling with Maximal Cliques for hunting DNA Motif). The GSMC algorithm consists of two steps. First, we employ the commonly used Gibbs sampling to generating initial motifs. Second, we utilize maximal cliques to cluster motifs according to Similarity with Position Information Contents (SPIC). Consequently, we raise the detection accuracy in a great degree, in the meantime holding comparative computation efficiency. In addition, we can find much more credible cofactor interacting motifs.

The identification of functional motifs in temporal gene expression analysis

The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR) and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur) binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

The ribosome assembly gene network is controlled by the feedback regulation of transcription elongation

Ribosome assembly requires the concerted expression of hundreds of genes, which are transcribed by all three nuclear RNA polymerases. Transcription elongation involves dynamic interactions between RNA polymerases and chromatin. We performed a synthetic lethal screening in Saccharomyces cerevisiae with a conditional allele of SPT6, which encodes one of the factors that facilitates this process. Some of these synthetic mutants corresponded to factors that facilitate pre-rRNA processing and ribosome biogenesis. We found that the in vivo depletion of one of these factors, Arb1, activated transcription elongation in the set of genes involved directly in ribosome assembly. Under these depletion conditions, Spt6 was physically targeted to the up-regulated genes, where it helped maintain their chromatin integrity and the synthesis of properly stable mRNAs. The mRNA profiles of a large set of ribosome biogenesis mutants confirmed the existence of a feedback regulatory network among ribosome assembly genes. The transcriptional response in this network depended on both the specific malfunction and the role of the regulated gene. In accordance with our screening, Spt6 positively contributed to the optimal operation of this global network. On the whole, this work uncovers a feedback control of ribosome biogenesis by fine-tuning transcription elongation in ribosome assembly factor-coding genes.

Feedback regulation of ribosome assembly

Ribosome biogenesis is a crucial process for growth and constitutes the major consumer of cellular resources. This pathway is subjected to very stringent regulation to ensure correct ribosome manufacture with a wide variety of environmental and metabolic changes, and intracellular insults. Here we summarise our current knowledge on the regulation of ribosome biogenesis in Saccharomyces cerevisiae by particularly focusing on the feedback mechanisms that maintain ribosome homeostasis. Ribosome biogenesis in yeast is controlled mainly at the level of the production of both pre-rRNAs and ribosomal proteins through the transcriptional and post-transcriptional control of the TORC1 and protein kinase A signalling pathways. Pre-rRNA processing can occur before or after the 35S pre-rRNA transcript is completed; the switch between these two alternatives is regulated by growth conditions. The expression of both ribosomal proteins and the large family of transacting factors involved in ribosome biogenesis is co-regulated. Recently, it has been shown that the synthesis of rRNA and ribosomal proteins, but not of trans-factors, is coupled. Thus the so-called CURI complex sequesters specific transcription factor Ifh1 to repress ribosomal protein genes when rRNA transcription is impaired. We recently found that an analogue system should operate to control the expression of transacting factor genes in response to actual ribosome assembly performance. Regulation of ribosome biogenesis manages situations of imbalanced ribosome production or misassembled ribosomal precursors and subunits, which have been closely linked to distinct human diseases.

Abf1 and other general regulatory factors control ribosome biogenesis gene expression in budding yeast

Ribosome biogenesis in Saccharomyces cerevisiae involves a regulon of >200 genes (Ribi genes) coordinately regulated in response to nutrient availability and cellular growth rate. Two cis-acting elements called PAC and RRPE are known to mediate Ribi gene repression in response to nutritional downshift. Here, we show that most Ribi gene promoters also contain binding sites for one or more General Regulatory Factors (GRFs), most frequently Abf1 and Reb1, and that these factors are enriched in vivo at Ribi promoters. Abf1/Reb1/Tbf1 promoter association was required for full Ribi gene expression in rich medium and for its modulation in response to glucose starvation, characterized by a rapid drop followed by slow recovery. Such a response did not entail changes in Abf1 occupancy, but it was paralleled by a quick increase, followed by slow decrease, in Rpd3L histone deacetylase occupancy. Remarkably, Abf1 site disruption also abolished Rpd3L complex recruitment in response to starvation. Extensive mutational analysis of the DBP7 promoter revealed a complex interplay of Tbf1 sites, PAC and RRPE in the transcriptional regulation of this Ribi gene. Our observations point to GRFs as new multifaceted players in Ribi gene regulation both during exponential growth and under repressive conditions.

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

BACKGROUNDS

With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.

RESULTS

We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.

CONCLUSION

Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Synthetic promoter design for new microbial chassis

The judicious choice of promoter to drive gene expression remains one of the most important considerations for synthetic biology applications. Constitutive promoter sequences isolated from nature are often used in laboratory settings or small-scale commercial production streams, but unconventional microbial chassis for new synthetic biology applications require well-characterized, robust and orthogonal promoters. This review provides an overview of the opportunities and challenges for synthetic promoter discovery and design, including molecular methodologies, such as saturation mutagenesis of flanking regions and mutagenesis by error-prone PCR, as well as the less familiar use of computational and statistical analyses for de novo promoter design.

Transcriptional control of yeast ribosome biogenesis: A multifaceted role for general regulatory factors

In Saccharomyces cerevisiae, a group of more than 200 co-regulated genes (Ribi genes) is involved in ribosome biogenesis. This regulon has recently been shown to rely on a small set of transcriptional regulators (mainly Abf1, but also Reb1, Tbf1 and Rap1) previously referred to as general regulatory factors (GRFs) because of their widespread binding and action at many promoters and other specialized genomic regions. Intriguingly, Abf1 binding to Ribi genes is differentially modulated in response to distinct nutrition signaling pathways. Such a dynamic promoter association has the potential to orchestrate both activation and repression of Ribi genes in synergy with neighboring regulatory sites and through the functional interplay of histone acetyltransferases and deacetylases.

Microarray and network-based identification of functional modules and pathways of active tuberculosis

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB.

Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer

Analysis of gene co-expression networks is a powerful "data-driven" tool, invaluable for understanding cancer biology and mechanisms of tumor development. Yet, despite of completion of thousands of studies on cancer gene expression, there were few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. Here we describe an integrated approach to cancer expression meta-analysis, which combines generation of "data-driven" co-expression networks with detailed statistical detection of promoter sequence motifs within the co-expression clusters. First, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) workflow and Pearson's correlation to generate a comprehensive set of over 3000 co-expression clusters in 82 normalized microarray datasets from nine cancers of different origin. Next, we designed a genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. The approach, realized as cisExpress software module, was specifically designed for analysis of very large data sets such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node.We discovered that although co-expression modules are populated with different sets of genes, they share distinct stable patterns of co-regulation based on promoter sequence analysis. The number of motifs per co-expression cluster varies widely in accordance with cancer tissue of origin, with the largest number in colon (68 motifs) and the lowest in ovary (18 motifs). The top scored motifs are typically shared between several tissues; they define sets of target genes responsible for certain functionality of cancerogenesis. Both the co-expression modules and a database of precalculated motifs are publically available and accessible for further studies.