Multiple Expectation Maximization For Motif Elicitation Research Articles

Gene co-expression network identifies critical genes, pathways and regulatory motifs mediating the progression of rift valley fever in Bostaurus

Rift Valley Fever (RVF) is a mosquito-borne viral disease caused by the Rift Valley Fever Virus. The disease is a zoonosis that largely affects domestic animals, including sheep, goats, and cattle, resulting in severe morbidity and mortality marked by massive storm abortions. To halt human and livestock deaths due to RVF, the development of efficacious vaccines and therapeutics is a compelling and urgent priority. We sought to identify potential key modules (gene clusters), hub genes, and regulatory motifs involved in the pathogenesis of RVF in Bos taurus that are amenable to inhibition. We analyzed 39 Bos taurus RNA-Seq samples using the weighted gene co-expression network analysis (WGCNA) R package and uncovered significantly enriched modules containing genes with potential pivotal roles in RVF progression. Moreover, regulatory motif analysis conducted using the Multiple Expectation Maximization for Motif Elicitation (MEME) suite identified motifs that probably modulate vital biological processes. Gene ontology terms associated with identified motifs were inferred using the GoMo human database. The gene co-expression network constructed in WGCNA using 5000 genes contained seven (7) modules, out of which four were significantly enriched for terms associated with response to viruses, response to interferon-alpha, innate immune response, and viral defense. Additionally, several biological pathways implicated in developmental processes, anatomical structure development, and multicellular organism development were identified. Regulatory motifs analysis identified short, repeated motifs whose function(s) may be amenable to disruption by novel therapeutics. Predicted functions of identified motifs include tissue development, embryonic organ development, and organ morphogenesis. We have identified several hub genes in enriched co-expressed gene modules and regulatory motifs potentially involved in the pathogenesis of RVF in B. taurus that are likely viable targets for disruption by novel therapeutics.

Computational analysis of heat shock protein (HSP) in Citrus X Sinensis

Heat shock proteins (HSPs) are molecular chaperones and one of the cell’s most important regulatory proteins present in all species. HSPs are a multigene family classified into six families according to their molecular weight range between 8KDa to 110KDa: HSP100, HSP90, HSP70, HSP60, small heat shock proteins (sHSPs), and ubiquitin. Citrus X Sinensis is very economical, and widely grown in the world. It is one of the best sources of nutrition, vitamin C, fiber, thiamine, folate, and antioxidants. It has a variety of health benefits i.e., protects against cell damage, boosts the immune system, heals wounds, etc. For highlighting the aspect of HSPs of citrus in computing protein, this work has great significance. Present studies are based on the identification and characterization of heat shock proteins in Citrus sinuses. In silico research was performed; Arabidopsis thaliana protein sequences of HSP60, HSP70 and HSP90 were used as a reference to identify these molecular chaperones in Citrus sinuses through Blast Phytozome. Furthermore, physiochemical properties, multiple sequence alignment, Multiple Expectation Maximization for Motif Elicitation (MEME) and phylogeny were constructed for 44 sequences of HSP60, 22 sequences of HSP70 and 23 for HSP90. The best expression of HSPs was found in Endoplasmic Reticulum. The findings of the present studies provide a good understanding of the HSP60, HSP70 and HSP90 families' function and expression site in the cell of Citrus sinuses. It also provides the basic knowledge for further investigations of the putative structure of dynamic proteins.

GENOME-WIDE IDENTIFICATION OF THE GATA TRANSCRIPTION FACTOR FAMILY IN Dichanthelium oligosanthes

The GATA transcription factors (TFs) play a crucial role in regulating various physiological processes in plants. Identification and characterization of the GATA TF family has been carried out in several important grass species, including rice, maize, and bamboo. However, no information is available on the GATA TFs in the C3 grass species Dichanthelium oligosanthes. In the current study, 31 GATA genes have been identified in the D. oligosanthes genome by stringent bioinformatics analysis. The exon-intron arrangement analysis of the DoGATAs via the Gene Structure Display Server (GSDS 2.0) revealed the redundancy and differences in their gene structural organization. In addition, the sequence comparisons within the DoGATAs via BLAST revealed 11 numbers of putative paralogs. Similarly, the BLAST comparisons among the OsGATAs and DoGATAs resulted in the identification of 21 orthologs. Structural analysis of the identified DoGATAs through Simple Modular Architecture Research Tool (SMART), Conserved Domain Database (CDD), and Multiple Expectation Maximization for Motif Elicitation (MEME) revealed that all of them possess the signature GATA domain and the C-X2-C-X18-C-X2-C consensus sequence. The phylogenetic analysis via MEGA divided the DoGATAs into four groups along with rice and Arabidopsis GATAs. In addition, the subcellular localization, gene ontology, and other peptide functional prediction results further supported the DoGATAs to be putative GATA genes. Moreover, the findings of this study can serve as a basic framework for the isolation and functional characterization of GATA genes in D. oligosanthes.

DNA motif discovery using chemical reaction optimization

DNA motif discovery means to find short similar sequence elements within a set of nucleotide sequences. It has become a compulsory need in bioinformatics for its useful applications such as compression, summarization, and clustering algorithms. Motif discovery is an NP-hard problem and exact algorithms cannot solve it in polynomial time. Many optimization algorithms were proposed to solve this problem. However, none of them can show its supremacy by overcoming all the obstacles. Chemical Reaction Optimization (CRO) is a population based metaheuristic algorithm that can easily fit for the optimization problem. Here, we have proposed an algorithm based on Chemical Reaction Optimization technique to solve the DNA motif discovery problem. The four basic operators of CRO have been redesigned for this problem to search the solution space locally as well as globally. Two additional operators (repair functions) have been proposed to improve the quality of the solutions. They have been applied to the final solution after the iteration stage of CRO to get a better one. Using the flexible mechanism of elementary operators of CRO along with the additional operators (repair functions), it is possible to determine motif more precisely. Our proposed method is compared with other traditional algorithms such as Gibbs sampler, AlignACE (Aligns Nucleic Acid Conserved Elements), MEME (Multiple Expectation Maximization for Motif Elicitation), and ACRI (Ant-Colony-Regulatory-Identification) by testing real-world datasets. The experimental results show that the proposed algorithm can give better results than other traditional algorithms in quality and in less running time. Besides, statistical tests have been performed to show the superiority of the proposed algorithm over other state-of-the-arts in this area.

In silico Characterisation of Novel Rice Transcripts Differentially Expressed in Phosphorus Deficient Conditions Suggests a Role of These Transcripts in Multiple Abiotic Stresses

Phosphorus deficiency adversely affects crop productivity. The mechanism of tolerance in plants is not well understood. The current study successfully annotated a set of highly significant (Log2 RPKM ≥3) nine novel sequences up-regulated in P deficient condition identified from a low P tolerant rice genotype. Sequence annotation identified two transcripts (Os01g37260 and Os02g11060) carrying known domains, F-box and WD, respectively. Multiple Expectation maximization for Motif Elicitation (MEME) revealed presence of conserved domains like D[LP][HY][CL]D[CM][DT]C[AP][DQ][IQ]C, [EH][DN]HN[HS] [ER][FY][EP]I[HN]H which might play a role in phosphorus deficiency tolerance. Analysis of the upstream regions indicated presence of stress responsive elements like E Box, ABRE, and MYBCORE suggesting regulation of the novel transcripts by DNA binding. Protein localization prediction tool suggests that these novel proteins might be targeted to nucleus, chloroplast and cell wall. Transcripts Os02g03640 and Os02g10250 revealed potential target sites for microRNA binding suggesting role of novel miRNAs in low phosphorus response. Our analysis suggests that an F-box protein, Os01g37260 (OSFBx14) might be a promising candidate gene playing a role in multiple abiotic stresses including P deficiency.

Genome-wide identification, phylogenetic relationships, and expression analysis of the carotenoid cleavage oxygenase gene family in pepper.

Carotenoid cleavage oxygenases (CCOs) are a family of dioxygenases, which specifically catalyze the cleavage of conjugated double bonds in carotenoids and apocarotenoids in plants. In this study, genome-wide analysis of CCO genes in pepper plants was performed using bioinformatic methods. At least 11 members of the CCO gene family were identified in the pepper genome. Phylogenetic analysis showed that pepper and tomato CCO genes could be divided into two groups (CCDs and NCEDs). The CCD group included five sub-groups (CCD1, CCD4, CCD7, CCD8, and CCD-like). These results indicate that there is a close genetic relationship between the two species. Sequence analysis using the online tool, Multiple Expectation Maximization for Motif Elicitation (MEME), showed that the CCO proteins comprise multiple conserved motifs, with 20 to 41 amino acids. In addition, multiple cis-acting elements in the promoter of CCO genes were identified using the online tool PlantCARE, and were found to be involved in light responsiveness, plant hormone regulation, and biotic and abiotic stresses, suggesting potential roles of these proteins under different conditions. RNA-seq analysis revealed that the CCO genes exhibit distinct patterns of expression in the roots, stems, leaves, and fruit. These findings suggest that the CCO genes have important roles in the vegetative and reproductive development of pepper plants.

Identification and expression analysis of the LRR-RLK gene family in tomato (Solanum lycopersicum) Heinz 1706.

As the largest subfamily of receptor-like kinases (RLKs), leucine-rich repeat receptor-like kinases (LRR-RLKs) regulate the growth, development, and stress responses of plants. Through a reiterative process of sequence analysis and re-annotation, 234 LRR-RLK genes were identified in the genome of tomato (Solanum lycopersicum) 'Heinz 1706', which were further grouped into 10 major groups based on their sequence similarity. In comparison to the significant role of tandem duplication in the expansion process of this gene family in other species, only approximately 12% (29 out of 234) of SlLRR-RLK genes arose from tandem duplication. Using the multiple expectation maximization for motif elicitation (MEME) method, the motif composition and arrangement were found to be variably conserved within each SlLRR-RLK group, indicating their different extent of functional divergence. Expression profiling analyses by qRT-PCR data revealed that SlLRR-RLK genes were differentially expressed in various tomato organs and tissues, and some SlLRR-RLK genes exhibited preferential expression in fruits at distinct developmental stages, suggesting that SlLRR-RLK may take important roles in fruit development and ripening process. The results of this study provide an overview of the LRR-RLK gene family in tomato Heinz 1706, one important species of Solanaceae, and will be helpful for future functional analysis of this important protein family in fleshy fruit-bearing species.

Genome-Wide Analysis of BURP Domain-Containing Genes in Populus trichocarpa

BURP domain-containing proteins have a conserved structure and are found extensively in plants. The functions of the proteins in this family are diverse, but remain unknown in Populus trichocarpa. In the present study, a complete genome of P. trichocarpa was analyzed bioinformatically. A total of 18 BURP family genes, named PtBURPs, were identified and characterized according to their physical positions on the P. trichocarpa chromosomes. A phylogenetic tree was generated from alignments of PtBURP protein sequences, while phylogenetic relationships were also examined between PtBURPs and BURP family genes in other plants, including rice, soybean, maize and sorghum. BURP genes in P. trichocarpa were classified into five classes, namely PG1β-like, BNM2-like, USP-like, RD22-like and BURP V. The multiple expectation maximization for motif elicitation (MEME) and multiple protein sequence alignments of PtBURPs were also performed. Results from the transcript level analyses of 10 PtBURP genes under different stress conditions revealed the expression patterns in poplar and led to a discussion on genome duplication and evolution, expression profiles and function of PtBURP genes.

Discovering Interesting Motif-Sets for Multi-Class Protein Sequence Classification

In this article, we propose an effective data mining technique for multi-class protein sequence classification. The technique, which can discover discriminative motif-sets for classification, performs its tasks in two phases. In Phase 1, it makes use of a popular motif discovery algorithm called MEME (Multiple Expectation Maximization for Motif Elicitation) to discover a set of highly conserved motifs in each protein family of training sequences. The highly conserved motif-sets discovered in each family may overlap with each other and may therefore not be unique enough to allow them to be used for classification. Phase 2, therefore, makes use of a pattern discovery approach to discover the interesting motif-sets in each protein family that are useful for classification with a single classifier. Based on these motif-sets, the functional family of each independent testing sequence can then be determined. For experimentation, the proposed technique has been tested with different sets of protein sequences. Experimental results show that it outperforms other existing protein sequence classifiers and can effectively classify proteins into their corresponding functional families. In addition, the motif-sets discovered during the training process have been found to be biologically meaningful.

Identification of New Genes Regulated by the Crt1 Transcription Factor, an Effector of the DNA Damage Checkpoint Pathway in Saccharomyces cerevisiae

The Crt1 (RFX1) protein in Saccharomyces cerevisiae is an effector of the DNA damage checkpoint pathway. It recognizes a 13-bp cis-regulatory element in the 5'-untranslated region (5'-UTR) of the ribonucleotide reductase genes RNR2, RNR3, and RNR4; the HUG1 gene; and itself. We calculated the weight matrix representing the Crt1p binding site motif according to analysis of the 5'-UTR sequences of the genes that are under its regulation. We subsequently searched the 5'-UTR sequences of all the genes in the yeast genome for the occurrence of this motif. The motif was found in regulatory regions of 30 genes. A statistical analysis showed that it is unlikely that a random gene cluster contains the motif conserved as well as the Crt1p binding site. Analysis of microarray data provided supporting evidence for five putative Crt1p targets: FSH3, YLR345W, UBC5, NDE2, and NTH2. We used reverse transcription-PCR to compare the expression levels of these genes in wild-type and crt1Delta strains. Our results indicated that FSH3, YLR345W, and NTH2 are indeed under the regulation of Crt1p. Sequence analysis of the FSH3p indicated that this protein may be involved in folate metabolism either by carrying serine hydrolase activity required for the novel metabolic pathway involving dihydrofolate reductase (DHFR) or by directly interacting with the DHFR enzyme. We postulate that Crt1p may influence deoxyribonucleotide synthesis not only by regulating expression of the RNR genes but also by modulating DHFR activity. FSH3p shares significant sequence similarity with the product of the human tumor suppressor gene OVCA2. YLR345Wp and NTH2p are enzymes involved in the central metabolism under stress conditions.

2HAPI: a microarray data analysis system.

2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.

An artificial intelligence approach to motif discovery in protein sequences: application to steriod dehydrogenases.

MEME (Multiple Expectation-maximization for Motif Elicitation) is a unique new software tool that uses artificial intelligence techniques to discover motifs shared by a set of protein sequences in a fully automated manner. This paper is the first detailed study of the use of MEME to analyse a large, biologically relevant set of sequences, and to evaluate the sensitivity and accuracy of MEME in identifying structurally important motifs. For this purpose, we chose the short-chain alcohol dehydrogenase superfamily because it is large and phylogenetically diverse, providing a test of how well MEME can work on sequences with low amino acid similarity. Moreover, this dataset contains enzymes of biological importance, and because several enzymes have known X-ray crystallographic structures, we can test the usefulness of MEME for structural analysis. The first six motifs from MEME map onto structurally important alpha-helices and beta-strands on Streptomyces hydrogenans 20beta-hydroxysteroid dehydrogenase. We also describe MAST (Motif Alignment Search Tool), which conveniently uses output from MEME for searching databases such as SWISS-PROT and Genpept. MAST provides statistical measures that permit a rigorous evaluation of the significance of database searches with individual motifs or groups of motifs. A database search of Genpept90 by MAST with the log-odds matrix of the first six motifs obtained from MEME yields a bimodal output, demonstrating the selectivity of MAST. We show for the first time, using primary sequence analysis, that bacterial sugar epimerases are homologs of short-chain dehydrogenases. MEME and MAST will be increasingly useful as genome sequencing provides large datasets of phylogenetically divergent sequences of biomedical interest.

Hidden Markov Model Analysis of Motifs in Steroid Dehydrogenases and Their Homologs

The increasing size of protein sequence databases is straining methods of sequence analysis, even as the increased information offers opportunities for sophisticated analyses of protein structure, function, and evolution. Here we describe a method that uses artificial intelligence-based algorithms to build models of families of protein sequences. These models can be used to search protein sequence databases for remote homologs. The MEME (Multiple Expectation-maximization for Motif Elicitation) software package identifies motif patterns in a protein family, and these motifs are combined into a hidden Markvov model (HMM) for use as a database searching tool. Meta-MEME is sensitive and accurate, as well as automated and unbiased, making it suitable for the analysis of large datasets. We demonstrate Meta-MEME on a family of dehydrogenases that includes mammalian 11β-hydroxysteroid and 17β-hydroxysteroid dehydrogenase and their homologs in the short chain alcohol dehydrogenase family. We chose this dataset because it is large and phylogenetically diverse, providing a good test of the sensitivity and selectivity of Meta-MEME on a protein family of biological interest. Indeed, Meta-MEME identifies at least 350 members of this family in Genpept96 and clearly separates these sequences from non-homologous proteins. We also show how the MEME motif output can be used for phylogenetic analysis.