Multiple non-collinear TF-map alignments of promoter regions

<p>Abstract</p> <p>Background</p> <p>The analysis of the promoter sequence of genes with similar expression patterns is a basic tool to annotate common regulatory elements. Multiple sequence alignments are on the basis of most comparative approaches. The characterization of regulatory regions from co-expressed genes at the sequence level, however, does not yield satisfactory results in many occasions as promoter regions of genes sharing similar expression programs often do not show nucleotide sequence conservation.</p> <p>Results</p> <p>In a recent approach to circumvent this limitation, we proposed to align the maps of predicted transcription factors (referred as TF-maps) instead of the nucleotide sequence of two related promoters, taking into account the label of the corresponding factor and the position in the primary sequence. We have now extended the basic algorithm to permit multiple promoter comparisons using the progressive alignment paradigm. In addition, non-collinear conservation blocks might now be identified in the resulting alignments. We have optimized the parameters of the algorithm in a small, but well-characterized collection of human-mouse-chicken-zebrafish orthologous gene promoters.</p> <p>Conclusion</p> <p>Results in this dataset indicate that TF-map alignments are able to detect high-level regulatory conservation at the promoter and the 3'UTR gene regions, which cannot be detected by the typical sequence alignments. Three particular examples are introduced here to illustrate the power of the multiple TF-map alignments to characterize conserved regulatory elements in absence of sequence similarity. We consider this kind of approach can be extremely useful in the future to annotate potential transcription factor binding sites on sets of co-regulated genes from high-throughput expression experiments.</p

Optimizing the GATA-3 position weight matrix to improve the identification of novel binding sites

BACKGROUND: The identifying of binding sites for transcription factors is a key component of gene regulatory network analysis. This is often done using position-weight matrices (PWMs). Because of the importance of in silico mapping of tentative binding sites, we previously developed an approach for PWM optimization that substantially improves the accuracy of such mapping. RESULTS: The present work implements the optimization algorithm applied to the existing PWM for GATA-3 transcription factor and builds a new di-nucleotide PWM. The existing available PWM is based on experimental data adopted from Jaspar. The optimized PWM substantially improves the sensitivity and specificity of the TF mapping compared to the conventional applications. The refined PWM also facilitates in silico identification of novel binding sites that are supported by experimental data. We also describe uncommon positioning of binding motifs for several T-cell lineage specific factors in human promoters. CONCLUSION: Our proposed di-nucleotide PWM approach outperforms the conventional mono-nucleotide PWM approach with respect to GATA-3. Therefore our new di-nucleotide PWM provides new insight into plausible transcriptional regulatory interactions in human promoters

Finding exact optimal motifs in matrix representation by partitioning

Motivation: Finding common patterns, or motifs, in the promoter regions of co-expressed genes is an important problem in bioinformatics. A common representation of the motif is by probability matrix or PSSM (position specific scoring matrix). However, even for a motif of length six or seven, there is no algorithm that can guarantee finding the exact optimal matrix from an infinite number of possible matrices. Results: T his paper introduces the first algorithm, called EOMM, for finding the exact optimal matrix-represented motif, or simply optimal motif. Based on branch-and-bound searching by partitioning the solution space recursively, EOMM can find the optimal motif of size up to eight or nine, and a motif of larger size with any desired accuracy on the principle that the smaller the error bound, the longer the running time. Experiments show that for some real and simulated data sets, EOMM finds the motif despite very weak signals when existing software, such as MEME and MITRA-PSSM, fails to do so. © The Author 2005. Published by Oxford University Press. All rights reserved.postprin

Targeted Computational Approaches for Mining Functional Elements in Metagenomes

Thesis (Ph.D.) - Indiana University, Informatics, 2012Metagenomics enables the genomic study of uncultured microorganisms by directly extracting the genetic material from microbial communities for sequencing. Fueled by the rapid development of Next Generation Sequencing (NGS) technology, metagenomics research has been revolutionizing the field of microbiology, revealing the taxonomic and functional composition of many microbial communities and their impacts on almost every aspect of life on Earth. Analyzing metagenomes (a metagenome is the collection of genomic sequences of an entire microbial community) is challenging: metagenomic sequences are often extremely short and therefore lack genomic contexts needed for annotating functional elements, while whole-metagenome assemblies are often poor because a metagenomic dataset contains reads from many different species. Novel computational approaches are still needed to get the most out of the metagenomes. In this dissertation, I first developed a binning algorithm (AbundanceBin) for clustering metagenomic sequences into groups, each containing sequences from species of similar abundances. AbundanceBin provides accurate estimations of the abundances of the species in a microbial community and their genome sizes. Application of AbundanceBin prior to assembly results in better assemblies of metagenomes--an outcome crucial to downstream analyses of metagenomic datasets. In addition, I designed three targeted computational approaches for assembling and annotating protein coding genes and other functional elements from metagenomic sequences. GeneStitch is an approach for gene assembly by connecting gene fragments scattered in different contigs into longer genes with the guidance of reference genes. I also developed two specialized assembly methods: the targeted-assembly method for assembling CRISPRs (Clustered Regularly Interspersed Short Palindromic Repeats), and the constrained-assembly method for retrieving chromosomal integrons. Applications of these methods to the Human Microbiome Project (HMP) datasets show that human microbiomes are extremely dynamic, reflecting the interactions between community members (including bacteria and viruses)

GWFASTA: server for FASTA search in eukaryotic and microbial genomes

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists

Transcription Factor Map Alignment of Promoter Regions

We address the problem of comparing and characterizing the promoter regions of genes with similar expression patterns. This remains a challenging problem in sequence analysis, because often the promoter regions of co-expressed genes do not show discernible sequence conservation. In our approach, thus, we have not directly compared the nucleotide sequence of promoters. Instead, we have obtained predictions of transcription factor binding sites, annotated the predicted sites with the labels of the corresponding binding factors, and aligned the resulting sequences of labels—to which we refer here as transcription factor maps (TF-maps). To obtain the global pairwise alignment of two TF-maps, we have adapted an algorithm initially developed to align restriction enzyme maps. We have optimized the parameters of the algorithm in a small, but well-curated, collection of human–mouse orthologous gene pairs. Results in this dataset, as well as in an independent much larger dataset from the CISRED database, indicate that TF-map alignments are able to uncover conserved regulatory elements, which cannot be detected by the typical sequence alignments

Integrated multiple sequence alignment

Sammeth M. Integrated multiple sequence alignment. Bielefeld (Germany): Bielefeld University; 2005.The thesis presents enhancements for automated and manual multiple sequence alignment: existing alignment algorithms are made more easily accessible and new algorithms are designed for difficult cases. Firstly, we introduce the QAlign framework, a graphical user interface for multiple sequence alignment. It comprises several state-of-the-art algorithms and supports their parameters by convenient dialogs. An alignment viewer with guided editing functionality can also highlight or print regions of the alignment. Also phylogenetic features are provided, e.g., distance-based tree reconstruction methods, corrections for multiple substitutions and a tree viewer. The modular concept and the platform-independent implementation guarantee an easy extensibility. Further, we develop a constrained version of the divide-and-conquer alignment such that it can be restricted by anchors found earlier with local alignments. It can be shown that this method shares attributes of both, local and global aligners, in the quality of results as well as in the computation time. We further modify the local alignment step to work on bipartite (or even multipartite) sets for sequences where repeats overshadow valuable sequence information. In the end a technique is established that can accurately align sequences containing eventually repeated motifs. Finally, another algorithm is presented that allows to compare tandem repeat sequences by aligning them with respect to their possible repeat histories. We describe an evolutionary model including tandem duplications and excisions, and give an exact algorithm to compare two sequences under this model