Prediction Models for DNA Transcription Termination Based on SOM Networks

This paper presents two efficient models for predicting transcription termination (TT) in human DNA. A neural network, self-organizing map, was used for finding features from a human polyadenylation (polyA) sites dataset. We derived prediction models related to different polyA signals. A program, "Dragon PolyAtt", for predicting TT regions was designed for the two most frequent polyA sites "AAUAAA" and "AUUAAA". In our tests, Dragon PolyAtt predicts TT regions with a sensitivity of 48.4% (13.6%) and specificity of 74% (79.1%) when searching for polyA signal "AAUAAA" ("AUUAAA"). Both tests were done on human chromosome 21. Results of Dragon PolyAtt system are substantially better than those obtained by the well-known "polyadq" program.

Towards high performance computing for molecular structure prediction using IBM Cell Broadband Engine--an implementation perspective

BACKGROUND

RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips.

METHODS

In this paper, we exploit the parallelism capabilities of the IBM Cell Broadband Engine to parallelize an existing Dynamic Programming (DP) algorithm for RNA secondary structure prediction. We design three different implementation strategies that exploit the inherent data, code and/or hybrid parallelism, referred to as C-Par, D-Par and H-Par, and analyze their performances. Our approach attempts to introduce parallelism in critical sections of the algorithm. We ran our experiments on SONY Play Station 3 (PS3), which is based on the IBM Cell chip.

RESULTS

Our results suggest that introducing parallelism in DP algorithm allows it to easily handle longer sequences which otherwise would consume a large amount of time in single core computers. The results further demonstrate the speed-up gain achieved in exploiting the inherent parallelism in the problem and also elicits the advantages of using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA.

CONCLUSION

The speed-up performance reported here is promising, especially when sequence length is long. To the best of our literature survey, the work reported in this paper is probably the first-of-its-kind to utilize the IBM Cell Broadband Engine (a heterogeneous multi-core chip) to implement a DP. The results also encourage using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA to predict its secondary structure.

Computational promoter analysis of mouse, rat and human antimicrobial peptide-coding genes

BACKGROUND

Mammalian antimicrobial peptides (AMPs) are effectors of the innate immune response. A multitude of signals coming from pathways of mammalian pathogen/pattern recognition receptors and other proteins affect the expression of AMP-coding genes (AMPcgs). For many AMPcgs the promoter elements and transcription factors that control their tissue cell-specific expression have yet to be fully identified and characterized.

RESULTS

Based upon the RIKEN full-length cDNA and public sequence data derived from human, mouse and rat, we identified 178 candidate AMP transcripts derived from 61 genes belonging to 29 AMP families. However, only for 31 mouse genes belonging to 22 AMP families we were able to determine true orthologous relationships with 30 human and 15 rat sequences. We screened the promoter regions of AMPcgs in the three species for motifs by an ab initio motif finding method and analyzed the derived promoter characteristics. Promoter models were developed for alpha-defensins, penk and zap AMP families. The results suggest a core set of transcription factors (TFs) that regulate the transcription of AMPcg families in mouse, rat and human. The three most frequent core TFs groups include liver-, nervous system-specific and nuclear hormone receptors (NHRs). Out of 440 motifs analyzed, we found that three represent potentially novel TF-binding motifs enriched in promoters of AMPcgs, while the other four motifs appear to be species-specific.

CONCLUSION

Our large-scale computational analysis of promoters of 22 families of AMPcgs across three mammalian species suggests that their key transcriptional regulators are likely to be TFs of the liver-, nervous system-specific and NHR groups. The computationally inferred promoter elements and potential TF binding motifs provide a rich resource for targeted experimental validation of TF binding and signaling studies that aim at the regulation of mouse, rat or human AMPcgs.

The transcriptional landscape of the mammalian genome

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

Detection and preliminary analysis of motifs in promoters of anaerobically induced genes of different plant species

BACKGROUND AND AIMS

Plants can suffer from oxygen limitation during flooding or more complete submergence and may therefore switch from Kreb's cycle respiration to fermentation in association with the expression of anaerobically inducible genes coding for enzymes involved in glycolysis and fermentation. The aim of this study was to clarify mechanisms of transcriptional regulation of these anaerobic genes by identifying motifs shared by their promoter regions.

METHODS

Statistically significant motifs were detected by an in silico method from 13 promoters of anaerobic genes. The selected motifs were common for the majority of analysed promoters. Their significance was evaluated by searching for their presence in transcription factor-binding site databases (TRANSFAC, PlantCARE and PLACE). Using several negative control data sets, it was tested whether the motifs found were specific to the anaerobic group.

KEY RESULTS

Previously, anaerobic response elements have been identified in maize (Zea mays) and arabidopsis (Arabidopsis thaliana) genes. Known functional motifs were detected, such as GT and GC motifs, but also other motifs shared by most of the genes examined. Five motifs detected have not been found in plants hitherto but are present in the promoters of animal genes with various functions. The consensus sequences of these novel motifs are 5'-AAACAAA-3', 5'-AGCAGC-3', 5'-TCATCAC-3', 5'-GTTT(A/C/T)GCAA-3' and 5'-TTCCCTGTT-3'.

CONCLUSIONS

It is believed that the promoter motifs identified could be functional by conferring anaerobic sensitivity to the genes that possess them. This proposal now requires experimental verification.

Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining

We present Dragon TF Association Miner (DTFAM), a system for text-mining of PubMed documents for potential functional association of transcription factors (TFs) with terms from Gene Ontology (GO) and with diseases. DTFAM has been trained and tested in the selection of relevant documents on a manually curated dataset containing >3000 PubMed abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80% with specificity of 82%. DTFAM provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. DTFAM complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of TFs and help decipher parts of gene transcriptional regulatory networks. DTFAM summarizes information from a large volume of documents saving time and making analysis simpler for individual users. DTFAM is freely available for academic and non-profit users at http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.

ANTIMIC: a database of antimicrobial sequences

Antimicrobial peptides (AMPs) are important components of the innate immune system of many species. These peptides are found in eukaryotes, including mammals, amphibians, insects and plants, as well as in prokaryotes. Other than having pathogen-lytic properties, these peptides have other activities like antitumor activity, mitogen activity, or they may act as signaling molecules. Their short length, fast and efficient action against microbes and low toxicity to mammals have made them potential candidates as peptide drugs. In many cases they are effective against pathogens that are resistant to conventional antibiotics. They can serve as natural templates for the design of novel antimicrobial drugs. Although there are vast amounts of data on natural AMPs, they are not available through one central resource. We have developed a comprehensive database (ANTIMIC, http://research.i2r. a-star.edu.sg/Templar/DB/ANTIMIC/) of known and putative AMPs, which contains approximately 1700 of these peptides. The database is integrated with tools to facilitate efficient extraction of data and their analysis at molecular level, as well as search for new AMPs. These tools include BLAST, PDB structure viewer and the Antimic profile module.

Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates

This paper introduces a new computer system for recognition of functional transcription start sites (TSSs) in RNA polymerase II promoter regions of vertebrates. This system allows scanning complete vertebrate genomes for promoters with significantly reduced number of false positive predictions. It can be used in the context of gene finding through its recognition of the 5' end of genes. The implemented recognition model uses a composite-hierarchical approach, artificial intelligence, statistics, and signal processing techniques. It also exploits the separation of promoter sequences into those that are C+G-rich or C+G-poor. The system was evaluated on a large and diverse human sequence-set and exhibited several times higher accuracy than several publicly available TSS-finding programs. Results obtained using human chromosome 22 data showed even greater specificity than the evaluation set results. The system has been implemented in the Dragon Promoter Finder package, which can be accessed at http://sdmc.krdl.org.sg:8080/promoter/.