The ctenophore genome and the evolutionary origins of neural systems.

The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

Spoof plasmons enable giant Raman scattering enhancement in Near-Infrared region.

Exceptionally strong enhancement of the Raman signal exceeding eight orders of magnitude for near-infrared (1064 nm) excitation is demonstrated for an array of dielectric submicron pillars covered by a relatively thick metal layer. The microstructure is designed to support 'spoof' plasmon-polariton excitations with resonant frequencies significantly below the fundamental surface plasmon resonance. Experiments reveal a relatively narrow range of spatial parameters for the optimal resonant scattering enhancement. They include a period close to the excitation wavelength, a specific ratio of the pillar planar size to the period, and optimal heights of both the pillars and the covering silver metal layer. The realized microstructures can be produced by fab-compatible photolithography techniques, and their outstanding sensing possibilities open the venue for the biomedical applications.

TSSPlant: a new tool for prediction of plant Pol II promoters.

Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). Moreover, TSS selection depends on cell/tissue, development stage and environmental conditions. Such complex promoter structures make their computational identification notoriously difficult. Here, we present TSSPlant, a novel tool that predicts both TATA and TATA-less promoters in sequences of a wide spectrum of plant genomes. The tool was developed by using large promoter collections from ppdb and PlantProm DB. It utilizes eighteen significant compositional and signal features of plant promoter sequences selected in this study, that feed the artificial neural network-based model trained by the backpropagation algorithm. TSSPlant achieves significantly higher accuracy compared to the next best promoter prediction program for both TATA promoters (MCC≃0.84 and F1-score≃0.91 versus MCC≃0.51 and F1-score≃0.71) and TATA-less promoters (MCC≃0.80, F1-score≃0.89 versus MCC≃0.29 and F1-score≃0.50). TSSPlant is available to download as a standalone program at http://www.cbrc.kaust.edu.sa/download/.

Slow plasmon-polaritons in a bilayer metallic structure revealed by the lower-energy resonances of surface-enhanced Raman scattering.

Apart from the main plasmon-polariton resonance of the surface-enhanced Raman scattering (SERS) occurring at 480 - 530 nm, an additional resonance was observed for substrates with two silver layers separated by a dielectric layer which support extra plasmon modes with decreased group velocities. The novel SERS resonance is shifted towards lower energies and has comparable amplitude, its exact energy position being determined by the thickness of the dielectric interlayer. The experimental findings provide a ground for the engineering of SERS-substrates with the spectral position of the additional resonance matched with the photon energy of the pump laser over a fairly wide range of laser wavelengths.

Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements.

UNLABELLED: Gene transcription is mostly conducted through interactions of various transcription factors and their binding sites on DNA (regulatory elements, REs). Today, we are still far from understanding the real regulatory content of promoter regions. Computer methods for identification of REs remain a widely used tool for studying and understanding transcriptional regulation mechanisms. The Nsite, NsiteH and NsiteM programs perform searches for statistically significant (non-random) motifs of known human, animal and plant one-box and composite REs in a single genomic sequence, in a pair of aligned homologous sequences and in a set of functionally related sequences, respectively. AVAILABILITY AND IMPLEMENTATION: Pre-compiled executables built under commonly used operating systems are available for download by visiting http://www.molquest.kaust.edu.sa and http://www.softberry.com. CONTACT: solovictor@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Community structure and metabolism through reconstruction of microbial genomes from the environment.

Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.

Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.

Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

PlantProm: a database of plant promoter sequences.

PlantProm DB, a plant promoter database, is an annotated, non-redundant collection of proximal promoter sequences for RNA polymerase II with experimentally determined transcription start site(s), TSS, from various plant species. The first release (2002.01) of PlantProm DB contains 305 entries including 71, 220 and 14 promoters from monocot, dicot and other plants, respectively. It provides DNA sequence of the promoter regions (-200 : +51) with TSS on the fixed position +201, taxonomic/promoter type classification of promoters and Nucleotide Frequency Matrices (NFM) for promoter elements: TATA-box, CCAAT-box and TSS-motif (Inr). Analysis of TSS-motifs revealed that their composition is different in dicots and monocots, as well as for TATA and TATA-less promoters. The database serves as learning set in developing plant promoter prediction programs. One such program (TSSP) based on discriminant analysis has been created by Softberry Inc. and the application of a support ftp: vector machine approach for promoter identification is under development. PlantProm DB is available at http://mendel.cs.rhul.ac.uk/ and http://www.softberry.com/.

Towards the integration of genomics, epidemiological and clinical data.

A report on 'A Wellcome Trust Scientific Conference: Applied Bioinformatics and Public Health Microbiology 2011', Hinxton, Cambridge, 1-3 June, 2011.

Identification of promoter regions and regulatory sites.

Promoter sequences are the main regulatory elements of gene expression. Their recognition by computer algorithms is fundamental for understanding gene expression patterns, cell specificity and development. This chapter describes the advanced approaches to identify promoters in animal, plant and bacterial sequences. Also, we discuss an approach to identify statistically significant regulatory motifs in genomic sequences.

Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment.

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends. RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions. CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment.

Sequence alignment kernel for recognition of promoter regions.

UNLABELLED: In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 sigma70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data. AVAILABILITY: The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/

Abundance of plastid DNA insertions in nuclear genomes of rice and Arabidopsis.

Pairwise comparison of whole plastid and draft nuclear genomic sequences of Arabidopsis thaliana and Oryza sativa L. ssp. indica shows that rice nuclear genomic sequences contain homologs of plastid DNA covering about 94 kb (83%) of plastid genome and including one or more full-length intact (without mutations resulting in premature stop codons) homologues of 26 known protein-coding (KPC) plastid genes. By contrast, only about 20 kb (16%) of chloroplast DNA, including a single intact plastid-derived KPC gene, is presented in the nucleus of A. thaliana. Sixteen rice plastid genes have at least one nuclear copy without any mutation or with only synonymous substitutions. Nuclear copies for other ten plastid genes contain both synonymous and non-synonymous substitutions. Multiple ESTs for 25 out of 26 KPC genes were also found, as well as putative promoters for some of them. The study of substitutions pattern shows that some of nuclear homologues of plastid genes may be functional and/or are under the pressure of the positive natural selection. The similar comparative analysis performed on rice chromosome 1 revealed 27 contigs containing plastid-derived sequences, totalling about 84 kb and covering two thirds of chloroplast DNA, with the intact nuclear copies of 26 different KPC genes. One of these contigs, AP003280, includes almost 57 kb (45%) of chloroplast genome with the intact copies of 22 KPC genes. At the same time, we observed that relative locations of homologues in plastid DNA and the nuclear genome are significantly different.