Three-dimensional sequence stratigraphy and subtle stratigraphic traps associated with systems tracts: West Cameron region, offshore Louisiana, Gulf of Mexico

Three-dimensional sequence stratigraphy is a potent exploration and development tool for the discovery of subtle stratigraphic traps. Reservoir morphology, heterogeneity and subtle stratigraphic trapping mechanisms can be better understood through systematic horizontal identification of sedimentary facies of systems tracts provided by three-dimensional attribute maps used as an important complement to the sequential analysis on the two-dimensional seismic lines and the well log data. On new prospects as well as on already-producing fields, the additional input of sequential analysis on three-dimensional data enables the identification, location and precise delimitation of new potentially productive zones. The first part of this paper presents four typical horizontal seismic facies assigned to the successive systems tracts of a third- or fourth-order sequence deposited in inner to outer neritic conditions on a elastic shelf. The construction of this synthetic representative sequence is based on the observed reproducibility of the horizontal seismic facies response to cyclic eustatic events on more than 35 sequences registered in the Gulf coast Plio-Pleistocene and Late Miocene, offshore Louisiana in the West Cameron region of the Gulf of Mexico. The second part shows how three-dimensional sequence stratigraphy can contribute in localizing and understanding sedimentary facies associated with productive zones. A case study in the early Middle Miocene Cibicides opima sands shows multiple stacked gas accumulations in the top slope fan, prograding wedge and basal transgressive systems tract of the third-order sequence between SB15.5 and SB 13.8 Ma.

Cross-species and cross-platform gene expression studies with the Bioconductor-compliant R package 'annotationTools'.

Background: The variety of DNA microarray formats and datasets presently available offers an unprecedented opportunity to perform insightful comparisons of heterogeneous data. Cross-species studies, in particular, have the power of identifying conserved, functionally important molecular processes. Validation of discoveries can now often be performed in readily available public data which frequently requires cross-platform studies.Cross-platform and cross-species analyses require matching probes on different microarray formats. This can be achieved using the information in microarray annotations and additional molecular biology databases, such as orthology databases. Although annotations and other biological information are stored using modern database models ( e. g. relational), they are very often distributed and shared as tables in text files, i.e. flat file databases. This common flat database format thus provides a simple and robust solution to flexibly integrate various sources of information and a basis for the combined analysis of heterogeneous gene expression profiles.Results: We provide annotationTools, a Bioconductor-compliant R package to annotate microarray experiments and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file databases. First, annotationTools contains a specialized set of functions for mining this widely used database format in a systematic manner. It thus offers a straightforward solution for annotating microarray experiments. Second, building on these basic functions and relying on the combination of information from several databases, it provides tools to easily perform cross-species analyses of gene expression data.Here, we present two example applications of annotationTools that are of direct relevance for the analysis of heterogeneous gene expression profiles, namely a cross-platform mapping of probes and a cross-species mapping of orthologous probes using different orthology databases. We also show how to perform an explorative comparison of disease-related transcriptional changes in human patients and in a genetic mouse model.Conclusion: The R package annotationTools provides a simple solution to handle microarray annotation and orthology tables, as well as other flat molecular biology databases. Thereby, it allows easy integration and analysis of heterogeneous microarray experiments across different technological platforms or species.

Simple finite field nuclear relaxation method for calculating vibrational contribution to degenerate four-wave mixing

A simple extended finite field nuclear relaxation procedure for calculating vibrational contributions to degenerate four-wave mixing (also known as the intensity-dependent refractive index) is presented. As a by-product one also obtains the static vibrationally averaged linear polarizability, as well as the first and second hyperpolarizability. The methodology is validated by illustrative calculations on the water molecule. Further possible extensions are suggested

Simple finite field method for calculation of static and dynamic vibrational hyperpolarizabilities: curvature contributions

In the static field limit, the vibrational hyperpolarizability consists of two contributions due to: (1) the shift in the equilibrium geometry (known as nuclear relaxation), and (2) the change in the shape of the potential energy surface (known as curvature). Simple finite field methods have previously been developed for evaluating these static field contributions and also for determining the effect of nuclear relaxation on dynamic vibrational hyperpolarizabilities in the infinite frequency approximation. In this paper the finite field approach is extended to include, within the infinite frequency approximation, the effect of curvature on the major dynamic nonlinear optical processes

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Advantages of extended bottom-up proteomics using sap9 for analysis of monoclonal antibodies.

Despite the recent advances in structural analysis of monoclonal antibodies with bottom-up, middle-down, and top-down mass spectrometry (MS), further improvements in analysis accuracy, depth, and speed are needed. The remaining challenges include quantitatively accurate assignment of post-translational modifications, reduction of artifacts introduced during sample preparation, increased sequence coverage per liquid chromatography (LC) MS experiment, and ability to extend the detailed characterization to simple antibody cocktails and more complex antibody mixtures. Here, we evaluate the recently introduced extended bottom-up proteomics (eBUP) approach based on proteolysis with secreted aspartic protease 9, Sap9, for analysis of monoclonal antibodies. Key findings of the Sap9-based proteomics analysis of a single antibody include: (i) extensive antibody sequence coverage with up to 100% for the light chain and up to 99-100% for the heavy chain in a single LC-MS run; (ii) connectivity of complementarity-determining regions (CDRs) via Sap9-produced large proteolytic peptides (3.4 kDa on average) containing up to two CDRs per peptide; (iii) reduced artifact introduction (e. g., deamidation) during proteolysis with Sap9 compared to conventional bottom-up proteomics workflows. The analysis of a mixture of six antibodies via Sap9-based eBUP produced comparable results. Due to the reasons specified above, Sap9-produced proteolytic peptides improve the identification confidence of antibodies from the mixtures compared to conventional bottom-up proteomics dealing with shorter proteolytic peptides.

A repeated GGA motif is critical for the activity and stability of the riboregulator RsmY of Pseudomonas fluorescens.

The riboregulator RsmY of Pseudomonas fluorescens strain CHA0 is an example of small regulatory RNAs belonging to the global Rsm/Csr regulatory systems controlling diverse cellular processes such as glycogen accumulation, motility, or formation of extracellular products in various bacteria. By binding multiple molecules of the small regulatory protein RsmA, RsmY relieves the negative effect of RsmA on the translation of several target genes involved in the biocontrol properties of strain CHA0. RsmY and functionally related riboregulators have repeated GGA motifs predicted to be exposed in single-stranded regions, notably in the loops of hairpins. The secondary structure of RsmY was corroborated by in vivo cleavage with lead acetate. RsmY mutants lacking three or five (out of six) of the GGA motifs showed reduced ability to derepress the expression of target genes in vivo and failed to bind the RsmA protein efficiently in vitro. The absence of GGA motifs in RsmY mutants resulted in reduced abundance of these transcripts and in a shorter half-life (< or = 6 min as compared with 27 min for wild type RsmY). These results suggest that both the interaction of RsmY with RsmA and the stability of RsmY strongly depend on the GGA repeats and that the ability of RsmY to interact with small regulatory proteins such as RsmA may protect this RNA from degradation.

The gene for the ligand binding chain of the human interferon gamma receptor.

In order to characterize the gene encoding the ligand binding (1(st); alpha) chain of the human IFN-gamma receptor, two overlapping cosmid clones were analyzed. The gene spans over 25 kilobases (kb) of the genomic DNA and has seven exons. The extracellular domain is encoded by exons 1 to 5 and by part of exon 6. The transmembrane region is also encoded by exon 6. Exon 7 encodes the intracellular domain and the 3' untranslated portion. The gene was located on chromosome 6q23.1, as determined by in situ hybridization. The 4 kb region upstream (5') of the gene was sequenced and analyzed for promoter activity. No consensus-matching TATA or CAAT boxes in the 5' region were found. Potential binding sites for Sp1, AP-1, AP-2, and CREB nuclear factors were identified. Compatible with the presence of the Sp1/AP-2 sites and the lack of TATA box, S1-nuclease mapping experiments showed multiple transcription initiation sites. Promoter activity of the 5' flanking region was analyzed with two different reporter genes: the Escherichia coli chloramphenicol acetyltransferase and human growth hormone. The smallest 5' region of the gene that still had full promoter activity was 692 base pairs in length. In addition, we found sequences belonging to the oldest family of Alu repeats, 2 - 3 kb upstream of the gene, which could be useful for genetic studies.

SGP-1: prediction and validation of homologous genes based on sequence alignments

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors.

Islands of euchromatin-like sequence and expressed polymorphic sequences within the short arm of human chromosome 21

The goals of the human genome project did not include sequencing of the heterochromatic regions. We describe here an initial sequence of 1.1 Mb of the short arm of human chromosome 21 (HSA21p), estimated to be 10% of 21p. This region contains extensive euchromatic-like sequence and includes on average one transcript every 100 kb. These transcripts show multiple inter- and intrachromosomal copies, and extensive copy number and sequence variability. The sequencing of the "heterochromatic" regions of the human genome is likely to reveal many additional functional elements and provide important evolutionary information.

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library

The construction of metagenomic libraries has permitted the study of microorganisms resistant to isolation and the analysis of 16S rDNA sequences has been used for over two decades to examine bacterial biodiversity. Here, we show that the analysis of random sequence reads (RSRs) instead of 16S is a suitable shortcut to estimate the biodiversity of a bacterial community from metagenomic libraries. We generated 10,010 RSRs from a metagenomic library of microorganisms found in human faecal samples. Then searched them using the program BLASTN against a prokaryotic sequence database to assign a taxon to each RSR. The results were compared with those obtained by screening and analysing the clones containing 16S rDNA sequences in the whole library. We found that the biodiversity observed by RSR analysis is consistent with that obtained by 16S rDNA. We also show that RSRs are suitable to compare the biodiversity between different metagenomic libraries. RSRs can thus provide a good estimate of the biodiversity of a metagenomic library and, as an alternative to 16S, this approach is both faster and cheaper.