Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]     

Extent of Antigenic Diversity in the V3 Region of the Surface Glycoprotein,gp120, of Human Immunodeficiency Virus Type 1 Group M and Consequences for Serotyping

Human immunodeficiency virus type 1 (HIV-1) may be studied by molecular or immunological approaches. Most analyses have been performed by genetic comparison of isolates and have led to the definition of clades or subtypes within the major (M) group of HIV-1. Five subtypes (A to E) were initially identified by comparison of genomic sequences. Four new subtypes (F to I) were identified more recently. Amino acid differences in the immunogenic V3 loop between isolates have also been studied, leading to a phenetic classification of at least 14 clusters (1 to 14) of sequences (B. T. M. Korber, K. McInnes, R. F. Smith, and G. Myers, J. Virol. 68:6730–6744, 1994). In this study, we compared the antigenicity of the V3 consensus sequences defined by phylogenetic analysis to the antigenicity of those defined by phenetic analysis. We used a recently developed subtype-specific enzyme immunoassay (SSEIA) that uses the principle of blocking with an excess of peptide in the liquid phase. Two SSEIAs were performed, the first with five V3 sequences defined by phylogenetic analysis and the second with 14 V3 sequences defined by phenetic analysis. A total of 168 HIV-1 sera taken from seropositive individuals from seven different countries or regions were studied. Experimental and statistical data, including correlation matrix and cluster analyses, demonstrated associations between the genetic subtypes and phenetically associated groups. Most of these were predicted by Korber et al. (J. Virol. 68:6730–6744, 1994) by theoretical analysis. We also found that V3 sequences can be grouped into between three and five antigenically unrelated categories. Residues that may be responsible for major antigenic differences were identified at the apex of the V3 loop, within the octapeptide xIGPGxxx, where x represents the critical positions. Our study provides evidence that there is a limited number of V3 serotypes which could be easily monitored by serological assays to study the diversity and dynamics of HIV-1 strains.The diversity of human immunodeficiency virus type 1 (HIV-1) is a major problem in the development of an effective vaccine against AIDS. Many HIV-1 sequences are now available, and phylogenetic analysis resulting in a continuously developing classification into subtypes or clades is possible (45). HIV-1 isolates are classified into the M group (for major) or O group (for outlier). The O group contains only a few variants, all from a limited area of Africa (19, 27, 50). The M group includes variants responsible for the present AIDS pandemic. It contains at least five subtypes (A to E), to which have been added more recently four other subtypes (F to I) (23, 28, 34, 36, 37). Subtypes A, C, D, G, and H are common in Africa (21, 35, 37, 38). Subtype B is the most common in America and Europe (24, 26, 51). Subtype E occurs mainly in Asia (25, 30, 41), and subtype F has been detected in Brazil and Romania (3, 28, 34). These distributions are not restrictive. Subtype C is also present in Asia (India and China), and subtype G is also present in Russia (7, 12, 29). The African subtypes (A, C, and D) and the Asian subtype (E) have also been identified in North America and in European countries (9, 13, 14, 32, 48). All the subtypes are present in Africa, including B (detected in West Africa), E (Central African Republic), and F (Cameroon) (1, 35, 38). Analysis of the genetic diversity of HIV-1 is becoming more difficult due to the increasing frequency of coinfections and recombinations (15, 20, 44).Phylogenetic trees have been generated with gag, env, or tat nucleotide sequences. Shorter DNA sequences encoding the functionally important V3 region of the envelope protein are most frequently used to provide reliable subtype designations (37). The diversity of the immunogenic V3 loop has also been studied by comparing the amino acids of different isolates, leading to a phenetic classification of at least 14 clusters of sequences, each one characterized by a consensus sequence based on the most common amino acid in a given position (22).The heterogeneity of HIV-1 strains is studied mostly by molecular characterization of genomic sequences. This involves sequencing fragments amplified by the PCR or the use of the heteroduplex mobility assay (10, 11). However, although these methods allow direct subtype classification, they are time-consuming and expensive and require highly trained workers. Serotyping of HIV-1 by antibody (Ab) binding to the V3 region has been suggested as an alternative approach (8, 40, 49, 51). Such an approach may make it possible to identify subtypes based on antigenic rather than genetic properties. This immunological information about antigenic diversity might be of value in vaccine development. We recently developed a subtype-specific enzyme immunoassay (SSEIA) which gave results consistent with those of genotyping (4, 48). This assay used V3 consensus sequences defined by genetic classification, so we wanted to compare the antigenicity of these V3 consensus sequences to the antigenicity of those defined by phenetic analysis. The phenetic clustering of V3 loop amino acid sequences is not always consistent with phylogenetic analysis. Our results suggested that a limited number of serotypes may exist and identified amino acids at the tip of the V3 loop that may be responsible for serological discrimination.     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence

The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks (PBNs) from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks, a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean networks; (2) given a subsequence, infer a Boolean network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean networks, and the selection probabilities governing which network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-network connectivity can be discovered with decent accuracy for relatively small time-course sequences.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]