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.

An assessment of gene prediction accuracy in large DNA sequences

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Comparative gene prediction in human and mouse

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.

A threefold dose intensity treatment with ifosfamide, carboplatin, and etoposide for patients with small cell lung cancer: a randomized trial.

BACKGROUND: The dose intensity of chemotherapy can be increased to the highest possible level by early administration of multiple and sequential high-dose cycles supported by transfusion with peripheral blood progenitor cells (PBPCs). A randomized trial was performed to test the impact of such dose intensification on the long-term survival of patients with small cell lung cancer (SCLC). METHODS: Patients who had limited or extensive SCLC with no more than two metastatic sites were randomly assigned to high-dose (High, n = 69) or standard-dose (Std, n = 71) chemotherapy with ifosfamide, carboplatin, and etoposide (ICE). High-ICE cycles were supported by transfusion with PBPCs that were collected after two cycles of treatment with epidoxorubicin at 150 mg/m(2), paclitaxel at 175 mg/m(2), and filgrastim. The primary outcome was 3-year survival. Comparisons between response rates and toxic effects within subgroups (limited or extensive disease, liver metastases or no liver metastases, Eastern Cooperative Oncology Group performance status of 0 or 1, normal or abnormal lactate dehydrogenase levels) were also performed. RESULTS: Median relative dose intensity in the High-ICE arm was 293% (range = 174%-392%) of that in the Std-ICE arm. The 3-year survival rates were 18% (95% confidence interval [CI] = 10% to 29%) and 19% (95% CI = 11% to 30%) in the High-ICE and Std-ICE arms, respectively. No differences were observed between the High-ICE and Std-ICE arms in overall response (n = 54 [78%, 95% CI = 67% to 87%] and n = 48 [68%, 95% CI = 55% to 78%], respectively) or complete response (n = 27 [39%, 95% CI = 28% to 52%] and n = 24 [34%, 95% CI = 23% to 46%], respectively). Subgroup analyses showed no benefit for any outcome from High-ICE treatment. Hematologic toxicity was substantial in the Std-ICE arm (grade > or = 3 neutropenia, n = 49 [70%]; anemia, n = 17 [25%]; thrombopenia, n = 17 [25%]), and three patients (4%) died from toxicity. High-ICE treatment was predictably associated with severe myelosuppression, and five patients (8%) died from toxicity. CONCLUSIONS: The long-term outcome of SCLC was not improved by raising the dose intensity of ICE chemotherapy by threefold.

Multiple non-collinear TF-map alignments of promoter regions

Background: The analysis of the promoter sequence of genes with similar expression patterns isa basic tool to annotate common regulatory elements. Multiple sequence alignments are on thebasis of most comparative approaches. The characterization of regulatory regions from coexpressedgenes at the sequence level, however, does not yield satisfactory results in manyoccasions as promoter regions of genes sharing similar expression programs often do not shownucleotide sequence conservation.Results: In a recent approach to circumvent this limitation, we proposed to align the maps ofpredicted transcription factors (referred as TF-maps) instead of the nucleotide sequence of tworelated promoters, taking into account the label of the corresponding factor and the position in theprimary sequence. We have now extended the basic algorithm to permit multiple promotercomparisons using the progressive alignment paradigm. In addition, non-collinear conservationblocks might now be identified in the resulting alignments. We have optimized the parameters ofthe algorithm in a small, but well-characterized collection of human-mouse-chicken-zebrafishorthologous gene promoters.Conclusion: Results in this dataset indicate that TF-map alignments are able to detect high-levelregulatory conservation at the promoter and the 3'UTR gene regions, which cannot be detectedby the typical sequence alignments. Three particular examples are introduced here to illustrate thepower of the multiple TF-map alignments to characterize conserved regulatory elements inabsence of sequence similarity. We consider this kind of approach can be extremely useful in thefuture to annotate potential transcription factor binding sites on sets of co-regulated genes fromhigh-throughput expression experiments.

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.

Lifespan extension by calorie restriction relies on the Sty1 MAP kinase stress pathway

Either calorie restriction, loss of function of the nutrient-dependent PKA or TOR/SCH9 pathways, or activation of stress defences improves longevity in different eukaryotes. However, the molecular links between glucose depletion, nutrient-dependent pathways and stress responses are unknown. Here we show that either calorie restriction or inactivation of nutrient-dependent pathways induces life-span extension in fission yeast, and that such effect is dependent on the activation of the stress-dependent Sty1 MAP kinase. During transition to stationary phase in glucose-limiting conditions, Sty1 becomes activated and triggers a transcriptional stress program, whereas such activation does not occur under glucose-rich conditions. Deletion of the genes coding for the SCH9-homologue Sck2 or the Pka1 kinases, or mutations leading to constitutive activation of the Sty1 stress pathway increase life span under glucose-rich conditions, and importantly such beneficial effects depend ultimately on Sty1. Furthermore, cells lacking Pka1 display enhanced oxygen consumption and Sty1 activation under glucose-rich conditions. We conclude that calorie restriction favours oxidative metabolism, reactive oxygen species production and Sty1 MAP kinase activation, and this stress pathway favours life-span extension.

Nicotine gum treatment before smoking cessation: a randomized trial.

BACKGROUND: New ways of improving the efficacy of nicotine therapy need to be explored. We tested whether starting nicotine polacrilex gum treatment 4 weeks before the quit date improved smoking abstinence rates compared with starting treatment on the quit date. METHODS: An open randomized trial of 314 daily smokers (mean, 23.7 cigarettes/d) enrolled through the Internet and by physicians in Switzerland from November 2005 to January 2007. In the precessation treatment group, participants received nicotine polacrilex gum (4 mg, unflavored) by mail for 4 weeks before and 8 weeks after their target quit date, and they were instructed to decrease their cigarette consumption by half before quitting. In the usual care group, participants received the same nicotine gum for 8 weeks after their quit date and were instructed to quit abruptly. Instructions were limited to a booklet sent by mail and access to a smoking cessation Web site. Results are expressed as self-reported abstinence rates at the end of treatment and as biochemically verified smoking abstinence (cotinine plus carbon monoxide) after 12 months. RESULTS: Eight weeks after the target quit date, self-reported 4-week abstinence rates were 41.6% in the precessation treatment group and 44.4% in the usual care group (P = .61). One year after the target quit date, biochemically verified 4-week smoking abstinence rates were 20.8% in the precessation treatment group and 19.4% in the usual care group (P = .76). CONCLUSION: Starting nicotine gum treatment 4 weeks before the target quit date was no more effective than starting treatment on the quit date.

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background: Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.Results: Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.Conclusion: We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.

Prediction of enzyme function by combining sequence similarity and protein interactions

Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

Randomized controlled trial of olanzapine or chlorpromazine as addition to lithium in the treatment of a first manic episode with psychotic features: an 8-week, flexible dose, single-blind trial

Background: Association of mood stabiliser and antipsychotic medication is indicated in psychotic mania, but specific guidelines for the treatment of a first episode of psychotic mania are needed. Aims: To compare safety and efficacy profiles of chlorpromazine and olanzapine augmentation of lithium treatment in a first episode of psychotic mania. Methods: A total of 83 patients were randomised to either lithium + chlorpromazine or lithium + olanzapine in an 8-week trial. Data was collected on side effects, vital signs and weight modifications, as well as on clinical variables. Results: There were no differences in the safety profiles of both medications, but patients in the olanzapine group were significantly more likely to have reached mania remission criteria after 8 weeks. Mixed effects models repeated measures analysis of variance showed that patients in the olanzapine group reached mania remission significantly earlier than those in the chlorpromazine group. Conclusions: These results suggest that while olanzapine and chlorpromazine have a similar safety profile in a cohort of patients with first episode of psychotic mania, the former has a greater efficacy on manic symptoms. On this basis, it may be a better choice for such conditions.

Long-term Plan for Concrete Pavement Research and Technology: The CP Road Map, November 2004, Volume 1 of 2

An Iowa State University–led team facilitated development of the CP Road Map. They developed a database of existing research. They gathered input, face to face, from the highway community. They identified gaps in research that became the basis for problem statements, which they organized into a cohesive, strategic research plan.

Long-term Plan for Concrete Pavement Research and Technology: The CP Road Map, November 2004, Volume 2 of 2

An Iowa State University–led team facilitated development of the CP Road Map. They developed a database of existing research. They gathered input, face to face, from the highway community. They identified gaps in research that became the basis for problem statements, which they organized into a cohesive, strategic research plan.

Long-term Plan for Concrete Pavement Research and Technology: The CP Road Map, November 2004

An Iowa State University–led team facilitated development of the CP Road Map. They developed a database of existing research. They gathered input, face to face, from the highway community. They identified gaps in research that became the basis for problem statements, which they organized into a cohesive, strategic research plan.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.