The Mouse Genome Analysis Consortium aligned the human and mouse genome sequences for a variety of purposes, using alignment programs that suited the various needs. For investigating issues regarding genome evolution, a particularly sensitive method was needed to permit alignment of a large proportion of the neutrally evolving regions. We selected a program called BLASTZ, an independent implementation of the Gapped BLAST algorithm specifically designed for aligning two long genomic sequences. BLASTZ was subsequently modified, both to attain efficiency adequate for aligning entire mammalian genomes and to increase its sensitivity. This work describes BLASTZ, its modifications, the hardware environment on which we run it, and several empirical studies to validate its results. (+info)
Computational analysis of core promoters in the Drosophila genome.
(26/288)
BACKGROUND: The core promoter, a region of about 100 base-pairs flanking the transcription start site (TSS), serves as the recognition site for the basal transcription apparatus. Drosophila TSSs have generally been mapped by individual experiments; the low number of accurately mapped TSSs has limited analysis of promoter sequence motifs and the training of computational prediction tools. RESULTS: We identified TSS candidates for about 2,000 Drosophila genes by aligning 5' expressed sequence tags (ESTs) from cap-trapped cDNA libraries to the genome, while applying stringent criteria concerning coverage and 5'-end distribution. Examination of the sequences flanking these TSSs revealed the presence of well-known core promoter motifs such as the TATA box, the initiator and the downstream promoter element (DPE). We also define, and assess the distribution of, several new motifs prevalent in core promoters, including what appears to be a variant DPE motif. Among the prevalent motifs is the DNA-replication-related element DRE, recently shown to be part of the recognition site for the TBP-related factor TRF2. Our TSS set was then used to retrain the computational promoter predictor McPromoter, allowing us to improve the recognition performance to over 50% sensitivity and 40% specificity. We compare these computational results to promoter prediction in vertebrates. CONCLUSIONS: There are relatively few recognizable binding sites for previously known general transcription factors in Drosophila core promoters. However, we identified several new motifs enriched in promoter regions. We were also able to significantly improve the performance of computational TSS prediction in Drosophila. (+info)
Feasibility and reliability of on-line automated microemboli detection after carotid endarterectomy. A transcranial Doppler study.
(27/288)
OBJECTIVES: recently, a new algorithm for transcranial Doppler (TCD) ultrasound detection of microembolic signals (MES) was developed. In the present study, we investigated its on-line performance in TCD monitoring after carotid endarterectomy (CEA) and assessed off-line its accuracy in detecting MES. MATERIALS AND METHODS: first, the feasibility of MES detection in TCD monitoring after CEA in a routine clinical setting was evaluated in 50 patients. Second, to test the reliability of the software a 2-h digital audio study tape was made and analysed by the algorithm and five human experts. The "gold standard" was defined as the agreement between human experts: a MES was considered to be present if at least three human observers agreed. RESULTS: TCD monitoring for emboli detection after CEA was well tolerated by the patients and could be performed reliably. In the study tape, the human gold standard detected 107 MES, with 93 MES having an intensity of > or =7 dB. The software detected 81 and 77 MES, respectively. Using the 7 dB intensity threshold, the software had no false positives and 16 false negatives. The kappa value between the human gold standard and the software was 0.91, the proportion of specific agreement was 0.83. CONCLUSIONS: the tested algorithm provides a reliable method for automated on-line microemboli detection after CEA. This makes monitoring of the effectiveness of antiplatelet agents in the prevention of stroke after CEA more practicable. (+info)
Validation and reproducibility of computerised cell-viability analysis of tissue slices.
(28/288)
BACKGROUND: The identification of live cells using membrane integrity dyes has become a frequently used technique, especially with articular cartilage and chondrocytes in situ where tissue slices are used to assess cell recovery as a function of location. The development of a reproducible computerised method of cell evaluation would eliminate many variables associated with manual counting and significantly reduce the amount of time required to evaluate experimental results. METHODS: To validate a custom computerised counting program, intra-person and inter-person cell counts of nine human evaluators (three groups - unskilled, novice, and experienced) were compared with repeated pixel counts of the custom program on 15 digitised images (in triplicate) of chondrocytes in situ stained with fluorescent dyes. RESULTS: Results indicated increased reproducibility with increased experience within evaluators [Intraclass Correlation Coefficient (ICC) range = 0.67 (unskilled) to 0.99 (experienced)] and between evaluators [ICC = 0.47 (unskilled), 0.85 (novice), 0.93 (experienced)]. The computer program had perfect reproducibility (ICC = 1.0). There was a significant relationship between the average of the experienced evaluators results and the custom program results (ICC = 0.77). CONCLUSIONS: This study demonstrated that increased experience in cell counting resulted in increased reproducibility both within and between human evaluators but confirmed that the computer program was the most reproducible. There was a good correlation between the intact cell recovery determined by the computer program and the experienced human evaluators. The results of this study showed that the computer counting program was a reproducible tool to evaluate intact cell recovery after use of membrane integrity dyes on chondrocytes in situ. This and the significant decrease in the time used to count the cells by the computer program advocate its use in future studies because it has significant advantages. (+info)
The composite solubility versus pH profile and its role in intestinal absorption prediction.
(29/288)
The purpose of this study was to examine absorption of basic drugs as a function of the composite solubility curve and intestinally relevant pH by using a gastrointestinal tract (GIT) absorption simulation based on the advanced compartmental absorption and transit model. Absorption simulations were carried out for virtual monobasic drugs having a range of pKa, log D, and dose values as a function of presumed solubility and permeability. Results were normally expressed as the combination that resulted in 25% absorption. Absorption of basic drugs was found to be a function of the whole solubility/pH relationship rather than a single solubility value at pH 7. In addition, the parameter spaces of greatest sensitivity were identified. We compared 3 theoretical scenarios: the GIT pH range overlapping (1) only the salt solubility curve, (2) the salt and base solubility curves, or (3) only the base curve. Experimental solubilities of 32 compounds were determined at pHs of 2.2 and 7.4, and they nearly all fitted into 2 of the postulated scenarios. Typically, base solubilities can be simulated in silico, but salt solubilities at low pH can only be measured. We concluded that quality absorption simulations of candidate drugs in most cases require experimental solubility determination at 2 pHs, to permit calculation of the whole solubility/pH profile. (+info)
The validity of computerized orthognathic predictions.
(30/288)
OBJECTIVE: utilizing OPAL cephalometric prediction software. DESIGN: A retrospective investigation involving the random selection of Class II orthognathic patients from surgical records. SUBJECTS: These 25 cases had undergone treatment aimed at producing Class I incisors. This involved fixed orthodontic appliances and a mandibular advancement osteotomy with rigid internal fixation. METHODS: Lateral cephalographs from three key stages were digitized and processed using the OPAL software. Pre-treatment predictions were generated and compared with the actual clinical changes. RESULTS: Prediction of some of the principal OPAL variables (SNA, ANB, LAFH%, OJ, OB) was reasonably accurate in terms of mean values. However, there were large individual variations for most measurements, and prediction of Wits, MxP/MnP, LAFH, and LPFH was prone to systematic error. In particular, there was a tendency towards over-prediction of the surgically-induced backward mandibular rotation. CONCLUSION: In lieu of further validation caution should be exercised with the interpretation of individual OPAL predictions, especially vertical skeletal changes, and an explanation given to patients that orthognathic predictions are based on generalizations. (+info)
EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora.
(31/288)
Plant pathogenic microbes have the remarkable ability to manipulate biochemical, physiological, and morphological processes in their host plants. These manipulations are achieved through a diverse array of effector molecules that can either promote infection or trigger defense responses. We describe a general functional genomics approach aimed at identifying extracellular effector proteins from plant pathogenic microorganisms by combining data mining of expressed sequence tags (ESTs) with virus-based high-throughput functional expression assays in plants. PexFinder, an algorithm for automated identification of extracellular proteins from EST data sets, was developed and applied to 2147 ESTs from the oomycete plant pathogen Phytophthora infestans. The program identified 261 ESTs (12.2%) corresponding to a set of 142 nonredundant Pex (Phytophthora extracellular protein) cDNAs. Of these, 78 (55%) Pex cDNAs were novel with no significant matches in public databases. Validation of PexFinder was performed using proteomic analysis of secreted protein of P. infestans. To identify which of the Pex cDNAs encode effector proteins that manipulate plant processes, high-throughput functional expression assays in plants were performed on 63 of the identified cDNAs using an Agrobacterium tumefaciens binary vector carrying the potato virus X (PVX) genome. This led to the discovery of two novel necrosis-inducing cDNAs, crn1 and crn2, encoding extracellular proteins that belong to a large and complex protein family in Phytophthora. Further characterization of the crn genes indicated that they are both expressed in P. infestans during colonization of the host plant tomato and that crn2 induced defense-response genes in tomato. Our results indicate that combining data mining using PexFinder with PVX-based functional assays can facilitate the discovery of novel pathogen effector proteins. In principle, this strategy can be applied to a variety of eukaryotic plant pathogens, including oomycetes, fungi, and nematodes. (+info)
Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.
(32/288)
BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. (+info)