New decoding algorithms for Hidden Markov Models using distance measures on labellings

<p>Abstract</p> <p>Background</p> <p>Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries.</p> <p>Results</p> <p>We give a set of algorithms to compute the conditional probability of all labellings "near" a reference labelling <it>λ </it>for a sequence <it>y </it>for a variety of definitions of "near". In addition, we give optimization algorithms to find the best labelling for a sequence in the robust sense of having all of its feature boundaries nearly correct. Natural problems in this domain are <it>NP</it>-hard to optimize. For membrane proteins, our algorithms find the approximate topology of such proteins with comparable success to existing programs, while being substantially more accurate in estimating the positions of transmembrane helix boundaries.</p> <p>Conclusion</p> <p>More robust HMM decoding may allow for better analysis of sequence features, in reasonable runtimes.</p

Association between characteristics of pain and stiffness and the functional status of patients with incident polymyalgia rheumatica from primary care.

This paper aims to examine the relationship between different characteristics of pain and stiffness and the functional status of patients with newly diagnosed polymyalgia rheumatica (PMR). Baseline analysis of an inception cohort study was conducted. Patients aged ≥18 years, with a new diagnosis of PMR were recruited from 382 English general practices. Participants were mailed a baseline questionnaire, including separate pain and stiffness manikins and numerical rating scales (NRS), a question on their ability to raise their arms above their head and the modified Health Assessment Questionnaire (mHAQ) to examine participants' functional status. Linear regression analysis, reported as regression co-efficients (95% confidence intervals (95% CI)), was used to assess the association of pain and stiffness with function, initially unadjusted and then adjusted for age, gender, deprivation status, smoking status, BMI, anxiety and depression. Six hundred fifty two patients responded to the baseline survey (88.5%). The majority (88.2%) reported no, or mild impairment in their functional status. Adjusted linear regression analysis demonstrated that high (NRS ≥8) pain (0.20 (95% CI 0.10-0.28)) or stiffness (0.18 (0.09-0.26)) ratings, an increasing number of sites of pain (0.18 (0.06-0.29)) or stiffness (0.19 (0.08-0.31)) and shoulder pain (0.18 (0.05-0.31)), stiffness (0.10 (0.01-0.20)) and difficulty raising arms above one's head (0.19 (0.10-0.28)) were all associated with increased functional impairment. The majority of newly diagnosed PMR patients reported no or minimal functional difficulty. However, those who experience severe or widespread pain or stiffness often have significant functional limitation in performing their daily activities and may be a subset worthy of additional focus in primary care

A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons

BACKGROUND: An important challenge in eukaryotic gene prediction is accurate identification of alternatively spliced exons. Functional transcripts can go undetected in gene expression studies when alternative splicing only occurs under specific biological conditions. Non-expression based computational methods support identification of rarely expressed transcripts. RESULTS: A non-expression based statistical method is presented to annotate alternatively spliced exons using a single genome sequence and evidence from cross-species sequence conservation. The computational method is implemented in the program ExAlt and an analysis of prediction accuracy is given for Drosophila melanogaster. CONCLUSION: ExAlt identifies the structure of most alternatively spliced exons in the test set and cross-species sequence conservation is shown to improve the precision of predictions. The software package is available to run on Drosophila genomes to search for new cases of alternative splicing

Obesity and motor skills among 4 to 6-year-old children in the united states: nationally-representative surveys

Few population-based studies have assessed relationships between body weight and motor skills in young children. Our objective was to estimate the association between obesity and motor skills at 4 years and 5-6 years of age in the United States. We used repeated cross-sectional assessments of the national sample from the Early Childhood Longitudinal Survey-Birth Cohort (ECLS-B) of preschool 4-year-old children (2005-2006; n = 5 100) and 5-6-year-old kindergarteners (2006-2007; n = 4 700). Height, weight, and fine and gross motor skills were assessed objectively via direct standardized procedures. We used categorical and continuous measures of body weight status, including obesity (Body Mass Index (BMI) ≥ 95th percentile) and BMI z-scores. Multivariate logistic and linear models estimated the association between obesity and gross and fine motor skills in very young children adjusting for individual, social, and economic characteristics and parental involvement.info:eu-repo/semantics/publishe

Occupancy Classification of Position Weight Matrix-Inferred Transcription Factor Binding Sites

BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors

Differential analysis for high density tiling microarray data

<p>Abstract</p> <p>Background</p> <p>High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The <it>ab initio </it>probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an <it>in-vivo </it>system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program.</p> <p>Results</p> <p>We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias.</p> <p>Conclusion</p> <p>The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003; it is most significant at the 5' end of genes, at a p-value < 10^-13. The prototype R code has been made available as supplementary material [see Additional file <supplr sid="S1">1</supplr>].</p> <suppl id="S1"> <title> <p>Additional file 1</p> </title> <text> <p>gsam_prototypercode.zip. File archive comprising of prototype R code for gSAM implementation including readme and examples.</p> </text> <file name="1471-2105-8-359-S1.zip"> <p>Click here for file</p> </file> </suppl

P53 in human melanoma fails to regulate target genes associated with apoptosis and the cell cycle and may contribute to proliferation

<p>Abstract</p> <p>Background</p> <p>Metastatic melanoma represents a major clinical problem. Its incidence continues to rise in western countries and there are currently no curative treatments. While mutation of the <it>P53 </it>tumour suppressor gene is a common feature of many types of cancer, mutational inactivation of <it>P53 </it>in melanoma is uncommon; however, its function often appears abnormal.</p> <p>Methods</p> <p>In this study whole genome bead arrays were used to examine the transcript expression of P53 target genes in extracts from 82 melanoma metastases and 6 melanoma cell lines, to provide a global assessment of aberrant P53 function. The expression of these genes was also examined in extracts derived from diploid human melanocytes and fibroblasts.</p> <p>Results</p> <p>The results indicated that P53 target transcripts involved in apoptosis were under-expressed in melanoma metastases and melanoma cell lines, while those involved in the cell cycle were over-expressed in melanoma cell lines. There was little difference in the transcript expression of P53 target genes between cell lines with null/mutant <it>P53 </it>compared to those with wild-type <it>P53</it>, suggesting that altered expression in melanoma was not related to <it>P53 </it>status. Similarly, down-regulation of P53 by short-hairpin RNA (shRNA) had limited effect on P53 target gene expression in melanoma cells, whereas there were a large number of P53 target genes whose mRNA expression was significantly altered by P53 inhibition in melanocytes. Analysis of whole genome gene expression profiles indicated that the ability of P53 to regulate genes involved in the cell cycle was significantly reduced in melanoma cells. Moreover, inhibition of P53 in melanocytes induced changes in gene expression profiles that were characteristic of melanoma cells and resulted in increased proliferation. Conversely, knockdown of P53 in melanoma cells resulted in decreased proliferation.</p> <p>Conclusions</p> <p>These results indicate that P53 target genes involved in apoptosis and cell cycle regulation are aberrantly expressed in melanoma and that this aberrant functional activity of P53 may contribute to the proliferation of melanoma.</p

Non-protein coding RNA biomarkers and differential expression in cancers: a review

<p>Abstract</p> <p>Background</p> <p>In these years a huge number of human transcripts has been found that do not code for proteins, named non-protein coding RNAs. In most cases, small (miRNAs, snoRNAs) and long RNAs (antisense RNA, dsRNA, and long RNA species) have many roles, functioning as regulators of other mRNAs, at transcriptional and post-transcriptional level, and controlling protein ubiquitination and degradation. Various species of npcRNAs have been found differentially expressed in different types of cancer. This review discusses the published data and new results on the expression of a subset of npcRNAs.</p> <p>Conclusion</p> <p>These results underscore the complexity of the RNA world and provide further evidence on the involvement of functional RNAs in cancer cell growth control.</p