bluuebunny/crossref_metadata_2025_split huggingface dataset:
PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship
Authors: Jung Inkyung , Matsuyama Akihisa , Yoshida Minoru , Kim Dongsup 
Published: January 2010

Abstract

Background
Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.

Results
Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.

Conclusion
Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.
HORI: a web server to compute Higher Order Residue Interactions in protein structures
Authors: Sundaramurthy Pandurangan , Shameer Khader , Sreenivasan Raashi , Gakkhar Sunita , Sowdhamini Ramanathan 
Published: January 2010

Abstract

Background
Folding of a protein into its three dimensional structure is influenced by both local and global interactions within a protein. Higher order residue interactions, like pairwise, triplet and quadruplet ones, play a vital role in attaining the stable conformation of the protein structure. It is generally agreed that higher order interactions make significant contribution to the potential energy landscape of folded proteins and therefore it is important to identify them to estimate their contributions to overall stability of a protein structure.

Results
We developed HORI [Higher order residue interactions in proteins], a web server for the calculation of global and local higher order interactions in protein structures. The basic algorithm of HORI is designed based on the classical concept of four-body nearest-neighbour propensities of amino-acid residues. It has been proved that higher order residue interactions up to the level of quadruple interactions plays a major role in the three-dimensional structure of proteins and is an important feature that can be used in protein structure analysis.

Conclusion
HORI server will be a useful resource for the structural bioinformatics community to perform analysis on protein structures based on higher order residue interactions. HORI server is a highly interactive web server designed in three modules that enables the user to analyse higher order residue interactions in protein structures. HORI server is available from the URL: http://caps.ncbs.res.in/hori
A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles
Authors: Chin Chia-Hao , Chen Shu-Hwa , Ho Chin-Wen , Ko Ming-Tat , Lin Chung-Yen 
Published: January 2010

Abstract

Background
Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.

Results
In this paper, we propose a novel hu b-attachment based method to detect functional modules from confidence-scored protein inte ractions and expression pr ofiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology.

Conclusion
A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.
Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)
Authors: Agarwal Sumeet , Vaz Candida , Bhattacharya Alok , Srinivasan Ashwin 
Published: January 2010

Abstract

Background
It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification.

Results
The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively.

Conclusion
The results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as an approach for ab initio identification of novel miRNAs.
Active machine learning for transmembrane helix prediction
Authors: Osmanbeyoglu Hatice U , Wehner Jessica A , Carbonell Jaime G , Ganapathiraju Madhavi K 
Published: January 2010

Abstract

Background
About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.

Results
An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins.

Conclusion
Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.
An integrative modular approach to systematically predict gene-phenotype associations
Authors: Mehan Michael R , Nunez-Iglesias Juan , Dai Chao , Waterman Michael S , Zhou Xianghong Jasmine 
Published: January 2010

Abstract

Background
Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes. We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms.

Results
We applied this method to 338 microarray datasets, covering 178 phenotype classes, and identified 193,145 phenotype-specific coexpression modules. We trained random forest classifiers for each phenotype and predicted a total of 6,558 gene-phenotype associations. We showed that 40.9% genes are pleiotropic, highlighting that pleiotropy is more prevalent than previously expected. We collected 77 ChIP-chip datasets studying 69 transcription factors binding over 16,000 targets under various phenotypic conditions. Utilizing this unique data source, we confirmed that dynamic transcriptional regulation is an important force driving the formation of phenotype specific gene modules.

Conclusion
We created a genome-wide gene to phenotype mapping that has many potential implications, including providing potential new drug targets and uncovering the basis for human disease phenotypes. Our analysis of these phenotype-specific coexpression modules reveals a high prevalence of gene pleiotropy, and suggests that phenotype-specific transcription factor binding may contribute to phenotypic diversity. All resources from our study are made freely available on our online Phenotype Prediction Database [1].
Proteomic analysis of peach fruit mesocarp softening and chilling injury using difference gel electrophoresis (DIGE)
Authors: Nilo Ricardo , Saffie Carlos , Lilley Kathryn , Baeza-Yates Ricardo , Cambiazo Verónica , Campos-Vargas Reinaldo , González Mauricio , Meisel Lee A , Retamales Julio , Silva Herman , Orellana Ariel 
Published: January 2010

Abstract

Background
Peach fruit undergoes a rapid softening process that involves a number of metabolic changes. Storing fruit at low temperatures has been widely used to extend its postharvest life. However, this leads to undesired changes, such as mealiness and browning, which affect the quality of the fruit. In this study, a 2-D DIGE approach was designed to screen for differentially accumulated proteins in peach fruit during normal softening as well as under conditions that led to fruit chilling injury.

Results
The analysis allowed us to identify 43 spots -representing about 18% of the total number analyzed- that show statistically significant changes. Thirty-nine of the proteins could be identified by mass spectrometry. Some of the proteins that changed during postharvest had been related to peach fruit ripening and cold stress in the past. However, we identified other proteins that had not been linked to these processes. A graphical display of the relationship between the differentially accumulated proteins was obtained using pairwise average-linkage cluster analysis and principal component analysis. Proteins such as endopolygalacturonase, catalase, NADP-dependent isocitrate dehydrogenase, pectin methylesterase and dehydrins were found to be very important for distinguishing between healthy and chill injured fruit. A categorization of the differentially accumulated proteins was performed using Gene Ontology annotation. The results showed that the 'response to stress', 'cellular homeostasis', 'metabolism of carbohydrates' and 'amino acid metabolism' biological processes were affected the most during the postharvest.

Conclusions
Using a comparative proteomic approach with 2-D DIGE allowed us to identify proteins that showed stage-specific changes in their accumulation pattern. Several proteins that are related to response to stress, cellular homeostasis, cellular component organization and carbohydrate metabolism were detected as being differentially accumulated. Finally, a significant proportion of the proteins identified had not been associated with softening, cold storage or chilling injury-altered fruit before; thus, comparative proteomics has proven to be a valuable tool for understanding fruit softening and postharvest.
Expression profiling of prospero in the Drosophila larval chemosensory organ: Between growth and outgrowth
Authors: Guenin Laure , Raharijaona Mahatsangy , Houlgatte Rémi , Baba-Aissa Fawzia 
Published: January 2010

AbstractBackgroundThe antenno-maxilary complex (AMC) forms the chemosensory system of theDrosophilalarva and is involved in gustatory and olfactory perception. We have previously shown that a mutant allele of the homeodomain transcription factor Prospero (prosVoila1,V1), presents several developmental defects including abnormal growth and altered taste responses. In addition, many neural tracts connecting the AMC to the central nervous system (CNS) were affected. Our earlier reports on larval AMC did not argue in favour of a role ofprosin cell fate decision, but strongly suggested thatproscould be involved in the control of other aspect of neuronal development. In order to identify these functions, we used microarray analysis of larval AMC and CNS tissue isolated from the wild type, and three other previously characterisedprosperoalleles, including theV1mutant, considered as a null allele for the AMC.ResultsA total of 17 samples were first analysed with hierarchical clustering. To determine those genes affected by loss ofprosfunction, we calculated a discriminating score reflecting the differential expression betweenV1mutant and otherprosalleles. We identified a total of 64 genes in the AMC. Additional manual annotation using all the computed information on the attributed role of these genes in theDrosophilalarvae nervous system, enabled us to identify one functional category of potential Prospero target genes known to be involved in neurite outgrowth, synaptic transmission and more specifically in neuronal connectivity remodelling. The second category of genes found to be differentially expressed between the null mutant AMC and the other alleles concerned the development of the sensory organs and more particularly the larval olfactory system. Surprisingly, a third category emerged from our analyses and suggests an association ofproswith the genes that regulate autophagy, growth and insulin pathways. Interestingly, EGFR and Notch pathways were represented in all of these three functional categories. We now propose that Pros could perform all of these different functions through the modulation of these two antagonistic and synergic pathways.ConclusionsThe current data contribute to the clarification of theprosperofunction in the larval AMC and show thatprosregulates different function in larvae as compared to those controlled by this gene in embryos. In the future, the possible mechanism by which Pros could achieve its function in the AMC will be explored in detail.
Expression profiling and cross-species RNA interference (RNAi) of desiccation-induced transcripts in the anhydrobiotic nematode Aphelenchus avenae
Authors: Reardon Wesley , Chakrabortee Sohini , Pereira Tiago Campos , Tyson Trevor , Banton Matthew C , Dolan Katharine M , Culleton Bridget A , Wise Michael J , Burnell Ann M , Tunnacliffe Alan 
Published: January 2010

AbstractBackgroundSome organisms can survive extreme desiccation by entering a state of suspended animation known as anhydrobiosis. The free-living mycophagous nematodeAphelenchus avenaecan be induced to enter anhydrobiosis by pre-exposure to moderate reductions in relative humidity (RH) prior to extreme desiccation. This preconditioning phase is thought to allow modification of the transcriptome by activation of genes required for desiccation tolerance.ResultsTo identify such genes, a panel of expressed sequence tags (ESTs) enriched for sequences upregulated inA. avenaeduring preconditioning was created. A subset of 30 genes with significant matches in databases, together with a number of apparently novel sequences, were chosen for further study. Several of the recognisable genes are associated with water stress, encoding, for example, two new hydrophilic proteins related to the late embryogenesis abundant (LEA) protein family. Expression studies confirmed EST panel members to be upregulated by evaporative water loss, and the majority of genes was also induced by osmotic stress and cold, but rather fewer by heat. We attempted to use RNA interference (RNAi) to demonstrate the importance of this gene set for anhydrobiosis, but foundA. avenaeto be recalcitrant with the techniques used. Instead, therefore, we developed a cross-species RNAi procedure usingA. avenaesequences in another anhydrobiotic nematode,Panagrolaimus superbus, which is amenable to gene silencing. Of 20A. avenaeESTs screened, a significant reduction in survival of desiccation in treatedP. superbuspopulations was observed with two sequences, one of which was novel, while the other encoded a glutathione peroxidase. To confirm a role for glutathione peroxidases in anhydrobiosis, RNAi with cognate sequences fromP. superbuswas performed and was also shown to reduce desiccation tolerance in this species.ConclusionsThis study has identified and characterised the expression profiles of members of the anhydrobiotic gene set inA. avenae. It also demonstrates the potential of RNAi for the analysis of anhydrobiosis and provides the first genetic data to underline the importance of effective antioxidant systems in metazoan desiccation tolerance.
Association between novel TARDBP mutations and Chinese patients with amyotrophic lateral sclerosis
Authors: Xiong Hui-Ling , Wang Jin-Yang , Sun Yi-Min , Wu Jian-Jun , Chen Yan , Qiao Kai , Zheng Qiao-Juan , Zhao Gui-xian , Wu Zhi-Ying 
Published: January 2010

Abstract

Background

TARDBP mutations have been reported in patients with amyotrophic lateral sclerosis (ALS) in different populations except Chinese. The present aim is to investigate the association between TARDBP mutations and Chinese patients with ALS.

Methods
71 SALS patients and 5 FALS families with non-SOD1 mutations were screened for TARDBP mutations via direct sequencing.

Results
A novel heterozygous variation, Ser292Asn (875G>A), was identified in the proband and 4 asymptomatic relatives including the children of the dead patient from a FALS family. Thus the dead patient, the proband's brother, was speculated to carry Ser292Asn though his sample was unavailable to the detection. This variation was not found in 200 unrelated control subjects. A homology search of the TDP-43 protein in different species demonstrated that it was highly conserved. Also, it was predicted to be deleterious to protein function with SIFT-calculated probabilities of 0.00. Therefore, Ser292Asn is predicted to be a pathogenic mutation. In addition, we have found two silent mutations (Gly40Gly and Ala366Ala) and one novel polymorphism (239-18t>c).

Conclusions
The present data have extended the spectrum of TARDBP mutations and polymorphisms, and supported the pathological role of TDP-43 in Chinese ALS patients.
Impact of gene variants on sex-specific regulation of human Scavenger receptor class B type 1 (SR-BI) expression in liver and association with lipid levels in a population-based study
Authors: Chiba-Falek Ornit , Nichols Marshall , Suchindran Sunil , Guyton John , Ginsburg Geoffrey S , Barrett-Connor Elizabeth , McCarthy Jeanette J 
Published: January 2010

Abstract

Background
Several studies have noted that genetic variants of SCARB1, a lipoprotein receptor involved in reverse cholesterol transport, are associated with serum lipid levels in a sex-dependent fashion. However, the mechanism underlying this gene by sex interaction has not been explored.

Methods
We utilized both epidemiological and molecular methods to study how estrogen and gene variants interact to influence SCARB1 expression and lipid levels. Interaction between 35 SCARB1 haplotype-tagged polymorphisms and endogenous estradiol levels was assessed in 498 postmenopausal Caucasian women from the population-based Rancho Bernardo Study. We further examined associated variants with overall and SCARB1 splice variant (SR-BI and SR-BII) expression in 91 human liver tissues using quantitative real-time PCR.

Results
Several variants on a haplotype block spanning intron 11 to intron 12 of SCARB1 showed significant gene by estradiol interaction affecting serum lipid levels, the strongest for rs838895 with HDL-cholesterol (p = 9.2 × 10-4) and triglycerides (p = 1.3 × 10-3) and the triglyceride:HDL cholesterol ratio (p = 2.7 × 10-4). These same variants were associated with expression of the SR-BI isoform in a sex-specific fashion, with the strongest association found among liver tissue from 52 young women <45 years old (p = 0.002).

Conclusions
Estrogen and SCARB1 genotype may act synergistically to regulate expression of SCARB1 isoforms and impact serum levels of HDL cholesterol and triglycerides. This work highlights the importance of considering sex-dependent effects of gene variants on serum lipid levels.