bluuebunny/crossref_metadata_2025_split huggingface dataset:
OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif
Authors: Drawid Amar , Gupta Nupur , Nagaraj Vijayalakshmi H , Gélinas Céline , Sengupta Anirvan M 
Published: July 2009

Abstract

Background
DNA sequence binding motifs for several important transcription factors happen to be self-overlapping. Many of the current regulatory site identification methods do not explicitly take into account the overlapping sites. Moreover, most methods use arbitrary thresholds and fail to provide a biophysical interpretation of statistical quantities. In addition, commonly used approaches do not include the location of a site with respect to the transcription start site (TSS) in an integrated probabilistic framework while identifying sites. Ignoring these features can lead to inaccurate predictions as well as incorrect design and interpretation of experimental results.

Results
We have developed a tool based on a Hidden Markov Model (HMM) that identifies binding location of transcription factors with preference for self-overlapping DNA motifs by combining the effects of their alternative binding modes. Interpreting HMM parameters as biophysical quantities, this method uses the occupancy probability of a transcription factor on a DNA sequence as the discriminant function, earning the algorithm the name OHMM: O ccupancy via H idden M arkov M odel. OHMM learns the classification threshold by training emission probabilities using unaligned sequences containing known sites and estimating transition probabilities to reflect site density in all promoters in a genome. While identifying sites, it adjusts parameters to model site density changing with the distance from the transcription start site. Moreover, it provides guidance for designing padding sequences in gel shift experiments. In the context of binding sites to transcription factor NF-κB, we find that the occupancy probability predicted by OHMM correlates well with the binding affinity in gel shift experiments. High evolutionary conservation scores and enrichment in experimentally verified regulated genes suggest that NF-κB binding sites predicted by our method are likely to be functional.

Conclusion
Our method deals specifically with identifying locations with multiple overlapping binding sites by computing the local occupancy of the transcription factor. Moreover, considering OHMM as a biophysical model allows us to learn the classification threshold in a principled manner. Another feature of OHMM is that we allow transition probabilities to change with location relative to the TSS. OHMM could be used to predict physical occupancy, and provides guidance for proper design of gel-shift experiments. Based upon our predictions, new insights into NF-κB function and regulation and possible new biological roles of NF-κB were uncovered.
Self-renewal and differentiation capabilities are variable between human embryonic stem cell lines I3, I6 and BG01V
Authors: Tavakoli Tahereh , Xu Xiangru , Derby Eric , Serebryakova Yevgeniya , Reid Yvonne , Rao Mahendra S , Mattson Mark P , Ma Wu 
Published: July 2009

AbstractBackgroundA unique and essential property of embryonic stem cells is the ability to self-renew and differentiate into multiple cell lineages. However, the possible differences in proliferation and differentiation capabilities among independently-derived human embryonic stem cells (hESCs) are not well known because of insufficient characterization. To address this question, a side-by-side comparison of 1) the ability to maintain an undifferentiated state and to self-renew under standard conditions; 2) the ability to spontaneously differentiate into three primary embryonic germ lineages in differentiating embryoid bodies; and 3) the responses to directed neural differentiation was made between three NIH registered hES cell lines I3 (TE03), I6 (TE06) and BG01V. Lines I3 and I6 possess normal XX and a normal XY karyotype while BG01V is a variant cell line with an abnormal karyotype derived from the karyotypically normal cell line BG01.ResultsUsing immunocytochemistry, flow cytometry, qRT-PCR and MPSS, we found that all three cell lines actively proliferated and expressed similar "stemness" markers including transcription factors POU5F1/Oct3/4 and NANOG, glycolipids SSEA4 and TRA-1-81, and alkaline phosphatase activity. All cell lines differentiated into three embryonic germ lineages in embryoid bodies and into neural cell lineages when cultured in neural differentiation medium. However, a profound variation in colony morphology, growth rate, BrdU incorporation, and relative abundance of gene expression in undifferentiated and differentiated states of the cell lines was observed. Undifferentiated I3 cells grew significantly slower but their differentiation potential was greater than I6 and BG01V. Under the same neural differentiation-promoting conditions, the ability of each cell line to differentiate into neural progenitors varied.ConclusionOur comparative analysis provides further evidence for similarities and differences between three hESC lines in self-renewal, and spontaneous and directed differentiation. These differences may be associated with inherited variation in the sex, stage, quality and genetic background of embryos used for hESC line derivation, and/or changes acquired during passaging in culture.
Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facet of oncolytic therapy
Authors: Worschech Andrea , Chen Nanhai , Yu Yong A , Zhang Qian , Pos Zoltan , Weibel Stephanie , Raab Viktoria , Sabatino Marianna , Monaco Alessandro , Liu Hui , Monsurró Vladia , Buller R Mark , Stroncek David F , Wang Ena , Szalay Aladar A , Marincola Francesco M 
Published: July 2009

Abstract

Background
GLV-1h68 is an attenuated recombinant vaccinia virus (VACV) that selectively colonizes established human xenografts inducing their complete regression.

Results
Here, we explored xenograft/VACV/host interactions in vivo adopting organism-specific expression arrays and tumor cell/VACV in vitro comparing VACV replication patterns. There were no clear-cut differences in vitro among responding and non-responding tumors, however, tumor rejection was associated in vivo with activation of interferon-stimulated genes (ISGs) and innate immune host's effector functions (IEFs) correlating with VACV colonization of the xenografts. These signatures precisely reproduce those observed in humans during immune-mediated tissue-specific destruction (TSD) that causes tumor or allograft rejection, autoimmunity or clearance of pathogens. We recently defined these common pathways in the "immunologic constant of rejection" hypothesis (ICR).

Conclusion
This study provides the first prospective validation of a universal mechanism associated with TSD. Thus, xenograft infection by oncolytic VACV, beyond offering a promising therapy of established cancers, may represent a reliable pre-clinical model to test therapeutic strategies aimed at modulating the central pathways leading to TSD; this information may lead to the identification of principles that could refine the treatment of cancer and chronic infection by immune stimulation or autoimmunity and allograft rejection through immune tolerance.
High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics
Authors: Yang Mary Qu , Athey Brian D , Arabnia Hamid R , Sung Andrew H , Liu Qingzhong , Yang Jack Y , Mao Jinghe , Deng Youping 
Published: July 2009

Abstract
The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) – 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14–17 and received great success. Supported by International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design (IJCBDD), International Journal of Functional Informatics and Personalized Medicine (IJFIPM) and the leading research laboratories from Harvard, M.I.T., Purdue, UIUC, UCLA, Georgia Tech, UT Austin, U. of Minnesota, U. of Iowa etc, the conference received thousands of research papers. Each submitted paper was reviewed by at least three reviewers and accepted papers were required to satisfy reviewers' comments. Finally, the review board and the committee decided to select only 19 high-quality research papers for inclusion in this supplement to BMC Genomics based on the peer reviews only. The conference committee was very grateful for the Plenary Keynote Lectures given by: Dr. Brian D. Athey (University of Michigan Medical School), Dr. Vladimir N. Uversky (Indiana University School of Medicine), Dr. David A. Patterson (Member of United States National Academy of Sciences and National Academy of Engineering, University of California at Berkeley) and Anousheh Ansari (Prodea Systems, Space Ambassador). The theme of the conference to promote synergistic research and education has been achieved successfully.
Differences in duplication age distributions between human GPCRs and their downstream genes from a network prospective
Authors: Huang Yong , Zheng Ying , Su Zhixi , Gu Xun 
Published: July 2009

Abstract

Background
How gene duplication has influenced the evolution of gene networks is one of the core problems in evolution. Current duplication-divergence theories generally suggested that genes on the periphery of the networks were preferentially retained after gene duplication. However, previous studies were mostly based on gene networks in invertebrate species, and they had the inherent shortcoming of not being able to provide information on how the duplication-divergence process proceeded along the time axis during major speciation events.

Results
In this study, we constructed a model system consisting of human G protein-coupled receptors (GPCRs) and their downstream genes in the GPCR pathways. These two groups of genes offered a natural partition of genes in the peripheral and the backbone layers of the network. Analysis of the age distributions of the duplication events in human GPCRs and "downstream genes" gene families indicated that they both experienced an explosive expansion at the time of early vertebrate emergence. However, we found only GPCR families saw a continued expansion after early vertebrates, mostly prominently in several small subfamilies of GPCRs involved in immune responses and sensory responses.

Conclusion
In general, in the human GPCR model system, we found that the position of a gene in the gene networks has significant influences on the likelihood of fixation of its duplicates. However, for a super gene family, the influence was not uniform among subfamilies. For super families, such as GPCRs, whose gene basis of expression diversity was well established at early vertebrates, continued expansions were mostly prominent in particular small subfamilies mainly involved in lineage-specific functions.
Reverse engineering module networks by PSO-RNN hybrid modeling
Authors: Zhang Yuji , Xuan Jianhua , de los Reyes Benildo G , Clarke Robert , Ressom Habtom W 
Published: July 2009

Abstract

Background
Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.

Results
This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information.

Conclusion
The reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.
A distribution-free convolution model for background correction of oligonucleotide microarray data
Authors: Chen Zhongxue , McGee Monnie , Liu Qingzhong , Kong Megan , Deng Youping , Scheuermann Richard H 
Published: July 2009

Abstract

Introduction
Affymetrix GeneChip® high-density oligonucleotide arrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high-level classification and cluster analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. Many proposed preprocessing methods are parametric, in that they assume that the background noise generated by microarray data is a random sample from a statistical distribution, typically a normal distribution. The quality of the final results depends on the validity of such assumptions.

Results
We propose a Distribution Free Convolution Model (DFCM) to circumvent observed deficiencies in meeting and validating distribution assumptions of parametric methods. Knowledge of array structure and the biological function of the probes indicate that the intensities of mismatched (MM) probes that correspond to the smallest perfect match (PM) intensities can be used to estimate the background noise. Specifically, we obtain the smallest q2 percent of the MM intensities that are associated with the lowest q1 percent PM intensities, and use these intensities to estimate background.

Conclusion
Using the Affymetrix Latin Square spike-in experiments, we show that the background noise generated by microarray experiments typically is not well modeled by a single overall normal distribution. We further show that the signal is not exponentially distributed, as is also commonly assumed. Therefore, DFCM has better sensitivity and specificity, as measured by ROC curves and area under the curve (AUC) than MAS 5.0, RMA, RMA with no background correction (RMA-noBG), GCRMA, PLIER, and dChip (MBEI) for preprocessing of Affymetrix microarray data. These results hold for two spike-in data sets and one real data set that were analyzed. Comparisons with other methods on two spike-in data sets and one real data set show that our nonparametric methods are a superior alternative for background correction of Affymetrix data.
Genome-wide prediction of cis-acting RNA elements regulating tissue-specific pre-mRNA alternative splicing
Authors: Wang Xin , Wang Kejun , Radovich Milan , Wang Yue , Wang Guohua , Feng Weixing , Sanford Jeremy R , Liu Yunlong 
Published: July 2009

Abstract

Background
Human genes undergo various patterns of pre-mRNA splicing across different tissues. Such variation is primarily regulated by trans-acting factors that bind on exonic and intronic cis-acting RNA elements (CAEs). Here we report a computational method to mechanistically identify cis-acting RNA elements that contribute to the tissue-specific alternative splicing pattern. This method is an extension of our previous model, SplicingModeler, which predicts the significant CAEs that contribute to the splicing differences between two tissues. In this study, we introduce tissue-specific functional levels estimation step, which allows evaluating regulatory functions of predicted CAEs that are involved in more than two tissues.

Results
Using a publicly available Affymetrix Genechip® Human Exon Array dataset, our method identifies 652 cis-acting RNA elements (CAEs) across 11 human tissues. About one third of predicted CAEs can be mapped to the known RBP (RNA binding protein) binding sites or match with other predicted exonic splicing regulator databases. Interestingly, the vast majority of predicted CAEs are in intronic regulatory regions. A noticeable exception is that many exonic elements are found to regulate the alternative splicing between cerebellum and testes. Most identified elements are found to contribute to the alternative splicing between two tissues, while some are important in multiple tissues. This suggests that genome-wide alternative splicing patterns are regulated by a combination of tissue-specific cis-acting elements and "general elements" whose functional activities are important but differ across multiple tissues.

Conclusion
In this study, we present a model-based computational approach to identify potential cis-acting RNA elements by considering the exon splicing variation as the combinatorial effects of multiple cis-acting regulators. This methodology provides a novel evaluation on the functional levels of cis-acting RNA elements by estimating their tissue-specific functions on various tissues.
Annotating the human genome with Disease Ontology
Authors: Osborne John D , Flatow Jared , Holko Michelle , Lin Simon M , Kibbe Warren A , Zhu Lihua , Danila Maria I , Feng Gang , Chisholm Rex L 
Published: July 2009

Abstract

Background
The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.

Results
We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.

Conclusion
The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.
Identification, characterization and expression of novel Sex Hormone Binding Globulin alternative first exons in the human prostate
Authors: Pinós Tomàs , Barbosa-Desongles Anna , Hurtado Antoni , Santamaria-Martínez Albert , de Torres Inés , Morote Joan , Reventós Jaume , Munell Francina 
Published: July 2009

AbstractBackgroundThe human Sex Hormone Binding Globulin (SHBG) gene, located at 17p13.1, comprises, at least, two different transcription units regulated by two different promoters. The first transcription unit begins with the exon 1 sequence and is responsible for the production of plasma SHBG by the hepatocytes, while the second begins with an alternative exon 1 sequence, which replaces the exon 1 present in liver transcripts. Alternative exon 1 transcription and translation has only been demonstrated in the testis of transgenic mice containing an 11-kb human SHBG transgene and in the human testis. Our goal has been to further characterize the 5' end of the SHBG gene and analyze the presence of the SHBG alternative transcripts in human prostate tissue and derived cell lines.ResultsUsing a combination ofin silicoandin vitrostudies, we have demonstrated that the SHBG gene, along with exon 1 and alternative exon 1 (renamed here exon 1A), contains four additional alternative first exons: the novel exons 1B, 1C, and 1E, and a previously identified exon 1N, which has been further characterized and renamed as exon 1D. We have shown that these four alternative first exons are all spliced to the same 3' splice site of SHBG exon 2, and that exon 1A and the novel exon 1B can be spliced to exon 1. We have also demonstrated the presence of SHBG transcripts beginning with exons 1B, 1C and 1D in prostate tissues and cell lines, as well as in several non-prostatic cell lines. Finally, the alignment of the SHBG mammalian sequences revealed that, while exons 1C, 1D and 1E are very well conserved phylogenetically through non-primate mammal species, exon 1B probably aroused in apes due to a single nucleotide change that generated a new 5' splice site in exon 1B.ConclusionThe identification of multiple transcription start sites (TSS) upstream of the annotated first exon of human SHBG, and the detection of the alternative transcripts in human prostate, concur with the prediction of the ENCODE (ENCyclopedia of DNA Elements) project, and suggest that the regulation of SHBG is much more complex than previously reported.