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Identification of novel and candidate miRNAs in rice by high throughput sequencing

BMC PLANT BIOLOGY 2008年第1期8卷 25-25页

作者： Sunkar, Ramanjulu Zhou, Xuefeng Zheng, Yun Zhang, Weixiong Zhu, Jian-Kang Oklahoma State Univ Dept Biochem & Mol Biol Stillwater OK 74078 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Univ Calif Riverside Dept Bot & Plant Sci Riverside CA 92521 USA

Background: Small RNA-guided gene silencing at the transcriptional and post-transcriptional levels has emerged as an important mode of gene regulation in plants and animals. Thus far, conventional sequencing of small RNA libraries from rice led to the identification of most of the conserved miRNAs. Deep sequencing of small RNA libraries is an effective approach to uncover rare and lineage- and/or species-specific microRNAs (miRNAs) in any organism. Results: In order to identify new miRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing. A total of 58,781, 43,003 and 80,990 unique genome-matching small RNAs were obtained from the control, drought and salt stress libraries, respectively. Sequence analysis confirmed the expression of most of the conserved miRNAs in rice. Importantly, 23 new miRNAs mostly each derived from a unique locus in rice genome were identified. Six of the new miRNAs are conserved in other monocots. Additionally, we identified 40 candidate miRNAs. Allowing not more than 3 mis-matches between a miRNA and its target mRNA, we predicted 20 targets for 9 of the new miRNAs. Conclusion: Deep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate miRNAs in rice.

关键词： Rice Genome Rice Seedling support vector machine model miRNA Family Candidate miRNAs

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Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and support vector machine (SVM) algorithm

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MICROCHEMICAL JOURNAL 2024年 207卷

作者： Cioccia, Guilherme Wenceslau, Rodrigo Ribeiro, Matheus Senesi, Giorgio S. Cabral, Jader Nicolodelli, Gustavo Cena, Cicero Marangoni, Bruno UFMS Univ Fed Mato Grosso do Sul Programa Posgrad Ciencia Mat Campo Grande MS Brazil Coordenadoria Geral Pericias Coordenador Reg Unida SEJUSP Secretaria Estado Just & Seguranca Publ Costa Rica MS Brazil CNR Ist Sci & Tecnol Plasmi ISTP Sede Bari Via Amendola 122-D I-70126 Bari Italy Univ Fed Uberlandia Inst Fis POB 593 BR-38400902 Uberlandia MG Brazil UFSC Univ Fed Santa Catarina Florianopolis SC Brazil

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The "gold standard" technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic support vector machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol's significant reduction in the likelihood of false positives.

关键词： Gunshot residues Laser-Induced Breakdown Spectroscopy Principal Component Analysis support vector machine model

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Identification of Parkinson's disease-related pathways and potential risk factors

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JOURNAL OF INTERNATIONAL MEDICAL RESEARCH 2020年第10期48卷 300060520957197-300060520957197页

作者： Shen, Jun Chen, Xiao-Chang Li, Wang-Jun Han, Qiu Chen, Chun Lu, Jing-Min Zheng, Jin-Yu Xue, Shou-Ru Soochow Univ Dept Neurol Affiliated Hosp 1 188 Shizi Rd Suzhou 215006 Jiangsu Peoples R China Xuzhou Med Univ Dept Neurol Affiliated Huaian Hosp Huaian Jiangsu Peoples R China Second Peoples Hosp Huaian Huaian Jiangsu Peoples R China Hongze Huaian Dist Peoples Hosp Dept Neurol Huaian Jiangsu Peoples R China Yangzhou Univ Dept Neurol Changshu Peoples Hosp 2 Clin Med Coll 5 Changshu Jiangsu Peoples R China Nanjing Med Univ Dept Neurol Huaian Peoples Hosp 1 Affiliated Huaian Peoples Hosp 1 Huaian Jiangsu Peoples R China

Objective To identify Parkinson's disease (PD)-associated deregulated pathways and genes, to further elucidate the pathogenesis of PD. Methods Dataset GSE100054 was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) in PD samples were identified. Functional enrichment analyses were conducted for the DEGs. The top 10 hub genes in the protein-protein interaction (PPI) network were screened out and used to construct a support vector machine (SVM) model. The expression of the top 10 genes was then validated in another dataset, GSE46129, and a clinical patient cohort. Results A total of 333 DEGs were identified. The DEGs were clustered into two gene sets that were significantly enriched in 12 pathways, of which 8 were significantly deregulated in PD, including cytokine-cytokine receptor interaction, gap junction, and actin cytoskeleton regulation. The signature of the top 10 hub genes in the PPI network was used to construct the SVM model, which had high performance for predicting PD. Of the 10 genes,GP1BA,GP6,ITGB5, andP2RY12were independent risk factors of PD. Conclusion Genes such asGP1BA,GP6,P2RY12,andITGB5play critical roles in PD pathology through pathways including cytokine-cytokine receptor interaction, gap junctions, and actin cytoskeleton regulation.

关键词： Parkinson's disease deregulated pathway differentially expressed gene clustering analysis protein-protein interactions support vector machine model genetic risk factors

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Entropy-based gene ranking without selection bias for the predictive classification of microarray data

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BMC BIOINFORMATICS 2003年第1期4卷 54-54页

作者： Furlanello, C Serafini, M Merler, S Jurman, G ITC Irst Trento Italy

Background: We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). Results: With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions: Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

关键词： support vector machine support vector machine Classifier support vector machine model Feature Ranking Recursive Feature Elimination

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support vector machines for predicting protein structural class

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BMC BIOINFORMATICS 2001年第3期2卷 3-3页

作者： Cai, Yu-Dong Liu, Xiao-Jun Xu, Xue-biao Zhou, Guo-Ping Chinese Acad Sci Shanghai Res Ctr Biotechnol Shanghai 200233 Peoples R China Univ Edinburgh Inst Cell Anim & Populat Biol Edinburgh EH9 3JT Midlothian Scotland Univ Wales Coll Cardiff Coll Cardiff Dept Comp Sci Cardiff CF2 3XF S Glam Wales Burnham Inst Dept Biol Struct La Jolla CA 92037 USA

Background: We apply a new machine learning method, the so-called support vector machine method, to predict the protein structural class. support vector machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure. Results: High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition. Conclusions: It is expected that the support vector machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.

关键词： support vector machine Amino Acid Composition support vector machine model Neural Network Method support vector machine Method

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Cell-type specificity of ChIP-predicted transcription factor binding sites

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BMC GENOMICS 2012年第1期13卷 372-372页

作者： Handstad, Tony Rye, Morten Mocnik, Rok Drablos, Finn Saetrom, Pal Norwegian Univ Sci & Technol Dept Canc Res & Mol Med NO-7491 Trondheim Norway Norwegian Univ Sci & Technol Dept Comp & Informat Sci NO-7491 Trondheim Norway

Background: Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results: We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions: Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

关键词： Transcription Factor Binding Site support vector machine model Peak Region Chromatin Accessibility Peak Cluster

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An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts

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CHEMISTRY CENTRAL JOURNAL 2010年第s1期4卷 S2-S2页

作者： Ferrari, Thomas Gini, Giuseppina Politecn Milan Dept Elect & Informat DEI I-20133 Milan Italy

Background: Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%;this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models. A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives. Results: A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test. Conclusions: The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result.

关键词： support vector machine support vector machine Classifier machine Learning Algorithm support vector machine model Ames Test

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Predicting adverse side effects of drugs

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BMC GENOMICS 2011年第Sup5期12卷 S11-S11页

作者： Huang, Liang-Chin Wu, Xiaogang Chen, Jake Y. MedeoLinx LLC Indianapolis IN 46280 USA Indiana Univ Sch Informat Indianapolis IN 46202 USA

Background: Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy. Results: We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments. Conclusions: Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.

关键词： support vector machine Gene Ontology Drug Target support vector machine model System Pharmacology

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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features

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BMC CANCER 2019年第1期19卷 263-263页

作者： Yu, Jiaxian Hu, Yueming Xu, Yafei Wang, Jue Kuang, Jiajie Zhang, Wei Shao, Jianlin Guo, Dianjing Wang, Yejun Shenzhen Univ Hlth Sci Ctr Dept Cell Biol & Genet Shenzhen 518060 Peoples R China Chinese Univ Hong Kong State Key Lab Agrobiotechnol Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Sch Life Sci Shatin Hong Kong Peoples R China Sehnzhen GenRead Technol Co Ltd Shenzhen 518000 Peoples R China Zhejiang Hosp 12 Lingyin Rd Hangzhou 310003 Zhejiang Peoples R China

BackgroundLung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis *** obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different *** were found with higher mutation rates in good ( 3-year survival) than in poor (<3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. support vector machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC *** prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.

关键词： Lung adenocarcinomas Somatic mutational Personal medicine support vector machine model machine learning

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Evidence for sequence biases associated with patterns of histone methylation

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BMC GENOMICS 2012年第1期13卷 367-367页

作者： Wang, Zhong Willard, Huntington F. Duke Univ Duke Inst Genome Sci & Policy Genome Biol Grp Durham NC 27708 USA US DOE Joint Genome Inst Walnut Creek CA 94598 USA

Background: Combinations of histone variants and modifications, conceptually representing a histone code, have been proposed to play a significant role in gene regulation and developmental processes in complex organisms. While various mechanisms have been implicated in establishing and maintaining epigenetic patterns at specific locations in the genome, they are generally believed to be independent of primary DNA sequence on a more global scale. Results: To address this systematically in the case of the human genome, we have analyzed primary DNA sequences underlying patterns of 19 different methylated histones in human primary T-cells and patterns of three methylated histones across additional human cell lines. We report strong sequence biases associated with most of these histone marks genome-wide in each cell type. Furthermore, the sequence characteristics for such association are distinct for different groups of histone marks. Conclusions: These findings provide evidence of an influence of genomic sequence on patterns of histone modification associated with gene expression and chromatin programming, and they suggest that the mechanisms responsible for global histone modifications may interpret genomic sequence in various ways.

关键词： support vector machine Transcription Start Site Histone Modification Area Under Curve support vector machine model

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