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Full-Text Articles in Life Sciences
Using Phylogenetically-Informed Annotation (Pia) To Search For Light-Interacting Genes In Transcriptomes From Non-Model Organisms, Daniel Isaac Speiser, M Sabrina Pankey, Alexandera K. Zaharoff, Barbara A. Battelle, Heather D. Bracken-Grissom, Jesse W. Breinholt, Seth M. Bybee, Thomas W. Cronin, Anders Garm, Annie R. Lindgren, Nipam H. Patel, Megan L. Porter, Meredith E. Protas, Ajna S. Rivera, Jeanne M. Serb, Kirk S. Zigler, Keith A. Crandall, Todd H. Oakley
Using Phylogenetically-Informed Annotation (Pia) To Search For Light-Interacting Genes In Transcriptomes From Non-Model Organisms, Daniel Isaac Speiser, M Sabrina Pankey, Alexandera K. Zaharoff, Barbara A. Battelle, Heather D. Bracken-Grissom, Jesse W. Breinholt, Seth M. Bybee, Thomas W. Cronin, Anders Garm, Annie R. Lindgren, Nipam H. Patel, Megan L. Porter, Meredith E. Protas, Ajna S. Rivera, Jeanne M. Serb, Kirk S. Zigler, Keith A. Crandall, Todd H. Oakley
Faculty Publications
Background
Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis …
Seqnls: Nuclear Localization Signal Prediction Based On Frequent Pattern Mining And Linear Motif Scoring, J.-R. Lin, Jianjun Hu
Seqnls: Nuclear Localization Signal Prediction Based On Frequent Pattern Mining And Linear Motif Scoring, J.-R. Lin, Jianjun Hu
Faculty Publications
Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking.
The experiment results on …
Hemebind: A Novel Method For Heme Binding Residue Prediction By Combining Structural And Sequence Information, R. Liu, Jianjun Hu
Hemebind: A Novel Method For Heme Binding Residue Prediction By Combining Structural And Sequence Information, R. Liu, Jianjun Hu
Faculty Publications
Background
Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues.
Results
Here …
Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu
Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu
Faculty Publications
Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based …
Bayesmotif: De Novo Protein Sorting Motif Discovery From Impure Datasets, Jianjun Hu, F. Zhang
Bayesmotif: De Novo Protein Sorting Motif Discovery From Impure Datasets, Jianjun Hu, F. Zhang
Faculty Publications
Background
Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms.
Methods
We formulated the protein sorting motif discovery problem as a classification problem …
Integrative Disease Classification Based On Cross-Platform Microarray Data, C.-C. Liu, Jianjun Hu, M. Kalakrishnan, H. Huang, X. J. Zhou
Integrative Disease Classification Based On Cross-Platform Microarray Data, C.-C. Liu, Jianjun Hu, M. Kalakrishnan, H. Huang, X. J. Zhou
Faculty Publications
Background
Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification.
Results
In this study, we tested the feasibility of disease classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank …
Integrative Missing Value Estimation For Microarray Data, Jianjun Hu, H. Li, M. S. Waterman, X. J. Zhou
Integrative Missing Value Estimation For Microarray Data, Jianjun Hu, H. Li, M. S. Waterman, X. J. Zhou
Faculty Publications
Background
Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples.
Results
We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets …