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Full-Text Articles in Life Sciences

A Course-Based Research Experience: How Benefits Change With Increased Investment In Instructional Time, Christopher D. Shaffer, Consuelo J. Alvarez, April E. Bednarski, David Dunbar, Anya L. Goodman, Catherine Reinke, Anne G. Rosenwald, Michael J. Wolyniak, Cheryl Bailey, Daron Barnard, Christopher Bazinet, Dale L. Beach, James E.J. Bedard, Satish Bhalla, John Braverman, Martin Burg, Vidya Chandrasekaran, Hui-Min Chung, Kari Clase, Randall J. Dejong, Justin R. Diangelo, Chunguang Du, Todd T. Eckdahl, Heather Eisler, Julia A. Emerson, Amy Frary, Donald Frohlich, Yuying Gosser, Shubha Govind, Adam Haberman, Amy T. Hark, Charles Hauser, Arlene Hoogewerf, Laura L.M. Hoopes, Carina E. Howell, Diana Johnson, Christopher J. Jones, Lisa Kadlec, Marian Kaehler, S. Catherine Silver Key, Adam Kleinschmit, Nighat P. Kokan, Olga Kopp, Gary Kuleck, Judith Leatherman, Jane Lopilato, Christy Mackinnon, Juan Carlos Martinez-Cruzado, Gerard Mcneil, Stephanie Mel, Hemlata Mistry, Alexis Nagengast, Paul Overvoorde, Don W. Paetkau, Susan Parrish, Celeste N. Peterson, Mary Preuss, Laura K. Reed, Dennis Revie, Srebrenka Robic, Jennifer Roecklein-Canfield, Michael R. Rubin, Kenneth Saville, Stephanie Schroeder, Karim Sharif, Mary Shaw, Gary Skuse, Christopher D. Smith, Mary A. Smith, Sheryl T. Smith, Eric Spana, Mary Spratt, Aparna Sreenivasan, Joyce Stamm, Paul Szauter, Jeffrey S. Thompson, Matthew Wawersik, James Youngblom, Leming Zhou, Elaine R. Mardis, Jeremy Buhler, Wilson Leung, David Lopatto, Sarah C.R. Elgin Jan 2014

A Course-Based Research Experience: How Benefits Change With Increased Investment In Instructional Time, Christopher D. Shaffer, Consuelo J. Alvarez, April E. Bednarski, David Dunbar, Anya L. Goodman, Catherine Reinke, Anne G. Rosenwald, Michael J. Wolyniak, Cheryl Bailey, Daron Barnard, Christopher Bazinet, Dale L. Beach, James E.J. Bedard, Satish Bhalla, John Braverman, Martin Burg, Vidya Chandrasekaran, Hui-Min Chung, Kari Clase, Randall J. Dejong, Justin R. Diangelo, Chunguang Du, Todd T. Eckdahl, Heather Eisler, Julia A. Emerson, Amy Frary, Donald Frohlich, Yuying Gosser, Shubha Govind, Adam Haberman, Amy T. Hark, Charles Hauser, Arlene Hoogewerf, Laura L.M. Hoopes, Carina E. Howell, Diana Johnson, Christopher J. Jones, Lisa Kadlec, Marian Kaehler, S. Catherine Silver Key, Adam Kleinschmit, Nighat P. Kokan, Olga Kopp, Gary Kuleck, Judith Leatherman, Jane Lopilato, Christy Mackinnon, Juan Carlos Martinez-Cruzado, Gerard Mcneil, Stephanie Mel, Hemlata Mistry, Alexis Nagengast, Paul Overvoorde, Don W. Paetkau, Susan Parrish, Celeste N. Peterson, Mary Preuss, Laura K. Reed, Dennis Revie, Srebrenka Robic, Jennifer Roecklein-Canfield, Michael R. Rubin, Kenneth Saville, Stephanie Schroeder, Karim Sharif, Mary Shaw, Gary Skuse, Christopher D. Smith, Mary A. Smith, Sheryl T. Smith, Eric Spana, Mary Spratt, Aparna Sreenivasan, Joyce Stamm, Paul Szauter, Jeffrey S. Thompson, Matthew Wawersik, James Youngblom, Leming Zhou, Elaine R. Mardis, Jeremy Buhler, Wilson Leung, David Lopatto, Sarah C.R. Elgin

Faculty Publications

There is widespread agreement that science, technology, engineering, and mathematics programs should provide undergraduates with research experience. Practical issues and limited resources, however, make this a challenge. We have developed a bioinformatics project that provides a course-based research experience for students at a diverse group of schools and offers the opportunity to tailor this experience to local curriculum and institution-specific student needs. We assessed both attitude and knowledge gains, looking for insights into how students respond given this wide range of curricular and institutional variables. While different approaches all appear to result in learning gains, we find that a significant ...


Seqnls: Nuclear Localization Signal Prediction Based On Frequent Pattern Mining And Linear Motif Scoring, J.-R. Lin, Jianjun Hu Jan 2013

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 ...


Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu Jan 2011

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 ...


Hemebind: A Novel Method For Heme Binding Residue Prediction By Combining Structural And Sequence Information, R. Liu, Jianjun Hu Jan 2011

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 ...


Incorporating Genomics And Bioinformatics Across The Life Sciences Curriculum, Jayna L. Ditty, Christopher A. Kvaal, Brad Goodner, Sharyn K. Freyermuth, Cheryl Bailey, Robert A. Britton, Stuart G. Gordon, Sabine Heinhorst, Kelyenne Reed, Zhaohui Xu, Erin R. Sanders-Lorenz, Seth Axen, Edwin Kim, Mitrick Johns, Kathleen Scott, Cheryl A. Kerfeld Aug 2010

Incorporating Genomics And Bioinformatics Across The Life Sciences Curriculum, Jayna L. Ditty, Christopher A. Kvaal, Brad Goodner, Sharyn K. Freyermuth, Cheryl Bailey, Robert A. Britton, Stuart G. Gordon, Sabine Heinhorst, Kelyenne Reed, Zhaohui Xu, Erin R. Sanders-Lorenz, Seth Axen, Edwin Kim, Mitrick Johns, Kathleen Scott, Cheryl A. Kerfeld

Faculty Publications

No abstract provided.


Bayesmotif: De Novo Protein Sorting Motif Discovery From Impure Datasets, Jianjun Hu, F. Zhang Jan 2010

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 Jan 2009

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 Jan 2006

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 ...