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104. Establishing bioinformatics research in the Asia Pacific

Author

Tammi Martti, Ranganathan Shoba, Gribskov Michael, and Tan Tin Wee

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract In 1998, the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation was set up to champion the advancement of bioinformatics in the Asia Pacific. By 2002, APBioNet was able to gain sufficient critical mass to initiate the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2006 Conference was organized as the 5th annual conference of the Asia-Pacific Bioinformatics Network, on Dec. 18–20, 2006 in New Delhi, India, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand) and Busan (South Korea). This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. It exemplifies a typical snapshot of the growing research excellence in bioinformatics of the region as we embark on a trajectory of establishing a solid bioinformatics research culture in the Asia Pacific that is able to contribute fully to the global bioinformatics community.

Published
2006
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105. Prediction of desmoglein-3 peptides reveals multiple shared T-cell epitopes in HLA DR4- and DR6- associated Pemphigus vulgaris

Author

Tan Tin, Sinha Animesh A, Tong Joo, and Ranganathan Shoba

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Pemphigus vulgaris (PV) is a severe autoimmune blistering skin disorder that is strongly associated with major histocompatibility complex class II alleles DRB1*0402 and DQB1*0503. The target antigen of PV, desmoglein 3 (Dsg3), is crucial for initiating T-cell response in early disease. Although a number of T-cell specificities within Dsg3 have been reported, the number is limited and the role of T-cells in the pathogenesis of PV remains poorly understood. We report here a structure-based model for the prediction of peptide binding to DRB1*0402 and DQB1*0503. The scoring functions were rigorously trained, tested and validated using experimentally verified peptide sequences. Results High predictivity is obtained for both DRB1*0402 (r2 = 0.90, s = 1.20 kJ/mol, q2 = 0.82, spress = 1.61 kJ/mol) and DQB1*0503 (r2 = 0.95, s = 1.20 kJ/mol, q2 = 0.75, spress = 2.15 kJ/mol) models, compared to experimental data. We investigated the binding patterns of Dsg3 peptides and illustrate the existence of multiple immunodominant epitopes that may be responsible for both disease initiation and propagation in PV. Further analysis reveals that DRB1*0402 and DQB1*0503 may share similar specificities by binding peptides at different binding registers, thus providing a molecular mechanism for the dual HLA association observed in PV. Conclusion Collectively, the results of this study provide interesting new insights into the pathology of PV. This is the first report illustrating high-level of cross-reactivity between both PV-implicated alleles, DRB1*0402 and DQB1*0503, as well as the existence of a potentially large number of T-cell epitopes throughout the entire Dsg3 extracellular domain (ECD) and transmembrane region. Our results reveal that DR4 and DR6 PV may initiate in the ECD and transmembrane region respectively, with implications for immunotherapeutic strategies for the treatment of this autoimmune disease.

Published
2006
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106. SVM-based prediction of caspase substrate cleavage sites

Author

Ranganathan Shoba, Tan Tin, and Wee Lawrence JK

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. Conclusion This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

Published
2006
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108. Towards BioDBcore: a community-defined information specification for biological databases

Author

Gaudet, Pascale, Bairoch, Amos, Field, Dawn, Sansone, Susanna-Assunta, Taylor, Chris, Attwood, Teresa K., Bateman, Alex, Blake, Judith A., Bult, Carol J., Cherry, J. Michael, Chisholm, Rex L., Cochrane, Guy, Cook, Charles E., Eppig, Janan T., Galperin, Michael Y., Gentleman, Robert, Goble, Carole A., Gojobori, Takashi, Hancock, John M., Howe, Douglas G., Imanishi, Tadashi, Kelso, Janet, Landsman, David, Lewis, Suzanna E., Mizrachi, Ilene Karsch, Orchard, Sandra, Ouellette, B. F. Francis, Ranganathan, Shoba, Richardson, Lorna, Rocca-Serra, Philippe, Schofield, Paul N., Smedley, Damian, Southan, Christopher, Tan, Tin Wee, Tatusova, Tatiana, Whetzel, Patricia L., White, Owen, and Yamasaki, Chisato

Published
2011
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115. The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution

Author

Elsik, Christine G., Tellam, Ross L., Worley, Kim C., Gibbs, Richard A., Elsik, Christine G., Tellam, Ross L, Gibbs, Richard A., Muzny, Donna M., Weinstock, George M., Adelson, David L, Eichler, Evan E., Elnitski, Laura, Elsik, Christine G., Guigó, Roderic, Hamernik, Debora L., Kappes, Steve M., Lewin, Harris A, Lynn, David J., Nicholas, Frank W., Reymond, Alexandre, Rijnkels, Monique, Skow, Loren C., Tellam, Ross L, Worley, Kim C., Zdobnov, Evgeny M., Gibbs, Richard A., Kappes, Steve M., Schook, Lawrence, Skow, Loren C., Weinstock, George M., Womack, James, Alioto, Tyler, Antonarakis, Stylianos E., Astashyn, Alex, Chapple, Charles E., Chen, Hsiu-Chuan, Chrast, Jacqueline, Câmara, Francisco, Elsik, Christine G., Ermolaeva, Olga, Guigó, Roderic, Henrichsen, Charlotte N., Hlavina, Wratko, Kapustin, Yuri, Kiryutin, Boris, Kitts, Paul, Kokocinski, Felix, Landrum, Melissa, Maglott, Donna, Pruitt, Kim, Reymond, Alexandre, Sapojnikov, Victor, Searle, Stephen M., Solovyev, Victor, Souvorov, Alexandre, Ucla, Catherine, Weinstock, George M., Wyss, Carine, Alioto, Tyler, Antonarakis, Stylianos E., Chapple, Charles E., Chrast, Jacqueline, Câmara, Francisco, Guigó, Roderic, Henrichsen, Charlotte N., Reymond, Alexandre, Ucla, Catherine, Wyss, Carine, Anzola, Juan M., Gerlach, Daniel, Zdobnov, Evgeny M., Elhaik, Eran, Elsik, Christine G., Graur, Dan, Reese, Justin T., Adelson, David L., Edgar, Robert C., McEwan, John C., Payne, Gemma M., Raison, Joy M., Junier, Thomas, Kriventseva, Evgenia V., Zdobnov, Evgeny M., Chrast, Jacqueline, Eyras, Eduardo, Henrichsen, Charlotte N., Plass, Mireya, Reymond, Alexandre, Donthu, Ravikiran, Larkin, Denis M., Lewin, Harris A., Nicholas, Frank W., Elnitski, Laura, Larkin, Denis M., Lewin, Harris A., Reecy, James, Yang, Mary Q., Adelson, David L, Chen, Lin, Cheng, Ze, Chitko-McKown, Carol G., Eichler, Evan E., Elnitski, Laura, Elsik, Christine G., Liu, George E., Matukumalli, Lakshmi K., Song, Jiuzhou, Zhu, Bin, Elsik, Christine G., Lynn, David J., Reese, Justin T., Bradley, Daniel G., Brinkman, Fiona S.L, Lau, Lilian P.L., Lynn, David J., Whiteside, Matthew D., Tellam, Ross L, Walker, Angela, Wheeler, Thomas T., Casey, Theresa, German, Bruce J., Lemay, Danielle G., Lynn, David J., Maqbool, Nauman J., Molenaar, Adrian J., Rijnkels, Monique, Lewin, Harris A., Seo, Seongwon, Stothard, Paul, Baldwin, Cynthia L., Baxter, Rebecca, Brinkmeyer-Langford, Candice L., Brown, Wendy C., Childers, Christopher P., Connelley, Timothy, Ellis, Shirley A., Fritz, Krista, Glass, Elizabeth J., Herzig, Carolyn T.A., livanainen, Antti, Lahmers, Kevin K., Skow, Loren C., Bennett, Anna K., Childers, Christopher P., Dickens, Michael C., Elsik, Christine G., Gilbert, James G.R., Hagen, Darren E., Reese, Justin T., Salih, Hanni, Aerts, Jan, Caetano, Alexandre R., Dalrymple, Brian, Elsik, Christine G., Fernando Garcia, Jose, Gibbs, Richard A., Gill, Clare A., Hamernik, Debora L, Hiendleder, Stefan G., Memili, Erdogan, Nicholas, Frank W., Reecy, James, Rijnkels, Monique, Skow, Loren C., Spurlock, Diane, Stothard, Paul, Tellam, Ross L, Weinstock, George M., Williams, John L, Worley, Kim C., Alexander, Lee, Brownstein, Michael J., Guan, Leluo, Holt, Robert A., Jones, Steven J.M., Marra, Marco A., Moore, Richard, Moore, Stephen S., Roberts, Andy, Taniguchi, Masaaki, Waterman, Richard C., Chacko, Joseph, Chandrabose, Mimi M., Cree, Andy, Diep Dao, Marvin, Dinh, Huyen H., Ayiesha Gabisi, Ramatu, Hines, Sandra, Hume, Jennifer, Jhangiani, Shalini N., Joshi, Vandita, Kovar, Christie L., Lewis, Lora R., Liu, Yih-shin, Lopez, John, Morgan, Margaret B., Muzny, Donna M., Bich Nguyen, Ngoc, Okwuonu, Geoffrey O., Ruiz, San Juana, Santibanez, Jireh, Wright, Rita A., Buhay, Christian, Ding, Yan, Dugan-Rocha, Shannon, Herdandez, Judith, Holder, Michael, Sabo, Aniko, Egan, Amy, Goodell, Jason, Wilczek-Boney, Katarzyna, Fowler, Gerald R., Edward Hitchens, Matthew, Lozado, Ryan J., Moen, Charles, Steffen, David, Warren, James T., Zhang, Jingkun, Chiu, Readman, Jones, Steven J.M., Marra, Marco A., Schein, Jacqueline E., James Durbin, K., Havlak, Paul, Jiang, Huaiyang, Liu, Yue, Qin, Xiang, Ren, Yanru, Shen, Yufeng, Song, Henry, Weinstock, George M., Worley, Kim C., Nicole Bell, Stephanie, Davis, Clay, Jolivet Johnson, Angela, Lee, Sandra, Nazareth, Lynne V., Patel, Bella Mayurkumar, Pu, Ling-Ling, Vattathil, Selina, Williams, Rex Lee, Jr., Curry, Stacey, Hamilton, Cerissa, Sodergren, Erica, Nazareth, Lynne V., Wheeler, David A., Adelson, David L, Aerts, Jan, Barris, Wes, Bennett, Gary L, Dalrymple, Brian, Eggen, André, Gill, Clare A., Green, Ronnie D., Harhay, Gregory P., Hobbs, Matthew, Jann, Oliver, Kappes, Steve M., Keele, John W., Kent, Matthew P., Larkin, Denis M., Lewin, Harris A., Lien, Sigbjørn, McEwan, John C., McKay, Stephanie D., McWilliam, Sean, Moore, Stephen S., Nicholas, Frank W., Payne, Gemma M., Ratnakumar, Abhirami, Salih, Hanni, Schnabel, Robert D., Smith, Timothy, Snelling, Warren M., Sonstegard, Tad S., Stone, Roger T., Sugimoto, Yoshikazu, Takasuga, Akiko, Taylor, Jeremy F., Tellam, Ross L., Van Tassell, Curtis P., Williams, John L, MacNeil, Michael D., Abatepaulo, Antonio R.R., Abbey, Colette A., Aerts, Jan, Ahola, Virpi, Almeida, Iassudara G., Amadio, Ariel F., Anatriello, Elen, Bahadue, Suria M., Baldwin, Cynthia L., Baxter, Rebecca, Bennett, Anna K., Biase, Fernando H., Boldt, Clayton R., Brinkmeyer-Langford, Candice L, Brown, Wendy C., Caetano, Alexandre R., Carroll, Jeffery A., Carvalho, Wanessa A, Casey, Theresa, Cervelatti, Eliane P., Chacko, Elsa, Chapin, Jennifer E., Cheng, Ye, Childers, Christopher P., Choi, Jungwoo, Colley, Adam J., Connelley, Timothy, de Campos, Tatiana A., De Donato, Marcos, de Miranda Santos, Isabel K.F., de Oliveira, Carlo J.F., Deobald, Heather, Devinoy, Eve, Dickens, Michael C., Donohue, Kaitlin E., Dovc, Peter, Eberlein, Annett, Ellis, Shirley A., Fitzsimmons, Carolyn J., Franzin, Alessandra M., Fritz, Krista, Garcia, Gustavo R., Fernando Garcia, Jose, Genini, Sem, German, Bruce J., Gilbert, James G.R., Gill, Clare A., Gladney, Cody J., Glass, Elizabeth J., Grant, Jason R., Greaser, Marion L., Green, Jonathan A., Hadsell, Darryl L., Hagen, Darren E., Hakimov, Hatam A., Halgren, Rob, Harrow, Jennifer L, Hart, Elizabeth A., Hastings, Nicola, Hernandez, Marta, Herzig, Carolyn T.A., Hiendleder, Stefan G., Hobbs, Matthew, Hu, Zhi-Liang, livanainen, Antti, Ingham, Aaron, Iso-Touru, Terhi, Jamis, Catherine, Jann, Oliver, Jensen, Kirsty, Kapetis, Dimos, Kerr, Tovah, Khalil, Sari S., Khatib, Hasan, Kolbehdari, Davood, Kumar, Charu G., Kumar, Dinesh, Leach, Richard, Lee, Justin C-M, Lemay, Danielle G., Li, Changxi, Liu, George E., Logan, Krystin M., Malinverni, Roberto, Maqbool, Nauman J., Marques, Elisa, Martin, William F., Martins, Natalia F., Maruyama, Sandra R., Mazza, Raffaele, McLean, Kim L, Medrano, Juan F., Memili, Erdogan, Molenaar, Adrian J., Moreno, Barbara T., Moré, Daniela D., Muntean, Carl T., Nandakumar, Hari P., Nogueira, Marcelo F.G., Olsaker, Ingrid, Pant, Sameer D., Panzitta, Francesca, Pastor, Rosemeire C.P., Poli, Mario A., Poslusny, Nathan, Rachagani, Satyanarayana, Ranganathan, Shoba, Razpet, Andrej, Reecy, James, Riggs, Penny K., Rijnkels, Monique, Rincon, Gonzalo, Rodriguez-Osorio, Nelida, Rodriguez-Zas, Sandra L., Romero, Natasha E., Rosenwald, Anne, Sando, Lillian, Schmutz, Sheila M., Seo, Seongwon, Shen, Libing, Sherman, Laura, Skow, Loren C., Southey, Bruce R., Spurlock, Diane, Strandberg Lutzow, Ylva, Sweedler, Jonathan V., Tammen, Imke, Taniguchi, Masaaki, Tellam, Ross L, Telugu, Bhanu Prakash V.L, Urbanski, Jennifer M., Utsunomiya, Yuri T., Verschoor, Chris P., Waardenberg, Ashley J., Walker, Angela, Wang, Zhiquan, Ward, Robert, Weikard, Rosemarie, Welsh, Thomas H., Jr., Wheeler, Thomas T., White, Stephen N., Williams, John L, Wilming, Laurens G., Wunderlich, Kris R., Yang, Jianqi, and Zhao, Feng-Qi

Published
2009

118. MOESM1 of Prediction of novel mouse TLR9 agonists using a random forest approach

Author

Varun Khanna, Li, Lei, Fung, Johnson, Ranganathan, Shoba, and Petrovsky, Nikolai

Subjects
hemic and immune systems, respiratory system
Abstract

Additional file 1. Sequence motifs in mTLR9 active ODNs having an absolute difference in the occurrences above 10% in high and low activity groups of ODNs, arranged in a clockwise manner. The width of the ribbon shows the average percentage composition of the motifs each group.

Published
2019
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119. Additional File 1

Author

Hardianto, Ari, Varun Khanna, Liu, Fei, and Ranganathan, Shoba

Abstract

This file contains Notes 1 and 2, Figures S1-S8. and Tables S1-S9. and relevant references. (PDF 3690 kb)

Published
2019
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129. Encyclopedia of Bioinformatics and Computational Biology : ABC of Bioinformatics

Author

Ranganathan, Shoba, Nakai, Kenta, Schönbach, Christian, Ranganathan, Shoba, Nakai, Kenta, and Schönbach, Christian

Subjects
Bioinformatics--Encyclopedias, Computational biology--Encyclopedias
Abstract

Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. - Brings together information from computer science, information technology, mathematics, statistics and biotechnology - Written and reviewed by leading experts in the field, providing a unique and authoritative resource - Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications - Includes interactive images, multimedia tools and crosslinking to further resources and databases

Published
2019

135. Additional file 2: of Computational analysis of the receptor binding specificity of novel influenza A/H7N9 viruses

Author

Xinrui Zhou, Zheng, Jie, Fransiskus Ivan, Yin, Rui, Ranganathan, Shoba, Chow, Vincent, and Chee-Keong Kwoh

Abstract

Superimpose the best and worst docked HA-ligand complexes. Visualize the structure of docked SH13-LSTa, SH13-LSTc, TW17-LSTa and TW17-LSTc complexes with the highest and the lowest binding affinities. (PDF 466 kb)

Published
2018
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137. Additional file 2: of A bioinformatics potpourri

Author

SchĂśnbach, Christian, Jinyan Li, Ma, Lan, Horton, Paul, Sjaugi, Muhammad, and Ranganathan, Shoba

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, GeneralLiterature_MISCELLANEOUS, ComputingMilieux_MISCELLANEOUS
Abstract

InCoB2017 Best Paper Awards. (PDF 73Â kb)

Published
2018
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142. Mass spectrometry–based protein identification in proteomics—a review.

Author

Noor, Zainab, Ahn, Seong Beom, Baker, Mark S, Ranganathan, Shoba, and Mohamedali, Abidali

Subjects
PROTEOMICS, MASS spectrometry, SOCIAL interaction, PROTEIN-protein interactions, BIOCHEMISTRY
Abstract

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification. [ABSTRACT FROM AUTHOR]

Published
2021
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143. A transcriptomic analysis of the adult stage of the bovine lungworm, Dictyocaulus viviparus

Author

Strube Christina, Hu Min, Nagaraj Shivashankar H, Ranganathan Shoba, Schnieder Thomas, and Gasser Robin B

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Lungworms of the genus Dictyocaulus (family Dictyocaulidae) are parasitic nematodes of major economic importance. They cause pathological effects and clinical disease in various ruminant hosts, particularly in young animals. Dictyocaulus viviparus, called the bovine lungworm, is a major pathogen of cattle, with severe infections being fatal. In this study, we provide first insights into the transcriptome of the adult stage of D. viviparus through the analysis of expressed sequence tags (ESTs). Results Using our EST analysis pipeline, we estimate that the present dataset of 4436 ESTs is derived from 2258 genes based on cluster and comparative genomic analyses of the ESTs. Of the 2258 representative ESTs, 1159 (51.3%) had homologues in the free-living nematode C. elegans, 1174 (51.9%) in parasitic nematodes, 827 (36.6%) in organisms other than nematodes, and 863 (38%) had no significant match to any sequence in the current databases. Of the C. elegans homologues, 569 had observed 'non-wildtype' RNAi phenotypes, including embryonic lethality, maternal sterility, sterility in progeny, larval arrest and slow growth. We could functionally classify 776 (35%) sequences using the Gene Ontologies (GO) and established pathway associations to 696 (31%) sequences in Kyoto Encyclopedia of Genes and Genomes (KEGG). In addition, we predicted 85 secreted proteins which could represent potential candidates for developing novel anthelmintics or vaccines. Conclusion The bioinformatic analyses of ESTs data for D. viviparus has elucidated sets of relatively conserved and potentially novel genes. The genes discovered in this study should assist research toward a better understanding of the basic molecular biology of D. viviparus, which could lead, in the longer term, to novel intervention strategies. The characterization of the D. viviparus transcriptome also provides a foundation for whole genome sequence analysis and future comparative transcriptomic analyses.

Published
2007
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144. SPdb – a signal peptide database

Author

Tan Tin, Choo Khar, and Ranganathan Shoba

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The signal peptide plays an important role in protein targeting and protein translocation in both prokaryotic and eukaryotic cells. This transient, short peptide sequence functions like a postal address on an envelope by targeting proteins for secretion or for transfer to specific organelles for further processing. Understanding how signal peptides function is crucial in predicting where proteins are translocated. To support this understanding, we present SPdb signal peptide database http://proline.bic.nus.edu.sg/spdb, a repository of experimentally determined and computationally predicted signal peptides. Results SPdb integrates information from two sources (a) Swiss-Prot protein sequence database which is now part of UniProt and (b) EMBL nucleotide sequence database. The database update is semi-automated with human checking and verification of the data to ensure the correctness of the data stored. The latest release SPdb release 3.2 contains 18,146 entries of which 2,584 entries are experimentally verified signal sequences; the remaining 15,562 entries are either signal sequences that fail to meet our filtering criteria or entries that contain unverified signal sequences. Conclusion SPdb is a manually curated database constructed to support the understanding and analysis of signal peptides. SPdb tracks the major updates of the two underlying primary databases thereby ensuring that its information remains up-to-date.

Published
2005
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145. DEDB: a database of Drosophila melanogaster exons in splicing graph form

Author

Tan Tin, Lee Bernett TK, and Ranganathan Shoba

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background A wealth of quality genomic and mRNA/EST sequences in recent years has provided the data required for large-scale genome-wide analysis of alternative splicing. We have capitalized on this by constructing a database that contains alternative splicing information organized as splicing graphs, where all transcripts arising from a single gene are collected, organized and classified. The splicing graph then serves as the basis for the classification of the various types of alternative splicing events. Description DEDB http://proline.bic.nus.edu.sg/dedb/index.html is a database of Drosophila melanogaster exons obtained from FlyBase arranged in a splicing graph form that permits the creation of simple rules allowing for the classification of alternative splicing events. Pfam domains were also mapped onto the protein sequences allowing users to access the impact of alternative splicing events on domain organization. Conclusions DEDB's catalogue of splicing graphs facilitates genome-wide classification of alternative splicing events for genome analysis. The splicing graph viewer brings together genome, transcript, protein and domain information to facilitate biologists in understanding the implications of alternative splicing.

Published
2004
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146. Additional file 3: Figure S3. of Exploration of charge states of balanol analogues acting as ATP-competitive inhibitors in kinases

Author

Hardianto, Ari, Yusuf, Muhammad, Liu, Fei, and Ranganathan, Shoba

Abstract

A. Trajectory of ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination I to PKCε and B. the respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 258 kb)

Published
2017
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147. Additional file 2: Figure S2. of Exploration of charge states of balanol analogues acting as ATP-competitive inhibitors in kinases

Author

Hardianto, Ari, Yusuf, Muhammad, Liu, Fei, and Ranganathan, Shoba

Abstract

A. Trajectory of ∆ G MMGB SA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination II to PKA and B. The respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 255 kb)

Published
2017
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148. Additional file 1: Figure S1. of Exploration of charge states of balanol analogues acting as ATP-competitive inhibitors in kinases

Author

Hardianto, Ari, Yusuf, Muhammad, Liu, Fei, and Ranganathan, Shoba

Abstract

A. Trajectory of ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination I to PKA and B. The respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 298 kb)

Published
2017
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149. Infectious disease informatics

Author

Tong, Joo Chuan and Ranganathan, Shoba

Subjects
infectious disease modeling, sequence analysis, molecular evolution, information entropy, infectious disease informatics, Article
Abstract

Throughout history, infectious diseases have posed a serious burden to mankind. More recently, there has been an alarming increase in drug-resistant microbes. Furthermore, new pathogens are emerging due to microbial evolution and adaptation. The spread of these diseases is a result of pathogen mutations and changes in human behavior patterns. Then, there are diseases that are lurking in the background, waiting for the right conditions before they strike again. In the war against these diseases, we have come to understand the behaviors of microbes in a heterogeneous world and the mechanisms governing disease transmission. These works have profoundly shaped modern knowledge of emerging and re-emerging infections. More recently, computational techniques have led the way into this new era by allowing rapid high-throughput analysis of pathogens which was previously not possible using traditional laboratory techniques. This chapter introduces methods in mathematical modeling, computational biology, and bioinformatics that have been used to study infectious diseases.

Published
2014

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