Volume 10 Supplement 11
Proceedings of the 2009 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference
© Wren et al; licensee BioMed Central Ltd. 2009
Published: 8 October 2009
MCBIOS 2009 was held February 20–21, 2009 at the Hunter Henry Center in Starkville, Mississippi on the Mississippi State University campus. The conference was hosted by the four research universities in Mississippi comprising the Mississippi Computational Biology Consortium – Jackson State University (JSU), Mississippi State University (MSU), the University of Mississippi (UM), and the University of Southern Mississippi (USM). Dr. Dawn Wilkins of UM and Dr. Susan Bridges of MSU served as conference co-chairs. Keynote speakers were Dr. Laura Elnitski, Head of the Genomic Functional Analysis Section at the National Human Genome Research Institute (NHGRI), Howard Cash, President and CEO of Gene Codes Corporation, and Dr. Cathy Wu, Director of the Protein Information Resource (PIR).
Dr. Raphael Isokpehi of JSU organized a panel discussion entitled Careers in Computational Biology and Bioinformatics: Perspective from Employers and Students. Panelists were Dr. Ed Perkins of the U.S. Army Engineering Research and Development Center, Robert Cottingham of Oak Ridge National Laboratory (ORNL), Dr. Doris M. Kupfer, Federal Aviation Administration, James C. Fuscoe, FDA National Center for Toxicological Research, and Cynthia Jeffries, student intern at ORNL. Over 40 student and faculty attendees also participated in the Speed Networking event organized by Dr. Isokpehi and featured in a recent Science Careers article . Each person in the event spent 3 minutes talking to twenty other participants in the two hour session providing students an opportunity to interact with employers and with students from other universities in the region.
Dr. Bindu Nanduri and Dr. Andy Perkins, both of MSU managed the setup, judging, and scoring of 80 posters. Monetary awards were provided by Dr. Ed Perkins. Student poster award winners were: first place – Eric Morales of the University of New Orleans (UNO), second place Teresia Buza of MSU, third place – Prashanti Manda of MSU and honorable mention to Amanda Alba of the UNO, Surya Saha of MSU and Sudhir Chowbina of the Indiana University School of Informatics. Student winners for oral presentations were: first place – Enis Afgan of the University of Alabama at Birmingham, second place – Lipi R. Archarya of UNO, and third place – Anastasia Chueva of Mississippi Valley State University.
Thirty-two papers from the 2009 conference were submitted to be considered for inclusion in this supplement, and of them a total of 20 were accepted (62.5% accept rate), making this year's Proceedings the most stringent to date. The number of submitted papers exceeded that of the 2008 MCBIOS Proceedings [2–20]. As in prior conferences, we strove to be inclusive yet rigorous in the peer-review process, so that the end result is both high quality and reflective of the work presented. Papers generally fell into five categories:
A means of better understanding how systems work and interconnect as opposed to the traditional approach of isolating subcomponents and focusing on them is called Systems Biology and has become an increasingly active area of study in bioinformatics [21–34]. Andrey Ptitsyn offers a systems biology approach to the analysis of microarray data , partitioning gene expression space into a multi-dimensional taxonomy. Differences between "taxa" are then studied using biological pathway analysis software to associate gene expression patterns with specific phenotypes. For example, the application identifies a link between glycine metabolism aberrations and metabolic biomarkers for metastatic prostate cancer.
Gao et al.  describe an approach to better integrate data from disparate sources to reach better conclusions about biological response to stimulus than could be obtained from any of the component sources alone. Their study combined the Gene Ontology (GO) with microarray data using a bigraph data structure and associated algorithms.
Chowbina et al. address data integration from a different perspective, describing a technical resource for regulatory, signaling and biochemical reactions in human biomolecular pathways. Their resource, Human Pathway Database (HPD), integrates several existing resources to provide a web interface to access approximately one thousand pathways.
Zhang et al. present text empirics in the context of the PathBinder system . Text empirics refers to properties of texts that are derived by examining the texts themselves. It differs from the machine learning approach in that derived properties are manually curated and presented as facts that system builders can use when they are designing their systems.
"OMICS" is a broad category, based on the fact that fields of study for biological entities often end with the suffix "omics" (e.g., transcriptomics, proteomics) [39–45]. Pendarvis et al.  describe the creation of a workflow system to analyze mass spectrometry replicate datasets to generate a list of identified proteins and expression changes and link them to biological knowledge. Because it is compliant with proteomic data submission guidelines, users can readily publish data to public proteomic data repositories.
Mete et al.  present a novel digital image analysis approach to the automated evaluation of angiogenesis in Liver Cancer. The method identifies regional, morphological and fractal features and helps measure micro-vessel density on digitized images of liver tumor sections. Their results show agreement between automated calculations and manual counting of micro-vessels.
Microarrays have long been a subject of bioinformatics analysis not only for better understanding transcription, but also as a model for large-scale analysis of genetic behavior [48–56]. Van Den Berg et al. compared gene annotation enrichment tools for functional modeling of agricultural microarray data , a category that is underrepresented among current tools. Chavan et al. describe Network Analysis Toolbox in R (NATbox), which provides a menu-driven GUI for modeling and analysis of functional relationships inferred from gene expression profiles. It is suited for interdisciplinary researchers and biologists with minimal programming experience who would like to use systems biology approaches without delving into the algorithmic aspects, and can be a useful demonstration/teaching tool.
Perkins and Langston  identify a means of effectively selecting thresholds for gene expression on microarrays by identifying "cliques" or subgroups of co-expressed genes. Roughly, the idea being that individual measurements are prone to error, but when a gene is seen co-expressed with other genes, then this reinforces the validity of the expression being biologically significant.
Li et al.  investigate inter- and intra-platform consistency of microarray measurements. Using pathway data as a guide for identifying biologically consistent results, the authors demonstrate that highly consistent biological information can be generated from different microarray platforms.
T.J. Jankun-Kelly and colleagues present a method to visually explore conserved domains on Multiple Sequence Alignments , simultaneously communicating the relative similarity of proteins across species and the differences in how function is expressed via conserved domains. It quickly identifies conserved domains and allows both macro (sequence-long) and micro (small amino-acid neighborhood) views.
Xu et al.  report the results of an extensive comparative genomics analysis of plant lignin biosynthesis gene families. Lignin is a major component of plant cell walls and appears to be the major cause of cell wall recalcitrance in energy production. Their analysis confirms that, among the species analyzed, the first complete lignin biosynthesis pathway appeared in moss. The major expansion of biosynthesis families occurred after the divergence of monocots and dicots and duplicated genes had many different fates. They conclude that transgenic lignin modification strategies to bioenergy feedstock may only be successful between closely related species.
T. Buza et al. report a method of automated functional annotation of the chicken genome, improving both the breadth and quality of annotation of genes . The method can facilitate functional annotation of other microarrays via an Array GO Mapper (AGOM) tool to help researchers quickly retrieve corresponding functional information.
Zhou et al.  develop CPTRA (Cross-Platform Transcriptome Analysis) to analyze Next Generation Sequencing based on Transcriptome profiling in species with limited genome information.
Garcia-Reyero et al.  study gene expression in fathead minnows (Pimephales promelas) in well-characterized sites adjacent to sewage treatment plants and find short-term exposure to effluents were able to induce a site-specific gene expression pattern in the fathead minnow gonad and liver and to affect fish sexual behavior.
MCBIOS 2010 will be held in Jonesboro, Arkansas, and hosted by Arkansas State University and the Arkansas Biosciences Institute. Daniel Berleant of University of Arkansas at Little Rock is the 2009–2010 President of MCBIOS and will be chair of MCBIOS 2010. Ulisses Braga-Neto of Texas A&M University is the new President-elect of the society. See http://www.MCBIOS.org for information regarding MCBIOS and future meetings. MCBIOS is a regional affiliate of the International Society for Computational Biology http://www.ISCB.org.
We would like to thank the Mississippi NSF EPSCoR for providing support for keynote speakers and the hard work of the peer-reviewers to help produce this supplement.
This article has been published as part of BMC Bioinformatics Volume 10 Supplement 11, 2009: Proceedings of the Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/10?issue=S11.
- Holmes L: Perspective: Speed Networking for Scientists. Science Careers 2009.Google Scholar
- Bottoms CA, Xu D: Wanted: unique names for unique atom positions. PDB-wide analysis of diastereotopic atom names of small molecules containing diphosphate. BMC Bioinformatics 2008, 9(Suppl 9):S16. 10.1186/1471-2105-9-S9-S16PubMed CentralView ArticlePubMedGoogle Scholar
- Chae M, Shmookler Reis RJ, Thaden JJ: An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks. BMC Bioinformatics 2008, 9(Suppl 9):S15. 10.1186/1471-2105-9-S9-S15PubMed CentralView ArticlePubMedGoogle Scholar
- Churbanov A, Winters-Hilt S: Clustering ionic flow blockade toggles with a mixture of HMMs. BMC Bioinformatics 2008, 9(Suppl 9):S13. 10.1186/1471-2105-9-S9-S13PubMed CentralView ArticlePubMedGoogle Scholar
- Dozmorov MG, Kyker KD, Hauser PJ, Saban R, Buethe DD, Dozmorov I, Centola MB, Culkin DJ, Hurst RE: From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells. BMC Bioinformatics 2008, 9(Suppl 9):S4. 10.1186/1471-2105-9-S9-S4PubMed CentralView ArticlePubMedGoogle Scholar
- Frank RL, Kandoth C, Ercal F: Validation of an NSP-based (negative selection pattern) gene family identification strategy. BMC Bioinformatics 2008, 9(Suppl 9):S2. 10.1186/1471-2105-9-S9-S2PubMed CentralView ArticlePubMedGoogle Scholar
- Giles CB, Wren JD: Large-scale directional relationship extraction and resolution. BMC Bioinformatics 2008, 9(Suppl 9):S11. 10.1186/1471-2105-9-S9-S11PubMed CentralView ArticlePubMedGoogle Scholar
- Hong H, Su Z, Ge W, Shi L, Perkins R, Fang H, Xu J, Chen JJ, Han T, Kaput J, et al.: Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples. BMC Bioinformatics 2008, 9(Suppl 9):S17. 10.1186/1471-2105-9-S9-S17PubMed CentralView ArticlePubMedGoogle Scholar
- Huan T, Sivachenko AY, Harrison SH, Chen JY: ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining. BMC Bioinformatics 2008, 9(Suppl 9):S5. 10.1186/1471-2105-9-S9-S5PubMed CentralView ArticlePubMedGoogle Scholar
- Kulkarni V, Errami M, Barber R, Garner HR: Exhaustive prediction of disease susceptibility to coding base changes in the human genome. BMC Bioinformatics 2008, 9(Suppl 9):S3. 10.1186/1471-2105-9-S9-S3PubMed CentralView ArticlePubMedGoogle Scholar
- Lee T, Desai VG, Velasco C, Reis RJ, Delongchamp RR: Testing for treatment effects on gene ontology. BMC Bioinformatics 2008, 9(Suppl 9):S20. 10.1186/1471-2105-9-S9-S20PubMed CentralView ArticlePubMedGoogle Scholar
- Mete M, Tang F, Xu X, Yuruk N: A structural approach for finding functional modules from large biological networks. BMC Bioinformatics 2008, 9(Suppl 9):S19. 10.1186/1471-2105-9-S9-S19PubMed CentralView ArticlePubMedGoogle Scholar
- Ptitsyn A: Comprehensive analysis of circadian periodic pattern in plant transcriptome. BMC Bioinformatics 2008, 9(Suppl 9):S18. 10.1186/1471-2105-9-S9-S18PubMed CentralView ArticlePubMedGoogle Scholar
- Ptitsyn AA, Weil MM, Thamm DH: Systems biology approach to identification of biomarkers for metastatic progression in cancer. BMC Bioinformatics 2008, 9(Suppl 9):S8. 10.1186/1471-2105-9-S9-S8PubMed CentralView ArticlePubMedGoogle Scholar
- Quest D, Dempsey K, Shafiullah M, Bastola D, Ali H: MTAP: the motif tool assessment platform. BMC Bioinformatics 2008, 9(Suppl 9):S6. 10.1186/1471-2105-9-S9-S6PubMed CentralView ArticlePubMedGoogle Scholar
- Rawat A, Seifert GJ, Deng Y: Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data. BMC Bioinformatics 2008, 9(Suppl 9):S7. 10.1186/1471-2105-9-S9-S7PubMed CentralView ArticlePubMedGoogle Scholar
- Roux B, Winters-Hilt S: Hybrid MM/SVM structural sensors for stochastic sequential data. BMC Bioinformatics 2008, 9(Suppl 9):S12. 10.1186/1471-2105-9-S9-S12PubMed CentralView ArticlePubMedGoogle Scholar
- Shi L, Jones WD, Jensen RV, Harris SC, Perkins RG, Goodsaid FM, Guo L, Croner LJ, Boysen C, Fang H, et al.: The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics 2008, 9(Suppl 9):S10. 10.1186/1471-2105-9-S9-S10PubMed CentralView ArticlePubMedGoogle Scholar
- Su Z, Hong H, Fang H, Shi L, Perkins R, Tong W: Very Important Pool (VIP) genes – an application for microarray-based molecular signatures. BMC Bioinformatics 2008, 9(Suppl 9):S9. 10.1186/1471-2105-9-S9-S9PubMed CentralView ArticlePubMedGoogle Scholar
- Zielinski JS, Bouaynaya N, Schonfeld D, O'Neill W: Time-dependent ARMA modeling of genomic sequences. BMC Bioinformatics 2008, 9(Suppl 9):S14. 10.1186/1471-2105-9-S9-S14PubMed CentralView ArticlePubMedGoogle Scholar
- Centler F, Kaleta C, di Fenizio PS, Dittrich P: Computing chemical organizations in biological networks. Bioinformatics 2008, 24: 1611–1618. 10.1093/bioinformatics/btn228View ArticlePubMedGoogle Scholar
- Li Y, Agarwal P, Rajagopalan D: A global pathway crosstalk network. Bioinformatics 2008, 24: 1442–1447. 10.1093/bioinformatics/btn200View ArticlePubMedGoogle Scholar
- Mazurie A, Bonchev D, Schwikowski B, Buck GA: Phylogenetic distances are encoded in networks of interacting pathways. Bioinformatics 2008, 24: 2579–2585. 10.1093/bioinformatics/btn503PubMed CentralView ArticlePubMedGoogle Scholar
- Vyshemirsky V, Girolami M: BioBayes: a software package for Bayesian inference in systems biology. Bioinformatics 2008, 24: 1933–1934. 10.1093/bioinformatics/btn338View ArticlePubMedGoogle Scholar
- Weidemann A, Richter S, Stein M, Sahle S, Gauges R, Gabdoulline R, Surovtsova I, Semmelrock N, Besson B, Rojas I, et al.: SYCAMORE – a systems biology computational analysis and modeling research environment. Bioinformatics 2008, 24: 1463–1464. 10.1093/bioinformatics/btn207View ArticlePubMedGoogle Scholar
- Xiong H, Choe Y: Structural systems identification of genetic regulatory networks. Bioinformatics 2008, 24: 553–560. 10.1093/bioinformatics/btm623View ArticlePubMedGoogle Scholar
- Suderman M, Hallett M: Tools for visually exploring biological networks. Bioinformatics 2007, 23: 2651–2659. 10.1093/bioinformatics/btm401View ArticlePubMedGoogle Scholar
- Saeys Y, Inza I, Larranaga P: A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23: 2507–2517. 10.1093/bioinformatics/btm344View ArticlePubMedGoogle Scholar
- Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 2007, 23: 2692–2699. 10.1093/bioinformatics/btm403View ArticlePubMedGoogle Scholar
- Avila-Campillo I, Drew K, Lin J, Reiss DJ, Bonneau R: BioNetBuilder: automatic integration of biological networks. Bioinformatics 2007, 23: 392–393. 10.1093/bioinformatics/btl604View ArticlePubMedGoogle Scholar
- Schmidt H: SBaddon: high performance simulation for the Systems Biology Toolbox for MATLAB. Bioinformatics 2007, 23: 646–647. 10.1093/bioinformatics/btl668View ArticlePubMedGoogle Scholar
- Schmidt H, Drews G, Vera J, Wolkenhauer O: SBML export interface for the systems biology toolbox for MATLAB. Bioinformatics 2007, 23: 1297–1298. 10.1093/bioinformatics/btm105View ArticlePubMedGoogle Scholar
- Shah AR, Singhal M, Klicker KR, Stephan EG, Wiley HS, Waters KM: Enabling high-throughput data management for systems biology: the Bioinformatics Resource Manager. Bioinformatics 2007, 23: 906–909. 10.1093/bioinformatics/btm031View ArticlePubMedGoogle Scholar
- Li P, Oinn T, Soiland S, Kell DB: Automated manipulation of systems biology models using libSBML within Taverna workflows. Bioinformatics 2008, 24: 287–289. 10.1093/bioinformatics/btm578View ArticlePubMedGoogle Scholar
- Ptitsyn A: Computational analysis of gene expression space associated with metastatic cancer. BMC Bioinformatics 2009, 10(Suppl 11):S6. 10.1186/1471-2105-10-S11-S6PubMed CentralView ArticlePubMedGoogle Scholar
- Gao C, Dang X, Chen Y, Wilkins D: Graph ranking for exploratory gene data analysis. BMC Bioinformatics 2009, 10(Suppl 11):S20. 10.1186/1471-2105-10-S11-S19View ArticleGoogle Scholar
- Chowbina SR, Wu X, Zhang F, Li PM, Pandy R, Kasamsetty HN, Chen JY: HPD: An Online Integrated Human Pathway Database Enabling Systems Biology Studies. BMC Bioinformatics 2009, 10(Suppl 11):S5. 10.1186/1471-2105-10-S11-S5PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang L, Berleant D, Ding J, Cao T, Wurtele ES: PathBinder – text empirics and automatic extraction of biomolecular interactions. BMC Bioinformatics 2009, 10(Suppl 11):S18. 10.1186/1471-2105-10-S1-S18View ArticleGoogle Scholar
- Toyoda T, Mochizuki Y, Player K, Heida N, Kobayashi N, Sakaki Y: OmicBrowse: a browser of multidimensional omics annotations. Bioinformatics 2007, 23: 524–526. 10.1093/bioinformatics/btl523View ArticlePubMedGoogle Scholar
- Polpitiya AD, Qian WJ, Jaitly N, Petyuk VA, Adkins JN, Camp DG 2nd, Anderson GA, Smith RD: DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24: 1556–1558. 10.1093/bioinformatics/btn217PubMed CentralView ArticlePubMedGoogle Scholar
- Xia T, Dickerson JA: OmicsViz: Cytoscape plug-in for visualizing omics data across species. Bioinformatics 2008, 24: 2557–2558. 10.1093/bioinformatics/btn473PubMed CentralView ArticlePubMedGoogle Scholar
- Gong L, Owen RP, Gor W, Altman RB, Klein TE: PharmGKB: an integrated resource of pharmacogenomic data and knowledge. Curr Protoc Bioinformatics 2008, Chapter 14(Unit 14):17.Google Scholar
- Kao HL, Gunsalus KC: Browsing multidimensional molecular networks with the generic network browser (N-Browse). Curr Protoc Bioinformatics 2008, Chapter 9(Unit 9):11.PubMedGoogle Scholar
- Martens L, Jones P, Cote R: Using the Proteomics Identifications Database (PRIDE). Curr Protoc Bioinformatics 2008, Chapter 13(Unit 13):18.Google Scholar
- Yeung N, Cline MS, Kuchinsky A, Smoot ME, Bader GD: Exploring biological networks with Cytoscape software. Curr Protoc Bioinformatics 2008, Chapter 8(Unit 8):13.PubMedGoogle Scholar
- Pendarvis K, Kumar R, Burgess SC, Nanduri B: An automated proteomic data analysis workflow for mass spectrometry. BMC Bioinformatics 2009, 10(Suppl 9):S17. 10.1186/1471-2105-10-S11-S17PubMed CentralView ArticlePubMedGoogle Scholar
- Mete M, Hennings L, Spencer HJ, Topaloglu U: Automatic Identification of Angiogenesis in Double Stained Images of Liver Tissue. BMC Bioinformatics 2009, 10(Suppl 11):S13. 10.1186/1471-2105-10-S11-S13PubMed CentralView ArticlePubMedGoogle Scholar
- Burkart MF, Wren JD, Herschkowitz JI, Perou CM, Garner HR: Clustering microarray-derived gene lists through implicit literature relationships. Bioinformatics 2007, 23: 1995–2003. 10.1093/bioinformatics/btm261View ArticlePubMedGoogle Scholar
- Hong F, Breitling R: A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 2008, 24: 374–382. 10.1093/bioinformatics/btm620View ArticlePubMedGoogle Scholar
- Li H, Zhan M: Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data. Bioinformatics 2008, 24: 1874–1880. 10.1093/bioinformatics/btn332PubMed CentralView ArticlePubMedGoogle Scholar
- Yang D, Li Y, Xiao H, Liu Q, Zhang M, Zhu J, Ma W, Yao C, Wang J, Wang D, et al.: Gaining confidence in biological interpretation of the microarray data: the functional consistence of the significant GO categories. Bioinformatics 2008, 24: 265–271. 10.1093/bioinformatics/btm558View ArticlePubMedGoogle Scholar
- Hermans F, Tsiporkova E: Merging microarray cell synchronization experiments through curve alignment. Bioinformatics 2007, 23: e64–70. 10.1093/bioinformatics/btl320View ArticlePubMedGoogle Scholar
- Martin S, Zhang Z, Martino A, Faulon JL: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 2007, 23: 866–874. 10.1093/bioinformatics/btm021View ArticlePubMedGoogle Scholar
- Novikov E, Barillot E: Software package for automatic microarray image analysis (MAIA). Bioinformatics 2007, 23: 639–640. 10.1093/bioinformatics/btl644View ArticlePubMedGoogle Scholar
- Royce TE, Rozowsky JS, Gerstein MB: Assessing the need for sequence-based normalization in tiling microarray experiments. Bioinformatics 2007, 23: 988–997. 10.1093/bioinformatics/btm052View ArticlePubMedGoogle Scholar
- Schumacher M, Binder H, Gerds T: Assessment of survival prediction models based on microarray data. Bioinformatics 2007, 23: 1768–1774. 10.1093/bioinformatics/btm232View ArticlePubMedGoogle Scholar
- Berg BHJ, Thanthiriwatte C, Manda P, Bridges SM: Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data. BMC Bioinformatics 2009, 10(Suppl 11):S9. 10.1186/1471-2105-10-S11-S9PubMed CentralView ArticlePubMedGoogle Scholar
- Chavan SS, Bauer MA, Scutari M, Nagarajan R: NATbox: A Network Analysis Toolbox in R. BMC Bioinformatics 2009, 10(Suppl 11):S14. 10.1186/1471-2105-10-S11-S14PubMed CentralView ArticlePubMedGoogle Scholar
- Perkins AD, Langston MA: Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC Bioinformatics 2009, 10(Suppl 11):S4. 10.1186/1471-2105-10-S11-S4PubMed CentralView ArticlePubMedGoogle Scholar
- Li Z, Su Z, Wen Z, Shi L, Chen T: Microarray Platform Consistency Is Revealed by Biologically Functional Analysis of Gene Expression Profiles. BMC Bioinformatics 2009, 10(Suppl 11):S12. 10.1186/1471-2105-10-S11-S12PubMed CentralView ArticlePubMedGoogle Scholar
- Jankun-Kelly TJ, Lindeman AD, Bridges SM: Exploratory Visual Analysis of Conserved Domains on Multiple Sequence Alignments. BMC Bioinformatics 2009, 10(Suppl 11):S7. 10.1186/1471-2105-10-S11-S7PubMed CentralView ArticlePubMedGoogle Scholar
- Xu Z, Zhang D, Hu J, Zhou X, Ye X, Reichel KL, Stewart NR, Syrenne RD, Yang X, Gao P, et al.: Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom. BMC Bioinformatics 2009, 10(Suppl 11):S3. 10.1186/1471-2105-10-S11-S3PubMed CentralView ArticlePubMedGoogle Scholar
- Buza TJ, Kumar R, Gresham CR, Burgess SC, McCarthy FM: Facilitating Functional Annotation of Chicken Microarray Data. BMC Bioinformatics 2009, 10(Suppl 11):S2. 10.1186/1471-2105-10-S11-S2PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou X, Su Z, Sammons RD, Peng Y, Tranel PJ, Stewart N Jr, Yuan JS: Novel Software Package for Cross-Platform Transcriptome Analysis (CPTRA). BMC Bioinformatics 2009, 10(Suppl 11):S16. 10.1186/1471-2105-10-S11-S16PubMed CentralView ArticlePubMedGoogle Scholar
- Garcia-Reyero N, Adelman IR, Martinović D, Liu L, Denslow ND: Site-specific impacts on gene expression and behavior in fathead minnows (Pimephales promelas) exposed in situ to streams adjacent to sewage treatment plants. BMC Bioinformatics 2009, 10(Suppl 11):S11. 10.1186/1471-2105-10-S11-S11PubMed CentralView ArticlePubMedGoogle Scholar
- Chen B, Johnson M: Protein Local 3D Structure Prediction by Super Granule Support Vector Machines (Super GSVM). BMC Bioinformatics 2009, 10(Suppl 11):S15. 10.1186/1471-2105-10-S1-S15PubMed CentralView ArticlePubMedGoogle Scholar
- Bright L, Swiderski C, Burgess S, Chowdhary B, McCarthy FM: Structural and Functional-annotation of an Equine Whole Genome Oligoarray. BMC Bioinformatics 2009, 10(Suppl 11):S8. 10.1186/1471-2105-10-S11-S8PubMed CentralView ArticlePubMedGoogle Scholar
- Zollanvari A, Cunningham MJ, Braga-Neto U, Dougherty ER: Analysis and Modeling of Time-Course Gene-Expression Profiles from Nanomaterial-Exposed Primary Human Epidermal Keratinocytes. BMC Bioinformatics 2009, 10(Suppl 11):S10. 10.1186/1471-2105-10-S11-S10PubMed CentralView ArticlePubMedGoogle Scholar
- Malone BM, Perkins AD, Bridges SM: Integrating phenotype and gene expression data for predicting gene function. BMC Bioinformatics 2009, 10(Suppl 11):S21. 10.1186/1471-2105-10-S11-S20View ArticleGoogle Scholar
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