Systems Support for Remote Visualization of Genomics Applications over Wide Area Networks

Microarray experiments can provide molecular-level insight into a variety of biological processes, from yeast cell cycle to tumorogenesis. However, analysis of both genomic and protein microarray data requires interactive collaborative investigation by biology and bioinformatics researchers. To assist collaborative analysis, remote collaboration tools for integrative analysis and visualization of microarray data are necessary. Such tools should: (i) provide fast response times when used with visualization-intensive genomics applications over a low-bandwidth wide area network, (ii) eliminate transfer of large and often sensitive datasets, (iii) work with any analysis software, and (iv) be platform-independent. Existing visualization systems do not satisfy all requirements. We have developed a remote visualization system called Varg that extends the platform-independent remote desktop system VNC with a novel global compression method. Our evaluations show that the Varg system can support interactive visualization-intensive genomic applications in a remote environment by reducing bandwidth requirements from 30:1 to 289:1.

[1]  Abraham Lempel,et al.  A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.

[2]  Didier Le Gall,et al.  MPEG: a video compression standard for multimedia applications , 1991, CACM.

[3]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[4]  A. Broder Some applications of Rabin’s fingerprinting method , 1993 .

[5]  Udi Manber,et al.  Finding Similar Files in a Large File System , 1994, USENIX Winter.

[6]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[7]  Air Force Air Force Materiel Command Hq FIPS-PUB-180-1 , 1995 .

[8]  Peter Deutsch,et al.  DEFLATE Compressed Data Format Specification version 1.3 , 1996, RFC.

[9]  Andrei Z. Broder,et al.  On the resemblance and containment of documents , 1997, Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).

[10]  Andy Hopper,et al.  Virtual Network Computing , 1998, IEEE Internet Comput..

[11]  J. Duane Northcutt,et al.  The interactive performance of SLIM: a stateless, thin-client architecture , 1999, SOSP.

[12]  S. P. Fodor,et al.  High density synthetic oligonucleotide arrays , 1999, Nature Genetics.

[13]  David Wetherall,et al.  A protocol-independent technique for eliminating redundant network traffic , 2000, SIGCOMM 2000.

[14]  Ka Yee Yeung,et al.  Validating clustering for gene expression data , 2001, Bioinform..

[15]  M K Kerr,et al.  Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  David Mazières,et al.  A low-bandwidth network file system , 2001, SOSP.

[17]  Klaus E. Schauser,et al.  Fast motion detection for thin client compression , 2002, Proceedings DCC 2002. Data Compression Conference.

[18]  Chris Vulpe,et al.  Discriminant analysis to evaluate clustering of gene expression data , 2002, FEBS letters.

[19]  A I Saeed,et al.  TM4: a free, open-source system for microarray data management and analysis. , 2003, BioTechniques.

[20]  J. Sydor,et al.  Protein expression profiling arrays: tools for the multiplexed high-throughput analysis of proteins , 2003, Proteome Science.

[21]  D. Cahill,et al.  Protein arrays and their role in proteomics. , 2003, Advances in biochemical engineering/biotechnology.

[22]  Ruo-Pan Huang Protein arrays, an excellent tool in biomedical research. , 2003, Frontiers in bioscience : a journal and virtual library.

[23]  P. Cutler Protein arrays: The current state‐of‐the‐art , 2003, Proteomics.

[24]  Susmita Datta,et al.  Comparisons and validation of statistical clustering techniques for microarray gene expression data , 2003, Bioinform..

[25]  A. Oleinikov,et al.  Self-assembling protein arrays using electronic semiconductor microchips and in vitro translation. , 2003, Journal of proteome research.

[26]  Kai Li,et al.  Visualization methods for statistical analysis of microarray clusters , 2005, BMC Bioinformatics.

[27]  Alok J. Saldanha,et al.  Java Treeview - extensible visualization of microarray data , 2004, Bioinform..

[28]  Anoop Gupta,et al.  Tools and applications for large-scale display walls , 2005, IEEE Computer Graphics and Applications.

[29]  Jason Nieh,et al.  THINC: a virtual display architecture for thin-client computing , 2005, SOSP '05.

[30]  Jason Nieh,et al.  On the performance of wide-area thin-client computing , 2006, TOCS.