- Open Access
POMO - Plotting Omics analysis results for Multiple Organisms
© Lin et al.; licensee BioMed Central Ltd. 2013
- Received: 19 September 2013
- Accepted: 18 December 2013
- Published: 24 December 2013
Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited.
We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly, yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input.
The POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration.
- Model organism
Modern high-throughput technologies measuring different omics types are constantly producing masses of new data [1–3]. Simultaneously, the various analysis algorithms and association analyses methods applied to these measurements are providing many different types of results [2–6]. Thus, the integration of the data and subsequent visualization of these results are becoming increasingly important and challenging .
The different types of analysis algorithms are resulting in various types of associations within the data. Often these methods include correlation-based or integrative data mining algorithms , and the results can include genomic feature to genomic feature associations across multiple data types, such as gene expression and chromosome rearrangements. The features can, for example, be genes or genomic positions such as regulatory regions, or they can be also clinical or sample annotations resulting for example from differential expression analysis [3, 8]. While the different values or types of data are related with each other, it also becomes necessary and challenging to be able to visualize different types of data and the results of their analysis [7, 9, 10]. Generally, the results of various analyses are given as text lists and visual illustrations are confounded by different formats, software platforms, and dependencies. However, because most of the genomic data can be organized by its genomic location, it is straightforward and advantageous to utilize the genomic position as a parameter in visualization. Since the majority of resulted omics associations can be linked to the physical chromosome positions, genome-wide illustrations can provide new insights to the investigator .
Traditional genome browsers such as Integrative Genomic Viewer , UCSC Genomic Browser  and GBrowse  are very useful for viewing biological data with multi-scaled linear tracks but they are not ideal to view gene networks. Cytoscape  fills this need and is adept at displaying network interactions and has released CytoscapeWeb  and Cytoscape.js beta libraries designed for web programming integration. Given that structural rearrangement events are likely more informative in the context of ordered chromosome circular layout context, there are a limited number of software tools available for circular illustration of the genomic association data, of which Circos  is most often used. Circos provides command line options to plot various types of data together into assorted attractive but static circular plots. Circos software requires local installation along with several mandatory Perl core and third party modules. The recent introduction of RCircos  successfully draws Circos images with R but implies that its usage is limited to experienced R programmers. DNAPlotter  plots interactive user-defined circular and linear genomic tracks. This standalone tool, improved from other published genomic viz tools such as CGView , GenomeDiagram , GenomePlot , GenoMap  and Microbial Genome Viewer  by combining Jemboss  and Artemis , flexibly accepts custom text files and relational databases, and the plotted tracks can be filtered and exported. DNAPlotter requires installation and does not support associations. Galaxy , web-based and very comprehensive for biomedical analysis and sharing, recently introduced Circster  a web-based Circos like visualization as part of its comprehensive pipeline. While Galaxy is available both publically and as a local install, Galaxy visualization functions are only available downstream of its workflows and thus limited to its ecosystem. As such, visualizing omics data with such a program requires a certain level of computational experience and multiple programs to illustrate, share and filter the data analysis results. In contrast, the UCSC Interaction Browser  and WikiPathways  both allow for web visualization and organization of network interactions, but they do not have genomic chromosomal context association views and they lack support for several important model organism references. In addition, as omics data includes often thousands of feature values, and there are at total thousands to millions resulted associations, it is vital to support filtering options for exploration and detection of sub-networks from dense and cluttered networks.
Supported organism references
H. Sapiens (GRCh37.p11)
D. melanogaster (BDGP5)
M. musculus (GRCm38.p1)
C. elegans (WBcel235)
S. cerevisiae (EF4)
D. rerio (Zv9)
A. thaliana (TAIR10)
O. sativa (MSU6)
S. lycopersicum (SL2.40)
It is widely accepted that visual networks are valuable for detecting and exploring patterns in large datasets. Genomic network visualizations with multiple perspectives, particularly within chromosomal context can offer insights of key proximal nodes and possible sub-networks. Data mining algorithms produce genome-wide association sets where individual associations are described with either a numerical ranking or weight. The option to filter and iteratively visualize these large data sets is of key importance in exploring and understanding the genomic associations. Our web application addresses and extends these requirements by combining different data types and including the reference genomes of multiple organisms by utilizing modern web programming technologies and components. POMO allows immediate visualization of genome-wide associations and annotations directly from text files while offering grid, Cytoscape and genomic circular context views. Within the genomic circular context, chromosomes are drawn as segments of the circumference; its length is normalized dependent on the nucleotide base length of the displayed organism. Omics nodes, which can be labelled as gene names or ids or explicit genomic positions, will be oriented/mapped to these segments, and the associations are represented as an edge between two genomic locations or genes. For additional visual differentiation, the notations are color encoded for different omics data types, such as gene expression, copy number variations, or proteomics data. Multiple annotation rings, with support for bar, histogram and heatmap graphs, can also be appended. Outer glyphs are used for representation of genomic features to unmapped nodes, which have no genomic location, such as phenotypic traits or disease state features.
Many labs studying data originating from omics studies of different organisms are lacking the personnel and expertise to write customized software for visualizing genome-wide associations. The inclusion of multiple organisms into POMO addresses this need by enhancing the utility and usability of visualization software. POMO supports the newest genome builds of the following organisms: human, mouse, nematode, fly, yeast, zebrafish, Arabidopsis, rice, tomato and E. coli (Table 1).
POMO is designed for illustrating omics associations directly from text files in circular genomic, network and tabular contexts with dynamic built in organism reference and annotation support. Following graph syntax from math, an edge is defined as two nodes having a link or association. In POMO, this edge can be ranked with a numeric weight, such as a p-value or correlation, or the user can directly mark this association with a color. Input associations can be derived from any data mining method as long as node labels are either gene names, identifiers such as ENSEMBL and ENTREZ or chromosome based positions. This flexibility allows for network nodes to be in non-coding DNA range which leads to complete inclusivity. Non-gene coding events such as promoter sites, copy number variation and other aberrations can easily be integrated and visualized. The program supports mixing gene and non-gene position based node labels. POMO node labels can be either ENSEMBL/ENTREZ id or gene label or position based. Position based nodes are labelled in the form chr:start:end. The nodes may be enhanced with a source type, such as genotype (GENO), gene expression (GEXP) or proteomics (PROT) data. These optional node annotations are encoded to a set of colors that lead to richer and differentiable graphical details. In addition, POMO supports multiple genome wide annotation rings, where the rings are defined in a text file and then uploaded. The syntax allows for pairing of values or colors to a gene or a segment in the chromosome. Syntax details and examples are provided in the Additional file 1. As exhibited in Additional file 1: Figure S10, annotation rings can be represented as bars, histograms and heat maps. Unmapped (PHENO) phenotype associations are visually portrait as outer glyph ticks, where the position represents the genomic position linked to the unmapped feature.
POMO inputs are text files containing genomic results such as interactions or associations. Each edge defines two nodes and the nodes are labelled with a gene name or ENSEMBLE or ENTREZ identifiers. The user can mix the node labels freely and Additional file 1: Table S1 provides more details and examples. Edges can optionally be rank with weights and also directly marked up with an HTML supported color. The supported delimiters along with the file type extensions are spaces (.txt), tabs (.tsv) and commas (.csv). Simple Interaction Format (.sif), which allows for multiple associations to be placed on one line, is also supported. We have also extended the sif format to allow an optional weight or color column.
Utilizing HTML5 FileReader API and modern web browsers, the tool allows uploading of association and annotation text files and then upon chromosome position translation immediately plots the resultant graph. Publically accessible cloud hosted omics association files can be read by POMO as an URL parameter. For testing and efficient plotting of small networks, one can declare association edges directly inside the URL parameter. Details and syntaxes are provided in the user guide, Additional file 1. The software includes comprehensive dialogs and messages to report if certain association node labels cannot be mapped to the selected reference. Association weight filtering can be accomplished if numeric values are provided. Moreover, POMO also allows for label set filtering, meaning, e.g., that a list of gene labels, such as members of a particular pathway, can be used to find subsets of the graph. The circular, grid and network views are automatically refreshed on each filtered submit and their iterated graph images can be exported as SVG image file, suitable for publishing or posters with its high definition presentation.
Big data is a large and routine part of modern day genomics research; along with troves of public databases, labs are generating different types of genome-wide data from new experiments and various instruments. Various sets of associations, often heterogeneous, are being extracted and by using POMO the investigators can gain insights from the different visual perspectives and layouts. Of particular interest is the genomic circular layout, where nodes are spatially mapped to chromosome arcs on the circumference and the associations are represented as edges between the genome-anchored nodes. Proximal and high degree nodes are revealed instantly, as well as sparse disjoint associations. With usage of filtering by association weight with multiple operators, gene label, or list of gene labels that can be for example pathways, investigators can intuitively find insights from previous uninformative dense networks. It is well known that genome wide visualizations, particularly in circular context, can have limited spatial capacities and dense graphs are not informative. To address this, POMO allows for filtering and edge bundling functions. The edge bundling allows for a node range window and groups the edges if the start and end nodes are within this window. Optionally, a score threshold can be set to exclude valued edges from the bundling (See Additional file 1 for more usage details).
Performance benchmarking on yeast protein-protein associations
Windows 7 4 GB RAM 2.6 GHz
Mac OS 10.8 8 GB RAM 1.8 GHz
POMO, freely available for non-commercial research, was designed for life science researchers to easily plot, filter and share genome-wide omics data and associations using an intuitive web interface. In supporting different labs studying different organisms, a comprehensive set of model organism genome references are fully integrated to allow for flexible association notations. The unique property, only available in POMO, is allowing the user to illustrate various organisms or closely related organisms together within single view. POMO also includes a detailed user guide, and several example associations and annotations are provided. In future, we will add support for other further organisms and appreciative of user feedbacks to improve the views and interface. For maximal visual impact, different visualization views and network layouts are supported and can be seamlessly toggled with simple clicks. Upon filtering, each view is dynamically filtered and text exports can serve as future inputs while the SVG image export can be converted to publishing quality presentations. POMO is an open sourced project and the code, builds and documentations are available at http://pomo.googlecode.com. In sum, as genome-wide visualizations, particularly interactive and web based, can help researchers to confirm theories and formulate new research questions, POMO can significantly facilitate researchers in finding new biological discoveries among their omics data.
Project name: POMO: Plotting Omics analysis results for Multiple Organisms
Project home page: http://pomo.cs.tut.fi
Operating system(s): Platform independent
License: POMO is available free of charge to academic and non-profit institutions.
Any restrictions to use by non-academics: Please contact authors for commercial use.
This work was supported by a strategic partnership between the ISB and the University of Luxembourg. The work of RA has been funded by Academy of Finland Finnish Programme no 134117 and 135257. The work of PM has been funded by “le plan Technologies de la Santé par le Gouvernment du Grand-Duché de Luxembourg” through Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg.
- Kircher M, Kelso J: High-throughput DNA sequencing – concepts and limitations. Bioessays. 2010, 32: 524-536. 10.1002/bies.200900181.View ArticlePubMedGoogle Scholar
- Schatz MC, Langmead B, Salzberg SL: Cloud computing and the DNA data race. Nat Biotechnol. 2010, 28: 691-693. 10.1038/nbt0710-691.PubMed CentralView ArticlePubMedGoogle Scholar
- Berger B, Peng J, Singh M: Computational solutions for omics data. Nat Rev Genet. 2013, 14: 333-346. 10.1038/nrg3433.PubMed CentralView ArticlePubMedGoogle Scholar
- Palsson B, Zengler K: The challenges of integrating multi-omic data sets. Nat Chem Biol. 2010, 6: 787-789.View ArticlePubMedGoogle Scholar
- Kirwan GM, Johansson E, Kleemann R, Verheij ER, Wheelock ÅM, Goto S, Trygg J, Wheelock CE: Building multivariate systems biology models. Anal Chem. 2012, 84: 7064-7071. 10.1021/ac301269r.View ArticlePubMedGoogle Scholar
- Liu Y, Devescovi V, Chen S, Nardini C: Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties. BMC Syst Biol. 2013, 7: 14-10.1186/1752-0509-7-14.PubMed CentralView ArticlePubMedGoogle Scholar
- Nielsen CB, Cantor M, Dubchak I, Gordon D, Wang T: Visualizing genomes: techniques and challenges. Nat Methods. 2010, 7 (3 Suppl): S5-S15.View ArticlePubMedGoogle Scholar
- Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M: Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009, 10: 184-194. 10.1038/nrg2537.PubMed CentralView ArticlePubMedGoogle Scholar
- Gehlenborg N, O’Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Kohlbacher O, Neuweger H, Schneider R, Tenenbaum D, Gavin AC: Visualization of omics data for systems biology. Nat Methods. 2010, 7 (3 Suppl): S56-S68.View ArticlePubMedGoogle Scholar
- Theocharidis A, van Dongen S, Enright AJ, Freeman TC: Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat Protoc. 2009, 4: 1535-1550. 10.1038/nprot.2009.177.View ArticlePubMedGoogle Scholar
- Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP: Integrative genomics viewer. Nat Biotechnol. 2011, 29: 24-26. 10.1038/nbt.1754.PubMed CentralView ArticlePubMedGoogle Scholar
- Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D: The Human Genome Browser at UCSC. Genome Res. 2002, 12: 996-1006.PubMed CentralView ArticlePubMedGoogle Scholar
- Stein LD, Mungall C, Shu S, Caudy M, Mangone M, Day A, Nickerson E, Stajich JE, Harris TW, Arva A, Lewis S: The generic genome browser: a building block for a model organism system database. Genome Res. 2002, 12: 1599-1610. 10.1101/gr.403602.PubMed CentralView ArticlePubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR, Vailaya A, Wang P-L, Adler A, Conklin BR, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner GJ, et al: Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007, 2: 2366-2382. 10.1038/nprot.2007.324.PubMed CentralView ArticlePubMedGoogle Scholar
- Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics Oxf Engl. 2010, 26: 2347-2348. 10.1093/bioinformatics/btq430.View ArticleGoogle Scholar
- Krzywinski M, Schein J, Birol İ, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA: Circos: an information aesthetic for comparative genomics. Genome Res. 2009, 19: 1639-1645. 10.1101/gr.092759.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang H, Meltzer P, Davis S: RCircos: an R package for Circos 2D track plots. BMC Bioinforma. 2013, 14: 244-10.1186/1471-2105-14-244.View ArticleGoogle Scholar
- Carver T, Thomson N, Bleasby A, Berriman M, Parkhill J: DNAPlotter: circular and linear interactive genome visualization. Bioinformatics. 2009, 25: 119-120. 10.1093/bioinformatics/btn578.PubMed CentralView ArticlePubMedGoogle Scholar
- Stothard P, Wishart DS: Circular genome visualization and exploration using CGView. Bioinformatics. 2005, 21: 537-539. 10.1093/bioinformatics/bti054.View ArticlePubMedGoogle Scholar
- Pritchard L, White JA, Birch PRJ, Toth IK: GenomeDiagram: a python package for the visualization of large-scale genomic data. Bioinformatics. 2006, 22: 616-617. 10.1093/bioinformatics/btk021.View ArticlePubMedGoogle Scholar
- Gibson R, Smith DR: Genome visualization made fast and simple. Bioinformatics. 2003, 19: 1449-1450. 10.1093/bioinformatics/btg152.View ArticlePubMedGoogle Scholar
- Sato N, Ehira S: GenoMap, a circular genome data viewer. Bioinformatics. 2003, 19: 1583-1584. 10.1093/bioinformatics/btg195.View ArticlePubMedGoogle Scholar
- Kerkhoven R, van Enckevort FHJ, Boekhorst J, Molenaar D, Siezen RJ: Visualization for genomics: the microbial genome viewer. Bioinformatics. 2004, 20: 1812-1814. 10.1093/bioinformatics/bth159.View ArticlePubMedGoogle Scholar
- Carver TJ, Mullan LJ: JAE: Jemboss Alignment Editor. Appl Bioinformatics. 2005, 4: 151-154. 10.2165/00822942-200504020-00010.View ArticlePubMedGoogle Scholar
- Carver T, Berriman M, Tivey A, Patel C, Böhme U, Barrell BG, Parkhill J, Rajandream M-A: Artemis and ACT: viewing, annotating and comparing sequences stored in a relational database. Bioinformatics. 2008, 24: 2672-2676. 10.1093/bioinformatics/btn529.PubMed CentralView ArticlePubMedGoogle Scholar
- Goecks J, Nekrutenko A, Taylor J, Team TG: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010, 11: R86-10.1186/gb-2010-11-8-r86.PubMed CentralView ArticlePubMedGoogle Scholar
- Goecks J, Eberhard C, Too T, Team TG, Nekrutenko A, Taylor J: Web-based visual analysis for high-throughput genomics. BMC Genomics. 2013, 14: 397-10.1186/1471-2164-14-397.PubMed CentralView ArticlePubMedGoogle Scholar
- Wong CK, Vaske CJ, Ng S, Sanborn JZ, Benz SC, Haussler D, Stuart JM: The UCSC interaction browser: multidimensional data views in pathway context. Nucleic Acids Res. 2013, 41: W218-W224. 10.1093/nar/gkt473.PubMed CentralView ArticlePubMedGoogle Scholar
- Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, Pico AR: WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012, 40 (Database issue): D1301-D1307.PubMed CentralView ArticlePubMedGoogle Scholar
- Flicek P, Ahmed I, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fairley S, Fitzgerald S, Gil L, García-Girón C, Gordon L, Hourlier T, Hunt S, Juettemann T, Kähäri AK, Keenan S, Komorowska M, Kulesha E, Longden I, Maurel T, McLaren WM, Muffato M, Nag R, Overduin B, Pignatelli M, Pritchard B, Pritchard E, et al: Ensembl 2013. Nucleic Acids Res. 2013, 41: D48-D55. 10.1093/nar/gks1236.PubMed CentralView ArticlePubMedGoogle Scholar
- Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE, the Mouse Genome Database Group: The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse. Nucleic Acids Res. 2012, 40: D881-D886. 10.1093/nar/gkr974.PubMed CentralView ArticlePubMedGoogle Scholar
- Sprague J, Bayraktaroglu L, Clements D, Conlin T, Fashena D, Frazer K, Haendel M, Howe DG, Mani P, Ramachandran S, Schaper K, Segerdell E, Song P, Sprunger B, Taylor S, Van Slyke CE, Westerfield M: The Zebrafish Information Network: the zebrafish model organism database. Nucleic Acids Res. 2006, 34 (suppl 1): D581-D585.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen N, Harris TW, Antoshechkin I, Bastiani C, Bieri T, Blasiar D, Bradnam K, Canaran P, Chan J, Chen C-K, Chen WJ, Cunningham F, Davis P, Kenny E, Kishore R, Lawson D, Lee R, Muller H-M, Nakamura C, Pai S, Ozersky P, Petcherski A, Rogers A, Sabo A, Schwarz EM, Van Auken K, Wang Q, Durbin R, Spieth J, Sternberg PW, et al: WormBase: a comprehensive data resource for Caenorhabditis biology and genomics. Nucleic Acids Res. 2005, 33 (suppl 1): D383-D389.PubMed CentralPubMedGoogle Scholar
- Marygold SJ, Leyland PC, Seal RL, Goodman JL, Thurmond J, Strelets VB, Wilson RJ, the FlyBase consortium: FlyBase: improvements to the bibliography. Nucleic Acids Res. 2013, 41: D751-D757. 10.1093/nar/gks1024.PubMed CentralView ArticlePubMedGoogle Scholar
- Kawahara Y, de la Bastide M, Hamilton J, Kanamori H, McCombie WR, Ouyang S, Schwartz D, Tanaka T, Wu J, Zhou S, Childs K, Davidson R, Lin H, Quesada-Ocampo L, Vaillancourt B, Sakai H, Lee SS, Kim J, Numa H, Itoh T, Buell CR, Matsumoto T: Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice. 2013, 6: 4-10.1186/1939-8433-6-4.View ArticlePubMedGoogle Scholar
- Tomato Genome Consortium: The tomato genome sequence provides insights into fleshy fruit evolution. Nature. 2012, 485: 635-641. 10.1038/nature11119.View ArticleGoogle Scholar
- Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, Karthikeyan AS, Lee CH, Nelson WD, Ploetz L, Singh S, Wensel A, Huala E: The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012, 40: D1202-D1210. 10.1093/nar/gkr1090.PubMed CentralView ArticlePubMedGoogle Scholar
- Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, Christie KR, Costanzo MC, Dwight SS, Engel SR, Fisk DG, Hirschman JE, Hitz BC, Karra K, Krieger CJ, Miyasato SR, Nash RS, Park J, Skrzypek MS, Simison M, Weng S, Wong ED: Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 2012, 40: D700-D705. 10.1093/nar/gkr1029.PubMed CentralView ArticlePubMedGoogle Scholar
- Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martinez C, Fulcher C, Huerta AM, Kothari A, Krummenacker M, Latendresse M, Muniz-Rascado L, Ong Q, Paley S, Schroder I, Shearer AG, Subhraveti P, Travers M, Weerasinghe D, Weiss V, Collado-Vides J, Gunsalus RP, Paulsen I, Karp PD: EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 2013, 41 (Database issue): D605-D612.PubMed CentralView ArticlePubMedGoogle Scholar
- Bostock M, Heer J: Protovis: a graphical toolkit for visualization. IEEE Trans Vis Comput Graph. 2009, 15: 1121-1128.View ArticlePubMedGoogle Scholar
- Närvä E, Autio R, Rahkonen N, Kong L, Harrison N, Kitsberg D, Borghese L, Itskovitz-Eldor J, Rasool O, Dvorak P, Hovatta O, Otonkoski T, Tuuri T, Cui W, Brustle O, Baker D, Maltby E, Moore HD, Benvenisty N, Andrews PW, Yli-Harja O, Lahesmaa R: High-resolution DNA analysis of human embryonic stem cell lines reveals culture-induced copy number changes and loss of heterozygosity. Nat Biotechnol. 2010, 28: 371-377. 10.1038/nbt.1615.View ArticlePubMedGoogle Scholar
- Hussein SM, Batada NN, Vuoristo S, Ching RW, Autio R, Narva E, Ng S, Sourour M, Hamalainen R, Olsson C, Lundin K, Mikkola M, Trokovic R, Peitz M, Brustle O, Bazett-Jones DP, Alitalo K, Lahesmaa R, Nagy A, Otonkoski T: Copy number variation and selection during reprogramming to pluripotency. Nature. 2011, 471: 58-62. 10.1038/nature09871.View ArticlePubMedGoogle Scholar
- Network CGA: Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012, 487: 330-337. 10.1038/nature11252.View ArticleGoogle Scholar
- Zheng S, Fu J, Vegesna R, Mao Y, Heathcock LE, Torres-Garcia W, Ezhilarasan R, Wang S, McKenna A, Chin L, Brennan CW, Yung WKA, Weinstein JN, Aldape KD, Sulman EP, Chen K, Koul D, Verhaak RGW: A survey of intragenic breakpoints in glioblastoma identifies a distinct subset associated with poor survival. Genes Dev. 2013, 27: 1462-1472. 10.1101/gad.213686.113.PubMed CentralView ArticlePubMedGoogle Scholar
- Von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002, 417: 399-403.View ArticlePubMedGoogle Scholar
- Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne J-B, Volkert TL, Fraenkel E, Gifford DK, Young RA: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science. 2002, 298: 799-804. 10.1126/science.1075090.View ArticlePubMedGoogle Scholar
- McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, Marcotte EM: Systematic discovery of nonobvious human disease models through orthologous phenotypes. Proc Natl Acad Sci USA. 2010, 107: 6544-6549. 10.1073/pnas.0910200107.PubMed CentralView ArticlePubMedGoogle Scholar
- Maul JE, Lilly JW, Cui L, dePamphilis CW, Miller W, Harris EH, Stern DB: The Chlamydomonas reinhardtii plastid chromosome: islands of genes in a sea of repeats. Plant Cell. 2002, 14: 2659-2679. 10.1105/tpc.006155.PubMed CentralView ArticlePubMedGoogle Scholar
- May P, Christian J-O, Kempa S, Walther D: ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii. BMC Genomics. 2009, 10: 209-10.1186/1471-2164-10-209.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.