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Read absence. A boolean where ‘one’ indicates complete absence of aligned reads. Visualization of whole-genome homology The BLASTatlas method [15] derives a map of per-‐ nucleotide numbers on a reference genome to visualize the matches in the alignment between the reference genome and a query. The query can constitute any number of genomic contigs, scaf-‐ folds, full genomes, or collections thereof. This provides a method to identify regions of a refer-‐ ence genome that are conserved throughout mul-‐ tiple samples, as well as those that are unique. The BLASTatlas method is integrated into the GeneWiz browser software to facilitate a user-‐friendly in-‐ terface. According to the BLAST algorithm chosen, DNA or protein sequences of the reference are aligned with the best match in the query (using either blastp, blastn, tblastn, or blastx). The align-‐ ment is then mapped back to the reference ge-‐ nome. A match adds a 'one' whereas a mismatch adds a 'zero' at each position along the chromo-‐ Hallin, et al. some. These ones and zeros translate into smooth color zones due to binning DNA properties and DNA destabilization Through the web interface it is currently possible to select from 36 different nucleotide composition and DNA structural properties [4,5,16-22]. In addi-‐ tion to this, calculations of so-‐called SIDD energy estimates are provided, offering an approximation of promoter regions. This method estimates the free energy required to open the DNA helix, calcu-‐ -‐0.035, -‐0.044, -‐0.055, using the SIDD algorithm [23]. All of these parameters can be applied in any combination to any of the prokaryotic genomes available from the web interface, or to a custom sequence provided by the user. Alternatively, the parameters may be applied as collections forming 8 standard atlases: Genome-‐, Base-‐, Structure-‐, Cruciform-‐, A-‐DNA-‐, Z-‐DNA-‐, the Repeat-‐atlas, and finally the SIDD atlas, which is introduced in this manuscript (Figure 3). Figure 3 Configuration and references for pre-defined groups of DNA sequence- and structural properties: Genome-, Base-, Structure-, Cruciform-, A-DNA-, Z-DNA-, Repeat-, and SIDD-atlas. Custom data A designated section of the GeneWiz browser is assigned for custom data. It allows the user to provide a per-‐nucleotide list of numerical values along with a desired color and data range. Al-‐ though not presented here, this allows for visuali-‐ zation of additional information such as microar-‐ ray data that has been pre-‐processed by the user, by mapping gene expression, regulation change, or p-values back to genomic coordinates. In addition to the main genome annotation covering CDSs, tRNAs, and rRNAs, the user may specify miscella-‐ neous and pseudo-‐gene annotations separately. A button allows the query of selected reference ge-‐ nomes against a replicate of pseudogenes.org [24]. Other annotations of possible pseudogenes can be added, such as GenePRIMP output (geneprimp.jgi-‐ psf.org/). Dynamic visualization The GeneWiz browser allows dynamic dispropor-‐ tional zooming, meaning that zooming occurs http://standardsingenomics.org 207
GeneWiz browser nearly instantly when requested by the user, by redrawing all the components like tracks, legends, marks and text for every view. This allows the browser to scale the plot to make use of the entire plotting area, by not rescaling all parts of the plot equally. For example, zooming 10 x will stretch a data lane 10 in genome position axis, however the lane height and distance to the neighbor lane will remain constant. The dynamic nature of the GeneWiz browser requires pre-‐binning of data for each zoom level, all of which are stored on a cen-‐ tral server; for improved efficiency only data re-‐ quested by the user are sent. The approach to store per-‐nucleotide information as table records in a database (e.g. MySQL) has proved unfeasible, as the number of records per genome exceeds millions, and the construction of indexes would be very time consuming. Instead, a memory mapping technique was chosen, that allows the server to directly obtain the values from binary files when provided with the zoom window and level, for any chromosome in the database. (Examples are pro-‐ vided as supplemental data, http://www.cbs.-‐ dtu.dk/services/gwBrowser/suppl/). The client is written as a JavaApplet, that obtains the data remotely from the server (http://ws.cbs.dtu.dk/cgi-‐bin/gwBrowser-‐ 0.91/server.cgi). The browser server is written in Perl/CGI, while a compiled c-‐program handles the access to the binary data files. The options cur-‐ rently supported are listed in Table 2. Table 2 GeneWiz Browser server options. Option description d The unique identifier for the atlas Feature type (e.g. CDS,rRNA,tRNA) when returning ft annotations f Data field to return b Begin of window e End of window l Zoom level z Enable zlib compression of output m=i Return the genome length m=avg/stddev/min/max Return aggregate data for window/genome m=d Return data values provided field, window and zoom level m=c Return colors provided two or three-step ranges m=n Return nucleotides provided the window m=a Return annotations (used together with option ‘ft’) and genes as well as numerical data associated These options (Table 2) can be incorporated into a with each nucleotide. The disproportional capabil-‐ single URL. For example, one could request all ity of the GeneWiz browser implies that all com-‐ ponents (legends, tracks, marks, etc.) are regene-‐ m-‐ rated for every view requested by the user. Figure 4 http://ws.cbs.dtu.dk/cgi-‐ outlines the GeneWiz browser workflow. bin/gwBrowser-‐ -‐ When submitting a job via the web interface, the request is assigned a job identifier, under which a-‐ all data lanes and configurations are kept. After tions are described in the xml record, which can the job has been processed the user may alter lane be downloaded from the web order, colors, ranges, and append various types of (http://ws.cbs.dtu.dk/cgi-‐bin/gwBrowser-‐ marks to the plot. The layout of a given browser 0.91/fetchxml.cgi?AL111168GENOMEatlas). Fur-‐ instance is governed by an XML file, located on the ther examples are provided in the supplemental server. When generating the graphical representa-‐ data section. tion of the genome, the client Java program will make requests to the server to acquire aggregated The GeneWiz workflow and data displayed values, such as the averages, standard deviations, The GeneWiz browser plots and provides dispro-‐ minima, and maxima as well as lane data and an-‐ portional zooming for data pertaining to features notations. 208 Standards in Genomic Sciences
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Peter Fischer Hallin | 2009 Peter F
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Preface This Ph.D. thesis is writte
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thesis, the work is just being publ
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ved at blive publiceret i Standards
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viii
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Paper VI [Lagesen K, Hallin P] 1 ,
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xii
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3.3.3 Refining E. coli and Shigella
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xvi
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xviii 2.17 Pan- and core-genome plo
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Chapter 1 Introduction Introduction
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Chapter 2 Comparative Genomics 2.1
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Comparative Genomics the publicly a
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Comparative Genomics source CDS tot
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Comparative Genomics 1 mysql -N -B
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Listing 2.8: R code to generate a 2
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1st U C A G U 2nd position C A G 3r
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1st U C A G U 2nd position C A G 3r
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Escherichia coli strain K-12, subst
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Comparative Genomics
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3M 2.5M 3.5M 2.5M 2M 0M 2M 0.5M B.
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Streptococcus Escherichia Bacillus
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2.4 Summary Comparative Genomics Th
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Comparative Genomics 2.5 Instant in
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‘ReSourCe is he best online submi
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up to a total of 41 different E. co
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Fig. 2 Genes (or segments) from eac
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Fig. 5 BLASTatlas of Clostridium bo
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different applications, such as ide
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1 Comparative Genomics 2.7 Paper II
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166 literally millions of bacterial
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168
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170 resistance genes on mobile gene
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172 involved in generating diversit
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174 recipient DNA. A feature observ
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176 Fig. 5 Genome length distributi
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178
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180 reasons why organisms remain un
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182 A final problem has to do with
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184 Middendorf B, Hochhut B, Leipol
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1 Comparative Genomics 2.8 Paper II
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2 O. N. Reva et al. Fig. 1. Genome
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4 O. N. Reva et al. decrease of the
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6 O. N. Reva et al. of which are kn
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8 O. N. Reva et al. compiled into a
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10 O. N. Reva et al. encoded by a c
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12 O. N. Reva et al. systems and ef
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1 2.9 Paper IV: The origins of Vibr
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phylogenies based on alternative ho
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Figure 1 Phylogenetic tree of the 1
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25000 20000 15000 10000 5000 0 Pan
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Gap F 2M 2.5M Gap E 875k 750k 625k
Page 108 and 109: Table 2 A selection of genes locate
Page 110 and 111: Open Access This article is distrib
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Page 114 and 115: 4314 74 Tools Abstract: Of the plet
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Page 162 and 163: Genome homology: Comparing multiple
Page 164 and 165: ing platform-‐independent Java
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Page 168 and 169: Chapter 4 Web Services and Interope
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Page 178 and 179: Chapter 5 Conclusion and perspectiv
Page 180 and 181: Appendix A Appendix: Workshops, tea
Page 182 and 183: Appendix B Appendix: Ph.D. study pl
Page 184 and 185: Danmarks Tekniske Universitet AFI,
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Page 188 and 189: Appendix C Appendix: Courses C.1 Gl
Page 190 and 191: D.2 Sample output from queryGenomes
Page 192 and 193: Appendix: Software 13 w a r n " $ o
Page 194 and 195: Appendix: Software 109 m y ( $ m i
Page 196 and 197: Appendix: Software 25 [ ] A l t e r
Page 198 and 199: BIBLIOGRAPHY J. Rogers, P. F. Stadl
Page 200 and 201: BIBLIOGRAPHY Q. Jin, Z. Yuan, J. Xu
Page 202 and 203: BIBLIOGRAPHY Velicer, F.-J. Vorholt
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