Computational tools and Interoperability in Comparative ... - CBS

More documents

Recommendations

Info

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
Page 1 and 2:
Peter Fischer Hallin | 2009 Peter F
Page 4:
Preface This Ph.D. thesis is writte
Page 7 and 8:
thesis, the work is just being publ
Page 9 and 10:
ved at blive publiceret i Standards
Page 11 and 12:
viii
Page 13 and 14:
Paper VI [Lagesen K, Hallin P] 1 ,
Page 15 and 16:
xii
Page 17 and 18:
3.3.3 Refining E. coli and Shigella
Page 19 and 20:
xvi
Page 21 and 22:
xviii 2.17 Pan- and core-genome plo
Page 24 and 25:
Chapter 1 Introduction Introduction
Page 26 and 27:
Chapter 2 Comparative Genomics 2.1
Page 28 and 29:
Comparative Genomics the publicly a
Page 30 and 31:
Comparative Genomics source CDS tot
Page 32 and 33:
Comparative Genomics 1 mysql -N -B
Page 34 and 35:
Listing 2.8: R code to generate a 2
Page 36 and 37:
1st U C A G U 2nd position C A G 3r
Page 38 and 39:
1st U C A G U 2nd position C A G 3r
Page 40 and 41:
Escherichia coli strain K-12, subst
Page 42 and 43:
Comparative Genomics
Page 44 and 45:
3M 2.5M 3.5M 2.5M 2M 0M 2M 0.5M B.
Page 46 and 47:
Streptococcus Escherichia Bacillus
Page 48 and 49:
2.4 Summary Comparative Genomics Th
Page 50 and 51:
Comparative Genomics 2.5 Instant in
Page 52 and 53:
‘ReSourCe is he best online submi
Page 54 and 55:
up to a total of 41 different E. co
Page 56 and 57:
Fig. 2 Genes (or segments) from eac
Page 58 and 59:
Fig. 5 BLASTatlas of Clostridium bo
Page 60 and 61:
different applications, such as ide
Page 62 and 63:
1 Comparative Genomics 2.7 Paper II
Page 64 and 65:
166 literally millions of bacterial
Page 66 and 67:
168
Page 68 and 69:
170 resistance genes on mobile gene
Page 70 and 71:
172 involved in generating diversit
Page 72 and 73:
174 recipient DNA. A feature observ
Page 74 and 75:
176 Fig. 5 Genome length distributi
Page 76 and 77:
178
Page 78 and 79:
180 reasons why organisms remain un
Page 80 and 81:
182 A final problem has to do with
Page 82 and 83:
184 Middendorf B, Hochhut B, Leipol
Page 84 and 85:
1 Comparative Genomics 2.8 Paper II
Page 86 and 87:
2 O. N. Reva et al. Fig. 1. Genome
Page 88 and 89:
4 O. N. Reva et al. decrease of the
Page 90 and 91:
6 O. N. Reva et al. of which are kn
Page 92 and 93:
8 O. N. Reva et al. compiled into a
Page 94 and 95:
10 O. N. Reva et al. encoded by a c
Page 96 and 97:
12 O. N. Reva et al. systems and ef
Page 98 and 99:
1 2.9 Paper IV: The origins of Vibr
Page 100 and 101:
phylogenies based on alternative ho
Page 102 and 103:
Figure 1 Phylogenetic tree of the 1
Page 104 and 105:
25000 20000 15000 10000 5000 0 Pan
Page 106 and 107:
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
Page 112 and 113: 1 Comparative Genomics 2.10 Paper V
Page 114 and 115: 4314 74 Tools Abstract: Of the plet
Page 116 and 117: 4316 74 Tools Size distribution of
Page 118 and 119: 4318 74 Tools Genome atlas Intrinsi
Page 120 and 121: 4320 74 Tools Genome atlas Intrinsi
Page 122 and 123: 4322 74 Tools for Comparison of Bac
Page 124 and 125: 4324 74 Tools for Comparison of Bac
Page 126 and 127: 4326 74 Tools information, as genet
Page 128 and 129: Chapter 3 rRNA operons and promoter
Page 130 and 131: tuB murI Fis III Fis II Fis I UP -3
Page 132 and 133: Bits 2.0 1.5 1.0 0.5 0.0 Bits 2.0 1
Page 134 and 135: RNA operons and promoter analysis O
Page 136 and 137: Bits 2.0 1.5 1.0 0.5 0.0 Bits T A T
Page 138 and 139: Code Meaning Example C Coding CCCCC
Page 140 and 141: RNA operons and promoter analysis 3
Page 142 and 143: P2 -10 -35 UP P1 -10 -35 UP FIS FIS
Page 144 and 145: 1 rRNA operons and promoter analysi
Page 146 and 147: Using HMMs also simplifies the use
Page 148 and 149: Information content Information con
Page 150 and 151: of the annotation. Some of the majo
Page 152 and 153: where match states stop around 10 c
Page 154 and 155: 1 rRNA operons and promoter analysi
Page 156 and 157: synthesis in flow cells to simultan
Page 160 and 161: Hallin, et al. Figure 4 | The dataf
Page 162 and 163: Genome homology: Comparing multiple
Page 164 and 165: ing platform-‐independent Java
Page 166 and 167: 34. Wang H, Noordewier M, Benham CJ
Page 168 and 169: Chapter 4 Web Services and Interope
Page 170 and 171: Web Services and Interoperability i
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,
Page 186 and 187: Danmarks Tekniske Universitet AFI,
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
show all

Computational tools and Interoperability in Comparative ... - CBS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?