Computational tools and Interoperability in Comparative ... - CBS

More documents

Recommendations

Info

Comparative Genomics source CDS total TP FP FN 3’off 5’off sens. shared U00096 (present) 4,321 - - - - - - - U00096 (2004) 4,254 4,172 82 109 1.02 -4.07 0.97 93% Glimmer 3.02 4,476 4,174 302 125 -0.6 -24.09 0.97 87% GeneMark-S 2.6 4,377 4,207 170 90 1.94 -20.17 0.98 91% EasyGene 1.2 4,056 4,017 39 256 -0.28 -19.07 0.94 91% Prodigal 1.1 4,332 4,200 132 97 0.54 -20.07 0.98 92% Table 2.1: Performance of prokaryotic gene finders. An older genbank record for E. coli K12 (U00096, 2002) has been included and the reference of all comparisons is the most recent shown at the top. The 3’ and 5’ off correspond to the number of base pairs that a query coordinate is downstream (positive number) or upstream (negative number) when compared to the reference. T P The sensitivity is estimated by binary classification, T P +F N where T P is the number of proteins shared between reference and query and F N are proteins unique to the reference, not found in the query. Calculating specificity (which requires a true negative count) is difficult as it is hard to identify regions of the chromosome that for certain does not contain protein coding genes (Larsen & Krogh, 2003). The rightmost column contains an estimate of the percentage of protein families shared between the query and the reference genome. The number is derived using the BLASTmatrix tool. (U00096 from 2004) were compared pairwise to the latest version of the GenBank entry. The number of unique genes in both reference and query genome was derived and for each overlapping pair of ORFs, the average inaccuracy of the 3’ and 5’ ends was calculated (table 2.1). In addition the encoded proteins were compared using the BLASTmatrix tool, described in section 2.3.6. This allows estimation of the number of protein families shared between the reference and the query genomes. 2.2.5 Finding tRNA and rRNA genes The tool tRNAscan-SE (Lowe & Eddy, 1997) has been implemented in the CBS Genome Atlas Database Web Service, and it predicts tRNA genes in contigs or genomes: 1 wget http :// www . cbs . dtu .dk/ws/ GenomeAtlas / examples / fasta . inc .pl 2 wget http :// www . cbs . dtu .dk/ws/ GenomeAtlas / examples / trnascan .pl 3 perl trnascan .pl < mapped . fsa > mapped . trna . fsa The RNAmmer method (Paper VI, chapter 3) can be used to consistently annotate rRNA genes in contigs and full genome sequences. This tool is implemented as a separate Web Service at CBS. Please refer to http://www.cbs.dtu.dk/ws/RNAmmer for full documentation. In listing 2.7 and example is provided showing the usage of the RNAmmer client script. Listing 2.7: Running RNAmmer on a genome sequence 1 wget http :// www . cbs . dtu .dk/ws/ GenomeAtlas / examples / fasta . inc .pl 2 wget http :// www . cbs . dtu .dk/ws/ RNAmmer / examples / rnammer .pl 3 perl rnammer .pl bac < mapped . fsa > mapped . rrna . fsa 2.3 Genome Comparisons The previous section has described some initial steps for annotating the bacterial genome which is required for further comparative studies. In this section emphasis will be placed on comparing annotated genomes both on the proteome level as well as using meta-data. 7
Genome Comparisons Right whisker ends at an observed data point, not exceeding 1.5 IQR 1.5 x IQR 95% confidence interval Q1 IQR Q3 1.5 x IQR median Right whisker ends at an observed data point, not exceeding 1.5 IQR Mild outliers between 1.5 and 3.0 IQR and extreme outliers more than 3 IQR away from Q1 and Q3 Figure 2.2: Construction of a box-and-whiskers plot. Notches is an estimate of the 95% confidence interval. The tools presented here have all been used widely during course activities and research projects. 2.3.1 Box-and-wiskers plot As the number of sequenced bacterial genomes grew from only two in 1995 to now close to a thousand at the time of writing, there began to be enough data to sample various genomic properties amongst the different phylogenetic groups. The box-and-wiskers plot (Tukey, 1977) is a useful tool for visualizing such differences. The plot shows a box between the first and the third quantile (figure 2.2). The distance between Q1 and Q3 is called the Inter Quantile Ratio (IQR) and whiskers are drawn through observations that are not exceeding 1.5 × IQR. A line is drawn within the box representing the median. Data between 1.5 × IQR and 3.0 × IQR are denoted ”mild” outliers whereas observations exceeding 3.0 × IQR are extreme outliers. Notches are sometimes drawn to denote the confidence interval. In the R implementation of the box-and-wiskers plot the 95% confidence interval is approximated by 1.5×IQR √ . When comparing two or more distributions, non-overlapping N notches marks significant differences. Distribution of genome size and base composition in prokaryotes To examine the base composition and genome size for different phylogenetic groups, a query to the CBS Genome Atlas Database can be done, grouping replicons into projects and summarizing / averaging within each project. Altough only possible from within CBS, the commands are listed below. 8
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 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 158 and 159:
Read absence. A boolean where ‘on
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 172 and 173:
Page 174 and 175:
Page 176 and 177:
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

Create successful ePaper yourself

Delete template?

Save as template?