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4320 74 Tools Genome atlas Intrinsic curvature dev avg 0.18 0.24 for Comparison of Bacterial Genomes dev avg Stacking energy –8.10 –7.21 dev avg Position preference 0.13 0.15 0.5M 0M M Annotations: CDS + CDS – rRNA tRNA 5M 1M 4.5M 1.5M M. acetivorans C2A 5,751,492 bp Global direct repeats fix avg 4 2M 5.00 7.50 3.5M 2.5M Global inverted repeats fix avg 5.00 7.50 3M GC skew dev avg –0.03 0.02 fix avg Percent AT 0.20 0.80 Resolution: 2301 Center for biological sequence analysis http://www.cbs.dtu.dk/ . Figure 3 Genome atlas of the main chromosome of the Archea Methanosarcina acetivorans.
Basic Local Alignment Search Tool, the most common (Altschul et al., 1990). However BLAST is not automatically suitable for large DNA input segments such as complete genomes. A more suitable program to align sequences in the range of megabases is Mummer, developed at TIGR, of which version 3 is now publicly available (Kurtz et al., 2004). Further, this method has been recently extended to include the average nucleotide identity in the conserved core genes of a set of genomes (Deloger et al., 2009). Moreover, graphical representation of the resulting alignment becomes an issue. Specific tools have been designed to align genome sequences and visualize such events. The Artemis Comparison Tool (ACT) is worth mentioning of which two versions are available: a downloadable version to be used on a local computer (Carver et al., 2005) and a web-based version with pre-computed comparisons between several hundred bacterial genomes. 3 BLAST results of entire bacterial chromosomes against each other have also been used to construct phylogenetic trees (Henz et al., 2005). Blast comparisons will be treated in Section 7 of this chapter. 5 Comparing the Coding Fraction of Genomes The typical coding density for a bacterial genome is about 90%, ranging from 95% for Pelagibacter ubique (an alpha-proteal marine bacterium that counts to the most numerous bacteria in the world) (Giovannoni et al., 2005) to around 75% for M. acetivorans. Intracellular bacteria can have a coding density as low as 50%. This means the majority of bacterial DNA codes for genes, which mostly are not spliced so that introns are absent (with very few exceptions). However, not every open reading frame is a gene, and it appears that many bacterial genomes are over-annotated, predicting 10–15% more genes than are real (Skovgaard et al., 2001). These over-annotated genes are frequently short open reading frames. In addition, genes can be missed in the annotation. A frequent mistake is that genes are annotated on the wrong strand, which can happen if the reading frame is open in either direction. The intergenic regions separating genes regulate transcription, and in intracellular bacteria frequently contain pseudogenes or repeats. Genes not coding for proteins include tRNA and rRNA genes, and some parts of intergenic regions can be transcribed into stable RNA that are transcribed but do not code for proteins. E. coli contains several hundred small non-coding RNA genes (ncRNA) (Chen et al., 2002) that can act as regulators (Gottesman, 2005). Their role in environmental bacteria is virtually unexplored. Although tRNA and rRNA genes are essential to life, they are sometimes missed in the annotation of a genome, a rather embarrassing omission, or occasionally annotated on the wrong strand (Lagesen et al., 2007). The number and location of rRNA operons in a genome can say something about an organism. It appears that organisms with short doubling times have larger numbers of rRNA and tRNA genes. Comparing > Figs. 2 and 3 it is likely that G. kaustrophilus with 9 rRNA copies, nearly all located close to the origin of replication (which boosts expression during replication as their copy number increases) can divide more quickly than M. acetivorans which only has three copies. Some really fast-growing bacteria can have 14 or more rRNA copies, as can be viewed from our list of genomes. 4 3 http://www.webact.org/WebACT/home 4 www.cbs.dtu.dk/services/GenomeAtlas/ Tools for Comparison of Bacterial Genomes 74 4321
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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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Paper VI [Lagesen K, Hallin P] 1 ,
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3.3.3 Refining E. coli and Shigella
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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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Web Services and Interoperability i
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Chapter 5 Conclusion and perspectiv
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Appendix A Appendix: Workshops, tea
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Appendix B Appendix: Ph.D. study pl
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Danmarks Tekniske Universitet AFI,
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Danmarks Tekniske Universitet AFI,
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Appendix C Appendix: Courses C.1 Gl
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D.2 Sample output from queryGenomes
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Appendix: Software 13 w a r n " $ o
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Appendix: Software 109 m y ( $ m i
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Appendix: Software 25 [ ] A l t e r
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BIBLIOGRAPHY J. Rogers, P. F. Stadl
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BIBLIOGRAPHY Q. Jin, Z. Yuan, J. Xu
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BIBLIOGRAPHY Velicer, F.-J. Vorholt
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