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1.  Dosage Sensitivity Shapes the Evolution of Copy-Number Varied Regions 
PLoS ONE  2010;5(3):e9474.
Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation.
doi:10.1371/journal.pone.0009474
PMCID: PMC2835737  PMID: 20224824
2.  Protein interactions in human genetic diseases 
Genome Biology  2008;9(1):R9.
A method is presented to identify residues that form part of an interaction interface, leading to the prediction that 1,428 OMIM mutations are related to an interaction defect.
We present a novel method that combines protein structure information with protein interaction data to identify residues that form part of an interaction interface. Our prediction method can retrieve interaction hotspots with an accuracy of 60% (at a 20% false positive rate). The method was applied to all mutations in the Online Mendelian Inheritance in Man (OMIM) database, predicting 1,428 mutations to be related to an interaction defect. Combining predicted and hand-curated sets, we discuss how mutations affect protein interactions in general.
doi:10.1186/gb-2008-9-1-r9
PMCID: PMC2395246  PMID: 18199329
3.  Reuse of structural domain–domain interactions in protein networks 
BMC Bioinformatics  2007;8:259.
Background
Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.
Results
We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between E. coli, S. cerevisiae and H. sapiens. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.
Conclusion
Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.
doi:10.1186/1471-2105-8-259
PMCID: PMC1940023  PMID: 17640363
4.  Pfam: clans, web tools and services 
Nucleic Acids Research  2005;34(Database issue):D247-D251.
Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (), the USA (), France () and Sweden ().
doi:10.1093/nar/gkj149
PMCID: PMC1347511  PMID: 16381856
5.  HMM Logos for visualization of protein families 
BMC Bioinformatics  2004;5:7.
Background
Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way.
Results
We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the state's expected contribution).
A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics .
Conclusions
We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras.
doi:10.1186/1471-2105-5-7
PMCID: PMC341448  PMID: 14736340
Hidden Markov Model; Sequence Logo; HMM Logo; profile; information content; hitting probability; dynamic programming; small GTPases

Results 1-5 (5)