1. |
|
|
2. |
- Carraro, Marco, et al.
(författare)
-
Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI
- 2017
-
Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 38:9, s. 1042-1050
-
Tidskriftsartikel (refereegranskat)abstract
- Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
|
|
3. |
- Daneshjou, Roxana, et al.
(författare)
-
Working toward precision medicine : Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
- 2017
-
Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 38:9, s. 1182-1192
-
Tidskriftsartikel (refereegranskat)abstract
- Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.
|
|
4. |
- Peters, Christoph, 1983-
(författare)
-
Topology Prediction of α-Helical Transmembrane Proteins
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Membrane proteins fulfil a number of tasks in cells, including signalling, cell-cell interaction, and the transportation of molecules. The prominence of these tasks makes membrane proteins an important target for clinical drugs. Because of the decreasing price of sequencing, the number of sequences known is increasing at such a rate that manual annotations cannot compete. Here, topology prediction is a way to provide additional information. It predicts the location and number of transmembrane helices in the protein and the orientation inside the membrane. An important factor to detect transmembrane helices is their hydrophobicity, which can be calculated using dedicated scales. In the first paper, we studied the difference between several hydrophobicity scales and evaluated their performance. We showed that while they appear to be similar, their performance for topology prediction differs significantly. The better performing scales appear to measure the probability of amino acids to be within a transmembrane helix, instead of just being located in a hydrophobic environment.Around 20% of the transmembrane helices are too hydrophilic to explain their insertion with hydrophobicity alone. These are referred to as marginally hydrophobic helices. In the second paper, we studied three of these helices experimentally and performed an analysis on membrane proteins. The experiments show that for all three helices positive charges on the N-terminal side of the subsequent helix are important to insert, but only two need the subsequent helix. Additionally, the analysis shows that not only the N-terminal helices are more hydrophobic, but also the C-terminal transmembrane helices.In Paper III, the finding from the second paper was used to improve the topology prediction. By extending our hidden Markov model with N- and C-terminal helix states, we were able to set stricter cut-offs. This improved the general topology prediction and in particular miss-prediction in large N- and C-terminal domains, as well the separation between transmembrane and non-transmembrane proteins.Lastly, we contribute several new features to our consensus topology predictor, TOPCONS. We added states for the detection of signal peptides to its hidden Markov model and thus reduce the over-prediction of transmembrane helices. With a new method for the generation of profile files, it is possible to increase the size of the database used to find homologous proteins and decrease the running time by 75%.
|
|