| 1. |
- Niroula, Abhishek, et al.
(författare)
-
PON-mt-tRNA: a multifactorial probability-based method for classification of mitochondrial tRNA variations.
- 2016
-
Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 1362-4962 .- 0305-1048. ; 44:5, s. 2020-2027
-
Tidskriftsartikel (refereegranskat)abstract
- Transfer RNAs (tRNAs) are essential for encoding the transcribed genetic information from DNA into proteins. Variations in the human tRNAs are involved in diverse clinical phenotypes. Interestingly, all pathogenic variations in tRNAs are located in mitochondrial tRNAs (mt-tRNAs). Therefore, it is crucial to identify pathogenic variations in mt-tRNAs for disease diagnosis and proper treatment. We collected mt-tRNA variations using a classification based on evidence from several sources and used the data to develop a multifactorial probability-based prediction method, PON-mt-tRNA, for classification of mt-tRNA single nucleotide substitutions. We integrated a machine learning-based predictor and an evidence-based likelihood ratio for pathogenicity using evidence of segregation, biochemistry and histochemistry to predict the posterior probability of pathogenicity of variants. The accuracy and Matthews correlation coefficient (MCC) of PON-mt-tRNA are 1.00 and 0.99, respectively. In the absence of evidence from segregation, biochemistry and histochemistry, PON-mt-tRNA classifies variations based on the machine learning method with an accuracy and MCC of 0.69 and 0.39, respectively. We classified all possible single nucleotide substitutions in all human mt-tRNAs using PON-mt-tRNA. The variations in the loops are more often tolerated compared to the variations in stems. The anticodon loop contains comparatively more predicted pathogenic variations than the other loops. PON-mt-tRNA is available at http://structure.bmc.lu.se/PON-mt-tRNA/.
|
|
| 2. |
- Niroula, Abhishek
(författare)
-
Tools and pipelines for interpreting the impacts of genetic variants
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Next generation sequencing (NGS) methods have been widely used for diagnosis. As time and cost of sequencing has reduced sharply during the last decade, genome and exome-wide sequencing have increasingly been used. The genome and exome projects produce large amounts of variation data and the clinical relevance of large proportions of them are not known. Among various types of genetic variations, the single nucleotide variations (SNVs) that lead to amino acid substitutions (AASs) are the most challenging to interpret. The best way to characterize the impacts of variations is by experimental studies. Since these experiments are expensive and time consuming, they cannot be performed for all identified variants. Computational tools can be used for scoring and ranking the variants and prioritizing them for experimental studies. Reliable and fast tools are necessary for accurate variation interpretation and to cope with the amounts of generated data. Several tools are available for predicting impacts of genetic variations. These tools use various types of information and have different performances. Various performance assessment studies have shown that most of the widely used tools have inconsistent and sub-optimal performance.In this study, we implemented a systematic approach to develop four computational tools for interpreting the impacts of genetic variations. The tools are based on machine learning algorithm. Benchmark variation datasets were obtained from various sources for training and testing the tools. A systematic feature selection technique was employed to identify relevant and non-redundant features for predicting variation impact. The benchmark datasets and the features were used for training the tools. Finally, the tools were tested by using independent datasets to estimate their performance for unseen data. The tools PON-P2, PON-MMR2, and PON-PS predict impacts of AASs in human proteins and the PON-mt-tRNA tool predicts the impacts of SNVs in human mitochondrial transfer RNAs (mt-tRNAs). All the tools showed better performance when compared with state-of-the-art tools. These tools have consistently shown the best performance in our studies as well as in independent studies.The tools developed in this study are useful for ranking variations and prioritizing the likely harmful ones for further evaluation. These tools were developed for different purposes. Three of the tools (PON-P2, PON-MMR2, and PON-mt-tRNA) predict pathogenicity of variations. While PON-P2 is a generic tool for predicting pathogenicity of AASs in all human proteins, PON-MMR2 and PON-mt-tRNA are specific tools for predicting pathogenicity of variations in mismatch repair proteins and mt-tRNA genes, respectively. PON-PS is the first tool for predicting disease severity due to AASs. Pathogenicity of variations indicate the relevance of variation to a disease but cannot predict severity of phenotype. Early identification of disease severity promotes personalized medicine by facilitating early interventions, such as preventive measures, clinical monitoring, and molecular tests, for patients and their family members.The developed computational tools were used for analysing the impacts of variations in DNA mismatch repair proteins, mt-tRNA genes, and somatic variations in cancer. The impacts of all possible AASs in four mismatch repair proteins (MLH1, MSH2, MSH6, and PMS2) were predicted using PON-MMR2 and the impacts of all possible SNVs in 22 human mt-tRNAs were predicted using PON-mt-tRNA. We also studied the distribution of predicted pathogenic and benign variations in the protein domains and 3-dimensional structures of proteins and mt-tRNAs. PON-P2 was used to identify harmful somatic AASs from among 5 million somatic variations from 7,042 genomes or exomes grouped into 30 types of cancer. Only a small fraction of the somatic variations were identified to be harmful. Although known cancer genes contained higher numbers of harmful variations, the proportion of harmful variations was only 40%. We prioritized the proteins that were implicated (containing harmful AASs) in the largest number of samples in each cancer type and studied the networks and pathways affected by them. In the functional interaction network, the prioritized proteins were centrally located. The significantly enriched pathways included several new pathways and previously known pathways implicated in cancer. Our findings facilitates prioritization of experimental studies in various cancer types as well as interpretation of variation impacts in mismatch repair proteins and mt-tRNA genes.
|
|
| 3. |
- Niroula, Abhishek, et al.
(författare)
-
Variation Interpretation Predictors : Principles, Types, Performance, and Choice
- 2016
-
Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 37:6, s. 579-597
-
Tidskriftsartikel (refereegranskat)abstract
- Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.
|
|
| 4. |
- Yang, Yang, et al.
(författare)
-
PON-Sol : Prediction of effects of amino acid substitutions on protein solubility
- 2016
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 32:13, s. 2032-2034
-
Tidskriftsartikel (refereegranskat)abstract
- Motivation: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. Results: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PONSol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible AASs in a protein to aid protein engineering.
|
|
