scholarly journals A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Bertl ◽  
Qianyun Guo ◽  
Malene Juul ◽  
Søren Besenbacher ◽  
Morten Muhlig Nielsen ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Mutation Rate ◽  
Specific Model ◽  
Whole Genome ◽  
Site Specific ◽  
Cancer Data ◽  
A Site

scholarly journals A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data

2017 ◽  
Author(s):  
Johanna Bertl ◽  
Qianyun Guo ◽  
Malene Juul ◽  
Søren Besenbacher ◽  
Morten Muhlig Nielsen ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Null Model ◽  
Specific Model ◽  
Cancer Development ◽  
Whole Genome ◽  
Site Specific ◽  
Explanatory Variables ◽  
Different Types ◽  
Cancer Types ◽  
Mutational Process

AbstractBackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.ResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.ConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

scholarly journals Author response: Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate

2017 ◽  
Author(s):  
Malene Juul ◽  
Johanna Bertl ◽  
Qianyun Guo ◽  
Morten Muhlig Nielsen ◽  
Michał Świtnicki ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Mutation Rate ◽  
Specific Model ◽  
Author Response ◽  
Cancer Driver

scholarly journals Comparison of Two PARAFAC Models of Dissolved Organic Matter Fluorescence for a Mid-Atlantic Forested Watershed in the USA

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Shatrughan Singh ◽  
Shreeram Inamdar ◽  
Durelle Scott
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Specific Model ◽  
Forested Watershed ◽  
Strongly Correlated ◽  
Site Specific ◽  
The Usa ◽  
Study Sites ◽  
Model Components ◽  
A Site

The composition of dissolved organic matter (DOM) in a mid-Atlantic forested watershed was evaluated using two fluorescence models—one based on previously validated model (Cory and McKnight, 2005) and the other developed specifically for our study site. DOM samples for the models were collected from multiple watershed sources over a two-year period. The previously validated parallel factor analysis (PARAFAC) model had 13 DOM components whereas our site-specific model yielded six distinct components including two terrestrial humic-like, two microbial-derived humic-like, and two protein-like components. The humic-like components were highest in surficial watershed sources and decreased from soil water to groundwater whereas the protein-like components were highest for groundwater sources. Discriminant analyses indicated that our site-specific model was more sensitive to subtle differences in DOM and the sum of the humic- and protein-like constituents yielded more pronounced differences among watershed sources as opposed to the prevalidated model. Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) concentrations and selected DOM metrics were also more strongly correlated with the site-specific model components. These results suggest that while the pre-validated model may capture broader trends in DOM composition and allow comparisons with other study sites, a site-specific model will be more sensitive for characterizing within-site differences in DOM.

scholarly journals Modeling site-specific nucleotide biases affecting Himar1 transposon insertion frequencies in TnSeq datasets

2021 ◽  
Author(s):  
Sanjeevani Choudhery ◽  
A Jacob Brown ◽  
Chidiebere D Akusobi ◽  
Eric J. Rubin ◽  
Christopher M Sassetti ◽  
...  
Keyword(s):  
Markov Models ◽  
A Priori ◽  
Growth Conditions ◽  
Specific Model ◽  
Site Specific ◽  
Transposon Insertion ◽  
Local Sequence ◽  
A Site ◽  
Analytical Approaches ◽  
Insertion Mutants

In bacterial TnSeq experiments, a library of transposons insertion mutants is generated, selected under various growth conditions, and sequenced to determine the profile of insertions at different sites in the genome, from which the fitness of mutant strains can be inferred. The widely used Himar1 transposon is known to be restricted to insertions at TA dinucleotides, but otherwise, few site-specific biases have been identified. As a result, most analytical approaches assume that insertion counts are expected a priori to be randomly distributed among TA sites in non-essential regions. However, recent analyses of independent Himar1 Tn libraries in M. tuberculosis have identified a local sequence pattern that is non-permissive for Himar1 insertion. This suggests there are site-specific biases that affect the frequency of insertions of the Himar1 transposon at different TA sites. In this paper, we use statistical and machine learning models to characterize patterns in the nucleotides surrounding TA sites associated with high and low insertion counts. We not only affirm that the previously discovered non-permissive pattern (CG)GnTAnC(CG) suppresses insertions, but conversely show that an A in the -3 position or T in the +3 position from the TA site encourages them. We demonstrate that these insertion preferences exist in Himar1 TnSeq datasets other than M. tuberculosis, including mycobacterial and non-mycobacterial species. We build predictive models of Himar1 insertion preferences as a function of surrounding nucleotides. The final predictive model explains about half of the variance in insertion counts, presuming the rest comes from stochastic variability between libraries or due to sampling differences during sequencing. Based on this model, we present a new method, called the TTN-Fitness method, to improve the identification of conditionally essential genes or genetic interactions, i.e., to better distinguish true biological fitness effects by comparing the observed counts to expected counts using a site-specific model of insertion preferences. Compared to previous methods like Hidden Markov Models, the TTN-Fitness method is able to classify the essentiality of many small genes (with few TA sites) that were previously characterized as Uncertain.

scholarly journals Functional expression of a yeast mitochondrial intron-encoded protein requires RNA processing at a conserved dodecamer sequence at the 3' end of the gene.

1989 ◽  
Vol 9 (4) ◽  
pp. 1507-1512 ◽  
Author(s):  
H Zhu ◽  
H Conrad-Webb ◽  
X S Liao ◽  
P S Perlman ◽  
R A Butow
Keyword(s):  
Genetic Analysis ◽  
Rna Processing ◽  
Fold Increase ◽  
Functional Expression ◽  
Rrna Gene ◽  
Yeast Mitochondria ◽  
Wild Type ◽  
Site Specific ◽  
Var1 Gene ◽  
A Site

All mRNAs of yeast mitochondria are processed at their 3' ends within a conserved dodecamer sequence, 5'-AAUAAUAUUCUU-3'. A dominant nuclear suppressor, SUV3-I, was previously isolated because it suppresses a dodecamer deletion at the 3' end of the var1 gene. We have tested the effects of SUV3-1 on a mutant containing two adjacent transversions within a dodecamer at the 3' end of fit1, a gene located within the 1,143-base-pair intron of the 21S rRNA gene, whose product is a site-specific endonuclease required in crosses for the quantitative transmission of that intron to 21S alleles that lack it. The fit1 dodecamer mutations blocked both intron transmission and dodecamer cleavage, neither of which was suppressed by SUV3-1 when present in heterozygous or homozygous configurations. Unexpectedly, we found that SUV3-1 completely blocked cleavage of the wild-type fit1 dodecamer and, in SUV3-1 homozygous crosses, intron conversion. In addition, SUV3-1 resulted in at least a 40-fold increase in the amount of excised intron accumulated. Genetic analysis showed that these phenotypes resulted from the same mutation. We conclude that cleavage of a wild-type dodecamer sequence at the 3' end of the fit1 gene is essential for fit1 expression.

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