17 research outputs found
autosome-ru/ADASTRA-pipeline: release-Susan
No full textThe pipeline used for ADASTRA data processing
Key changes and updates:
Estimating significance of individual ASBs: the weight parameter obtained by fitting the negative binomial mixture (applicable for scoring ASBs for BAD > 1) is now used as an informative prior, that is treated as the probability of the tested allele (the Reference allele for Ref-ASBs and the Alternative allele for Alt-ASBs) to have a higher copy number (compared to the other allele with a fixed read count), and thus to have a higher ChIP-Seq read count independently of TF binding.
The posterior was calculated for each particular SNV and used for ASB scoring, the Bayesian factor was calculated from the likelihood ratio
of obtaining the observed ChIP-Seq read count at the tested allele agreeing (the tested allele has higher DNA copy number) or contrasting
(conversely) with the DNA copy number (defined by BAD). This posterior weight was used to compute the P-value and the effect size for
individual SNVs.
This updated approach improves the statistical scoring of ASBs by reweighting the Negative binomial mixture and placing an emphasis on
the component that is more likely to be the source of the observed read counts. This is specifically important for cell type-ASBs, where the
allele with a larger ChIP-Seq read count is commonly shared between experiments.
This improvement marks the main difference with the published algorithm (doi:10.1101/2020.10.07.327643), which had a disadvantage that
different observations (experiments for the same SNV) having a common allele with a greater ChIP-Seq read count, in fact, did not comply
with the 'global' fit of the Negative Binomial Mixture model.
BAD calling procedure changes: the penalty for generating additional segments in the BABACHI algorithm (https://github.com/autosome-ru/BABACHI) was changed to CAIC4 (CAIC with the multiplier of 4) instead of 9 used in Soos. This provides a minor but consistent improvement in terms of BAD maps agreement with COSMIC
autosome-ru/BABACHI: BABACHI 2.0
No full textNew version of BABACHI. Now works with VCF files and saves the output as BED fil
autosome-ru/MixALime: Mixture models for Allelic Imbalance Estimation v 2.12.10
No full textMixture models for Allelic Imbalance Estimation v 2.12.1
autosome-ru/MixALime: Mixture models for Allelic Imbalance Estimation v 2.22.3
No full text<p>Mixture models for Allelic Imbalance Estimation v 2.22.3</p>
autosome-ru/MixALime: Mixture models for Allelic Imbalance Estimation v 2.23.3
No full text<p>Mixture models for Allelic Imbalance Estimation v 2.23.3</p>
autosome-ru/MixALime: MiXALime v 1.0.3
No full textMixture Models for Allelic Imbalance Estimation v 1.0.
Overweight and Obesity in the Russian Population: Prevalence in Adults and Association with Socioeconomic Parameters and Cardiovascular Risk Factors
No full textObjective: To evaluate the prevalence and geographic distribution of overweight and obesity in Russian adults aged 25-64 years as well as the association between chronic risk factors and obesity. Methods: Data were obtained from the survey "Epidemiology of Cardiovascular Diseases and Its Risk Factors in Some Regions of the Russian Federation" (ESSE-RF). This is a large cross-sectional multicenter population-based study that included interviews and medical examination (anthropometry, blood pressure [BP] measurement, and laboratory analysis) applied in 2012-2014. Results: The sample included 20,190 adults (response rate 79.4%) aged 25-64 years. Approximately one third of participants (30.3%) had obesity and another third (34.3%) were classified as overweight. BMI increased with age in both sexes. The prevalence of obesity between regions ranged from 24.4 to 35.5%. Overweight and obesity levels decreased with higher education (men only). Overall obesity rates were higher in rural than urban populations, but rates of overweight were similar in rural and urban populations. Participants with obesity were more likely to have BP > 160/100 mm Hg (odds ratio > 2.0) and also > 140/90 mm Hg, raised blood glucose, and high triglycerides. Conclusion: The prevalence of overweight and obesity in Russian adults aged 25-64 years is not evenly distributed geographically, but it is comparable to that of other European countries. Individuals with obesity were also more likely to have indicators of poor cardiovascular and metabolic health. © 2019 The Author(s) Published by S. Karger AG, Basel
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Landscape of allele-specific transcription factor binding in the human genome.
No full textSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry
