Infrastructure Coverage of the Ural Federal District Regions: Assessment Methodology and Diagnostics Results

The article examines the infrastructure as one of the essential elements in the economic system. The authors consider the development stages of this concept in the scientific community and provide the opinions of a number of researchers as to the role and place of the infrastructure in the economic system. The article provides a brief genesis of approaches to describing the infrastructure and conferring its functions on individual branches. The authors emphasize the higher importance of infrastructure coverage with the economy transition to machine production. Two key methodological approaches are identified to describe the substance and content of the infrastructure: industrial and functional.The authors offer their methodology of assessing the infrastructure coverage of regional-level territories. The methodology is based on identifying a combination of specific indicators the values of which can be used to evaluate the development level of individual infrastructure elements. The indicative analysis being the basis of the methodological apparatus helps make a judgment of any phenomenon by comparing the current observed values with the previously adopted threshold levels. Such comparison makes it possible to classify the observations by the «norm—pre-crisis—crisis» scale. An essential advantage of this method is the possibility of standardizing the indicators, or, in other words, bringing them to one comparable conditional value. Thus, you can get estimates for individual blocks of indicators and a complex assessment for the whole set in general. The authors have identified four main infrastructure elements: transport, communications, public utility services and healthcare. The methodology includes 21 indicators all together.The test estimates based on the authors’ methodology revealed the defects in the development of the Ural regions` infrastructure. The article provides a brief analysis of the obtained data with identifying individual indicators and areas

Detection of regulatory SNPs in human genome using ChIP-seq ENCODE data.

A vast amount of SNPs derived from genome-wide association studies are represented by non-coding ones, therefore exacerbating the need for effective identification of regulatory SNPs (rSNPs) among them. However, this task remains challenging since the regulatory part of the human genome is annotated much poorly as opposed to coding regions. Here we describe an approach aggregating the whole set of ENCODE ChIP-seq data in order to search for rSNPs, and provide the experimental evidence of its efficiency. Its algorithm is based on the assumption that the enrichment of a genomic region with transcription factor binding loci (ChIP-seq peaks) indicates its regulatory function, and thereby SNPs located in this region are more likely to influence transcription regulation. To ensure that the approach preferably selects functionally meaningful SNPs, we performed enrichment analysis of several human SNP datasets associated with phenotypic manifestations. It was shown that all samples are significantly enriched with SNPs falling into the regions of multiple ChIP-seq peaks as compared with the randomly selected SNPs. For experimental verification, 40 SNPs falling into overlapping regions of at least 7 TF binding loci were selected from OMIM. The effect of SNPs on the binding of the DNA fragments containing them to the nuclear proteins from four human cell lines (HepG2, HeLaS3, HCT-116, and K562) has been tested by EMSA. A radical change in the binding pattern has been observed for 29 SNPs, besides, 6 more SNPs also demonstrated less pronounced changes. Taken together, the results demonstrate the effective way to search for potential rSNPs with the aid of ChIP-seq data provided by ENCODE project

A variety of SNP effects on binding of the corresponding oligonucleotides to nuclear proteins from K562 cells.

<p>rs79734816:C>T (<i>A</i>), rs2071002:A>C (<i>B</i>), and rs74393987:C>T (<i>C</i>) change the number and intensity of bands, while rs75996864:G>T (<i>D</i>) affect only band intensity, and 7961894:C>T (<i>E</i>) do not have any. Changes in the binding of allelic variants with the nuclear proteins are indicated by arrows.</p

Summary of the experimental testing of putative rSNPs selected from S_omim.

<p>1- no influence, 2- influence of SNP on the band intensity, 3- influence of SNP on the band mobility.</p

enrichment of S_gwas sample and its high-confidence derivatives, S_pV, S_OR and S_int, as well as S_r sample with putative rSNPs as a function of cut-off number of overlapping TF binding loci (<i>i</i>) for defining OTFRs.

<p>500 bootstrap iterations were performed for each point. The resulting standard deviations and confidence intervals are shown by error bars and colour-filled areas,respectively. The subsamples of S_gwas were generated with filtering of SNPs by P-value <1 e–7 (S_pV), OR>3 and <0.3 (S_OR), and by both criteria (S_int). S_gwas sample was extracted from NHGRI GWAS catalog.</p

The used approach to genome-wide selection of rSNPs.

<p>Computational analysis was applied to identify the SNPs in the most likely regulatory regions of the human genome and predict rSNPs for experimental verification.</p

Localization of putative rSNPs within OTFR belonging to APC gene.

<p>SNPs from the different parts of OTFR were taken in EMSA to study the effect of SNP location within OTFR on the protein binding.</p

Summary of the experimental testing of putative rSNPs from different parts of OTFR belonging to APC gene.

<p>1- no influence, 2- influence of SNP on the band intensity, 3- influence of SNP on the band mobility.</p

The enrichment of functionally-associated S_omim, S_clinic, S_gwas samples, and S_r sample with putative rSNPs as a function of cut-off number of overlapping TF binding loci (<i>i</i>) for defining OTFRs.

<p>500 bootstrap iterations were performed for each point. The resulting standard deviations and confidence intervals are shown by error bars and colour-filled areas,respectively. S_clinic, S_omim, and S_gwas consist of SNPs associated with phenotypic manifestations and extracted from dbSNP NCBI Clinical/LSDB Submissions Resources, OMIM catalog, and NHGRI GWAS catalog, respectively. Sample S_r of random SNPs was created without applying any phenotypic preferences and used as control. Genomic region chr6:29,909,708-31,325,212 was excluded from the analysis of S_clinic sample, since it caused enrichment overestimation at lower <i>i</i> (a large number of SNPs concentrate in this region due to its extensive use in genotyping assays).</p