Integrating binding and expression data to predict transcription factors com- bined function Mahmoud Ahmed and Deok Ryong Kim Department of Biochemistry and Convergence Medical Sciences and Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju, Korea Summary Table 1: Functions of the target R package. Background Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Function Description Input Output Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These
pro- merge ranges Merge overlapping peaks & regions. peaks & regions Merged ranges teins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always find distance Calculate the distance between the centers peaks & regions Distances lead to a functional interaction. Results We propose a method to predict the combined functions of two factors using of peaks & regions. comparable binding and expression data (target) (Figure 1). We based this method on binding and expression target score peaks Calculate regulatory scores for peaks in Distances Peak
scores analysis (BETA), which we re-implemented in R and extended for this purpose (Table 1). target ranks the factor’s relation to regions. targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the score regions Calculate regulatory scores for regions. Peak scores & region Regions scores method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of IDs factors. Yin Yang 1 transcription factor (YY1) and YY2 are evolutionary and functionally related. The knockdown of rank product
Rank regions based on the regulatory po- Regions scores, expres- Regions rank products either factors produced wide changes in the gene expression of HeLa cells (Figure 2). We found that YY1 and YY2 tential & expression statistics. sion statistics & region have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets IDs (Figure 3 & Table 2). Conclusion We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of
two associated peaksSelect overlapping peaks & regions & peaks & regions Assigned peaks transcription factors. calculate a score for each peak in relation to a region. direct target Select & rank regions with overlapping peaks & regions Assigned targets Background peaks. plot predictions Plot the ECDF of the regions’ ranks by Ranks & group factor ECDF plot The integration of the overlapping binding sites and the effect of the gene expression of perturbed factors can be group. used to infer their combined function; cooperative or competitive. Two factors work cooperatively when they share a
test predictions Test the ECDF of the ranks in the regions Ranks & group factor t-statistics & p-values binding site and where they both induce or repress the gene [2]. By contrast, two factors may compete on a specific in each group are from different distribu- sites where the binding of either has an effect on the gene expression opposite to the other [3]. In this study, we pro- tion. vide an implementation of an algorithm to integrate the binding and expression data to predict transcription factors direct target and extend the method to predict the combined functions of two factors using
comparable binding and expression data. Results Figure 2: Differential expression of YY1 and YY2 in knockdown vs control HeLa cells. Probe intensities from microarrays of YY1 or YY2 (n = 3) knockdown and control (n = 3) were aggregated by gene and used to perform differential expression analysis (GSE14964). The gene expression in the YY1 and YY2-knockdown samples was compared to the control samples individually. A) Volcano plots show the fold-change (log ) and p-values (-log 0) 1 2 in each comparison. B) The fold-change (log ) of the YY1 and YY2-knockdown are shown as scatter plot. C) The 2
count of regulated (Up/Down) genes in by YY1 or YY2-knockdown and their intersections are shown as bars. Figure 1: Integrating binding and expression data to predict the combined function of transcription factors. The binding data from ChIP experiments of two factors are used to find the peaks in the genomic regions of interest. The distances between the peaks and the regions are used to calculate peak scores. The sum of the scores of all peaks in assigned to a region is its regulatory potential. The product of signed statistics from gene expression experiments of the factors perturbation is
used to determine the magnitude and the direction of their regulatory interactions. The rank product of the region score and statistics is the region significance. Implementation Binding and expression target analysis (BETA) The BETA algorithm in its simplest form, minus [6], is composed of three steps: 1. Select the peaks (p) within a certain range from the regions of interest (g). Figure 3: Predicted function of YY1 and YY2 on specific and shared targets in HeLa cells. The target analysis 2. Calculate the distance (∆) between the center of the peak and each of the regions expressed relative to
a distance was applied using two sets of data from the HeLa cells; expression data in YY1 and YY2-knockdown (GSE14964) of 100 kb. and two sets of ChIP peaks using antibodies for YY1 (GSE31417) and YY2 (GSE96878). Predicted targets were 3. Calculate the peak scores (S ) as the transformed exponential of the distance, ∆, as follows; ranked based on their distance to the transcription start sites (TSS) and their fold-change. The empirical distribution p function (ECDF) of each group of targets (Down, None or Up-regulated genes) of A) YY1 and B) YY2 was calcu- S = e −(0.5+4∆) lated. C) The shared
targets were ranked based on their distance to the TSS in which they had overlapping peaks p and the product of the corresponding fold-changes. The ECDF of each group of targets (Competitively, None or Cooperatively regulated genes) was calculated. 4. Calculate the region/gene regulatory potential (S ) as the sum of the scores, S [5], as follows: Table 2: Testing YY1 and YY2 target groups. g p Factor Test Statistic P Value k X S = S pi YY1 Down vs Up 0.79 0e+00 g i=1 YY2 Up vs Down 0.41 5e-13 Two Factors Cooperate vs Compete 0.97 0e+00 where p is {1, ..., k} peaks near the region of interest.
In BETA basic, another step is added to predict real region/gene targets. 5. Rank all regions based on their regulatory potential, S , to give their binding potential (R ) and based on their g gb differential expression (R ). The product of the two ranks predicts real region/gene targets. ge References R × R ge gb RP = [1] R. Breitling et al. “Rank products: A simple, yet powerful, new method to detect differentially regulated genes in g n 2 replicated microarray experiments”. In: FEBS Letters (2004). [2] C. Hernandez-Munain, J. L. Roberts, and M. S. Krangel. “Cooperation among Multiple
Transcription Factors Is where n is the number of regions g. Required for Access to Minimal T-Cell Receptor α-Enhancer Chromatin In Vivo”. In: Molecular and Cellular Biology (1998). Regulatory interaction (RI) term for predicting combined functions [3] L. J. Norton et al. “Direct competition between DNA binding factors highlights the role of Kr¨ uppel-like Factor 1 in the erythroid/megakaryocyte switch.” In: Scientific reports 7.1 (2017), p. 3137. To determine the relation of two factors x and y on a common peak near a region of interest, we define a new term; [4] A. Subramanian et al. “Gene set
enrichment analysis: A knowledge-based approach for interpreting genome-wide the regulatory interaction (RI) as the product of two signed statistics from comparable perturbation experiments. The expression profiles”. In: Proceedings of the National Academy of Sciences 102.43 (Oct. 2005), pp. 15545–15550. [5] Q. Tang et al. “A comprehensive view of nuclear receptor cancer cistromes”. In: Cancer Research (2011). rank of this term is used to calculate a rank product (PR ) for each region of interest as described above [1]. g [6] S. Wang et al. “Target analysis by integration of transcriptome and
ChIP-seq data with BETA”. In: Nature Proto- R × RI ge cols (2013). gb RI = x × y ge and RP = ge g g n 2 This term would represent the interaction magnitude assuming a linear relation between the two factor. The sign of the term would define the direction of the relation were positive means cooperative and negative means competitive. The scripts to reproduce this analysis, figures and tables are available here https://github. The regions can be divided into meaningful groups and tested for significance. The original BETA paper suggested com/BCMSLab/target_ranking or by directly scanning the QR
code. The github repository generating distribution functions for the groups and apply the one-tailed Kolmogorov-Smirnov test to test whether contains the instructions for setting up a software environment, obtaining the data and running the the groups are drawn from the same distribution [4]. analysis. [B. Bioinformatics and systems biology-2] Integrating binding and expression data to predict transcription factors combined function Mahmoud Ahmed¹, Deok Ryong Kim¹ ¹Convergence Medical Science, Gyeongsang National University, Jinju 52727, Korea Transcription factor binding on the regulatory
region of a gene induces or represses its expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. Finding protein overlapping binding sites is rarely enough to infer a functional interaction. We propose a method to predict the combined functions of two factors using comparable binding and expression data. The method is based on a previously suggested algorithm, which we re-implemented in R and extended for this purpose. The output of this method is the rank of the
factor's targets by importance and a prediction of the dominant kind of interaction between two transcription factors. We applied the method on simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 has antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a small number of shared targets. We developed an R package and a web application to apply the method of integrating binding and expression data to determine the cooperative or
competitive combined function. A Study on the Improvement of the Cryptographic Alogrithm for Identifying Genetic relatives 1 1 Gi Ju Lee , Minhyeok Jeong , Jong Wha J. Joo 1 * 1 Department of Computer Science and Engineering, Dongguk University-Seoul, 04620 Seoul, Republic of Korea ABSTRACT With the advent of high-throughput technology, genetic information is often compared to reveal individual relationships. Individual genetic information is one of the most important personal information, and efforts are being made to compare individual genetic information without compromising privacy. Of
these, GenoCrypt identifying genetic relatives without compromising privacy. However, it requires a lot of computing resources. In this study, we propose a method referred to as AdGenoCrypt while consuming less computing resources. METHODS In this study, to improve the encoding process of Fuzzy encryption, the GS(Genome Sketch) is divided into n GS pieces before encrypting as SGS(Secure Genome Sketch). GS contains hash information that divides Haplotypes into 300 SNP (Single Nucleotide Polymorphism) units, 4625 pieces. Each piece has 24 bits of data, so out of a total of 16,777,216 binary
data, the hash value of genetic info
