List of Titles

A case-control approach to assess variability in distribution of distance between transcription factor binding site and transcription start site

- Moos, Abdul Ragmaan

Authors: Moos, Abdul Ragmaan
Date: 2017
Subjects: Transcription factors , Proteomics , Chromatin , Chromatin immunoprecipitation
Language: English
Type: Thesis , Masters , MSc
Identifier: http://hdl.handle.net/10962/5315 , vital:20808
Description: Using the in-silico approach, with ENCODE ChIP-seq data for various transcription factors and different cell types; we systematically compared the distance between the transcription factor binding site (TFBS) and the transcription start (TSS). Our aim was to determine if the same transcription factor binds at a different position relative to the TSS in a normal and an abnormal cell type. We compare distribution of distance of binding sites from the TSS; to make description less verbose we call this “distance” where there is no possibility of confusion. We used a case-control methodology where the distance between the TFBS and the TSS in the normal, non-cancerous or untreated cell type is the control. The distance between the TFBS and the TSS in the cancerous or treated cell type is the case. We use the distance between the TFBS and the TSS in the control as the standard. We compared the distance between the TFBS and the TSS in the case and the control. If the distance between the TFBS and the TSS in the control was greater than the distance between the TFBS and the TSS in the case, we can infer the following. The transcription factor in the case binds closer to the TSS compared to the control. If the distance between the TFBS and the TSS in the control is smaller than the distance between the TFBS and the TSS in the case, we can infer the following. The TF in the case binds further away from the TSS compared to the control. Our method is a screening method whereby we compare ChIP-seq data to determine if there is a difference in the distribution distance between the TFBS and the TSS for normal and abnormal cell types. We used the R package ChIP-Enrich to compare the distribution of distance between ChIP-seq peak and the nearest TSS. ChIP-Enrich produces a histogram with the number of ChIP-seq peaks at a certain distance from the TSS. The results indicate for some transcription factors like GM12878-cMyc and K562-cMyc there is a difference between the distribution of distance between the TFBS and the nearest TSS. cMyc has more binding sites within a distance of 1kb from the TSS in GM12878 when compared to K562. GM12878-CTCF and K562-CTCF have slight differences when comparing their distribution of distance from the TSS. This means CTCF binds almost the same distance from the TSS in both GM12878 and K562. A549-gr treated with dexamethasone is interesting because with increase dose of dexamethasone the distribution of distance from the TSS changes as well.
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Transcription factor binding specificity and occupancy : elucidation, modelling and evaluation

- Kibet, Caleb Kipkurui

Authors: Kibet, Caleb Kipkurui
Date: 2017
Subjects: Transcription factors , Transcription factors -- Data processing , Motif Assessment and Ranking Suite
Language: English
Type: Thesis , Doctoral , PhD
Identifier: vital:21185 , http://hdl.handle.net/10962/6838
Description: The major contributions of this thesis are addressing the need for an objective quality evaluation of a transcription factor binding model, demonstrating the value of the tools developed to this end and elucidating how in vitro and in vivo information can be utilized to improve TF binding specificity models. Accurate elucidation of TF binding specificity remains an ongoing challenge in gene regulatory research. Several in vitro and in vivo experimental techniques have been developed followed by a proliferation of algorithms, and ultimately, the binding models. This increase led to a choice problem for the end users: which tools to use, and which is the most accurate model for a given TF? Therefore, the first section of this thesis investigates the motif assessment problem: how scoring functions, choice and processing of benchmark data, and statistics used in evaluation affect motif ranking. This analysis revealed that TF motif quality assessment requires a systematic comparative analysis, and that scoring functions used have a TF-specific effect on motif ranking. These results advised the design of a Motif Assessment and Ranking Suite MARS, supported by PBM and ChIP-seq benchmark data and an extensive collection of PWM motifs. MARS implements consistency, enrichment, and scoring and classification-based motif evaluation algorithms. Transcription factor binding is also influenced and determined by contextual factors: chromatin accessibility, competition or cooperation with other TFs, cell line or condition specificity, binding locality (e.g. proximity to transcription start sites) and the shape of the binding site (DNA-shape). In vitro techniques do not capture such context; therefore, this thesis also combines PBM and DNase-seq data using a comparative k-mer enrichment approach that compares open chromatin with genome-wide prevalence, achieving a modest performance improvement when benchmarked on ChIP-seq data. Finally, since statistical and probabilistic methods cannot capture all the information that determine binding, a machine learning approach (XGBooost) was implemented to investigate how the features contribute to TF specificity and occupancy. This combinatorial approach improves the predictive ability of TF specificity models with the most predictive feature being chromatin accessibility, while the DNA-shape and conservation information all significantly improve on the baseline model of k-mer and DNase data. The results and the tools introduced in this thesis are useful for systematic comparative analysis (via MARS) and a combinatorial approach to modelling TF binding specificity, including appropriate feature engineering practices for machine learning modelling.
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Analysis of predictive power of binding affinity of PBM-derived sequences

- Matereke, Lavious Tapiwa

Authors: Matereke, Lavious Tapiwa
Date: 2015
Subjects: Transcription factors , Protein binding , DNA-binding proteins , Chromatin , Protein microarrays
Language: English
Type: Thesis , Masters , MSc
Identifier: vital:4161 , http://hdl.handle.net/10962/d1018666
Description: A transcription factor (TF) is a protein that binds to specific DNA sequences as part of the initiation stage of transcription. Various methods of finding these transcription factor binding sites (TFBS) have been developed. In vivo technologies analyze DNA binding regions known to have bound to a TF in a living cell. Most widely used in vivo methods at the moment are chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing. In vitro methods derive TFBS based on experiments with TFs and DNA usually in artificial settings or computationally. An example is the Protein Binding Microarray which uses artificially constructed DNA sequences to determine the short sequences that are most likely to bind to a TF. The major drawback of this approach is that binding of TFs in vivo is also dependent on other factors such as chromatin accessibility and the presence of cofactors. Therefore TFBS derived from the PBM technique might not resemble the true DNA binding sequences. In this work, we use PBM data from the UniPROBE motif database, ChIP-seq data and DNase I hypersensitive sites data. Using the Spearman’s rank correlation and area under receiver operating characteristic curve, we compare the enrichment scores which the PBM approach assigns to its identified sequences and the frequency of these sequences in likely binding regions and the human genome as a whole. We also use central motif enrichment analysis (CentriMo) to compare the enrichment of UniPROBE motifs with in vivo derived motifs (from the JASPAR CORE database) in their respective TF ChIP-seq peak region. CentriMo is applied to 14 TF ChIP-seq peak regions from different cell lines. We aim to establish if there is a relationship between the occurrences of UniPROBE 8-mer patterns in likely binding regions and their enrichment score and how well the in vitro derived motifs match in vivo binding specificity. We did not come out with a particular trend showing failure of the PBM approach to predict in vivo binding specificity. Our results show Ets1, Hnf4a and Tcf3 show prediction failure by the PBM technique in terms of our Spearman’s rank correlation for ChIP-seq data and central motif enrichment analysis. However, the PBM technique also matched the in vivo binding specificities of FoxA2, Pou2f2 and Mafk. Failure of the PBM approach was found to be a result of variability in the TF’s binding specificity, the presence of cofactors, narrow binding specificity and the presence ubiquitous binding patterns.
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Comparison of protein binding microarray derived and ChIP-seq derived transcription factor binding DNA motifs

- Hlatshwayo, Nkosikhona Rejoyce

Authors: Hlatshwayo, Nkosikhona Rejoyce
Date: 2015
Subjects: Protein binding , DNA , DNA microarrays , Transcription factors , DNA-protein interactions , Gene regulatory networks
Language: English
Type: Thesis , Masters , MSc
Identifier: vital:4146 , http://hdl.handle.net/10962/d1017907
Description: Transcription factors (TFs) are biologically important proteins that interact with transcription machinery and bind DNA regulatory sequences to regulate gene expression by modulating the synthesis of the messenger RNA. The regulatory sequences comprise of short conserved regions of a specific length called motifs . TFs have very diverse roles in different cells and play a very significant role in development. TFs have been associated with carcinogenesis in various tissue types, as well as developmental and hormone response disorders. They may be responsible for the regulation of oncogenes and can be oncogenic. Consequently, understanding TF binding and knowing the motifs to which they bind is worthy of attention and research focus. Various projects have made the study of TF binding their main focus; nevertheless, much about TF binding remains confounding. Chromatin immunoprecipitation in conjunction with deep sequencing (ChIP-seq) techniques are a popular method used to investigate DNA-TF interactions in vivo. This procedure is followed by motif discovery and motif enrichment analysis using relevant tools. Protein Binding Microarrays (PBMs) are an in vitro method for investigating DNA-TF interactions. We use a motif enrichment analysis tools (CentriMo and AME) and an empirical quality assessment tool (Area under the ROC curve) to investigate which method yields motifs that are a true representation of in vivo binding. Motif enrichment analysis: On average, ChIP-seq derived motifs from the JASPAR Core database outperformed PBM derived ones from the UniPROBE mouse database. However, the performance of motifs derived using these two methods is not much different from each other when using CentriMo and AME. The E-values from Motif enrichment analysis were not too different from each other or 0. CentriMo showed that in 35 cases JASPAR Core ChIP-seq derived motifs outperformed UniPROBE mouse PBM derived motifs, while it was only in 11 cases that PBM derived motifs outperformed ChIP-seq derived motifs. AME showed that in 18 cases JASPAR Core ChIP-seq derived motifs did better, while only it was only in 3 cases that UniPROBE motifs outperformed ChIP-seq derived motifs. We could not distinguish the performance in 25 cases. Empirical quality assessment: Area under the ROC curve values computations followed by a two-sided t-test showed that there is no significant difference in the average performances of the motifs from the two databases (with 95% confidence, mean of differences=0.0088125 p-value= 0.4874, DF=47) .
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Analysis of transcription factor binding specificity using ChIP-seq data.

- Kibet, Caleb Kipkurui

Authors: Kibet, Caleb Kipkurui
Date: 2014
Subjects: Transcription factors , Chronic myeloid leukemia , Antioncogenes , Cancer cells -- Growth -- Regulation
Language: English
Type: Thesis , Masters , MSc
Identifier: vital:4115 , http://hdl.handle.net/10962/d1013131
Description: Transcription factors (TFs) are key regulators of gene expression whose failure has been implicated in many diseases, including cancer. They bind at various sites at different specificity depending on the prevailing cellular conditions, disease, development stage or environmental conditions of the cell. TF binding specificity is how well a TF distinguishes functional sites from potential non-functional sites to form a useful regulatory network. Owing to its role in diseases, various techniques have been used to determine TF binding specificity in vitro and in vivo, including chromatin immuno-precipitation followed by massively parallel sequencing (ChIP-seq). ChIP-seq is an in vivo technique that considers how the chromatin landscape affects TF binding. Motif enrichment analysis (MEA) tools are used to identify motifs that are over-represented in ChIP-seq peak regions. One such tool, CentriMo, finds over-represented motifs at the center since peak calling software are biased to declaring binding regions centered at the TF binding site. In this study, we investigate the use of CentriMo and other MEA tools to determine the difference in motif enrichment attributed presence of Chronic Myeloid leukemia (CML)), treatment with Interferon (IFN) and Dexamethasone (DEX) compared to control based on Fisher’s exact test; using uniform peaks ChIP-seq data generated by the ENCODE consortium. CentriMo proved to be capable. We observed differential motif enrichment of TFs with tumor promoter activity: YY1, CEBPA, Egr1, Cmyc family, Gata1 and JunD in K562 while Stat1, Irf1, and Runx1 in Gm12878. Enrichment of CTCF in Gm12878 with YY1 as the immuno-precipitated (ChIP-ed) factor and the presence of significant spacing (SpaMo analysis) of CTCF and YY1 in Gm12878 but not in K562 could show that CTCF, as a repressor, helps in maintaining the required YY1 level in a normal cell line. IFN might reduce Cmyc and the Jun family of TFs binding via the repressive action of CTCF and E2f2. We also show that the concentration of DEX treatment affects motif enrichment with 50nm being an optimum concentration for Gr binding by maintaining open chromatin via AP1 TF. This study has demonstrated the usefulness of CentriMo for TF binding specificity analysis.
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A central enrichment-based comparison of two alternative methods of generating transcription factor binding motifs from protein binding microarray data

- Mahaye, Ntombikayise

Authors: Mahaye, Ntombikayise
Date: 2013 , 2013-03-13
Subjects: Transcription factors , Bioinformatics , Protein binding , Protein microarrays , Cell lines
Language: English
Type: Thesis , Masters , MSc
Identifier: vital:3890 , http://hdl.handle.net/10962/d1003049 , Transcription factors , Bioinformatics , Protein binding , Protein microarrays , Cell lines
Description: Characterising transcription factor binding sites (TFBS) is an important problem in bioinformatics, since predicting binding sites has many applications such as predicting gene regulation. ChIP-seq is a powerful in vivo method for generating genome-wide putative binding regions for transcription factors (TFs). CentriMo is an algorithm that measures central enrichment of a motif and has previously been used as motif enrichment analysis (MEA) tool. CentriMo uses the fact that ChIP-seq peak calling methods are likely to be biased towards the centre of the putative binding region, at least in cases where there is direct binding. CentriMo calculates a binomial p-value representing central enrichment, based on the central bias of the binding site with the highest likelihood ratio. In cases where binding is indirect or involves cofactors, a more complex distribution of preferred binding sites may occur but, in many cases, a low CentriMo p-value and low width of maximum enrichment (about 100bp) are strong evidence that the motif in question is the true binding motif. Several other MEA tools have been developed, but they do not consider motif central enrichment. The study investigates the claim made by Zhao and Stormo (2011) that they have identified a simpler method than that used to derive the UniPROBE motif database for creating motifs from protein binding microarray (PBM) data, which they call BEEML-PBM (Binding Energy Estimation by Maximum Likelihood-PBM). To accomplish this, CentriMo is employed on 13 motifs from both motif databases. The results indicate that there is no conclusive difference in the quality of motifs from the original PBM and BEEML-PBM approaches. CentriMo provides an understanding of the mechanisms by which TFs bind to DNA. Out of 13 TFs for which ChIP-seq data is used, BEEML-PBM reports five better motifs and twice it has not had any central enrichment when the best PBM motif does. PBM approach finds seven motifs with better central enrichment. On the other hand, across all variations, the number of examples where PBM is better is not high enough to conclude that it is overall the better approach. Some TFs bind directly to DNA, some indirect or in combination with other TFs. Some of the predicted mechanisms are supported by literature evidence. This study further revealed that the binding specificity of a TF is different in different cell types and development stages. A TF is up-regulated in a cell line where it performs its biological function. The discovery of cell line differences, which has not been done before in any CentriMo study, is interesting and provides reasons to study this further.
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