A case-control approach to assess variability in distribution of distance between transcription factor binding site and transcription start site
- 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.
- Full Text:
- Date Issued: 2017
- 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.
- Full Text:
- Date Issued: 2017
Analysis of predictive power of binding affinity of PBM-derived sequences
- 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.
- Full Text:
- Date Issued: 2015
- 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.
- Full Text:
- Date Issued: 2015
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