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
Transcription factor binding specificity and occupancy : elucidation, modelling and evaluation
- 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.
- Full Text:
- Date Issued: 2017
- 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.
- Full Text:
- Date Issued: 2017
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