Limits...
Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data.

Kähärä J, Lähdesmäki H - BMC Bioinformatics (2013)

Bottom Line: We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy.We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number.Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear k-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative k-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of k-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for k-mer and PWMs, respectively. Finally, the k-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.

Show MeSH
Top k-mers aligned to FOXJ3 logo. The top k-mers chosen with the CV-scheme (left) and the top affinity k-mers from the most frequent approach (right) aligned to FOXJ3 sequence logo.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750486&req=5

Figure 6: Top k-mers aligned to FOXJ3 logo. The top k-mers chosen with the CV-scheme (left) and the top affinity k-mers from the most frequent approach (right) aligned to FOXJ3 sequence logo.

Mentions: The cross-validation scheme does not choose necessarily those k-mers to the model that are important originally with higher number of k-mers. It was investigated if the highest affinity k-mers would be included late in the cross-validation. There was little overlap between the ten last k-mers in the cross-validation and ten most important (highest affinity score) k-mers in the most-frequent approach. Nevertheless the k-mers are quite similar to each other in the sense that the top k-mers in the different methods align relatively well to the PWMs: examples of FOXJ3 and HSF2 are shown in Figures 6 and 7. The k-mers aligned to the logo in the left are from the latest rounds in the cross-validation and in the right the k-mers are top-affinity k-mers from the most-frequent approach. Both k-mers and their reverse complements are taken into account when aligning the top k-mers to the motifs.


Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data.

Kähärä J, Lähdesmäki H - BMC Bioinformatics (2013)

Top k-mers aligned to FOXJ3 logo. The top k-mers chosen with the CV-scheme (left) and the top affinity k-mers from the most frequent approach (right) aligned to FOXJ3 sequence logo.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750486&req=5

Figure 6: Top k-mers aligned to FOXJ3 logo. The top k-mers chosen with the CV-scheme (left) and the top affinity k-mers from the most frequent approach (right) aligned to FOXJ3 sequence logo.
Mentions: The cross-validation scheme does not choose necessarily those k-mers to the model that are important originally with higher number of k-mers. It was investigated if the highest affinity k-mers would be included late in the cross-validation. There was little overlap between the ten last k-mers in the cross-validation and ten most important (highest affinity score) k-mers in the most-frequent approach. Nevertheless the k-mers are quite similar to each other in the sense that the top k-mers in the different methods align relatively well to the PWMs: examples of FOXJ3 and HSF2 are shown in Figures 6 and 7. The k-mers aligned to the logo in the left are from the latest rounds in the cross-validation and in the right the k-mers are top-affinity k-mers from the most-frequent approach. Both k-mers and their reverse complements are taken into account when aligning the top k-mers to the motifs.

Bottom Line: We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy.We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number.Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear k-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative k-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of k-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for k-mer and PWMs, respectively. Finally, the k-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.

Show MeSH