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CodingMotif: exact determination of overrepresented nucleotide motifs in coding sequences.

Ding Y, Lorenz WA, Chuang JH - BMC Bioinformatics (2012)

Bottom Line: We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences.We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.

ABSTRACT

Background: It has been increasingly appreciated that coding sequences harbor regulatory sequence motifs in addition to encoding for protein. These sequence motifs are expected to be overrepresented in nucleotide sequences bound by a common protein or small RNA. However, detecting overrepresented motifs has been difficult because of interference by constraints at the protein level. Sampling-based approaches to solve this problem based on codon-shuffling have been limited to exploring only an infinitesimal fraction of the sequence space and by their use of parametric approximations.

Results: We present a novel O(N(log N)2)-time algorithm, CodingMotif, to identify nucleotide-level motifs of unusual copy number in protein-coding regions. Using a new dynamic programming algorithm we are able to exhaustively calculate the distribution of the number of occurrences of a motif over all possible coding sequences that encode the same amino acid sequence, given a background model for codon usage and dinucleotide biases. Our method takes advantage of the sparseness of loci where a given motif can occur, greatly speeding up the required convolution calculations. Knowledge of the distribution allows one to assess the exact non-parametric p-value of whether a given motif is over- or under- represented. We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.

Conclusions: CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences. We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions. A software implementation is available at http://bioinformatics.bc.edu/chuanglab/codingmotif.tar.

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DCM p-values for motifs with known splicing activity. We observe a correlation between -log(p) for CodingMotif (DCM) p-values and experimentally measured splicing activity (as described in [3]) with r2 = 0.26 (t-test p-value 0.02).
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Figure 3: DCM p-values for motifs with known splicing activity. We observe a correlation between -log(p) for CodingMotif (DCM) p-values and experimentally measured splicing activity (as described in [3]) with r2 = 0.26 (t-test p-value 0.02).

Mentions: As a test of the ability of CodingMotif to identify biologically relevant motifs, we analyzed the behavior of splicing motifs on the coding sequences in human chromosome 1. Our expectation was that motifs with known activity in coding regions, such as exonic splicing enhancers, would show overrepresentation. Figure 3 shows the log p-values from CodingMotif versus experimentally measured exonic splicing enhancer activity, for sequences assayed previously by [3] (activity values rounded to the nearest 5%). The splicing activities refer to rates of splicing rescue when a particular hexamer was inserted into exon2 of a pSXN reporter construct. We found that motifs with superior p-values indeed have greater splicing activities. For example, the motifs with the top 4 p-values all have splicing activities of at least 40%. Overall, we observe a correlation with R2 = 0.26 (t-test p-value = 0.02) between - log(p) and splicing activity. However, some motifs with strong overrepresentation do not show strong splicing activity. This illustrates the importance of dataset size, as it is likely that this large dataset may have a number of other functional motifs that are overrepresented but have functions unrelated to splicing.


CodingMotif: exact determination of overrepresented nucleotide motifs in coding sequences.

Ding Y, Lorenz WA, Chuang JH - BMC Bioinformatics (2012)

DCM p-values for motifs with known splicing activity. We observe a correlation between -log(p) for CodingMotif (DCM) p-values and experimentally measured splicing activity (as described in [3]) with r2 = 0.26 (t-test p-value 0.02).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3298695&req=5

Figure 3: DCM p-values for motifs with known splicing activity. We observe a correlation between -log(p) for CodingMotif (DCM) p-values and experimentally measured splicing activity (as described in [3]) with r2 = 0.26 (t-test p-value 0.02).
Mentions: As a test of the ability of CodingMotif to identify biologically relevant motifs, we analyzed the behavior of splicing motifs on the coding sequences in human chromosome 1. Our expectation was that motifs with known activity in coding regions, such as exonic splicing enhancers, would show overrepresentation. Figure 3 shows the log p-values from CodingMotif versus experimentally measured exonic splicing enhancer activity, for sequences assayed previously by [3] (activity values rounded to the nearest 5%). The splicing activities refer to rates of splicing rescue when a particular hexamer was inserted into exon2 of a pSXN reporter construct. We found that motifs with superior p-values indeed have greater splicing activities. For example, the motifs with the top 4 p-values all have splicing activities of at least 40%. Overall, we observe a correlation with R2 = 0.26 (t-test p-value = 0.02) between - log(p) and splicing activity. However, some motifs with strong overrepresentation do not show strong splicing activity. This illustrates the importance of dataset size, as it is likely that this large dataset may have a number of other functional motifs that are overrepresented but have functions unrelated to splicing.

Bottom Line: We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences.We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.

ABSTRACT

Background: It has been increasingly appreciated that coding sequences harbor regulatory sequence motifs in addition to encoding for protein. These sequence motifs are expected to be overrepresented in nucleotide sequences bound by a common protein or small RNA. However, detecting overrepresented motifs has been difficult because of interference by constraints at the protein level. Sampling-based approaches to solve this problem based on codon-shuffling have been limited to exploring only an infinitesimal fraction of the sequence space and by their use of parametric approximations.

Results: We present a novel O(N(log N)2)-time algorithm, CodingMotif, to identify nucleotide-level motifs of unusual copy number in protein-coding regions. Using a new dynamic programming algorithm we are able to exhaustively calculate the distribution of the number of occurrences of a motif over all possible coding sequences that encode the same amino acid sequence, given a background model for codon usage and dinucleotide biases. Our method takes advantage of the sparseness of loci where a given motif can occur, greatly speeding up the required convolution calculations. Knowledge of the distribution allows one to assess the exact non-parametric p-value of whether a given motif is over- or under- represented. We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.

Conclusions: CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences. We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions. A software implementation is available at http://bioinformatics.bc.edu/chuanglab/codingmotif.tar.

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