A model of k-mer surprisal to quantify local sequence information content surrounding splice regions
Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These meth...
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| Format: | Article |
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PeerJ Inc.
2020-11-01
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| Online Access: | https://peerj.com/articles/10063.pdf |
| _version_ | 1797419873210466304 |
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| author | Sam Humphrey Alastair Kerr Magnus Rattray Caroline Dive Crispin J. Miller |
| author_facet | Sam Humphrey Alastair Kerr Magnus Rattray Caroline Dive Crispin J. Miller |
| author_sort | Sam Humphrey |
| collection | DOAJ |
| description | Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment. |
| first_indexed | 2024-03-09T06:54:31Z |
| format | Article |
| id | doaj.art-c6f890ee67d44d59b6a444a2f4c4411e |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:54:31Z |
| publishDate | 2020-11-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj.art-c6f890ee67d44d59b6a444a2f4c4411e2023-12-03T10:14:19ZengPeerJ Inc.PeerJ2167-83592020-11-018e1006310.7717/peerj.10063A model of k-mer surprisal to quantify local sequence information content surrounding splice regionsSam Humphrey0Alastair Kerr1Magnus Rattray2Caroline Dive3Crispin J. Miller4CRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United KingdomCRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United KingdomDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United KingdomCRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United KingdomComputational Biology Group, CRUK Beatson Institute, Glasgow, United KingdomMolecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.https://peerj.com/articles/10063.pdfInformation theorySurprisalSplicingEntropy |
| spellingShingle | Sam Humphrey Alastair Kerr Magnus Rattray Caroline Dive Crispin J. Miller A model of k-mer surprisal to quantify local sequence information content surrounding splice regions PeerJ Information theory Surprisal Splicing Entropy |
| title | A model of k-mer surprisal to quantify local sequence information content surrounding splice regions |
| title_full | A model of k-mer surprisal to quantify local sequence information content surrounding splice regions |
| title_fullStr | A model of k-mer surprisal to quantify local sequence information content surrounding splice regions |
| title_full_unstemmed | A model of k-mer surprisal to quantify local sequence information content surrounding splice regions |
| title_short | A model of k-mer surprisal to quantify local sequence information content surrounding splice regions |
| title_sort | model of k mer surprisal to quantify local sequence information content surrounding splice regions |
| topic | Information theory Surprisal Splicing Entropy |
| url | https://peerj.com/articles/10063.pdf |
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