Stochastic models of sequence evolution including insertion-deletion events.
Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and geno...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2009
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| _version_ | 1797083062073294848 |
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| author | Miklós, I Novák, A Satija, R Lyngsø, R Hein, J |
| author_facet | Miklós, I Novák, A Satija, R Lyngsø, R Hein, J |
| author_sort | Miklós, I |
| collection | OXFORD |
| description | Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and genome analysis and is called alignment. This statistical approach is harder conceptually and computationally, than competing approaches based on choosing an alignment according to some optimality criteria. But it has major practical advantages in terms of testing evolutionary hypotheses and parameter estimation. Basic dynamic approaches can allow the analysis of up to 4-5 sequences. MCMC techniques can bring this to about 10-15 sequences. Beyond this, different or heuristic approaches must be used. Besides the computational challenges, increasing realism in the underlying models is presently being addressed. A recent development that has been especially fruitful is combining statistical alignment with the problem of sequence annotation, making statements about the function of each nucleotide/amino acid. So far gene finding, protein secondary structure prediction and regulatory signal detection has been tackled within this framework. Much progress can be reported, but clearly major challenges remain if this approach is to be central in the analyses of large incoming sequence data sets. |
| first_indexed | 2024-03-07T01:36:39Z |
| format | Journal article |
| id | oxford-uuid:956c04de-8b36-4d34-8970-5eadad0444ea |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:36:39Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:956c04de-8b36-4d34-8970-5eadad0444ea2022-03-26T23:46:00ZStochastic models of sequence evolution including insertion-deletion events.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:956c04de-8b36-4d34-8970-5eadad0444eaEnglishSymplectic Elements at Oxford2009Miklós, INovák, ASatija, RLyngsø, RHein, JComparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and genome analysis and is called alignment. This statistical approach is harder conceptually and computationally, than competing approaches based on choosing an alignment according to some optimality criteria. But it has major practical advantages in terms of testing evolutionary hypotheses and parameter estimation. Basic dynamic approaches can allow the analysis of up to 4-5 sequences. MCMC techniques can bring this to about 10-15 sequences. Beyond this, different or heuristic approaches must be used. Besides the computational challenges, increasing realism in the underlying models is presently being addressed. A recent development that has been especially fruitful is combining statistical alignment with the problem of sequence annotation, making statements about the function of each nucleotide/amino acid. So far gene finding, protein secondary structure prediction and regulatory signal detection has been tackled within this framework. Much progress can be reported, but clearly major challenges remain if this approach is to be central in the analyses of large incoming sequence data sets. |
| spellingShingle | Miklós, I Novák, A Satija, R Lyngsø, R Hein, J Stochastic models of sequence evolution including insertion-deletion events. |
| title | Stochastic models of sequence evolution including insertion-deletion events. |
| title_full | Stochastic models of sequence evolution including insertion-deletion events. |
| title_fullStr | Stochastic models of sequence evolution including insertion-deletion events. |
| title_full_unstemmed | Stochastic models of sequence evolution including insertion-deletion events. |
| title_short | Stochastic models of sequence evolution including insertion-deletion events. |
| title_sort | stochastic models of sequence evolution including insertion deletion events |
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