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A predictive model for regulatory sequences directing liver-specific transcription.

ABSTRACT: The identification and interpretation of the regulatory signals within the human genome remain among the greatest goals and most difficult challenges in genome analysis. The ability to predict the temporal and spatial control of transcription is likely to require a combination of methods to address the contribution of sequence-specific signals, protein-protein interactions and chromatin structure. We present here a new procedure to identify clusters of transcription factor binding sites characteristic of sequence modules experimentally verified to direct transcription selectively to liver cells. This algorithm is sufficiently specific to identify known regulatory sequences in genes selectively expressed in liver, promising acceleration of experimental promoter analysis. In combination with phylogenetic footprinting, this improvement in the specificity of predictions is sufficient to motivate a scan of the human genome. Potential regulatory modules were identified in orthologous human and rodent genomic sequences containing both known and uncharacterized genes.

SUBMITTER: Krivan W

PROVIDER: S-EPMC311083 | biostudies-literature | 2001 Sep

REPOSITORIES: biostudies-literature

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A predictive model for regulatory sequences directing liver-specific transcription.

Krivan W W Wasserman W W WW

Genome research 20010901 9

The identification and interpretation of the regulatory signals within the human genome remain among the greatest goals and most difficult challenges in genome analysis. The ability to predict the temporal and spatial control of transcription is likely to require a combination of methods to address the contribution of sequence-specific signals, protein-protein interactions and chromatin structure. We present here a new procedure to identify clusters of transcription factor binding sites characte ...[more]

PMID: 11544200

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Project description:UNLABELLED: BACKGROUND:Transposable elements (TEs) encode sequences necessary for their own transposition, including signals required for the termination of transcription. TE sequences within the introns of human genes show an antisense orientation bias, which has been proposed to reflect selection against TE sequences in the sense orientation owing to their ability to terminate the transcription of host gene transcripts. While there is evidence in support of this model for some elements, the extent to which TE sequences actually terminate transcription of human gene across the genome remains an open question. RESULTS:Using high-throughput sequencing data, we have characterized over 9,000 distinct TE-derived sequences that provide transcription termination sites for 5,747 human genes across eight different cell types. Rarefaction curve analysis suggests that there may be twice as many TE-derived termination sites (TE-TTS) genome-wide among all human cell types. The local chromatin environment for these TE-TTS is similar to that seen for 3' UTR canonical TTS and distinct from the chromatin environment of other intragenic TE sequences. However, those TE-TTS located within the introns of human genes were found to be far more cell type-specific than the canonical TTS. TE-TTS were much more likely to be found in the sense orientation than other intragenic TE sequences of the same TE family and TE-TTS in the sense orientation terminate transcription more efficiently than those found in the antisense orientation. Alu sequences were found to provide a large number of relatively weak TTS, whereas LTR elements provided a smaller number of much stronger TTS. CONCLUSIONS:TE sequences provide numerous termination sites to human genes, and TE-derived TTS are particularly cell type-specific. Thus, TE sequences provide a powerful mechanism for the diversification of transcriptional profiles between cell types and among evolutionary lineages, since most TE-TTS are evolutionarily young. The extent of transcription termination by TEs seen here, along with the preference for sense-oriented TE insertions to provide TTS, is consistent with the observed antisense orientation bias of human TEs.

Dataset Information

A predictive model for regulatory sequences directing liver-specific transcription.

Publications

A predictive model for regulatory sequences directing liver-specific transcription.

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