Limits...
Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle.

Zacher B, Lidschreiber M, Cramer P, Gagneur J, Tresch A - Mol. Syst. Biol. (2014)

Bottom Line: To overcome these limitations, we introduce bidirectional HMMs which infer directed genomic states from occupancy profiles de novo.Application to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells recovers the majority of transcribed loci, reveals gene-specific variations in the yeast transcription cycle and indicates the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome.We anticipate bidirectional HMMs to significantly improve the analyses of genome-associated directed processes.

View Article: PubMed Central - PubMed

Affiliation: Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Institute for Genetics, University of Cologne, Cologne, Germany.

No MeSH data available.


Related in: MedlinePlus

Roles of directed genomic states in the transcription cycle.Mean ChIP enrichment of factors (horizontal axis) indicates the composition of the transcription machinery in each state (vertical axis). Factors were ordered by hierarchical clustering, and states were ordered by position of their most frequent occurrence along the average gene.Each state was assigned to a phase in the transcription cycle by investigating the frequency (y-axis) of each state at an average transcript. This spatial state distribution was calculated from the genomic sate sequences (viterbi paths) of 4,362 genes.The flux diagram shows probabilities of state transitions calculated from the viterbi paths. Branches mark alternative successions of states at individual genes and thus reveal extensive variation in the transcription cycle as it is modeled by the genomic states. Each node (state) is positioned according to the most frequent position on a metagene. The diagram contains at least one incoming and one outgoing transition for each state as well as transitions observed with a frequency > 0.01 on the metagene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4300491&req=5

fig04: Roles of directed genomic states in the transcription cycle.Mean ChIP enrichment of factors (horizontal axis) indicates the composition of the transcription machinery in each state (vertical axis). Factors were ordered by hierarchical clustering, and states were ordered by position of their most frequent occurrence along the average gene.Each state was assigned to a phase in the transcription cycle by investigating the frequency (y-axis) of each state at an average transcript. This spatial state distribution was calculated from the genomic sate sequences (viterbi paths) of 4,362 genes.The flux diagram shows probabilities of state transitions calculated from the viterbi paths. Branches mark alternative successions of states at individual genes and thus reveal extensive variation in the transcription cycle as it is modeled by the genomic states. Each node (state) is positioned according to the most frequent position on a metagene. The diagram contains at least one incoming and one outgoing transition for each state as well as transitions observed with a frequency > 0.01 on the metagene.

Mentions: To understand how the 20 bdHMM states relate to phases of the transcription cycle, we analyzed their average frequencies along annotated, transcribed genes (Fig4B, Materials and Methods). The states showing a single frequency peak (18 out of 20 states) were grouped into six transcription phases, according to the location of their peak on the average gene: promoter (P, 2 states), promoter escape (PE, 2 states), early elongation (eE, 3 states), mid-elongation (mE, 5 states), late elongation (lE, 3 states) and termination (T, 2 states). Two states showed two peaks in frequency, in each case with one peak upstream of the transcription start site and one peak around the polyadenylation (pA) site. We interpreted these two states as mixed promoter and termination states and labeled them accordingly P/T1 and P/T2 (Fig4A and B). Hence, although overlapping transcription is not explicitly modeled by bdHMMs, this phenomenon could be captured by specific states.


Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle.

Zacher B, Lidschreiber M, Cramer P, Gagneur J, Tresch A - Mol. Syst. Biol. (2014)

Roles of directed genomic states in the transcription cycle.Mean ChIP enrichment of factors (horizontal axis) indicates the composition of the transcription machinery in each state (vertical axis). Factors were ordered by hierarchical clustering, and states were ordered by position of their most frequent occurrence along the average gene.Each state was assigned to a phase in the transcription cycle by investigating the frequency (y-axis) of each state at an average transcript. This spatial state distribution was calculated from the genomic sate sequences (viterbi paths) of 4,362 genes.The flux diagram shows probabilities of state transitions calculated from the viterbi paths. Branches mark alternative successions of states at individual genes and thus reveal extensive variation in the transcription cycle as it is modeled by the genomic states. Each node (state) is positioned according to the most frequent position on a metagene. The diagram contains at least one incoming and one outgoing transition for each state as well as transitions observed with a frequency > 0.01 on the metagene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4300491&req=5

fig04: Roles of directed genomic states in the transcription cycle.Mean ChIP enrichment of factors (horizontal axis) indicates the composition of the transcription machinery in each state (vertical axis). Factors were ordered by hierarchical clustering, and states were ordered by position of their most frequent occurrence along the average gene.Each state was assigned to a phase in the transcription cycle by investigating the frequency (y-axis) of each state at an average transcript. This spatial state distribution was calculated from the genomic sate sequences (viterbi paths) of 4,362 genes.The flux diagram shows probabilities of state transitions calculated from the viterbi paths. Branches mark alternative successions of states at individual genes and thus reveal extensive variation in the transcription cycle as it is modeled by the genomic states. Each node (state) is positioned according to the most frequent position on a metagene. The diagram contains at least one incoming and one outgoing transition for each state as well as transitions observed with a frequency > 0.01 on the metagene.
Mentions: To understand how the 20 bdHMM states relate to phases of the transcription cycle, we analyzed their average frequencies along annotated, transcribed genes (Fig4B, Materials and Methods). The states showing a single frequency peak (18 out of 20 states) were grouped into six transcription phases, according to the location of their peak on the average gene: promoter (P, 2 states), promoter escape (PE, 2 states), early elongation (eE, 3 states), mid-elongation (mE, 5 states), late elongation (lE, 3 states) and termination (T, 2 states). Two states showed two peaks in frequency, in each case with one peak upstream of the transcription start site and one peak around the polyadenylation (pA) site. We interpreted these two states as mixed promoter and termination states and labeled them accordingly P/T1 and P/T2 (Fig4A and B). Hence, although overlapping transcription is not explicitly modeled by bdHMMs, this phenomenon could be captured by specific states.

Bottom Line: To overcome these limitations, we introduce bidirectional HMMs which infer directed genomic states from occupancy profiles de novo.Application to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells recovers the majority of transcribed loci, reveals gene-specific variations in the yeast transcription cycle and indicates the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome.We anticipate bidirectional HMMs to significantly improve the analyses of genome-associated directed processes.

View Article: PubMed Central - PubMed

Affiliation: Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Institute for Genetics, University of Cologne, Cologne, Germany.

No MeSH data available.


Related in: MedlinePlus