A supervised hidden Markov model framework for efficiently segmenting tiling array data in transcriptional and ChIP-chip experiments: systematically incorporating validated biological knowledge.

Jiang Du — 2006-10-23T15:32:54-00:00

Bioinformatics (12 October 2006)

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array data sets into "active regions" (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. Methodology: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments. SUPPLEMENTARY INFORMATION: The supplementary materials are available at http://tiling.gersteinlab.org/hmm/.

Mapping the C. elegans noncoding transcriptome with a whole-genome tiling microarray

Housheng He — 2007-09-12T08:47:56-00:00

Genome Res. (4 September 2007), gr.6611807.

The number of annotated protein coding genes in the genome of Caenorhabditis elegans is similar to that of other animals, but the extent of its non-protein-coding transcriptome remains unknown. Expression profiling on whole-genome tiling microarrays applied to a mixed-stage C. elegans population verified the expression of 71% of all annotated exons. Only a small fraction (11%) of the polyadenylated transcription is non-annotated and appears to consist of [~]3200 missed or alternative exons and 7800 small transcripts of unknown function (TUFs). Almost half (44%) of the detected transcriptional output is non-polyadenylated and probably not protein coding, and of this, 70% overlaps the boundaries of protein-coding genes in a complex manner. Specific analysis of small non-polyadenylated transcripts verified 97% of all annotated small ncRNAs and suggested that the transcriptome contains [~]1200 small (<500 nt) unannotated noncoding loci. After combining overlapping transcripts, we estimate that at least 70% of the total C. elegans genome is transcribed. 10.1101/gr.6611807

CiteULike: xingxu tiling

A supervised hidden Markov model framework for efficiently segmenting tiling array data in transcriptional and ChIP-chip experiments: systematically incorporating validated biological knowledge.

Mapping the C. elegans noncoding transcriptome with a whole-genome tiling microarray