Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions.

Copy number variations (CNVs) are a significant source of genetic variation and have been found frequently associated with diseases such as cancers and autism. High-throughput sequencing data are increasingly being used to detect and quantify CNVs; however, the distributional properties of the data are not fully understood. A hidden Markov model (HMM) is proposed using inhomogeneous emission distributions based on negative binomial regression to account for the sequencing biases. The model is tested on the whole genome sequencing data and simulated data sets. An algorithm for CNV detection is implemented in the R package CNVfinder. The model based on negative binomial regression is shown to provide a good fit to the data and provides competitive performance compared with methods based on normalization of read counts.

Induction of apoptosis in hormone refractory breast cancer: horizontal modulation is superior to vertical.

Women with estrogen receptor positive (ER+) breast cancer receive treatment with tamoxifen or aromatase inhibitors as adjuvant hormone therapy, but their tumors frequently exhibit de novo or acquired resistance. Current strategies being developed to overcome resistance involve a combination of growth factor pathway inhibitors in addition to hormone therapy. Unfortunately, prolongation of responses with these new approaches is measured only in months. We reasoned that a pro-apoptotic strategy would be preferable since cell death would abrogate the process of adaptive reprogramming and eliminate the resistant cells rather than inhibiting their growth. Our hypothesis was that combinations of pro-apoptotic agents could be designed that would act synergistically as opposed to a merely additively. We examined two model strategies to determine which would result in the greatest synergy: a vertical approach that involved the targeting of two or more steps in a single pathway and a horizontal one, targeting steps in more than one parallel pathway. We found that combinations involving horizontal activation resulted in greater synergy than vertical. Combination index and isobologram analyses revealed that the horizontal combination of the small molecule 4-(4-chloro-2-methylphenoxy)-N-hydroxybutanamide (CMH) along with T-DM1 displayed the strongest synergy for inducing apoptosis in hormone refractory breast cancer cells. Both the reprogrammed, hormone resistant cells and the wild type responded to certain combinations with synergistic enhancement of apoptosis. These data suggest that combinations using T-DM1 are promising for further in vivo studies both in xenografts and in patients.

Predicting nucleosome positioning using a duration Hidden Markov Model.

BACKGROUND: The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function. RESULTS: We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download. CONCLUSIONS: Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

Preferentially quantized linker DNA lengths in Saccharomyces cerevisiae.

The exact lengths of linker DNAs connecting adjacent nucleosomes specify the intrinsic three-dimensional structures of eukaryotic chromatin fibers. Some studies suggest that linker DNA lengths preferentially occur at certain quantized values, differing one from another by integral multiples of the DNA helical repeat, approximately 10 bp; however, studies in the literature are inconsistent. Here, we investigate linker DNA length distributions in the yeast Saccharomyces cerevisiae genome, using two novel methods: a Fourier analysis of genomic dinucleotide periodicities adjacent to experimentally mapped nucleosomes and a duration hidden Markov model applied to experimentally defined dinucleosomes. Both methods reveal that linker DNA lengths in yeast are preferentially periodic at the DNA helical repeat ( approximately 10 bp), obeying the forms 10n+5 bp (integer n). This 10 bp periodicity implies an ordered superhelical intrinsic structure for the average chromatin fiber in yeast.