Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements
Weiwei Zhang, Tim D Spector, Panos Deloukas, Jordana T Bell, Barbara E Engelhardt
(Submitted on 9 Aug 2013)

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% — 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes.

Bayesian genome assembly and assessment by Markov Chain Monte Carlo sampling
Mark Howison, Felipe Zapata, Erika J. Edwards, Casey W. Dunn
(Submitted on 6 Aug 2013)

Most genome assemblers provide a point estimates of the true genome sequences, chosen from among many alternative hypotheses that are supported by the data. We present a Markov Chain Monte Carlo approach to sequence assembly that instead generates a distribution of assembly hypotheses with quantified probabilities. This statistically explicit Bayesian approach to assembly allows the investigator to evaluate alternative assembly hypotheses in a unified framework and propagate uncertainty about genomes assembly to downstream analyses. We implement this approach in a prototype assembler and illustrate its application to the genome of the bacteriophage $\Phi$X174.

XORRO: Rapid Paired-End Read Overlapper
Russell J. Dickson, Gregory B. Gloor
(Submitted on 16 Apr 2013)

Background: Computational analysis of next-generation sequencing data is outpaced by data generation in many cases. In one such case, paired-end reads can be produced from the Illumina sequencing method faster than they can be overlapped by downstream analysis. The advantages in read length and accuracy provided by overlapping paired-end reads demonstrates the necessity for software to efficiently solve this problem.
Results: XORRO is an extremely efficient paired-end read overlapping program. XORRO can overlap millions of short paired-end reads in a few minutes. It uses 64-bit registers with a two bit alphabet to represent sequences and does comparisons using low-level logical operations like XOR, AND, bitshifting and popcount.
Conclusions: As of the writing of this manuscript, XORRO provides the fastest solution to the paired-end read overlap problem. XORRO is available for download at: sourceforge.net/projects/xorro-overlap/

High-speed and accurate color-space short-read alignment with CUSHAW2
Yongchao Liu, Bernt Popp, Bertil Schmidt
(Submitted on 17 Apr 2013)

Summary: We present an extension of CUSHAW2 for fast and accurate alignments of SOLiD color-space short-reads. Our extension introduces a double-seeding approach to improve mapping sensitivity, by combining maximal exact match seeds and variable-length seeds derived from local alignments. We have compared the performance of CUSHAW2 to SHRiMP2 and BFAST by aligning both simulated and real color-space mate-paired reads to the human genome. The results show that CUSHAW2 achieves comparable or better alignment quality compared to SHRiMP2 and BFAST at an order-of-magnitude faster speed and significantly smaller peak resident memory size. Availability: CUSHAW2 and all simulated datasets are available at this http URL Contact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de

The Convergence of eQTL Mapping, Heritability Estimation and Polygenic Modeling: Emerging Spectrum of Risk Variation in Bipolar Disorder
Eric R. Gamazon, Hae Kyung Im, Chunyu Liu, Members of the Bipolar Disorder Genome Study (BiGS) Consortium, Dan L. Nicolae, Nancy J. Cox
(Submitted on 25 Mar 2013)

It is widely held that a substantial genetic component underlies Bipolar Disorder (BD) and other neuropsychiatric disease traits. Recent efforts have been aimed at understanding the genetic basis of disease susceptibility, with genome-wide association studies (GWAS) unveiling some promising associations. Nevertheless, the genetic etiology of BD remains elusive with a substantial proportion of the heritability – which has been estimated to be 80% based on twin and family studies – unaccounted for by the specific genetic variants identified by large-scale GWAS. Furthermore, functional understanding of associated loci generally lags discovery. Studies we report here provide considerable support to the claim that substantially more remains to be gained from GWAS on the genetic mechanisms underlying BD susceptibility, and that a large proportion of the variation in disease risk may be uncovered through integrative functional genomic approaches. We combine recent analytic advances in heritability estimation and polygenic modeling and leverage recent technological advances in the generation of -omics data to evaluate the nature and scale of the contribution of functional classes of genetic variation to a relatively intractable disorder. We identified cis eQTLs in cerebellum and parietal cortex that capture more than half of the total heritability attributable to SNPs interrogated through GWAS and showed that eQTL-based heritability estimation is highly tissue-dependent. Our findings show that a much greater resolution may be attained than has been reported thus far on the number of common loci that capture a substantial proportion of the heritability to disease risk and that the functional nature of contributory loci may be clarified en masse.