Hatice Ozel Abaan

Systematic comparison of three genomic enrichment methods for massively parallel DNA sequencing

Massively parallel DNA sequencing technologies have greatly increased our ability to generate large amounts of sequencing data at a rapid pace. Several methods have been developed to enrich for genomic regions of interest for targeted sequencing. We have compared three of these methods: Molecular Inversion Probes (MIP), Solution Hybrid Selection (SHS), and Microarray-based Genomic Selection (MGS). Using HapMap DNA samples, we compared each of these methods with respect to their ability to capture an identical set of exons and evolutionarily conserved regions associated with 528 genes (2.61 Mb). For sequence analysis, we developed and used a novel Bayesian genotype-assigning algorithm, Most Probable Genotype (MPG). All three capture methods were effective, but sensitivities (percentage of targeted bases associated with high-quality genotypes) varied for an equivalent amount of pass-filtered sequence: for example, 70% (MIP), 84% (SHS), and 91% (MGS) for 400 Mb. In contrast, all methods yielded similar accuracies of >99.84% when compared to Infinium 1M SNP BeadChip-derived genotypes and >99.998% when compared to 30-fold coverage whole-genome shotgun sequencing data. We also observed a low false-positive rate with all three methods; of the heterozygous positions identified by each of the capture methods, >99.57% agreed with 1M SNP BeadChip, and >98.840% agreed with the whole-genome shotgun data. In addition, we successfully piloted the genomic enrichment of a set of 12 pooled samples via the MGS method using molecular bar codes. We find that these three genomic enrichment methods are highly accurate and practical, with sensitivities comparable to that of 30-fold coverage whole-genome shotgun data.

A Transcriptomic Atlas of Mouse Neocortical Layers

Summary In the mammalian cortex, neurons and glia form a patterned structure across six layers whose complex cytoarchitectonic arrangement is likely to contribute to cognition. We sequenced transcriptomes from layers 1-6b of different areas (primary and secondary) of the adult (postnatal day 56) mouse somatosensory cortex to understand the transcriptional levels and functional repertoires of coding and noncoding loci for cells constituting these layers. A total of 5,835 protein-coding genes and 66 noncoding RNA loci are differentially expressed (“patterned”) across the layers, on the basis of a machine-learning model (naive Bayes) approach. Layers 2-6b are each associated with specific functional and disease annotations that provide insights into their biological roles. This new resource (http://genserv.anat.ox.ac.uk/layers) greatly extends currently available resources, such as the Allen Mouse Brain Atlas and microarray data sets, by providing quantitative expression levels, by being genome-wide, by including novel loci, and by identifying candidate alternatively spliced transcripts that are differentially expressed across layers.

Local DNA Topography Correlates with Functional Noncoding Regions of the Human Genome

The three-dimensional molecular structure of DNA, specifically the shape of the backbone and grooves of genomic DNA, can be dramatically affected by nucleotide changes, which can cause differences in protein-binding affinity and phenotype. We developed an algorithm to measure constraint on the basis of similarity of DNA topography among multiple species, using hydroxyl radical cleavage patterns to interrogate the solvent-accessible surface area of DNA. This algorithm found that 12% of bases in the human genome are evolutionarily constrained—double the number detected by nucleotide sequence–based algorithms. Topography-informed constrained regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history.

A new strategy for genome assembly using short sequence reads and reduced representation libraries

We have developed a novel approach for using massively parallel short-read sequencing to generate fast and inexpensive de novo genomic assemblies comparable to those generated by capillary-based methods. The ultrashort (<100 base) sequences generated by this technology pose specific biological and computational challenges for de novo assembly of large genomes. To account for this, we devised a method for experimentally partitioning the genome using reduced representation (RR) libraries prior to assembly. We use two restriction enzymes independently to create a series of overlapping fragment libraries, each containing a tractable subset of the genome. Together, these libraries allow us to reassemble the entire genome without the need of a reference sequence. As proof of concept, we applied this approach to sequence and assembled the majority of the 125-Mb Drosophila melanogaster genome. We subsequently demonstrate the accuracy of our assembly method with meaningful comparisons against the current available D. melanogaster reference genome (dm3). The ease of assembly and accuracy for comparative genomics suggest that our approach will scale to future mammalian genome-sequencing efforts, saving both time and money without sacrificing quality.

Genomics of CpG Methylation in Developing and Developed Zebrafish

DNA methylation is a dynamic process through which specific chromatin modifications can be stably transmitted from parent to daughter cells. A large body of work has suggested that DNA methylation influences gene expression by silencing gene promoters. However, these conclusions were drawn from data focused mostly on promoter regions. Regarding the entire genome, it is unclear how methylation and gene transcription patterns are related during vertebrate development. To identify the genome-wide distribution of CpG methylation, we created series of high-resolution methylome maps of Danio rerio embryos during development and in mature, differentiated tissues. We found that embryonic and terminal tissues have unique methylation signatures in CpG islands and repetitive sequences. Fully differentiated tissues have increased CpG and LTR methylation and decreased SINE methylation relative to embryonic tissues. Unsupervised clustering analyses reveal that the embryonic and terminal tissues can be classified solely by their methylation patterning. Novel analyses also identify a previously undescribed genome-wide exon methylation signature. We also compared whole genome methylation with genome-wide mRNA expression levels using publicly available RNA-seq datasets. These comparisons revealed previously unrecognized relationships between gene expression, alternative splicing, and exon methylation. Surprisingly, we found that exonic methylation is a better predictor of mRNA expression level than promoter methylation. We also found that transcriptionally skipped exons have significantly less methylation than retained exons. Our integrative analyses reveal highly complex interplay between gene expression, alternative splicing, development, and methylation patterning in zebrafish.

Mutational Signatures of De-Differentiation in Functional Non-Coding Regions of Melanoma Genomes

Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.

Replication and exploratory analysis of 24 candidate risk polymorphisms for neural tube defects.

BackgroundNeural tube defects (NTDs), which are among the most common congenital malformations, are influenced by environmental and genetic factors. Low maternal folate is the strongest known contributing factor, making variants in genes in the folate metabolic pathway attractive candidates for NTD risk. Multiple studies have identified nominally significant allelic associations with NTDs. We tested whether associations detected in a large Irish cohort could be replicated in an independent population.MethodsReplication tests of 24 nominally significant NTD associations were performed in racially/ethnically matched populations. Family-based tests of fifteen nominally significant single nucleotide polymorphisms (SNPs) were repeated in a cohort of NTD trios (530 cases and their parents) from the United Kingdom, and case-control tests of nine nominally significant SNPs were repeated in a cohort (190 cases, 941 controls) from New York State (NYS). Secondary hypotheses involved evaluating the latter set of nine SNPs for NTD association using alternate case-control models and NTD groupings in white, African American and Hispanic cohorts from NYS.ResultsOf the 24 SNPs tested for replication, ADA rs452159 and MTR rs10925260 were significantly associated with isolated NTDs. Of the secondary tests performed, ARID1A rs11247593 was associated with NTDs in whites, and ALDH1A2 rs7169289 was associated with isolated NTDs in African Americans.ConclusionsWe report a number of associations between SNP genotypes and neural tube defects. These associations were nominally significant before correction for multiple hypothesis testing. These corrections are highly conservative for association studies of untested hypotheses, and may be too conservative for replication studies. We therefore believe the true effect of these four nominally significant SNPs on NTD risk will be more definitively determined by further study in other populations, and eventual meta-analysis.

A Bioinformatics Approach for Determining Sample Identity from Different Lanes of High-Throughput Sequencing Data

The ability to generate whole genome data is rapidly becoming commoditized. For example, a mammalian sized genome (∼3Gb) can now be sequenced using approximately ten lanes on an Illumina HiSeq 2000. Since lanes from different runs are often combined, verifying that each lane in a genomes build is from the same sample is an important quality control. We sought to address this issue in a post hoc bioinformatic manner, instead of using upstream sample or “barcode” modifications. We rely on the inherent small differences between any two individuals to show that genotype concordance rates can be effectively used to test if any two lanes of HiSeq 2000 data are from the same sample. As proof of principle, we use recent data from three different human samples generated on this platform. We show that the distributions of concordance rates are non-overlapping when comparing lanes from the same sample versus lanes from different samples. Our method proves to be robust even when different numbers of reads are analyzed. Finally, we provide a straightforward method for determining the gender of any given sample. Our results suggest that examining the concordance of detected genotypes from lanes purported to be from the same sample is a relatively simple approach for confirming that combined lanes of data are of the same identity and quality.