SD3_HSat3A6ArraySizeEstimates

SD3_HSat3A6ArraySizeEstimates.txt: This file lists the 396 male samples for whom we estimated HSat3A6 array sizes. The first column lists the Sample ID, the second column lists the sample population (abbreviated according to the conventions used by the 1000 Genomes Project), the third and fourth columns specify the Y haplogroup assignment of each sample (from personal correspondence with Chris Tyler-Smith), and the fifth column lists our HSat3A6 array size estimate in bp

SD2_SubfamilySpecific24mers

SD2_SubfamilySpecific24mers.txt: This file lists all subfamily-specific 24-mers (column 1) along with their subfamily assignments (column 2). A subfamily-specific 24-mer is defined as a 24-mer that occurs on >1% of reads within a given HSat2,3 subfamily and on no more than 0.1% of reads in any other subfamily (and on no non-HSat2,3 reads). Column 3 lists the proportion of reads containing that 24-mer in that subfamily, and column 4 lists the proportion of High Quality (phred>20 for all bases) 24-bp windows matching that 24-mer in that subfamily

HSat3A6 (DYZ1) array size estimates in 396 individuals.

<p>Each circle represents an HSat3A6 size estimate for a single individual, and it is colored by the population designation of that individual. Individuals are grouped by Y haplogroup assignments, and boxplots illustrate the distribution of array sizes within each haplogroup. Brackets below the plot indicate pairs of haplogroups with p<0.001 in a pairwise, two-sided, two-sample Wilcoxon rank-sum test (with Holm correction for multiple testing), indicating a location shift in the distributions of array sizes between these haplogroups.</p

Sequence database description of HSat2,3 subfamilies.

†<p>these represent the predominant chromosomal localizations.</p><p>*localizations based on flow-sorted chromosome sequence coverage as well as published mappings of sequenced clones and HSat2,3 annotated on hg19 (representing arrays >50 kb).</p

Array size estimates for Y-haplogroups.

<p>Array size estimates for Y-haplogroups.</p

Genomic Characterization of Large Heterochromatic Gaps in the Human Genome Assembly

<div><p>The largest gaps in the human genome assembly correspond to multi-megabase heterochromatic regions composed primarily of two related families of tandem repeats, Human Satellites 2 and 3 (HSat2,3). The abundance of repetitive DNA in these regions challenges standard mapping and assembly algorithms, and as a result, the sequence composition and potential biological functions of these regions remain largely unexplored. Furthermore, existing genomic tools designed to predict consensus-based descriptions of repeat families cannot be readily applied to complex satellite repeats such as HSat2,3, which lack a consistent repeat unit reference sequence. Here we present an alignment-free method to characterize complex satellites using whole-genome shotgun read datasets. Utilizing this approach, we classify HSat2,3 sequences into fourteen subfamilies and predict their chromosomal distributions, resulting in a comprehensive satellite reference database to further enable genomic studies of heterochromatic regions. We also identify 1.3 Mb of non-repetitive sequence interspersed with HSat2,3 across 17 unmapped assembly scaffolds, including eight annotated gene predictions. Finally, we apply our satellite reference database to high-throughput sequence data from 396 males to estimate array size variation of the predominant HSat3 array on the Y chromosome, confirming that satellite array sizes can vary between individuals over an order of magnitude (7 to 98 Mb) and further demonstrating that array sizes are distributed differently within distinct Y haplogroups. In summary, we present a novel framework for generating initial reference databases for unassembled genomic regions enriched with complex satellite DNA, and we further demonstrate the utility of these reference databases for studying patterns of sequence variation within human populations.</p></div

Reads from flow-sorted chromosomes are useful in assigning genome-wide distributions of HSat2,3 subfamilies.

<p>Read feature vectors from flow-sorted chromosome datasets (colored according to targeted chromosome(s)) are overlaid on PCA projections of the read databases (colored black) for (A) HSat2A2, (B) HSat3A6, and (C) HSat3A4. The plots qualitatively show enrichment for chromosomes 1, Y, and the acrocentrics, respectively. This enrichment is quantitatively and precisely measured in order to infer the chromosomal localization of each subfamily (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003628#s4" target="_blank">Methods</a>).</p

Overview of approach used to characterize satellite sequences.

<p>This shows a simplified graphic representation of our overall approach for identifying satellite subfamilies given whole-genome shotgun read data. The actual spectral clustering algorithm is applied in the full 1024-dimension feature space using 50-nearest-neighbor edges weighted according to Euclidean distance.</p

Ring-like topology of HSat2,3 subfamily projections reflects tandem repeat unit organization.

<p>Feature vectors representing simulated reads from a 1.77 kb clone sequence from the HSat2 array on chromosome 1 are colored by their starting position on the clone sequence and overlaid on a PCA projection of HSat2A2 reads (black). Arrows below this plot illustrate the tandem nature of the 1.77 kb repeat, which yields the observed ring-like projection as reads are sampled from different subregions of the tandem repeat unit.</p

Recursive identification of highly connected subgraphs identifies fourteen HSat2,3 subfamilies.

<p>This tree illustrates the iterative binary divisions of the complete HSat2,3 dataset into subfamilies. Each plot is a PCA projection (on principal components 1 and 2) of the normalized 5-mer frequency vectors for all reads in that subgraph. Each point corresponds to one read, colored red or blue by its cluster assignment. Final cluster projections are colored black. The box at the upper right illustrates the concept of self-mate-pair frequencies within the first subgraph division. Arrows below each subgraph are labeled with the self-mate-pair frequency of each daughter cluster, and they are colored to match their cluster of origin in the parent subgraph.</p