(74% of regions are identified with a precision of 86%). This suggests the interesting model that paternal nucleosome inheri- tance might aid in the establishment of DNA methylation-free regions (Figure 6). There is evidence that the histone modification H3K4me3 can interfere with DNA methylation [48], and CpG islands that retain nucleosomes in sperm are also enriched for this mark (Figure 5). This suggests a possible mechanism for how nucleosome retention may influence DNA methylation in the embryo. Alternatively, the same sequence elements and factors may underlie both the establishment of methylation-free regions and the retention of nucleosomes in sperm. Sperm chromatin organization may drive fine-scale base composition variation in the human genome Finally, based on the striking relationship between base composition and nucleosome occupancy in sperm, we propose that chromatin organization in the male germline may be an important selective pressure on GC-content evolution in mam- malian genomes. By defining the regions at which nucleosomes are retained in the paternal germline, base composition establishes the organization of sperm chromatin and so the regions at which epigenetic information in the form of histone modifications can be transmitted from one generation to the next. It is interesting to speculate, therefore, that a requirement to transmit paternal epigenetic information to the zygote could be an important selective pressure on sequence evolution in mammalian genomes. Methods Nucleosome retention sites in human sperm The following measures of nucleosome retention in human sperm were used in this study. Regions of nucleosome retention defined using deep sequencing. We used the genomic positions of retained nucleosomes from four sperm donors as identified using micrococcal nuclease digestion and reported by Hammoud et al [5]. In brief, these regions were defined by the USeq package [59], using a 300-bp sliding window along the genome, and represent genomic windows where sequence reads are significantly more from the histone-bound fraction of the genome than from the genomic input control. Nucleosome retention score defined using deep sequen- cing. The raw unfiltered reads from the nucleosome fraction and genomic input control from four sperm donors were downloaded from Gene Expression Omnibus (GEO) [60]. We filtered reads keeping those matching the genome without mismatches and with an Eland alignment score $13 as in [5]. We then shifted the positions of the reads by 75 bp (which is half of the length of the sequenced nucleosome-bound fragments) in the direction of sequencing to transform the data from counts of 59 and 39 ends to central nucleosome positions. To account for a possible sequencing bias, we calculated the normalized nucleosome retention score in 150 bp windows genome-wide as the difference between the number of nucleosome reads and the number of genomic input reads within the window and divided by the square root of the sum [5,59]. Because the nucleosome fraction sample was sequenced to a greater depth compared to the genomic input control, for the calculation of the normalized nucleosome retention score the reads from the nucleosome fraction were randomly sub-sampled to generate a dataset with the same number of reads as the genomic input, as described in [5]. Repetitive windows of the genome were defined using the Duke uniqueness track downloaded from the UCSC browser website [61]. For the analyses of correlation between nucleosome Nucleosome Retention in Human Sperm retention and GC-content or nucleosome affinity, 150-bp windows containing non-unique 20mers were removed. Genome-wide nucleosome retention scores defined using microarrays. We also analyzed two additional nucleosome retention scores along the human genome from data generated by a second laboratory [3]. In these experiments the nucleosome- bound regions were isolated by two different experimental methods (micrococcal nuclease digestion (MND), and salt extraction followed by endonuclease digestion (SRD)) from four donors, and identified using genome-wide low-density CGH microarrays. In brief, sperm chromatin was digested with micrococcal nuclease and then centrifuged to separate the histone from the protamine fraction (MND experiment). Alternatively, sperm chromatin was treated with weak salt solutions, digested with two endonucleases and centrifuged to separate the histone and protamine fractions (SRD experiment). In both cases, the histone and the protamine fraction were hybridized to a two-colour CGH array consisting of 44 thousand genic and intergenic probes. The raw hybridization signal intensity data for these two experiments were downloaded from GEO. We normalized the downloaded raw microarray data using MA2C with the ‘‘Robust’’ normalization setting to adjust for dye and probe-sequence bias [62]. We also analyzed data from a third MNase digestion map [4], but found that it shows little agreement with data from the two other studies. The reasons for this are not clear, but may relate to a more extensive digestion of DNA. Retention of modified nucleosomes in human sperm We used the H3K4me3 and H3K27me3 ChIP-Seq data generated by Hammoud et al [5]. For each of the two datasets, to control for sequencing biases, we calculated a retention score, based on the binomial distribution, by normalizing against the input control. Genomic distribution of regions of nucleosome retention in sperm We classified genes as overlapping retained nucleosomes at their transcription start sites when one or more start site is within 50 bp of a nucleosome enrichment region defined by sequencing from Ham- moud et al [5]. Coordinates for all protein-coding and non-coding genes and transcripts were retrieved from Ensembl release 54 [63]. Nucleosome occupancy in T-cells We used the nucleosome occupancy data generated by Schones et al using MNase digestion and deep sequencing [51]. The positions of the uniquely mapped sequenced reads marking the ends of nucleosomes along the human genome were downloaded from the authors’ website. We filtered-out identical reads. As for the sperm nucleosome data, we shifted the positions of the reads in the direction of sequencing to transform the data from positions of fragment ends to central nucleosome positions. We then counted these transformed nucleosome positions along the human genome in 150 bp windows. Intrinsic nucleosome binding preferences Nucleosome binding preferences were predicted using the model of Kaplan et al [30], which is trained on the occupancy of chicken nucleosomes on naked yeast DNA. The nucleosome affinity score for human genome version hg18 was downloaded from the authors’ website. Gene expression data We retrieved gene expression data for ten tissues (testes, brain, breast, colon, heart, liver, lymph node, skeletal muscle and cerebellum) PLoS Genetics | www.plosgenetics.org 8 April 2011 | Volume 7 | Issue 4 | e1002036