Genomes assembled de novo from short reads are highly fragmented relative to the finished chromosomes of H. sapiens and key model organisms generated by the Human Genome Project. To address this, we need scalable, cost-effective methods enabling chromosome-scale contiguity. Here we show that genome-wide chromatin interaction datasets, such as those generated by Hi-C, are a rich source of long-range information for assigning, ordering and orienting genomic sequences to chromosomes, including across centromeres. To exploit this, we developed an algorithm that uses Hi-C data for ultra-long-range scaffolding of de novo genome assemblies. We demonstrate the approach by combining shotgun fragment and short jump mate-pair sequences with Hi-C data to generate chromosome-scale de novo assemblies of the human, mouse and Drosophila genomes, achieving – for human – 98% accuracy in assigning scaffolds to chromosome groups and 99% accuracy in ordering and orienting scaffolds within chromosome groups. Hi-C data can also be used to validate chromosomal translocations in cancer genomes.

Genetic studies of human evolution require high-quality contiguous ape genome assemblies that are not guided by the human reference. We coupled long-read sequence assembly, full-length cDNA sequencing with a multi-platform scaffolding approach to produce ab initio chimpanzee and orangutan genome assemblies. Comparing these with two long-read de novo human genome assemblies and a gorilla genome assembly, we characterized lineage-specific and shared great ape genetic variation ranging from single base-pair to megabase-sized variants. We identified ~17 thousand fixed human-specific structural variants identifying genic and putative regulatory changes that emerged in humans since divergence from nonhuman apes. Interestingly, these fixed human-specific structural variants are enriched near genes that are downregulated in human compared to chimpanzee cerebral organoids, particularly in cells analogous to radial glial neural progenitors.

SummaryThe HeLa cell line was established in 1951 from cervical cancer cells taken from a patient, Henrietta Lacks, marking the first successful attempt to continually culture human-derived cells in vitro1. HeLa’s robust growth and unrestricted distribution resulted in its broad adoption – both intentionally and through widespread cross-contamination2 – and for the past sixty years it has served a role analogous to that of a model organism3. Its cumulative impact is illustrated by the fact that HeLa is named in >74,000 or ~0.3% of PubMed abstracts. The genomic architecture of HeLa remains largely unexplored beyond its karyotype4, in part because like many cancers, its extensive aneuploidy renders such analyses challenging. We performed haplotype-resolved whole genome sequencing5 of the HeLa CCL-2 strain, discovering point and indel variation, mapping copy-number and loss of heterozygosity (LOH), and phasing variants across full chromosome arms. We further investigated variation and copy-number profiles for HeLa S3 and eight additional strains. Surprisingly, HeLa is relatively stable with respect to point variation, accumulating few new mutations since early passaging. Haplotype resolution facilitated reconstruction of an amplified, highly rearranged region at chromosome 8q24.21 at which the HPV-18 viral genome integrated as the likely initial event underlying tumorigenesis. We combined these maps with RNA-Seq6 and ENCODE Project7 datasets to phase the HeLa epigenome, revealing strong, haplotype-specific activation of the proto-oncogene MYC by the integrated HPV-18 genome ~500 kilobases upstream, and permitting global analyses of the relationship between gene dosage and expression. These data provide an extensively phased, high-quality reference genome for past and future experiments relying on HeLa, and demonstrate the value of haplotype resolution for characterizing cancer genomes and epigenomes.

We present single-cell combinatorial indexed Hi-C (sciHi-C), which applies the concept of combinatorial cellular indexing to chromosome conformation capture. In this proof-of-concept, we generate and sequence six sciHi-C libraries comprising a total of 10,696 single cells. We use sciHi-C data to separate cells by karytoypic and cell-cycle state differences and identify cell-to-cell heterogeneity in mammalian chromosomal conformation. Our results demonstrate that combinatorial indexing is a generalizable strategy for single-cell genomics.

Analysis of cell-free fetal DNA in maternal plasma holds great promise for the development of non-invasive prenatal genetic diagnostics. However, previous studies have been restricted to detection of fetal trisomies (1, 2) or specific, paternally inherited mutations (3), or to genotyping common polymorphisms using invasively sampled material (4). Here, we combine genome sequencing of two parents, genome-wide maternal haplotyping (5), and deep sequencing of maternal plasma to non-invasively determine the genome sequence of a human fetus at 18.5 weeks gestation. Inheritance was predicted at 2.8×106 parentally heterozygous sites with 98.1% accuracy. Furthermore, 39 of 44 de novo point mutations in the fetal genome were detected, albeit with limited specificity. Subsampling these data and analyzing a second family trio by the same approach indicate that ~300 kilobase parental haplotype blocks combined with shallow sequencing of maternal plasma are sufficient to substantially determine the inherited complement of a fetal genome. However, ultra-deep sequencing of maternal plasma is necessary for the practical detection of fetal de novo mutations genome-wide. Although technical and analytical challenges remain, we anticipate that non-invasive analysis of inherited variation and de novo mutations in fetal genomes will facilitate the comprehensive prenatal diagnosis of both recessive and dominant Mendelian disorders.

Haplotype information is essential to the complete description and interpretation of genomes 1 , genetic diversity 2 and genetic ancestry 3 . Although individual human genome sequencing is increasingly routine 4 , nearly all such genomes are unresolved with respect to haplotype. Here we combine the throughput of massively parallel sequencing 5 with the contiguity information provided by large-insert cloning 6 to experimentally determine the haplotype-resolved genome of a South Asian individual. A single fosmid library was split into a modest number of pools, each providing ~3% physical coverage of the diploid genome. Sequencing of each pool yielded reads overwhelmingly derived from only one homologous chromosome at any given location. These data were combined with whole-genome shotgun sequence to directly phase 94% of ascertained heterozygous single nucleotide polymorphisms (SNPs) into long haplotype blocks (N50 of 386 kilobases (kbp)). This method also facilitates the analysis of structural variation, for example, to anchor novel insertions 7,8 to specific locations and haplotypes.The high quality of the human reference genome derives from the hierarchical sequencing of large-insert clones, such that the assembly corresponding to each clone represents a single haplotype 9 . One of the first 'personal genomes' exploited clone-based mate pairing and long, accurate Sanger reads to resolve variants into haplotype blocks (N50 of 350 kbp; that is, 50% of resolved sequence is within blocks of at least 350 kbp) 1 . Although new technologies 5 have subsequently enabled >1,000-fold reduction in genome sequencing costs, the short read-lengths and paucity of contiguity information are such that it remains challenging to determine haplotypes at a genome-wide scale. Genomic phase, the assignment of alleles to homologous chromosomes, was determined for SNPs using mate-

We present an unbiased method to globally resolve RNA structures through pairwise contact measurements between interacting regions. RNA Proximity Ligation (RPL) uses proximity ligation of native RNA followed by deep sequencing to yield chimeric reads with ligation junctions in the vicinity of structurally proximate bases. We apply RPL in both baker's yeast (Saccharomyces cerevisiae) and human cells and generate contact probability maps for ribosomal and other abundant RNAs, including yeast snoRNAs, the RNA subunit of the signal recognition particle, and the yeast U2 spliceosomal RNA homolog. RPL measurements correlate with established secondary structures for these RNA molecules, including stem-loop structures and long-range pseudoknots. We anticipate that RPL will complement the current repertoire of computational and experimental approaches in enabling the high-throughput determination of secondary and tertiary RNA structures.

Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.