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Cancers develop as a result of driver mutations1,2 that lead to clonal outgrowth and the evolution of disease3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes5 and therapeutic vulnerabilities6. However, the serial genetic evolution of mutant cancer genes7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex-often neomorphic-functions of biological and therapeutic importance.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer1. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy2,3. The CLL epigenome is also an important disease-defining feature4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations6. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy. A single-cell approach is used to follow the heritable stochastic changes to DNA methylation that occur in primary chronic lymphocytic leukaemia and healthy B cells, allowing the tracing of cell lineage histories and evolution during treatment with ibrutinib.

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus-host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE. ispartof: VIRUSES-BASEL vol:12 issue:12 ispartof: location:Switzerland status: published

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and uses a simple criterion of homologous-identical recombination to convert the multiple sequence alignment into a graph. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Genetic and epigenetic heterogeneity of cancer cells fundamentally shapes cancer progression and relapse. In chronic lymphocytic leukemia (CLL), we previously reported that intra-leukemic epigenetic diversity in DNA methylation (DNAme) follows a stochastic pattern reminiscent of genetic 'trial and error' in cancer evolution. We measured stochastic DNAme from bulk sequencing by observing the proportion of discordantly methylated sequencing reads (PDR), and found that higher PDR was associated with greater cell-to-cell transcriptional heterogeneity and adverse clinical outcome. However, bulk DNAme sequencing does not allow to phase stochastic DNAme across distant genomic loci for a single cell, as it is limited to length of a short sequencing read. Thus, it can only provide the average PDR for a population of cells, rather than the cell-to-cell variation in this important epigenetic feature. Therefore, to define stochastic DNAme changes at the single cell level, we optimized a multiplexed single cell reduced representation bisulfite sequencing (MscRRBS) protocol to allow high-throughput single cell DNAme sequencing. This protocol significantly improves scalability by multiplexing cells with shorter inline-barcodes at an early stage and utilizing SPRI beads purification to eliminate adapter-dimers. We applied MscRRBS to 393 single CD19pos B cells from two healthy volunteers and 111 single cells from a CLL sample. 88% of cells were evaluable with greater than 100,000 covered CpGs (average of 436,230 CpGs per cell). We achieved bisulfite conversion rates of 99.7%+/-0.0001 (mean+/- SD), without a significant reduction in coverage. A downsampling analysis showed that 2.1 million reads per cell provided 85% of CpG coverage with only marginal increase in coverage with further sequencing. Biallelic coverage was observed in 4.6+/-2% of germline SNPs. With MscRRBS, we measure the PDR of each individual cell. As expected from our prior bulk RRBS analysis, we found that the average PDR across cells was higher in CLL compared with B cells from healthy adult volunteers (0.39+/-0.01 vs. 0.26+/-0.08, P The higher uniformity of PDR in CLL may reflect the relationship between epi-mutation rate (measured through PDR) and the evolutionary age of the cells. As additional stochastic DNAme changes are generated with each generation, we hypothesize that PDR estimates the number of generations in a cells' evolutionary history. Thus, CLL cells have uniformly high PDR reflecting a high but uniform number of generations in their history, consistent with a single common cell of origin. In contrast, normal B cells have diverse histories with newly formed naive cells intermixed with long-lived memory cells. Consistent with this hypothesis, healthy donor CD27neg cells showed lower PDR and less PDR cell-to-cell variation compared with CD27pos cells (mean absolute PDR difference 0.008+/-0.01 vs. 0.03+/-0.03, respectively, P As MscRRBS allows complete phasing of DNAme across distant genomic loci, we further calculated the odds ratio of concordance in methylation state between any two neighboring CpG as a function of their genomic distance, compared with any two randomly paired CpGs. Through this procedure we quantified the properties of DNAme concordance decay and uncovered different genomic scales of stochastically disordered methylation. Finally, the MscRRBS also allowed to reconstruct phylo(epi)genetic relationship between the cells, and provided accurate estimate of the rates of stochastic epi-mutation across the genome. Thus, single cell DNAme is a powerful novel tool to define epigenetic diversification and its impact on CLL evolution. Disclosures No relevant conflicts of interest to declare.