Semi-automated assembly of high-quality diploid human reference genomes

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements

A draft human pangenome reference

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample

Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.

Variant calling has been widely used for genotyping and for improving the consensus accuracy of long-read assemblies. Variant calls are commonly hard-filtered with user-defined cutoffs. However, it is impossible to define a single set of optimal cutoffs, as the calls heavily depend on the quality of the reads, the variant caller of choice and the quality of the unpolished assembly. Here, we introduce Merfin, a k-mer based variant-filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller's internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers

Community coping strategies for COVID-19 in Bangladesh: A nationwide cross-sectional survey

It is important to know the community coping strategies during the rapid uprise of a pandemic, as this helps to predict the consequences, especially in the mental health spectrum. This study aims to explore coping strategies used by Bangladeshi citizens during the major wave of the COVID-19 pandemic. Design: Prospective, cross-sectional survey of adults living in Bangladesh. Methods: Participants were interviewed for socio-demographic data and completed the Bengali-translated Brief-COPE Inventory. COPING indicators were categorized in four ways, such as approach, avoidant, humor, and religion. Results: Participants (N = 2001), aged 18 to 86 years, were recruited from eight administrative divisions within Bangladesh (mean age 31.85 ± 14.2 years). The male-to-female participant ratio was 53.4% (n = 1074) to 46.6% (n = 927). Higher scores were reported for approach coping styles (29.83 ± 8.9), with lower scores reported for avoidant coping styles (20.83 ± 6.05). Humor coping scores were reported at 2.68 ± 1.3, and religion coping scores at 5.64 ± 1.8. Both men and women showed similar coping styles. Multivariate analysis found a significant relationship between male gender and both humor and avoidant coping (p < 0.01). Male gender was found to be inversely related to both religion and approach coping (p < 0.01). Marital status and education were significantly related to all coping style domains (p < 0.01). The occupation was related to approach coping (p < 0.01). Rural and urban locations differed in participants’ coping styles (p < 0.01). Exploratory factor analysis revealed two cluster groups (factors 1 and 2) of mixed styles of coping. Conclusions: Participants in this study coped with the COVID-19 pandemic by utilizing mixed coping strategies. This study finds female gender, the married, elderly, and rural populations were adaptive to positive approaches to coping, whereas the male and educated population had the avoidant approach to coping

Community Coping Strategies for COVID-19 in Bangladesh: A Nationwide Cross-Sectional Survey

It is important to know the community coping strategies during the rapid uprise of a pandemic, as this helps to predict the consequences, especially in the mental health spectrum. This study aims to explore coping strategies used by Bangladeshi citizens during the major wave of the COVID-19 pandemic. Design: Prospective, cross-sectional survey of adults living in Bangladesh. Methods: Participants were interviewed for socio-demographic data and completed the Bengali-translated Brief-COPE Inventory. COPING indicators were categorized in four ways, such as approach, avoidant, humor, and religion. Results: Participants (N = 2001), aged 18 to 86 years, were recruited from eight administrative divisions within Bangladesh (mean age 31.85 ± 14.2 years). The male-to-female participant ratio was 53.4% (n = 1074) to 46.6% (n = 927). Higher scores were reported for approach coping styles (29.83 ± 8.9), with lower scores reported for avoidant coping styles (20.83 ± 6.05). Humor coping scores were reported at 2.68 ± 1.3, and religion coping scores at 5.64 ± 1.8. Both men and women showed similar coping styles. Multivariate analysis found a significant relationship between male gender and both humor and avoidant coping (p < 0.01). Male gender was found to be inversely related to both religion and approach coping (p < 0.01). Marital status and education were significantly related to all coping style domains (p < 0.01). The occupation was related to approach coping (p < 0.01). Rural and urban locations differed in participants’ coping styles (p < 0.01). Exploratory factor analysis revealed two cluster groups (factors 1 and 2) of mixed styles of coping. Conclusions: Participants in this study coped with the COVID-19 pandemic by utilizing mixed coping strategies. This study finds female gender, the married, elderly, and rural populations were adaptive to positive approaches to coping, whereas the male and educated population had the avoidant approach to coping

Nanopore sequencing and the {Shasta} toolkit enable efficient de novo assembly of eleven human genomes

De novo assembly of a human genome using nanopore long-read sequences has been reported, but it used more than 150,000 CPU hours and weeks of wall-clock time. To enable rapid human genome assembly, we present Shasta, a de novo long-read assembler, and polishing algorithms named MarginPolish and HELEN. Using a single PromethION nanopore sequencer and our toolkit, we assembled 11 highly contiguous human genomes de novo in 9 d. We achieved roughly 63× coverage, 42-kb read N50 values and 6.5× coverage in reads >100 kb using three flow cells per sample. Shasta produced a complete haploid human genome assembly in under 6 h on a single commercial compute node. MarginPolish and HELEN polished haploid assemblies to more than 99.9% identity (Phred quality score QV = 30) with nanopore reads alone. Addition of proximity-ligation sequencing enabled near chromosome-level scaffolds for all 11 genomes. We compare our assembly performance to existing methods for diploid, haploid and trio-binned human samples and report superior accuracy and speed

A draft human pangenome reference

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample