Abstract
Motivation
Reconstruction of haplotypes for human genomes is an important problem in medical and population genetics. Hi-C sequencing generates read pairs with long-range haplotype ...information that can be computationally assembled to generate chromosome-spanning haplotypes. However, the haplotypes have limited completeness and low accuracy. Haplotype information from population reference panels can potentially be used to improve the completeness and accuracy of Hi-C haplotyping.
Results
In this paper, we describe a likelihood based method to integrate short-range haplotype information from a population reference panel of haplotypes with the long-range haplotype information present in sequence reads from methods such as Hi-C to assemble dense and highly accurate haplotypes for individual genomes. Our method leverages a statistical phasing method and a maximum spanning tree algorithm to determine the optimal second-order approximation of the population-based haplotype likelihood for an individual genome. The population-based likelihood is encoded using pseudo-reads which are then used as input along with sequence reads for haplotype assembly using an existing tool, HapCUT2. Using whole-genome Hi-C data for two human genomes (NA19240 and NA12878), we demonstrate that this integrated phasing method enables the phasing of 97–98% of variants, reduces the switch error rates by 3–6-fold, and outperforms an existing method for combining phase information from sequence reads with population-based phasing. On Strand-seq data for NA12878, our method improves the haplotype completeness from 71.4 to 94.6% and reduces the switch error rate 2-fold, demonstrating its utility for phasing using multiple sequencing technologies.
Availability and implementation
Code and datasets are available at https://github.com/vibansal/IntegratedPhasing.
Motivation: Next-generation sequencing technologies have enabled the sequencing of several human genomes in their entirety. However, the routine resequencing of complete genomes remains infeasible. ...The massive capacity of next-generation sequencers can be harnessed for sequencing specific genomic regions in hundreds to thousands of individuals. Sequencing-based association studies are currently limited by the low level of multiplexing offered by sequencing platforms. Pooled sequencing represents a cost-effective approach for studying rare variants in large populations. To utilize the power of DNA pooling, it is important to accurately identify sequence variants from pooled sequencing data. Detection of rare variants from pooled sequencing represents a different challenge than detection of variants from individual sequencing. Results: We describe a novel statistical approach, CRISP Comprehensive Read analysis for Identification of Single Nucleotide Polymorphisms (SNPs) from Pooled sequencing that is able to identify both rare and common variants by using two approaches: (i) comparing the distribution of allele counts across multiple pools using contingency tables and (ii) evaluating the probability of observing multiple non-reference base calls due to sequencing errors alone. Information about the distribution of reads between the forward and reverse strands and the size of the pools is also incorporated within this framework to filter out false variants. Validation of CRISP on two separate pooled sequencing datasets generated using the Illumina Genome Analyzer demonstrates that it can detect 80–85% of SNPs identified using individual sequencing while achieving a low false discovery rate (3–5%). Comparison with previous methods for pooled SNP detection demonstrates the significantly lower false positive and false negative rates for CRISP. Availability: Implementation of this method is available at http://polymorphism.scripps.edu/∼vbansal/software/CRISP/ Contact: vbansal@scripps.edu
Whole-genome sequencing using sequencing technologies such as Illumina enables the accurate detection of small-scale variants but provides limited information about haplotypes and variants in ...repetitive regions of the human genome. Single-molecule sequencing (SMS) technologies such as Pacific Biosciences and Oxford Nanopore generate long reads that can potentially address the limitations of short-read sequencing. However, the high error rate of SMS reads makes it challenging to detect small-scale variants in diploid genomes. We introduce a variant calling method, Longshot, which leverages the haplotype information present in SMS reads to accurately detect and phase single-nucleotide variants (SNVs) in diploid genomes. We demonstrate that Longshot achieves very high accuracy for SNV detection using whole-genome Pacific Biosciences data, outperforms existing variant calling methods, and enables variant detection in duplicated regions of the genome that cannot be mapped using short reads.
To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell transcriptomics across various healthy human ...tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Intestinal goblet cells, enterocytes, and kidney proximal tubule cells appear highly permissive to SARS-CoV-2, consistent with clinical data. Our analysis also predicts non-canonical entry paths for lung and brain infections. Spermatogonial cells and prostate endocrine cells also appear to be permissive to SARS-CoV-2 infection, suggesting male-specific vulnerabilities. Both pro- and anti-viral factors are highly expressed within the nasal epithelium, with potential age-dependent variation, predicting an important battleground for coronavirus infection. Our analysis also suggests that early embryonic and placental development are at moderate risk of infection. Lastly, SCARF expression appears broadly conserved across a subset of primate organs examined. Our study establishes a resource for investigations of coronavirus biology and pathology.
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•Single-cell transcriptome profiling of SCARFs in various somatic and reproductive tissues•Intestine, kidneys, placenta, and spermatogonia appear most permissive for coronavirus•Nasal epithelium exhibits high expression of both promoting and restricting factors•SCARF expression is conserved across the major organs of human, chimpanzee, and macaque
Singh et al. provide a resource to predict the tropism and identify the plausible entry points for SARS-CoV-2 and other pathogenic coronaviruses throughout the human body. The RNA levels of 28 genes dubbed “SCARFs,” for SARS-CoV-2 and coronavirus-associated receptors and factors, are profiled in human tissues at the single-cell level.
Many tools have been developed for haplotype assembly-the reconstruction of individual haplotypes using reads mapped to a reference genome sequence. Due to increasing interest in obtaining ...haplotype-resolved human genomes, a range of new sequencing protocols and technologies have been developed to enable the reconstruction of whole-genome haplotypes. However, existing computational methods designed to handle specific technologies do not scale well on data from different protocols. We describe a new algorithm, HapCUT2, that extends our previous method (HapCUT) to handle multiple sequencing technologies. Using simulations and whole-genome sequencing (WGS) data from multiple different data types-dilution pool sequencing, linked-read sequencing, single molecule real-time (SMRT) sequencing, and proximity ligation (Hi-C) sequencing-we show that HapCUT2 rapidly assembles haplotypes with best-in-class accuracy for all data types. In particular, HapCUT2 scales well for high sequencing coverage and rapidly assembled haplotypes for two long-read WGS data sets on which other methods struggled. Further, HapCUT2 directly models Hi-C specific error modalities, resulting in significant improvements in error rates compared to HapCUT, the only other method that could assemble haplotypes from Hi-C data. Using HapCUT2, haplotype assembly from a 90× coverage whole-genome Hi-C data set yielded high-resolution haplotypes (78.6% of variants phased in a single block) with high pairwise phasing accuracy (∼98% across chromosomes). Our results demonstrate that HapCUT2 is a robust tool for haplotype assembly applicable to data from diverse sequencing technologies.
A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real ...observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.
The human genome contains hundreds of low-copy repeats (LCRs) that are challenging to analyze using short-read sequencing technologies due to extensive copy number variation and ambiguity in read ...mapping. Copy number and sequence variants in more than 150 duplicated genes that overlap LCRs have been implicated in monogenic and complex human diseases. We describe a computational tool, Parascopy, for estimating the aggregate and paralog-specific copy number of duplicated genes using whole-genome sequencing (WGS). Parascopy is an efficient method that jointly analyzes reads mapped to different repeat copies without the need for global realignment. It leverages multiple samples to mitigate sequencing bias and to identify reliable paralogous sequence variants (PSVs) that differentiate repeat copies. Analysis of WGS data for 2504 individuals from diverse populations showed that Parascopy is robust to sequencing bias, has higher accuracy compared to existing methods and enables prioritization of pathogenic copy number changes in duplicated genes.
Motivation: The goal of the haplotype assembly problem is to reconstruct the two haplotypes (chromosomes) for an individual using a mix of sequenced fragments from the two chromosomes. This problem ...has been shown to be computationally intractable for various optimization criteria. Polynomial time algorithms have been proposed for restricted versions of the problem. In this article, we consider the haplotype assembly problem in the most general setting, i.e. fragments of any length and with an arbitrary number of gaps. Results: We describe a novel combinatorial approach for the haplotype assembly problem based on computing max-cuts in certain graphs derived from the sequenced fragments. Levy et al. have sequenced the complete genome of a human individual and used a greedy heuristic to assemble the haplotypes for this individual. We have applied our method HapCUTto infer haplotypes from this data and demonstrate that the haplotypes inferred using HapCUT are significantly more accurate (20–25% lower maximum error correction scores for all chromosomes) than the greedy heuristic and a previously published method, Fast Hare. We also describe a maximum likelihood based estimator of the absolute accuracy of the sequence-based haplotypes using population haplotypes from the International HapMap project. Availability: A program implementing HapCUT is available on request. Contact: vibansal@cs.ucsd.edu
The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare ...variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.
Erythropoietin (EPO), named after its role in hematopoiesis, is also expressed in mammalian brain. In clinical settings, recombinant EPO treatment has revealed a remarkable improvement of cognition, ...but underlying mechanisms have remained obscure. Here, we show with a novel line of reporter mice that cognitive challenge induces local/endogenous hypoxia in hippocampal pyramidal neurons, hence enhancing expression of EPO and EPO receptor (EPOR). High-dose EPO administration, amplifying auto/paracrine EPO/EPOR signaling, prompts the emergence of new CA1 neurons and enhanced dendritic spine densities. Single-cell sequencing reveals rapid increase in newly differentiating neurons. Importantly, improved performance on complex running wheels after EPO is imitated by exposure to mild exogenous/inspiratory hypoxia. All these effects depend on neuronal expression of the Epor gene. This suggests a model of neuroplasticity in form of a fundamental regulatory circle, in which neuronal networks-challenged by cognitive tasks-drift into transient hypoxia, thereby triggering neuronal EPO/EPOR expression.
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