Title: Haplotype phasing in single-cell DNA-sequencing data
AbstractMotivation
Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.
Results
We show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 more »
kb—comparable to typical gene lengths—compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.
Availability and implementation
Source code is available at https://www.github.com/raphael-group.
Supplementary information
Supplementary data are available at Bioinformatics online.
Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enablingde novoassembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes.
Methods
Here we evaluatede novoassembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes.
Results
Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCRmore »cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.
Conclusions
PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improvedde novogenome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.
Amses, Kevin R.; Davis, William J.; James, Timothy Y.; Birol, ed., Inanc(
, Bioinformatics)
AbstractMotivation
Whole-genome sequencing of uncultured eukaryotic genomes is complicated by difficulties in acquiring sufficient amounts of tissue. Single-cell genomics (SCG) by multiple displacement amplification provides a technical workaround, yielding whole-genome libraries which can be assembled de novo. Downsides of multiple displacement amplification include coverage biases and exacerbation of contamination. These factors affect assembly continuity and fidelity, complicating discrimination of genomes from contamination and noise by available tools. Uncultured eukaryotes and their relatives are often underrepresented in large sequence data repositories, further impairing identification and separation.
Results
We compare the ability of filtering approaches to remove contamination and resolve eukaryotic draft genomes from SCG metagenomes, finding significant variation in outcomes. To address these inconsistencies, we introduce a consensus approach that is codified in the SCGid software package. SCGid parallelly filters assemblies using different approaches, yielding three intermediate drafts from which consensus is drawn. Using genuine and mock SCG metagenomes, we show that our approach corrects for variation among draft genomes predicted by individual approaches and outperforms them in recapitulating published drafts in a fast and repeatable way, providing a useful alternative to available methods and manual curation.
Availability and implementation
The SCGid package is implemented in python and R. Source code is availablemore »at http://www.github.com/amsesk/SCGid under the GNU GPL 3.0 license.
Supplementary information
Supplementary data are available at Bioinformatics online.
Abstract Motivation Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. Results We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on sixmore »human genomes HG002–HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. Availability and implementation https://github.com/huangnengCSU/NanoSNP.git. Supplementary information Supplementary data are available at Bioinformatics online.« less
Haplotype phasing maize genetic variants is important for genome interpretation, population genetic analysis and functional analysis of allelic activity. We performed an isoform-level phasing study using two maize inbred lines and their reciprocal crosses, based on single-molecule, full-length cDNA sequencing. To phase and analyze transcripts between hybrids and parents, we developed IsoPhase. Using this tool, we validated the majority of SNPs called against matching short-read data from embryo, endosperm and root tissues, and identified allele-specific, gene-level and isoform-level differential expression between the inbred parental lines and hybrid offspring. After phasing 6907 genes in the reciprocal hybrids, we annotated the SNPs and identified large-effect genes. In addition, we identified parent-of-origin isoforms, distinct novel isoforms in maize parent and hybrid lines, and imprinted genes from different tissues. Finally, we characterized variation in cis- and trans-regulatory effects. Our study provides measures of haplotypic expression that could increase accuracy in studies of allelic expression.
Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.
Satas, Gryte, and Raphael, Benjamin J. Haplotype phasing in single-cell DNA-sequencing data. Bioinformatics 34.13 Web. doi:10.1093/bioinformatics/bty286.
@article{osti_10413642,
place = {Country unknown/Code not available},
title = {Haplotype phasing in single-cell DNA-sequencing data},
url = {https://par.nsf.gov/biblio/10413642},
DOI = {10.1093/bioinformatics/bty286},
abstractNote = {Abstract MotivationCurrent technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants. ResultsWe show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb—comparable to typical gene lengths—compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations. Availability and implementationSource code is available at https://www.github.com/raphael-group. Supplementary informationSupplementary data are available at Bioinformatics online.},
journal = {Bioinformatics},
volume = {34},
number = {13},
publisher = {Oxford University Press},
author = {Satas, Gryte and Raphael, Benjamin J.},
}
