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Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.

Benchmark small variant calls are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. Here, as part of the Genome in a Bottle (GIAB) Consortium, we apply a reproducible, cloud-based pipeline to integrate multiple short- and linked-read sequencing datasets and provide benchmark calls for human genomes. We generate benchmark calls for one previously analyzed GIAB sample, as well as six genomes from the Personal Genome Project. These new genomes have broad, open consent, making this a 'first of its kind' resource that is available to the community for multiple downstream applications. We produce 17% more benchmark single nucleotide variations, 176% more indels and 12% larger benchmark regions than previously published GIAB benchmarks. We demonstrate that this benchmark reliably identifies errors in existing callsets and highlight challenges in interpreting performance metrics when using benchmarks that are not perfect or comprehensive. Finally, we identify strengths and weaknesses of callsets by stratifying performance according to variant type and genome context.

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Methods to deconvolve single-cell RNA sequencing (scRNAseq) data are necessary for samples containing a natural mixture of genotypes and for scRNAseq experiments that multiplex cells from different donors1. Multiplexing across donors is a popular experimental design with many benefits including avoiding batch effects2, reducing costs, and improving doublet detection. Using variants detected in the RNAseq reads, it is possible to assign cells to the individuals from which they arose. These variants can also be used to identify and remove cross-genotype doublet cells that may have highly similar transcriptional profiles precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA in the system. Ambient RNA is caused by cell lysis prior to droplet partitioning and is an important confounder of scRNAseq analysis3. Souporcell is a novel method to cluster cells using only the genetic variants detected within the scRNAseq reads. We show that it achieves high accuracy on genotype clustering, doublet detection, and ambient RNA estimation as demonstrated across a wide range of challenging scenarios.

Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (, Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. Here we profile the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then use our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a complex eukaryotic parasite and an open access reference data set for the study of malaria parasites.

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. We profiled the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire

A high-quality reference genome is a fundamental resource for functional genetics, comparative genomics, and population genomics, and is increasingly important for conservation biology. PacBio Single Molecule, Real-Time (SMRT) sequencing generates long reads with uniform coverage and high consensus accuracy, making it a powerful technology for de novo genome assembly. Improvements in throughput and concomitant reductions in cost have made PacBio an attractive core technology for many large genome initiatives, however, relatively high DNA input requirements (~5 &micro, g for standard library protocol) have placed PacBio out of reach for many projects on small organisms that have lower DNA content, or on projects with limited input DNA for other reasons. Here we present a high-quality de novo genome assembly from a single Anopheles coluzzii mosquito. A modified SMRTbell library construction protocol without DNA shearing and size selection was used to generate a SMRTbell library from just 100 ng of starting genomic DNA. The sample was run on the Sequel System with chemistry 3.0 and software v6.0, generating, on average, 25 Gb of sequence per SMRT Cell with 20 h movies, followed by diploid de novo genome assembly with FALCON-Unzip. The resulting curated assembly had high contiguity (contig N50 3.5 Mb) and completeness (more than 98% of conserved genes were present and full-length). In addition, this single-insect assembly now places 667 (&gt, 90%) of formerly unplaced genes into their appropriate chromosomal contexts in the AgamP4 PEST reference. We were also able to resolve maternal and paternal haplotypes for over 1/3 of the genome. By sequencing and assembling material from a single diploid individual, only two haplotypes were present, simplifying the assembly process compared to samples from multiple pooled individuals. The method presented here can be applied to samples with starting DNA amounts as low as 100 ng per 1 Gb genome size. This new low-input approach puts PacBio-based assemblies in reach for small highly heterozygous organisms that comprise much of the diversity of life.

Benchmark small variant calls from the Genome in a Bottle Consortium (GIAB) for the CEPH/HapMap genome NA12878 (HG001) have been used extensively for developing, optimizing, and demonstrating performance of sequencing and bioinformatics methods. Here, we improve and simplify the methods we use to integrate multiple sequencing datasets, with the intention of deploying a reproducible cloud-based pipeline for application to arbitrary human genomes. We use these reproducible methods to form high-confidence calls with respect to GRCh37 and GRCh38 for HG001 and 4 additional broadly-consented genomes from the Personal Genome Project that are available as NIST Reference Materials. Our new methods produce 17% more SNPs and 176% more indels than our previously published calls for HG001. We also phase 99.5% of the variants in HG001 and call about 90% of the reference genome with high-confidence, increased from 78% previously. Our calls only contain 108 differences from the Illumina Platinum Genomes calls in GRCh37, only 14 of which are ambiguous or likely to be errors in our calls. By comparing several callsets to our new calls, our previously published calls, and Illumina Platinum Genomes calls, we highlight challenges in interpreting performance metrics when benchmarking against imperfect high-confidence calls. Our new calls address some of these challenges, but performance metrics should always be interpreted carefully. Benchmarking tools from the Global Alliance for Genomics and Health are useful for stratifying performance metrics by variant type and genome context to elucidate strengths and weaknesses of a method. We also explore differences between comparing to high-confidence calls for the 5 GIAB genomes, and show that performance metrics for one pipeline are largely similar but not identical when comparing to the 5 genomes. Finally, to explore applicability of our methods for genomes that have fewer datasets, we form high-confidence calls using only Illumina and 10x Genomics, and find that they have more high-confidence calls but have a higher error rate. These newly characterized genomes have a broad, open consent with few restrictions availability of samples and data, enabling a uniquely diverse array of applications.

Large-scale population analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short-read whole-genome sequencing. However, these short-read approaches fail to give a complete picture of a genome. They struggle to identify structural events, cannot access repetitive regions, and fail to resolve the human genome into haplotypes. Here, we describe an approach that retains long range information while maintaining the advantages of short reads. Starting from ∼1 ng of high molecular weight DNA, we produce barcoded short-read libraries. Novel informatic approaches allow for the barcoded short reads to be associated with their original long molecules producing a novel data type known as “Linked-Reads”. This approach allows for simultaneous detection of small and large variants from a single library. In this manuscript, we show the advantages of Linked-Reads over standard short-read approaches for reference-based analysis. Linked-Reads allow mapping to 38 Mb of sequence not accessible to short reads, adding sequence in 423 difficult-to-sequence genes including disease-relevant genes STRC, SMN1, and SMN2. Both Linked-Read whole-genome and whole-exome sequencing identify complex structural variations, including balanced events and single exon deletions and duplications. Further, Linked-Reads extend the region of high-confidence calls by 68.9 Mb. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only ∼1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as ‘Linked-Reads’. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent–child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (

Haplotyping of human chromosomes is a prerequisite for cataloguing the full repertoire of genetic variation. We present a microfluidics-based, linked-read sequencing technology that can phase and haplotype germline and cancer genomes using nanograms of input DNA. This high-throughput platform prepares barcoded libraries for short-read sequencing and computationally reconstructs long-range haplotype and structural variant information. We generate haplotype blocks in a nuclear trio that are concordant with expected inheritance patterns and phase a set of structural variants. We also resolve the structure of the EML4-ALK gene fusion in the NCI-H2228 cancer cell line using phased exome sequencing. Finally, we assign genetic aberrations to specific megabase-scale haplotypes generated from whole-genome sequencing of a primary colorectal adenocarcinoma. This approach resolves haplotype information using up to 100 times less genomic DNA than some methods and enables the accurate detection of structural variants.

Studies have shown that somatic structural variation (SV) plays a key role in the oncogenic process. Traditionally SVs in the cancer genome have been detected using low resolution cytogenetic approaches, such as FISH, or microarray-based techniques. More recently, next-generation sequencing (NGS)-based technologies have been employed to detect SVs, including indels and translocations. However, both short- and long-read NGS-based approaches are limited in their ability to accurately identify SV events and delineate their breakpoints due to the limitations inherent in assembly of billions of short-read sequences across a heterogeneous cancer sample, as well as the costly and burdensome laboratory infrastructure associated with long-read sequencers. We utilized a novel technology that combines microfluidics and molecular barcoding to generate libraries that are sequenced with an Illumina system. Open-source bioinformatics software produces linked-reads that maintain long-range information and single molecule sensitivity. Cell lines and cancer samples were obtained from commercial sources, and genomic DNA was extracted. DNA sample indexing and partitioning was performed using the 10X Genomicx GemCode instrument. One ng of sample DNA was used as input for each reaction, and DNA molecules were partitioned into droplets to fragment the DNA and introduce molecular barcodes. Following barcoding, droplets were fractured, and library DNA was purified and sequenced on Illumina sequencers. The GemCode Long Ranger software suite was used to map sequencing reads back to original long molecules of DNA, generating reads linked to partition barcodes. Thus we can generate phased sequences covering many 10's to 100's of kilobases. We first benchmarked the ability to call multiple SV types using a well-characterized germline HapMap sample (NA12878) as well as two recently characterized haploid hydatidiform moles (CHM1 and CHM13) that have been studied with multiple orthogonal technologies. Regions with evidence for structural variation were reassembled into distinct haplotypes. The barcode information allowed us to both phase the structural variants we detected and disambiguate calls within highly repetitive regions, such as segmental duplications. We demonstrated high concordance with alternative approaches across all major classes of SVs, including long insertions and deletions as well as copy-neutral events. In cancer cell lines, we detected well-annotated gene fusions, such as the EML4/ALK and ALK/PTPN3 fusions in the lung cancer cell line NCI-H2228, and the SLC26A/PRKAR2A fusion in the triple negative breast cancer cell line HCC38. Citation Format: Sofia Kyriazopoulou-Panagiotopoulou, Patrick Marks, Haynes Heaton, Heather Ordonez, Kristina Giorda, Cassandra Jabara, Billy Lau, John M. Bell, Michael Schnall-Levin, Hanlee P. Ji. Linked-Reads enable detailed, phased resolution of structural variation in the cancer genome. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3602.