Long Read Single Cell and Spatial Benchmarking
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB83819 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR14111320.fastq.gz | Fastqsanger.gz | |||
| ERR14777945.fastq.gz | Fastqsanger.gz | |||
| ERR14787845.fastq.gz | Fastqsanger.gz | |||
| ERR14776290.fastq.gz | Fastqsanger.gz |
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Project description:Third-generation sequencing technologies provided by Pacific Biosciences and Oxford Nanopore Technologies generate read lengths in the scale of kilobasepairs. However, these reads display high error rates, and correction steps are necessary to realize their great potential in genomics and transcriptomics. Here, we compare properties of PacBio and Nanopore data and assess correction methods by Canu, MARVEL and proovread in various combinations. We found total error rates of around 13% in the raw datasets. PacBio reads showed a high rate of insertions (around 8%) whereas Nanopore reads showed similar rates for substitutions, insertions and deletions of around 4% each. In data from both technologies the errors were uniformly distributed along reads apart from noisy 5' ends, and homopolymers appeared among the most over-represented kmers relative to a reference. Consensus correction using read overlaps reduced error rates to about 1% when using Canu or MARVEL after patching. The lowest error rate in Nanopore data (0.45%) was achieved by applying proovread on MARVEL-patched data including Illumina short-reads, and the lowest error rate in PacBio data (0.42%) was the result of Canu correction with minimap2 alignment after patching. Our study provides valuable insights and benchmarks regarding long-read data and correction methods.
| S-EPMC7671305 | biostudies-literature
Project description:Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to couple long-read sequencing with single cell and spatial barcoding to explore alternative splicing in the single cell and spatial setting. Though Nanopore-based long reads sequencing are widelyhave been adopted applied to explore single cell alternative and spatially barcoded librariessplicing in recent research, there still exist technical issues have problems which could bias the hindered accurate single cell isoform-level quantification, which are not well addressed in such settings. First, Tthe relatively higher sequencing error of Nanopore long reads, despite the recent improvements, has limited the accuracy ofhinder cell barcode and unique molecular identifier (UMI) recovery, a necessary first step in the analysis of single cell/spatial sequencing data. Then Rread truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, further leads to the false detection of spurious new isoformsdegrade quantification accuracy. We show that these technical issues persist despite the recent improvements in long read sequencing accuracy. Beyond the initial data pre-processing, in downstream analysis we are lacking a statistical framework to quantify splicing variation within and between cells/spots. In light of these multiple challenges, we developed Longcell, a statistical framework and computational pipeline for isoform quantification using single cell and spatial spot barcoded Nanopore long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the single cell and spatial data modalities. On Visium long read sequencing data for multiple tissues, Longcell allows accurate identification of spatial isoform switching. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.
2025-05-23 | GSE295932 | GEO
Project description:Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.1 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200803 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.1/v1.3.1 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.3.0 was reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.7.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish, NextDenovo/NextPolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.
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Project description:Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.
2023-10-02 | PXD038699 | Pride
Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
2024-04-22 | E-MTAB-13991 | biostudies-arrayexpress
Project description:A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individual, which was cross validated with deep Illumina sequencing, population datasets, and well-established algorithms. It was a necessary effort to completely reanalyze the HuRef genome as its previously published variants were mostly reported five years ago, suffering from compatibility, organization, and accuracy issues that prevent their direct use in benchmarking. Our extensive analysis and validation resulted in a gold set with high specificity and sensitivity. In contrast to the current gold sets of the NA12878 or HS1011 genomes, our gold set is the first that includes small variants, deletion SVs and insertion SVs up to a hundred thousand base-pairs. We demonstrate the utility of our HuRef gold set to benchmark several published SV detection tools.
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Project description:BackgroundThe utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation.ResultsFor this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385.ConclusionsThese results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.
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2015-04-13 | E-GEOD-66688 | biostudies-arrayexpress
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