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Quantifying sequencing error and effective sequencing depth of liquid biopsy NGS with unique molecular identifier error correction

ABSTRACT: Quantifying sequencing error and effective sequencing depth of liquid biopsy NGS with unique molecular identifier error correction

PROVIDER: PRJEB39899 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR4467720_1.fastq.gz	Fastqsanger.gz
	ERR4467720_2.fastq.gz	Fastqsanger.gz
	ERR4467730.fastq.gz	Fastqsanger.gz
	ERR4467730_1.fastq.gz	Fastqsanger.gz
	ERR4467730_2.fastq.gz	Fastqsanger.gz

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Quantifying sequencing error and effective sequencing depth of liquid biopsy NGS with unique molecular identifier error correction

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A cell type specific error correction signal in posterior parietal cortex

Project description:Neurons in posterior parietal cortex contribute to the execution of goal-directed navigation and other decision-making tasks. Although molecular studies have catalogued over fifty cortical cell types, it remains unknown what distinct functions they serve during goal-directed navigation. Here, we identified a molecularly defined subset of somatostatin (Sst) inhibitory neurons that, in mouse posterior parietal cortex, carry a novel cell type-specific error correction signal for navigation. We obtained repeatable experimental access to these cells using an adeno-associated virus (AAV) in which gene expression is driven by an enhancer that functions specifically in a subset of Sst cells. We found that during goal-directed navigation in a virtual environment, this subset of Sst neurons activates in a synchronous pattern that is distinct from the activity of surrounding neurons, including other Sst neurons. Using in vivo two-photon photostimulation and ex vivo paired patch clamp recordings, we show that nearby cells of this Sst subtype excite each other through gap junctions, revealing a self-excitation circuit motif that contributes to the synchronous activity of this cell type. Remarkably, these cells selectively activate as mice execute course corrections for deviations in their virtual heading during navigation toward a reward location, both for self- and experimentally-induced deviations. We propose that this subtype of Sst neurons provides a self-reinforcing and cell type-specific error-correction signal in posterior parietal cortex that may aid the execution and learning of accurate goal-directed navigation trajectories.

2023-05-11 | GSE232200 | GEO

A cell type specific error correction signal in posterior parietal cortex

Project description:A cell type specific error correction signal in posterior parietal cortex

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High throughput error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing

Project description:Droplet-based single-cell sequencing techniques have provided unprecedented insight into cellular heterogeneities within tissues. However, these approaches only allow for the measurement of the distal parts of a transcript following short-read sequencing. Therefore, splicing and sequence diversity information is lost for the majority of the transcript. The application of long-read Nanopore sequencing to droplet-based methods is challenging because of the low base-calling accuracy currently associated with Nanopore sequencing. Although several approaches that use additional short-read sequencing to error-correct the barcode and UMI sequences have been developed, these techniques are limited by the requirement to sequence a library using both short- and long-read sequencing. Here we introduce a novel approach termed single-cell Barcode UMI Correction sequencing (scBUC-seq) to efficiently error-correct barcode and UMI oligonucleotide sequences synthesized by using blocks of dimeric nucleotides. The method can be applied to correct both short-read and long-read sequencing, thereby allowing users to recover more reads per cell that permits direct single-cell Nanopore sequencing for the first time. We illustrate our method by using species-mixing experiments to evaluate barcode assignment accuracy and multiple myeloma cell lines to evaluate differential isoform usage and Ewing’s sarcoma cells to demonstrate Ig fusion transcript analysis.

2021-01-31 | GSE162053 | GEO

DNA replication-mediated error correction of ectopic CENP-A deposition maintains centromere identity

Project description:DNA replication-mediated error correction of ectopic CENP-A deposition maintains centromere identity

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Error-correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

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DNA replication-mediated error correction of ectopic CENP-A deposition maintains centromere identity

Project description:Chromatin assembled with histone H3 variant CENP-A is the heritable epigenetic determinant of human centromere identity. Using genome-wide mapping and reference models for 23 human centromeres, CENP-A is shown in early G1 to be assembled into nucleosomes within megabase, repetitive a-satellite DNAs at each centromere and onto 11,390 sites on the chromosome arms. Centromere-bound CENP-A is found to be quantitatively maintained during DNA replication by coordinated action of the MCM2 helicase, CAF1, HJURP, and the CCAN network of constitutive centromere components. CCAN serves to tether CENP-A removed by MCM2, thereby enabling local reassembly onto both daughter centromeres with identical DNA sequence preferences and nucleosome phasing as the loading in G1 and independent of CENP-B. Conversely, without CCAN-mediated tethering, DNA replication removes CENP-A from sites on the chromosome arms. Our data identify an MCM2/CAF1/HJURP- and CCAN-dependent error correction mechanism that acts in S-phase to maintain CENP-A-dependent centromere identity.

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High throughput error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing

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Probabilistic error correction for RNA sequencing.

Project description:Sequencing of RNAs (RNA-Seq) has revolutionized the field of transcriptomics, but the reads obtained often contain errors. Read error correction can have a large impact on our ability to accurately assemble transcripts. This is especially true for de novo transcriptome analysis, where a reference genome is not available. Current read error correction methods, developed for DNA sequence data, cannot handle the overlapping effects of non-uniform abundance, polymorphisms and alternative splicing. Here we present SEquencing Error CorrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)-based method, which is the first to successfully address these problems. SEECER efficiently learns hundreds of thousands of HMMs and uses these to correct sequencing errors. Using human RNA-Seq data, we show that SEECER greatly improves on previous methods in terms of quality of read alignment to the genome and assembly accuracy. To illustrate the usefulness of SEECER for de novo transcriptome studies, we generated new RNA-Seq data to study the development of the sea cucumber Parastichopus parvimensis. Our corrected assembled transcripts shed new light on two important stages in sea cucumber development. Comparison of the assembled transcripts to known transcripts in other species has also revealed novel transcripts that are unique to sea cucumber, some of which we have experimentally validated. Supporting website: http://sb.cs.cmu.edu/seecer/.

| S-EPMC3664804 | biostudies-literature

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