Dataset Information

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

ABSTRACT: Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCR??), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCR?? reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50?bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCR?? reconstruction was achieved for 6 datasets (81% - 100%) with at least 0.25 millions (PE) reads of length >50?bp, while it failed for datasets with <30?bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCR?? and gene expression profiles from scRNA-seq data of T cells.

SUBMITTER: Rizzetto S

PROVIDER: S-EPMC5630586 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

Rizzetto Simone S Eltahla Auda A AA Lin Peijie P Bull Rowena R Lloyd Andrew R AR Ho Joshua W K JWK Venturi Vanessa V Luciani Fabio F

Scientific reports 20171006 1

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-s ...[more]

PMID: 28986563

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Project description:BACKGROUND: One of the concerns of assembling de novo transcriptomes is determining the amount of read sequences required to ensure a comprehensive coverage of genes expressed in a particular sample. In this report, we describe the use of Illumina paired-end RNA-Seq (PE RNA-Seq) reads from Hevea brasiliensis (rubber tree) bark to devise a transcript mapping approach for the estimation of the read amount needed for deep transcriptome coverage. FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts. CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.

Dataset Information

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

Publications

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

Similar Datasets

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