Sequencing depth and coverage: key considerations in genomic analyses

By D. Sims, Ian M. Sudbery, N. Ilott, Andreas Heger, Chris P. Ponting. Nature reviews genetics, 2014

1,328 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/21252bf6982bf24238a9136697e1d08725380715

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Citations in the corpus

A survey of best practices for RNA-seq data analysis (2016, background): “…some authors will argue that as few as five million mapped reads are sufficient to quantify accurately medium to highly expressed genes in most eukaryotic transcriptomes, others will sequence up to 100 million reads to quantify precisely genes and transcripts that have low expression levels [7].”
Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data (2015, background): “The sequencing technology itself has been improving continuously, allowing longer reads and deeper coverage at lower cost (Sims et al. , 2014).”
UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2016, background): “In order to prevent this bias propagating to the quantification estimates, it is common to remove reads or read pairs with the same alignment coordinates as they are assumed to arise through PCR amplification of the same molecule (Sims et al. 2014).”
Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications (2016, background): “[92] reviewed in critical detail the guidelines and precedents for optimal sequencing depth and coverage in regard to sequencing genomes, exomes, transcriptomes, methylomes, and epigenomes by chromatin immunopre‐ cipitation and sequencing and/or chromosome conformation capture.”
HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies (2017, background): “Illumina short read sequencing at ∼ 30 – 40× is considered the de facto standard approach to obtain reliable heterozygous calls (Sims et al. 2014).”
Measuring the biodiversity of microbial communities by flow cytometry (2016, background): “Although these techniques have shown a continuous decrease in their analysis costs, highfrequency 16S rRNA amplicon sequencing analyses are often budget-limited (Sims et al. 2014), time-consuming (van Dorst et al.”
MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization (2019, background): “A+T content plays an important role in HTS experiments since the known bias in the library preparation step leading to genome regions that possess extreme A+T content tend to have low sequencing depth and are difficult to be assembled (54).”
UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2017, neutral): “In order to prevent this bias propagating to the quantification estimates, it is common to remove reads or read pairs with the same alignment coordinates, because they are assumed to arise through PCR amplification of the same molecule (Sims et al. 2014).”
Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series (2014, background): “While initial RNA-seq took advantage of the accuracy of the technology to avoid replication, recent studies highlight the importance of appropriate replication for a sound RNA-seq data analysis (Tarazona et al., 2011; Liu et al., 2014; Sims et al., 2014).”
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression (2016, background): “Thus, sequencing samples at high depth (>80 million reads per sample) [23], which has been reported to improve the quantification of rare transcripts, may improve the accuracy of MCP-counter estimates from RNA-sequencing samples.”