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  • Review Article
  • Published:

Sequencing depth and coverage: key considerations in genomic analyses

Nature Reviews Genetics volume 15, pages 121–132 (2014)Cite this article

Subjects

Key Points

  • The average depth of sequencing coverage can be defined theoretically as LN/G, where L is the read length, N is the number of reads and G is the haploid genome length.

  • The breadth of coverage is the percentage of target bases that have been sequenced for a given number of times.

  • Hybrid sequencing approaches are being introduced to overcome problems in genome assembly and in placing highly repetitive sequence in a genome.

  • For DNA resequencing studies, the required sequencing capacity depends on the size of the regions of interest, the types of variant and the disease model being studied.

  • The accuracy of variant calling is affected by sequence quality, uniformity of coverage and the threshold of false-discovery rate that is used.

  • The power to identify and accurately quantify RNA molecules is dependent on their lengths and abundance, and on the number of sequenced reads.

  • In human cells, 80% of transcripts that are expressed at >10 fragments per kilobase of exon per million reads mapped (FPKM) can be accurately quantified with ~36 million 100-bp paired-end sequenced reads.

  • Depth of coverage is affected by the accuracy of genome alignment algorithms and by the uniqueness or the 'mappability' of sequencing reads within a target genome.

  • Sequence depth influences the accuracy by which rare events can be quantified in RNA sequencing, chromatin immunoprecipitation followed by sequencing (ChIP–seq) and other quantification-based assays.

  • Sequence depth must be traded off against the need for control samples and replicates.

Abstract

Sequencing technologies have placed a wide range of genomic analyses within the capabilities of many laboratories. However, sequencing costs often set limits to the amount of sequences that can be generated and, consequently, the biological outcomes that can be achieved from an experimental design. In this Review, we discuss the issue of sequencing depth in the design of next-generation sequencing experiments. We review current guidelines and precedents on the issue of coverage, as well as their underlying considerations, for four major study designs, which include de novo genome sequencing, genome resequencing, transcriptome sequencing and genomic location analyses (for example, chromatin immunoprecipitation followed by sequencing (ChIP–seq) and chromosome conformation capture (3C)).

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Figure 1: Sequencing depths for different applications.
Figure 2: The three different types of peaks in chromatin immunoprecipitation followed by sequencing experiments.

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Acknowledgements

The Computational Genomics Analysis and Training Centre is funded by a UK Medical Research Council Strategic Award.

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Authors and Affiliations

  1. Department of Physiology, Computational Genomics Analysis and Training Programme, Medical Research Council Functional Genomics Unit, Anatomy and Genetics, Le Gros Clark Building, University of Oxford, Parks Road, Oxford, OX1 3PT, UK

    David Sims, Ian Sudbery, Nicholas E. Ilott, Andreas Heger & Chris P. Ponting

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PowerPoint slides

Glossary

Depth

The average number of times that a particular nucleotide is represented in a collection of random raw sequences.

Sequence capture

The enrichment of fragmented DNA or RNA species of interest by hybridization to a set of sequence-specific DNA or RNA oligonucleotides.

GC bias

The difference between the observed GC content of sequenced reads and the expected GC content based on the reference sequence.

Variant calling

The process of identifying consistent differences between the sequenced reads and the reference genome; these differences include single base substitutions, small insertions and deletions, and larger copy number variants.

Low-complexity sequences

DNA regions that have a biased nucleotide composition, which are enriched with simple sequence repeats.

Clonal evolution

An iterative process of clonal expansion, genetic diversification and clonal selection that is thought to drive the evolution of cancers, which gives rise to metastasis and resistance to therapy.

Dynamic range

The range of expression levels over which genes and transcripts can be accurately quantified in gene expression analyses. In theory, RNA sequencing offers an infinite dynamic range, whereas microarrays are limited by the range of signal intensities.

Long non-coding RNAs

(lncRNAs). RNA molecules that are transcribed from non-protein-coding loci; such RNAs are >200 nt in length and show no predicted protein-coding capacity.

Cap analysis of gene expression

(CAGE). In contrast to RNA sequencing, CAGE produces short 'tag' sequences that represent the 5′ end of the RNA molecule. As CAGE does not sequence across an entire cDNA, it requires a lower depth of sequencing than RNA sequencing to quantify low-abundance transcripts.

Spike-in control RNAs

A pool of RNA molecules of known length, sequence composition and abundance that is introduced into an experiment to assess the performance of the technique.

Fragments per kilobase of exon per million reads mapped

(FPKM). A method for normalizing read counts over genes or transcripts. Read counts are first normalized by gene length and then by library size. After normalization, the expression value of each gene is less dependent on these variables.

Saturation

In the context of sequence depth, the point at which the addition of extra reads to an analysis yields no improvement in the number of significant effects identified.

Parametric methods

Methods that rely on assumptions regarding the distribution of sampled data. In RNA sequencing, differential expression analysis sampled reads are assumed to follow a Poisson or negative binomial distribution.

CLIP–seq

(Crosslinking immunoprecipitation followed by sequencing). A method for interrogating RNA–protein interactions, in which RNAs are crosslinked to proteins by ultraviolet radiation and then fragmented. After immunoprecipitation of the protein of interest, the RNA is converted to cDNA and sequenced.

iCLIP

(Individual nucleotide-resolution crosslinking and immunoprecipitation). An extension of CLIP–seq that produces base-pair resolution. It relies on the fact that most cDNA synthesis reactions terminate at the crosslinked bases of the RNA; these prematurely terminated bases are purified and sequenced.

PAR–CLIP

(Photoactivatable-ribonucleoside-enhanced crosslinking immunoprecipitation). An extension of CLIP–seq, in which the photoactivatable nucleotide uridine analogue 4SU is incorporated into RNA. Upon activation with ultraviolet radiation, these bases form covalent crosslinks with bound proteins. Following conversion to cDNA, uncrosslinked uridines become thymidines, whereas crosslinked uridines become cytosines, thus indicating the protein-binding sites in the RNA.

CHART

(Capture hybridization analysis of RNA targets). A method that uses biotinylated oligonucleotides to pull down complementary RNAs (which are generally long non-coding RNAs) and their associated DNA after crosslinking. The resulting DNA is then sequenced to identify sequences that are associated with the RNA.

CHiRP

(Chromatin isolation by RNA purification). A method to capture DNA that is associated with RNA (particularly long-non coding RNAs); it is based on a similar principle to CHART.

DNaseI-seq

(DNase I hypersensitive site sequencing). A method to identify regions of open chromatin. Regions of open chromatin are sensitive to DNase I digestion, whereas those in regions of close chromatin are not. Sequencing of fragment ends after DNase I digestion thus reveals the locations of open chromatin.

MeDIP–seq

(Methylated DNA immunoprecipitation followed by sequencing). A method to identify regions of methylated DNA, in which chromatin immunoprecipitation is carried out using an antibody that recognizes methylated cytosine and the resulting immunoprecipitated DNA fragments are subjected to sequencing.

CAP–seq

(CxxC affinity purification sequencing). A method to identify genomic regions that are enriched for unmethylated CpG dinucleotides on the basis of binding of the CxxC domain to such regions. A recombinant CxxC domain from the KDM2B protein is biotinylated and is bound to DNA. After fragmentation, DNA bound to the biotinylated CxxC domain is recovered and sequenced.

Peaks

Regions of the genome with an enrichment of mapped reads compared with a control track or a local background. Produced by peak callers, these are often the output of location-based experiments.

Point-source factor

A protein factor that yields narrow and localized peaks in chromatin immunoprecipitation followed by sequencing experiments, such as sequence-specific transcription factors or some modified histones that occur in localized regions.

Broad-source factor

A protein factor or modification that marks extended genomic regions, such as many modified histones.

Mixed-source factor

A protein factor or modification that produces peaks which are similar to those of both point-source and broad-source factors.

Technical replicates

Replicates that are derived from the same initial biological sample (as opposed to biological replicates). The variation between two such samples will be due to the variation that is introduced by the technique used rather than the underlying variation in the biology.

PCR duplicates

Pairs of reads that originated from the same molecule in the original biological sample and that are filtered out in many analyses.

Library complexity

The number of unique biological molecules that are represented in a sequencing library.

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Sims, D., Sudbery, I., Ilott, N. et al. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15, 121–132 (2014). https://doi.org/10.1038/nrg3642

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