Coverage (genetics)

Coverage (or depth) in DNA sequencing is the number of unique reads that include a given nucleotide in the reconstructed sequence.[1][2] Deep sequencing refers to the general concept of aiming for high number of unique reads of each region of a sequence.[3]

An overlap of the product of three sequencing runs, with the read depth at each point indicated.

Rationale

Even though the sequencing accuracy for each individual nucleotide is very high, the very large number of nucleotides in the genome means that if an individual genome is only sequenced once, there will be a significant number of sequencing errors. Furthermore, many positions in a genome contain rare single-nucleotide polymorphisms (SNPs). Hence to distinguish between sequencing errors and true SNPs, it is necessary to increase the sequencing accuracy even further by sequencing individual genomes a large number of times.

Ultra-deep sequencing

The term "ultra-deep" can sometimes also refer to higher coverage (>100-fold), which allows for detection of sequence variants in mixed populations.[4][5][6] In the extreme, error-corrected sequencing approaches such as Maximum-Depth Sequencing can make it so that coverage of a given region approaches the throughput of a sequencing machine, allowing coverages of >10^8.[7]

Transcriptome sequencing

Deep sequencing of transcriptomes, also known as RNA-Seq, provides both the sequence and frequency of RNA molecules that are present at any particular time in a specific cell type, tissue or organ.[8] Counting the number of mRNAs that are encoded by individual genes provides an indicator of protein-coding potential, a major contributor to phenotype.[9] Improving methods for RNA sequencing is an active area of research both in terms of experimental and computational methods.[10]

Calculation

The average coverage for a whole genome can be calculated from the length of the original genome (G), the number of reads (N), and the average read length (L) as . For example, a hypothetical genome with 2,000 base pairs reconstructed from 8 reads with an average length of 500 nucleotides will have 2× redundancy. This parameter also enables one to estimate other quantities, such as the percentage of the genome covered by reads (sometimes also called breadth of coverage). A high coverage in shotgun sequencing is desired because it can overcome errors in base calling and assembly. The subject of DNA sequencing theory addresses the relationships of such quantities.[2]

Physical coverage

Sometimes a distinction is made between sequence coverage and physical coverage. Where sequence coverage is the average number of times a base is read, physical coverage is the average number of times a base is read or spanned by mate paired reads.[2][11][12]

gollark: Yes, a horrible bodge which doesn't fix the first problem.
gollark: Except you filter the empty ones out, bodging it without solving anything.
gollark: I'm pretty sure that whichever strings will go in will just be blank, and also after every string will be another blank one.
gollark: I think tokenize is broken, although parse probably is too.
gollark: Because using find and replace on existing code is so hard.

References
  1. "Sequencing Coverage". illumina.com. Illumina education. Retrieved 2020-10-08. Check date values in: |access-date= (help)
  2. Sims, David; Sudbery, Ian; Ilott, Nicholas E.; Heger, Andreas; Ponting, Chris P. (2014). "Sequencing depth and coverage: key considerations in genomic analyses". Nature Reviews Genetics. 15 (2): 121–132. doi:10.1038/nrg3642. PMID 24434847.
  3. Mardis, Elaine R. (2008-09-01). "Next-Generation DNA Sequencing Methods". Annual Review of Genomics and Human Genetics. 9 (1): 387–402. doi:10.1146/annurev.genom.9.081307.164359. ISSN 1527-8204. PMID 18576944.
  4. Ajay SS, Parker SC, Abaan HO, Fajardo KV, Margulies EH (September 2011). "Accurate and comprehensive sequencing of personal genomes". Genome Res. 21 (9): 1498–505. doi:10.1101/gr.123638.111. PMC 3166834. PMID 21771779.
  5. Mirebrahim, Hamid; Close, Timothy J.; Lonardi, Stefano (2015-06-15). "De novo meta-assembly of ultra-deep sequencing data". Bioinformatics. 31 (12): i9–i16. doi:10.1093/bioinformatics/btv226. ISSN 1367-4803. PMC 4765875. PMID 26072514.
  6. Beerenwinkel, Niko; Zagordi, Osvaldo (2011-11-01). "Ultra-deep sequencing for the analysis of viral populations". Current Opinion in Virology. 1 (5): 413–418. doi:10.1016/j.coviro.2011.07.008. PMID 22440844.
  7. Jee, J.; Rasouly, A.; Shamovsky, I.; Akivis, Y.; Steinman, S.; Mishra, B.; Nudler, E. (2016). "Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing". Nature. 534 (7609): 693–696. Bibcode:2016Natur.534..693J. doi:10.1038/nature18313. PMC 4940094. PMID 27338792.
  8. Malone, John H.; Oliver, Brian (2011-01-01). "Microarrays, deep sequencing and the true measure of the transcriptome". BMC Biology. 9: 34. doi:10.1186/1741-7007-9-34. ISSN 1741-7007. PMC 3104486. PMID 21627854.
  9. Hampton M, Melvin RG, Kendall AH, Kirkpatrick BR, Peterson N, Andrews MT (2011). "Deep sequencing the transcriptome reveals seasonal adaptive mechanisms in a hibernating mammal". PLOS ONE. 6 (10): e27021. Bibcode:2011PLoSO...627021H. doi:10.1371/journal.pone.0027021. PMC 3203946. PMID 22046435.
  10. Heyer EE, Ozadam H, Ricci EP, Cenik C, Moore MJ (2015). "An optimized kit-free method for making strand-specific deep sequencing libraries from RNA fragments". Nucleic Acids Res. 43 (1): e2. doi:10.1093/nar/gku1235. PMC 4288154. PMID 25505164.
  11. Meyerson, M.; Gabriel, S.; Getz, G. (2010). "Advances in understanding cancer genomes through second-generation sequencing". Nature Reviews Genetics. 11 (10): 685–696. doi:10.1038/nrg2841. PMID 20847746.
  12. Ekblom, Robert; Wolf, Jochen B. W. (2014). "A field guide to whole‐genome sequencing, assembly and annotation". Evolutionary Applications. 7 (9): 1026–42. doi:10.1111/eva.12178. PMC 4231593. PMID 25553065.
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