Reverse-solve reads-per-sample, samples-per-run, runs-needed, and cost-per-sample for any sequencing project. Compare Illumina NovaSeq X / NextSeq 2000 / MiSeq, Element AVITI, Oxford Nanopore PromethION, and PacBio Revio side-by-side. Lander–Waterman math, runs entirely in your browser.
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| Platform | Reads / sample | Yield / sample | Samples / run | Runs | Cost / sample |
|---|---|---|---|---|---|
| Illumina NovaSeq X — 25B (2×150 PE) | 320.0M | 96.00 Gb | 81 | 1 | $192.00 |
| Illumina NovaSeq X — 10B (2×150 PE) | 320.0M | 96.00 Gb | 32 | 1 | $192.00 |
| Illumina NovaSeq X — 1.5B (2×150 PE) | 320.0M | 96.00 Gb | 5 | 2 | $192.00 |
| Illumina NextSeq 2000 — P3 (2×150 PE) | 320.0M | 96.00 Gb | 3 | 3 | $192.00 |
| Illumina NextSeq 2000 — P2 (2×150 PE) | 320.0M | 96.00 Gb | 1 | 8 | $192.00 |
| Illumina NextSeq 2000 — P1 (2×150 PE) | 320.0M | 96.00 Gb | 1 | 32 | $192.00 |
| Illumina MiSeq — v3 (2×300 PE) | 160.0M | 96.00 Gb | 1 | 64 | $192.00 |
| Illumina MiSeq — v2 (2×250 PE) | 192.0M | 96.00 Gb | 1 | 128 | $192.00 |
| Element AVITI — High Output (2×300 PE) | 160.0M | 96.00 Gb | 6 | 2 | $192.00 |
| Element AVITI — High Output (2×150 PE) | 320.0M | 96.00 Gb | 3 | 4 | $192.00 |
| Element AVITI — High Output (2×75 PE) | 640.0M | 96.00 Gb | 1 | 7 | $192.00 |
| ONT PromethION — single R10.4.1 flow cell | 9.6M | 96.00 Gb | 1 | 7 | $192.00 |
| PacBio Revio — single SMRT Cell HiFi | 5.3M | 96.00 Gb | 1 | 11 | $192.00 |
Cost-per-sample uses your single cost-per-Gb input across all platforms — adjust per platform for an accurate quote.
When to use
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A 2×150 PE run delivers 300 bp per read pair, not 150. The calculator stores combined read length so the formula stays N = C·G / L without per-platform special-cases. If you switch to a single-end mode for a long-read run, halve L manually.
Manufacturer headline yields are top-of-spec. Cluster density variability, library quality, and run failures typically take real-world yield to 80–100% of nominal. Build in 20% slack for production runs.
Reads land randomly. ~5% of bases sit below half-coverage. For confident germline variant calling on diploids, plan 2–3× the headline depth so the Poisson tail still has enough per-allele reads.
You cannot put 1,000 samples on one MiSeq just because the math says reads-per-sample fits. Index well-balance, well-to-well leakage, and minimum reads-for-demultiplexing all set practical floors. For most Illumina platforms, do not exceed 96-plex without index-hopping mitigation.
100× WES is roughly 6× WGS in raw bases — not because the math is different, but because the target is 50× smaller. Variant calling sensitivity at 100× WES on coding sequence is roughly equivalent to 30× WGS. Do not benchmark them as if they were the same metric.
Lander–Waterman coverage relation C = L·N / G, reverse-solved for reads-per-sample, samples-per-run, runs-needed, total yield, and cost-per-sample. Platform throughputs are manufacturer-published headline values from current spec sheets (Illumina, Element Biosciences, Oxford Nanopore, PacBio). Application defaults are drawn from GATK Best Practices, ENCODE guidelines, the Earth Microbiome Project, 10x Genomics user guides, and the NIH Roadmap Epigenomics consortium.
Last validated 2026-04-07. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
ConductScience NGS Read Coverage Calculator (v1.11.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/ngs-read-coverage-calculator
Lander ES, Waterman MS. Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics. 1988;2(3):231-239. doi:10.1016/0888-7543(88)90007-9
Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15(2):121-132. doi:10.1038/nrg3642
Van der Auwera GA, et al. From FastQ Data to High-Confidence Variant Calls: GATK Best Practices Pipeline. Curr Protoc Bioinformatics. 2013;43:11.10.1-11.10.33. doi:10.1002/0471250953.bi1110s43
Read coverage (also called sequencing depth) is the average number of reads that overlap each base in your reference. A 30× whole-genome sequencing project means every base in the human genome is covered, on average, by 30 reads.
Coverage is the single most important budget knob in an NGS experiment: it sets sensitivity for variant calling, the noise floor for differential expression, and the floor for detecting low-frequency clones in tumor or microbial samples. Picking the right coverage — and the right number of samples — for your platform of choice is what this calculator is for.
For random shotgun sequencing, the expected coverage is:
where C is the average coverage depth, L is the effective read length per read or read pair, N is the number of reads (or read pairs), and G is the genome or target size in base pairs.
The relation reverses cleanly: N = C · G / L gives the reads required to hit a coverage target. Multiply by sample count and you have your project budget. Divide by reads-per-run and you have the number of runs.
Lander & Waterman (1988) derived this in the context of physical mapping; the formula carries over directly to sequencing because both processes are random sampling on a target.
