Sequencing Project Basics
Coverage depth reflects how many times each base in a genome is sequenced. High coverage improves variant detection and assembly accuracy but increases cost. This tool approximates coverage for next-generation sequencing projects so researchers can budget for enough reads.
Coverage Equation
Coverage depends on total bases sequenced divided by genome size. With read length and number of reads , we find total sequenced bases as . Coverage equals:
where is genome size in base pairs. Enter genome size in megabases to keep units manageable.
Interpreting Results
Typical resequencing projects aim for 30× coverage or more, while de novo assemblies might exceed 60×. Once you know your desired depth, adjust read count or length to reach that goal. This calculator provides a quick reference while designing experiments or assessing whether existing data is sufficient.
Balancing Depth and Quality
Sequencing more reads does little if the underlying data are noisy. Library preparation, cleanup, and instrument settings all influence read quality. High-quality runs may reach your accuracy goals with less depth than expected, whereas lower-quality runs might require extra coverage to compensate for ambiguous bases.
Paired-End or Single-End?
Paired-end sequencing reads each fragment from both directions, improving alignments and revealing insert sizes. While generally more expensive, it provides better assembly metrics and variant calling in repetitive regions. Reflect this choice by doubling the read count when estimating coverage if you plan paired-end reads.
Cost-Saving Tips
Sequencing centers often discount larger batches or offer reduced pricing for longer reads. Combining samples into multiplexed runs or opting for slightly lower depth on exploratory projects can keep budgets under control. Don't forget to include library prep and any indexing kits in your total project cost.
Accounting for Usable Reads
Not every read that comes off a sequencer is usable. Adapter contamination, low-quality tails, and PCR duplicates can reduce the number of reads that align to the reference genome. The usable reads field estimates what proportion of raw data will contribute to coverage. For example, if only 85 percent of reads pass quality filters and mapping, entering 85 will scale the calculation accordingly. This adjustment prevents overestimating coverage and can highlight the need for more sequencing before the experiment begins.
Understanding Paired-End Mathematics
The paired-end option doubles the effective number of reads because each fragment yields two sequences—one from the forward direction and one from the reverse. Paired reads are especially useful for resolving structural variants, detecting insertions, and improving assembly continuity. However, the cost field remains per read pair, reflecting how most service providers price runs. If you budget for a million read pairs with a paired-end kit, the calculator multiplies coverage by two while keeping the total cost based on a million units.
Genome Size and Complexity
Genome size is not the only factor influencing coverage needs. Highly repetitive genomes, like those of many plants, often require greater depth to resolve ambiguous regions. Conversely, small bacterial genomes with low repeat content may assemble well at modest coverage. When in doubt, consult published studies of similar organisms to see what depths produced reliable results. Our calculator assumes uniform coverage across the genome, but real datasets exhibit unevenness due to GC bias or sequence context. Adding an extra 10–20 percent buffer to the desired coverage can compensate for these fluctuations.
Example Calculation
Suppose you plan to resequence a 3 Mbp bacterial genome using 150 bp reads and expect to generate 2 million read pairs. If the run is paired-end and you anticipate 90 percent of reads to be usable after filtering, the total bases sequenced would be 150 bp × 2 million pairs × 2 ends × 0.9 ≈ 540 million bases. Dividing by the 3 million-base genome results in an average coverage of 180×, which is more than sufficient for variant detection. The total cost field multiplies the read pair count by the price per pair, allowing you to test different scenarios before submitting samples.
Data Storage and Computational Planning
Deep sequencing creates large files. A single lane of short reads can produce tens or hundreds of gigabytes of FASTQ data. Planning for adequate storage and computing resources is as important as budgeting for wet-lab supplies. Coverage influences downstream analysis times: aligning 20× coverage of a human genome will take far longer than aligning 5×. Considering these logistics early helps avoid bottlenecks once the sequencing data arrives.
Library Complexity and Duplication
If a library has low complexity—perhaps due to over-amplification during PCR—the same fragments may be sequenced repeatedly, inflating nominal coverage without improving the breadth of the genome covered. Many pipelines mark duplicate reads and remove them from variant calling. The usable reads percentage can approximate this loss. Aim for libraries with sufficient unique fragments to minimize duplication; otherwise, you may spend money sequencing redundant information.
Coverage versus Breadth
Average coverage is only one metric of sequencing success. Breadth of coverage—the percentage of the genome with at least one read—is equally important. Highly uneven coverage might yield high averages but leave gaps. Techniques like random fragmentation, balanced PCR cycles, and optimized cluster densities on the sequencer improve uniformity. While our calculator focuses on average depth, understanding breadth encourages thoughtful library preparation and run configuration.
Scaling Up Experiments
As projects move from pilot studies to large cohorts, small savings per sample add up. The calculator’s cost output clarifies how different read lengths, efficiencies, or sequencing platforms affect the budget. It also highlights the trade-off between sequencing more individuals at lower coverage versus fewer individuals at higher coverage. Population genetics studies sometimes favor broader sampling at moderate depth, while clinical diagnostics may prioritize deep sequencing of critical regions.
Replicates and Validation
No calculator can replace biological replication. Technical and biological replicates confirm that observed variants are reproducible and not artifacts. When budgeting, remember to multiply coverage and cost estimates by the number of replicates required for statistical confidence. Planning ahead ensures that the study remains both scientifically rigorous and financially viable.
From Coverage to Action
Ultimately, coverage planning supports the broader goal of answering a biological question. Whether you are identifying rare mutations, assembling a novel genome, or surveying microbial diversity in a metagenomic sample, understanding how read counts translate to depth empowers you to design efficient experiments. Use this calculator iteratively as you refine library protocols, negotiate sequencing quotes, and project data management needs. A thoughtful plan today saves resources and headaches once the sequencing machine starts churning out reads.