Difference of coverage between amplicons

Difference of coverage between amplicons

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I have 2 fastq files and I generated BAM file (indexed and sorted) of some reads. I aligned them to a reference genome (hg19).

I am working with different primers.


Therefore I have different amplicons. How can I plot the coverage of these different amplicons. And what could explain big difference between them?

Thank you very much for your help.

Well, to plot the coverage I would use something like Python Matplotlib. Take a look at this example:

import matplotlib.pyplot as plt import matplotlib amplicons = ('TTGCCAGTTAACGTCTTCCTTCTCTCTCTG', 'CCCTTGTCTCTGTGTTCTTGTCCCCCCCA', 'TGATCTGTCCCTCACAGCAGGGTCTTCTCT', 'CACACTGACGTGCCTCTCCCTCCCTCCA') countAmpl = (1635, 4734, 2156, 3085) fig = plt.figure(figsize=(10,8.5)) subplt = fig.add_subplot(111) subplt.set_ylabel('Count') subplt.set_xlabel('Amplicons') subplt.plot(amplicons, countAmpl, linestyle="-", marker="o", color="blue") for tl in subplt.get_yticklabels(): tl.set_color('blue') plt.savefig("amplicons.eps")

Check other types of charts in Matplotlib if you think you need something else.

You can also try to open and visualize the BAM in IGV from the Broad Institute.

Regarding the difference in coverage, I would say that some are from a repetitive region. Or the amplification depth of a region is greater than for the others. Or maybe the aligner found similar regions and decided that the amplicon aligns well in all those regions.

BEDTools, among other software suites, will give you a coverage histogram. Biggest source of bias in PCR efficiency is just that some primers work better than others, there is sequence bias in the PCR amplification stage of the library prep too. GC content contributes to this.

Targeted Sequencing Approaches for NGS

While whole human genome sequencing has advanced discovery and human health, challenging regions of the genome are difficult to analyze using this approach, resulting in a population sequencing bias, and existing databases are noted to be neither complete nor accurate (1). For many research applications, the cost of whole genome sequencing can still be a burden, particularly when you take into account the computational processing and informatics needs for whole genome analysis. This added cost and complexity would be of little benefit when studying a specific region of interest for disease and translational research applications. To help address this issue, many researchers have adopted a targeted sequencing approach to improve coverage, to simplify analysis and interpretation, and to lower their total sequencing workflow costs.

First, the target regions of a genome or DNA sample are amplified by well-designed multiplex PCR primers with overhanging tails being partial adaptor sequences compatible with corresponding DNA sequencers, resulting in both target amplicons and non-specific PCR products including primer dimers.

Traditionally, when the panel size is large (e.g. more than 2,000 amplicons in a single pool), the non-specific PCR products can be overwhelming and significantly affect the downstream steps if there is no measure to remove them. Some amplicon-based methods utilize bead purification and size selection to remove smaller DNA fragments such as primer dimers. However, some complicated non-specific PCR products with sizes similar to the lengths of target amplicons and its resulting libraries can be difficult to remove using just size selection. The following Bioanalyzer trace shows significant background noise around a target library of 300bp.

/>CleanPlex Library Trace without Background Cleaning

CleanPlex overcomes this drawback with an innovative and patented enzymatic / chemical background cleaning step that removes non-specific PCR products including both primer dimers and more complicated and longer nonspecific PCR artifacts, resulting in very pure target libraries. The following Bioanalyzer trace shows the effect of CleanPlex background cleaning technology.

/>CleanPlex Library Trace with Background Cleaning

Subsequently, sample barcodes (for sample pooling purpose) are added by an indexing PCR step to get sequencing-ready libraries. The whole workflow only takes 3 hours and minimal hands-on time.

Assessment of the Precision ID Ancestry panel

The ability to provide accurate DNA-based forensic intelligence requires analysis of multiple DNA markers to predict the biogeographical ancestry (BGA) and externally visible characteristics (EVCs) of the donor of biological evidence. Massively parallel sequencing (MPS) enables the analysis of hundreds of DNA markers in multiple samples simultaneously, increasing the value of the intelligence provided to forensic investigators while reducing the depletion of evidential material resulting from multiple analyses. The Precision ID Ancestry Panel (formerly the HID Ion AmpliSeq™ Ancestry Panel) (Thermo Fisher Scientific) (TFS)) consists of 165 autosomal SNPs selected to infer BGA. Forensic validation criteria were applied to 95 samples using this panel to assess sensitivity (1 ng-15 pg), reproducibility (inter- and intra-run variability) and effects of compromised and forensic casework type samples (artificially degraded and inhibited, mixed source and aged blood and bone samples). BGA prediction accuracy was assessed using samples from individuals who self-declared their ancestry as being from single populations of origin (n = 36) or from multiple populations of origin (n = 14). Sequencing was conducted on Ion 318™ chips (TFS) on the Ion PGM™ System (TFS). HID SNP Genotyper v4.3.1 software (TFS) was used to perform BGA predictions based on admixture proportions (continental level) and likelihood estimates (sub-population level). BGA prediction was accurate at DNA template amounts of 125pg and 30pg using 21 and 25 PCR cycles respectively. HID SNP Genotyper continental level BGA assignments were concordant with BGAs for self-declared East Asian, African, European and South Asian individuals. Compromised, mixed source and admixed samples, in addition to sub-population level prediction, requires more extensive analysis.

This is a preview of subscription content, access via your institution.


Forensic phenotyping can provide useful intelligence regarding the biogeographical ancestry (BGA) and externally visible characteristics (EVCs) of the donor of an evidentiary sample. Currently, single nucleotide polymorphism (SNP) based inference of BGA and EVCs is performed most commonly using SNaPshot ® , a single base extension (SBE) assay. However, a single SNaPshot multiplex PCR is limited to 30–40 SNPs. Next generation sequencing (NGS) offers the potential to genotype hundreds to thousands of SNPs from multiple samples in a single experimental run. The PCR multiplexes from five SNaPshot assays (SNPforID 52plex, SNPforID 34plex, Eurasiaplex, IrisPlex and an unpublished BGA assay) were applied to three different DNA template amounts (0.1, 0.2 and 0.3 ng) in three samples (9947A and 007 control DNAs and a male donor). The pooled PCR amplicons containing 136 unique SNPs were sequenced using Life Technologies’ Ion Torrent™ PGM system. Approximately 72 Mb of sequence was generated from two 10 Mb Ion 314™ v1 chips. Accurate genotypes were readily obtained from all three template amounts. Of a total of 408 genotypes, 395 (97%) were fully concordant with SNaPshot across all three template amounts. Of those genotypes discordant with SNaPshot, six Ion Torrent sequences (1.5%) were fully concordant with Sanger sequencing across the three template amounts. Seven SNPs (1.7%) were either discordant between template amounts or discordant with Sanger sequencing. Sequence coverage observed in the negative control, and, allele coverage variation for heterozygous genotypes highlights the need to establish a threshold for background levels of sequence output and heterozygous balance. This preliminary study of the Ion Torrent PGM system has demonstrated considerable potential for use in forensic DNA analyses as a low to medium throughput NGS platform using established SNaPshot assays.

Q5® High-Fidelity DNA Polymerases

Q5 ® High-Fidelity DNA Polymerase (NEB #M0491) sets a new standard for both fidelity and robust performance. With the highest fidelity amplification available (>280 times higher than Taq), Q5 DNA Polymerase results in ultra-low error rates. Q5 DNA Polymerase is composed of a novel polymerase that is fused to the processivity-enhancing Sso7d DNA binding domain, improving speed, fidelity and reliability of performance. Q5 master mixes contain dNTPs, Mg++ and a proprietary broad-use buffer requiring only the addition of primers and DNA template for robust amplification, regardless of GC content.

NEW: Q5U Hot Start High-Fidelity DNA Polymerase (NEB #M0515). Q5U is a modified version of Q5 High-Fidelity DNA Polymerase containing a mutation in the uracil-binding pocket that enables the ability to read and amplify templates containing uracil and inosine bases. This is useful for amplifying bisulfite-converted, enzymatically-deaminated, or damaged DNA, preventing carryover contamination in PCR (when used with dUTP and UDG), and in USER cloning methods. Learn more about this product.

Comparison of High-Fidelity Polymerases

1 We continue to investigate improved assays to characterize Q5&rsquos very low error rate to ensure that we present the most accurate fidelity data possible (Potapov, V. and Ong, J.L. (2017) PLoS ONE. 12(1): e0169774).
2 PCR-based mutation screening in lacI (Agilent) or rpsL (Life).


  • Highest fidelity amplification (>280X higher than Taq)
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The Q5 buffer system is designed to provide superior performance with minimal optimization across a broad range of amplicons, regardless of GC content. For routine or complex amplicons up to

65% GC content, Q5 Reaction Buffer (NEB #B9027) provides reliable and robust amplification. For amplicons with high GC content (>65% GC), addition of the Q5 High GC Enhancer ensures continued maximum performance. Q5 and Q5 Hot Start DNA Polymerases are available as standalone enzymes, or in a master mix format for added convenience. Master mix formulations include dNTPs, Mg ++ and all necessary buffer components.

Robust Amplification with Q5 (A) and Q5 Hot Start (B) High-Fidelity DNA Polymerases

Amplification of a variety of human genomic amplicons from low to high GC content using either Q5 or Q5 Hot Start High-Fidelity DNA Polymerase. Reactions using Q5 Hot Start were set up at room temperature. All reactions were conducted using 30 cycles of amplification and visualized by microfluidic LabChip® analysis.

In contrast to chemically modified or antibody-based hot start polymerases, NEB's Q5 Hot Start (NEB #M0493) utilizes a unique synthetic aptamer. This molecule binds to the polymerase through non-covalent interactions, blocking activity during the reaction setup. The polymerase is activated during normal cycling conditions, allowing reactions to be set up at room temperature. Q5 Hot Start does not require a separate high temperature activation step, shortening reaction times and increasing ease-of-use. Q5 Hot Start Polymerase is an ideal choice for high specificity amplification and provides robust amplification of a wide variety of amplicons, regardless of GC content.

Amplification Performance Across a Wide Range of Genomic Targets

PCR was performed with a variety of amplicons, with GC content ranging from high AT to high GC, with Q5 and several other commercially available polymerases. All polymerases were cycled according to manufacturer's recommendations, including use of GC Buffers and enhancers when recommended. Yield and purity of reaction products were quantitated and represented, as shown in the figure key, by dot color and size. A large dark green dot represents the most successful performance. Q5 provides superior performance across the range of GC content.

Master Mix and Stand-Alone Formats Provide Convenience and Flexibility

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  1. Fidelity &ndash the highest fidelity amplification available (>100X higher than Taq)
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  4. Speed &ndash short extension times
  5. Amplicon length &ndash robust amplifications up to 20 kb for simple templates, and 10 kb for complex

NEB offers a guidelines for choosing the correct DNA polymerase for your application by providing a list of specific properites. Several factors govern which polymerase should be used in a given application, including:

Template/product specificity: Is RNA or DNA involved? Is the 3´ terminus at a gap, nick or at the end of the template?

Removal of existing nucleotides: Will the nucleotide(s) be removed from the existing polynucleotide chain as part of the protocol? If so, will they be removed from the 5´ or the 3´ end?

Thermal stability: Does the polymerase need to survive incubation at high temperature or is heat inactivation desirable?

Fidelity: Will subsequent sequence analysis or expression depend on the fidelity of the synthesized products?

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What's planned

- will add cookie support to remember individually assigned input parameters from one session to the next. If you use a standard set of parameters, you will no longer need to re-enter them each time you visit the muPlex server. Use of the cookies will be entirely optional. If cookies are turned off in your browser, the interface will silently revert to its default parameters.

- will introduce a muPlex forum (wiki?) to provide support for our expanding list of users!

- Do you have ideas for improving muPlex? Our on-going development efforts are primarily in response to suggestions made by our users. (See Questions and Feedback below.)

Controversy about HPV Vaccine

During a debate between Republican presidential candidates in 2011, Michele Bachmann, one of the candidates, implied that the vaccine for HPV is unsafe for children and can cause mental retardation. Scientists and other healthcare professionals immediately produced evidence to refute this claim. A USA Today article, “No Evidence HPV Vaccines Are Dangerous” (September 19, 2011), described two studies by the Centers for Disease Control and Prevention (CDC) that track the safety of the vaccine. Here is an excerpt from the article:

  • First, the CDC monitors reports to the Vaccine Adverse Event Reporting System, a database to which anyone can report a suspected side effect. CDC officials then investigate to see whether reported problems could possibly be caused by vaccines or are simply a coincidence. Second, the CDC has been following girls who receive the vaccine over time, comparing them with a control group of unvaccinated girls….Again, the HPV vaccine has been found to be safe.

According to an article by Elizabeth Rosenthal, “Drug Makers’ Push Leads to Cancer Vaccines’ Rise” (New York Times, August 19, 2008), the FDA and CDC said that “with millions of vaccinations, by chance alone some serious adverse effects and deaths will occur in the time period following vaccination, but have nothing to do with the vaccine.” The article stated that the FDA and CDC monitor data to determine if more serious effects occur than would be expected from chance alone.

According to another source, the CDC data suggests that serious health problems after vaccination occur at a rate of about 3 in 100,000. This is a proportion of 0.00003. But are these health problems due to the vaccine? Is the rate of similar health problems any different for those who don’t receive the vaccine? Let’s assume that there are no differences in the rate of serious health problems between the treatment and control groups. That is, let’s assume that the proportion of serious health problems in both groups is 0.00003.

Suppose the CDC follows a random sample of 100,000 girls who had the vaccine and a random sample of 200,000 girls who did not have the vaccine. Over time, they calculate the proportion in each group who have serious health problems.

Question: How much of a difference in these sample proportions is unusual if the vaccine has no effect on the occurrence of serious health problems?

To answer this question, we need to see how much variation we can expect in random samples if there is no difference in the rate that serious health problems occur, so we use the sampling distribution of differences in sample proportions.

  • Spread: The large samples will produce a standard error that is very small. The standard error of the differences in sample proportions is

Answer: We can view random samples that vary more than 2 standard errors from the mean as unusual. If there is no difference in the rate that serious health problems occur, the mean is 0. So differences in rates larger than 0 + 2(0.00002) = 0.00004 are unusual. This is equivalent to about 4 more cases of serious health problems in 100,000. With such large samples, we see that a small number of additional cases of serious health problems in the vaccine group will appear unusual. But are 4 cases in 100,000 of practical significance given the potential benefits of the vaccine? This is an important question for the CDC to address.

Try It

According to a 2008 study published by the AFL-CIO, 78% of union workers had jobs with employer health coverage compared to 51% of nonunion workers. In 2009, the Employee Benefit Research Institute cited data from large samples that suggested that 80% of union workers had health coverage compared to 56% of nonunion workers. Let’s suppose the 2009 data came from random samples of 3,000 union workers and 5,000 nonunion workers.

Try It

The following is an excerpt from a press release on the AFL-CIO website published in October of 2003.

  • Wal-Mart exemplifies the harmful trend among America’s large employers to shirk health insurance responsibilities at the cost of their workers and community…. With reduced coverage and increased workers’ premium fees, Wal-Mart – the largest private employer in the U.S. – sets a troubling standard. Fewer than half of Wal-Mart workers are insured under the company plan – just 46 percent. This rate is dramatically lower than the 66 percent of workers at large private firms who are insured under their companies’ plans, according to a new Commonwealth Fund study released today which documents the growing trend among large employers to drop health insurance for their workers.

Suppose we want to see if this difference reflects insurance coverage for workers in our community. We select a random sample of 50 Wal-Mart employees and 50 employees from other large private firms in our community. Suppose that 20 of the Wal-Mart employees and 35 of the other employees have insurance through their employer.

AmpliSeq for Illumina Custom and Community Panels FAQs

No, a new version is not currently planned. The off-the-shelf panels and Community panels are based on hg19. Conversion of pre-designed panels may be considered in the future based on market demand.


What are the target amplicon sizes?

Amplicon can have a range where you select the maximum amplicon size.

Are amplicon sizes equivalent to insert sizes?

Because we digest the primers during the Partially Digest Amplicons step, the resulting insert sizes will be smaller than the amplicon size. Depending on read length chosen, we recommend adapter trimming.

Why are some targets difficult to design in DesignStudio?
  • Homologs: Having homologs in the same design can lead to low designability. Split homologs into separate pools.
  • GC Content: Regions with greater than 80% GC content can be difficult to design against, particularly when these regions are greater than 500 bp in length.
  • Homopolymer sequences and Repetitive elements: DesignStudio avoids these regions to make sure that probes have better specificity in the genome.
  • Poor Specificity: DesignStudio will assess the specificity of probes and exclude those which will not provide satisfactory on-target coverage.
How does DesignStudio select primers?

Optimal probes are chosen using an algorithm that considers melting temperature (Tm), % GC, length, secondary structure, uniqueness in the genome, and the presence of underlying SNPs (based on dbSNP). For more information, see the DesignStudio online help.

How can I improve the designability and coverage in my DesignStudio Project?
  • Increasing the size of the target to design against can rescue previously ‘undesignable’ regions. The increased size of a target gives DesignStudio a little more flexibility to fit a higher scoring amplicon over the desired target bases.
  • Change the context of the panel – for example, putting a highly homologous or high GC rich target sequence into the same multiplex design can be problematic for designing probes to amplify each target discretely. Moving problematic regions into a separate design can frequently improve the designability.
  • Change the stringency levels.
Can I create a dual pool design?

No. This is a multiplex PCR which will not be able to discern between the top and bottom strand.

Can I edit the content of my design after I’ve submitted it?

No. However, you can use the “Modify Design” button to copy the content to a new panel and then edit it.

Can I edit my design after I’ve placed an order?

No. After you’ve placed an order, you cannot edit the design. The files necessary for analysis by BaseSpace Sequence Hub and Local Run Manager must remain in sync with the material you ordered.

What is the current turnaround time for a submitted design with respect to the target size or number of targets?

A design smaller than 250 kb has an expected turnaround time of 48 hours or less. Designs greater than 250 kb or with many targets can take longer than 48 hours to return.

On-Demand designs have a shorter turnaround time than other submissions. On-Demand designs of 250 kb or less should be returned in less than 2 hours.

What determines the amplicon design criteria?

Currently, DesignStudio allows users to choose an amplicon size of 140, 175, 275, or 375 (recommended for MiSeq) for each design. The amplicon size includes the primer sequences and the insert regions. We recommend using 175 bp for FFPE DNA, 140 bp for cfDNA, and 275 bp for normal DNA.

Is it possible to use an AmpliSeq for Illumina design to screen many SNPs (up to 1000 or more) for many individuals (up to 1000 or more)?

Yes, DesignStudio enables SNP genotyping by sequencing.

What is the largest design that I can submit to the AmpliSeq for Illumina pipeline?

You can submit designs of up to 500 kb directly to the pipeline. The pipeline is capable of processing designs up to 5 Mb, but such designs are costly and take up a large amount of computational resources.

We recommend that you only submit designs up to 2 Mb. For designs between 2 Mb and 5 Mb, we recommend that you contact your sales specialist.

What panels can I use to add amplicons to a new design?

You can copy amplicons from custom, community, and fixed AmpliSeq for Illumina panels using the same species as your design. For information on the available community and fixed panels, contact Illumina Technical Support.

DesignStudio - Primer Bioinformatics

What is the level of overlap among primers?

Primers in the same pool/tube do not overlap.

With AmpliSeq for Illumina projects in DesignStudio, are primer sets designed automatically (with a computer program), without interrogation from a research scientist?

The process is an automated pipeline, optimized to provide the maximum coverage with reliable primer sets.

DesignStudio - Oligo Ordering

Can I add a few more genes to a set of previously ordered primers?

No. You must modify the design, add the new genes, and submit a new order.

If I have regular primers for a region and I know that they are working, can I add them to my AmpliSeq for Illumina design?
Can I add primers manually, postdesign, to cover a region completely?

No. We use specially modified primers, so standard primers will not allow for library construction.

Is there a minimum order for AmpliSeq for Illumina Gene and Hotspot designs?

AmpliSeq for Illumina custom panels range from 12 amplicons to 3,072 amplicons per pool. Target regions can be as small as 1 bp, but because designs must include 12 amplicons, you would need 12 sets of 1 bp regions.

All orders have a minimum price equivalent to the cost of an order containing 48 amplicons.

In what container format should I expect to receive my custom primers?

Each custom primer pool is delivered as a pre-pooled tube.

How can I find out the status of a design submission to AmpliSeq for Illumina Custom Panel?

Email [email protected] Use your AmpliSeq for Illumina Design ID number or Solution ID number when referring to your order.

DesignStudio - Troubleshooting and Validating

Suppose that I am targeting a region and DesignStudio suggests a design consisting of two primer pools. For each sample, should I prepare a library for each amplification (each pool)? Or should I combine the two amplifications (the products of the two amplified pools) and then prepare the library?

If your design results in multiple pools, each pool is processed independently through “Amplify DNA/cDNA Targets” as referenced in the AmpliSeq for Illumina Custom and Community Panel Reference Guide. The pools then are combined prior to the “Partially Digest Amplicons” step. They continue as one sample through index ligation and final library amplification.

How many base pairs separate the primers from the target region?

To make sure that an entire exon is covered, by default, we add 25 bp of padding up and down-stream of the selected target region. This padding allows for room to place the primers. Padding ensures high-quality sequencing at the ends of the exons and allows some sequencing into the splice junction regions. Primer regions are not considered covered. Therefore, if coverage obtained from the initial design is less than 100%, we can try one more time to extend the primer further into the intron to capture the whole exon.


What DNA input amount is required?

The assay uses between 1 and 100 ng of DNA per primer pool, with most designs using 10 ng per pool.

What quality of DNA is required and how should DNA quality be assessed?

We’ve seen success with low quality inputs using the protocol modifications indicated in the user guides. Commercially available or laboratory validated DNA extraction methods typically yield DNA that is compatible with this assay. DNA purity should have an A260/A280 ratio of 1.8–2.0. PicoGreen is recommended for an accurate quantification.

Are FFPE samples supported?

Only use FFPE-derived DNA when using short amplicon lengths of 140 or 175 bp. Shorter amplicons provide better amplification than longer ones when the sample input is fragmented FFPE-derived DNA.

How much DNA can be targeted with this kit?

There is a limit of 12-6,144 primer pairs per pool. If generating target region greater than 5Mb, we recommend selecting an enrichment option.


Does this assay use the standard Nextera or TruSeq Adapters?

The adapters used in this assay are optimized for the AmpliSeq workflow. Nextera or TruSeq Adapters are not compatible with this assay.

What is required to purchase from Illumina?
Can I perform two or three different amplifications and then pool them before going into library prep?

It is possible to run 3 different AmpliSeq for Illumina designs each with barcodes on the same sequencing run. However, your target amplicon size and required coverage must be achieved in a single run.


What read length is recommended for sequencing?

A 2×150 bp paired-end read is recommended for 140-275 bp amplicon sizes. Up to 2x300 bp paired-end run on the MiSeq is recommended for 375 bp amplicon sizes.

How many samples can be sequenced at a time?

This kit has integrated sample barcodes that enable pooling of up to 96 samples per sequencing run. However, the actual number of samples that can be pooled together per sequencing run depends on the number of amplicons and the desired depth of sequencing coverage. An online calculator is provided in DesignStudio to help with these calculations.


What tools are offered for data analysis?

Local Run Manager and BaseSpace Sequence Hub have apps available for analysis. The DNA Amplicon Analysis App and RNA Amplicon Analysis App are available on BaseSpace Sequence Hub. Further analysis can be performed on any variant calls using BaseSpace Variant Interpreter. Local Run Manager has a similar DNA Amplicon Analysis Module and RNA Amplicon Analysis Module which utilizes the same workflow and algorithm as the BaseSpace Sequence Hub Apps.

The DNA Amplicon analysis workflow can be used to perform alignment and variant calling and the RNA Amplicon analysis workflow for fusion calling. Additionally, OncoCNV caller, a BaseSpace Lab Apps is available for CNV analysis.

Is there sample data I can view?

Yes, there are example data sets in BaseSpace Public Data.

What actual assay performance can I expect from my design?

DesignStudio returns high confidence amplicon designs that have delivered unprecedented amplicon multiplexing performance. Since each design is unique and sample input can vary, performance of the design will need to be tested empirically.

Are there non-encrypted manifest files available for my RNA panels (custom or fixed) containing fusions?

No. Manifest files for any RNA panel containing fusions are unavailable in a non-encrypted format. Only the encrypted manifest file is available.

Where can I find the breakpoint details for fusion panels (custom or fixed) included in the design?

Information about exact breakpoints contained in all RNA fusion panel designs is not provided. The result files produced by Illumina software analysis tools provide details of any RNA fusion events identified by the software. For information on which gene pairs are evaluated for your panel, see the panel's data sheet.

Where can I find my alignment files (eg, BAM files) from my analysis of RNA panels containing fusions?

Illumina software packages, including BaseSpace Sequence Hub Apps, do not provide alignment files as output from the analysis. At this time, only the final reporting of the results from the analysis are provided. For more details, consult the software's documentation.

Is there any information about potential false negatives or uncalled fusions from analysis of RNA panels containing fusions?

No. The software only reports detected fusion events. For information on which gene pairs are evaluated for your panel, see the panel's data sheet.

AmpliSeq for Illumina On-Demand

What is the minimum number of genes I can order in an On-Demand panel?

We’ve set an ordering minimum of 1 gene or 24 amplicons per panel. Designs must also have at least 2 pools and 12 amplicons per pool.

What is the maximum number of genes I can order in an On-Demand panel?

We have set an ordering maximum of 500 genes or 15,000 amplicons per panel due to manufacturing restrictions. We are always making improvements, so this limit is likely to increase. You may be able to order larger designs in the future.

What annotation source and version is used to recognize gene symbols when creating an On-Demand Panel?

Illumina uses RefGene v74 as the source of annotations.

Are untranslated regions (UTRs) included in an On-Demand gene’s design?

No, only the coding DNA sequence (CDS) region of a gene is included as part of an On-Demand gene design.

What is “Gene Amplicon Uniformity”?

Gene amplicon uniformity is the percentage of amplicons for a gene with greater than 0.2 times the mean coverage of all amplicons targeting that gene. It represents the observed wet-lab uniformity calculated from NextSeq data with the Illumina DNA Amplicon workflow.

Do On-Demand panels support UTR-only genes? What about pseudogenes?

No. On-Demand panels only support genes containing CDS regions. Pseudogenes are not supported.

What is the padding used for On-Demand gene designs?

The padding for every On-Demand gene design is 5 bp on the 5′ and 3′ ends of the exon.

Have all possible gene combinations been tested for primer-primer interactions?

No. The number of possible combinations is astronomical. It is not feasible to test for all possible combinations in the lab. However, through computer-based searches, we have reduced the occurrence of primer-primer interactions as much as possible. In addition, when synthesizing many genes simultaneously in large batches, we have observed less than 1% amplicon drop-out due to suspected primer-primer interactions.

Why are the number of primer pairs per pool indicated on the tube and box labels different than the number of amplicons per pool indicated in DesignStudio?

The number of amplicons per pool in DesignStudio reflects the number of unique amplicons in each pool. The number of primer pairs per pool on the tube and box labels reflects the total number of oligos per pool. Either value can be used when preparing libraries according to the AmpliSeq for Illumina On-Demand, Custom and Community Panels Reference Guide (Table 4. X cycles and X minutes). If the values fall into different cycle categories, the higher PCR cycle number is recommended.

AmpliSeq for Illumina On-Demand – IGV Viewer

What is the “observed coverage” track in the IGV viewer?

The “observed coverage” track indicates the number of observed reads for each amplicon of each targeted gene during validation experiments on a NextSeq. Use this track as general guidance for the likely performance when running an experiment. While values can vary among assays, the general coverage trend should remain consistent.

What are “Gaps”?

Gaps occur where there are no amplicons to provide coverage for the intended target. We have made every effort to minimize the occurrence of these regions in our On-Demand designs.

What is the scale on the Y-axis?

The Y-axis represents the observed coverage normalized by the mean amplicon coverage for the gene.

Can I use coordinates to navigate the IGV viewer?

No. The IGV viewer can only focus on your gene of interest. In the Grid View, select a gene, and the IGV viewer updates automatically to center on that gene.

I notice that the “observed coverage” track for an amplicon occasionally does not appear to contain information. Why is that?

All amplicons in the design contain reads that are visualized in the “observed coverage” track. If the number of reads covering an amplicon is relatively small in comparison to neighboring amplicons, the “observed coverage” track appears empty. However, if you change the scale to a lower value, you will then be able to visualize the lower number of reads. If the observed coverage track is not present, the designer notifies you why that track is not available.

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COVER: a priori estimation of coverage for metagenomic sequencing

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.

Systems Biology Programme, Centro Nacional de Biotecnología (CNB-CSIC). C/Darwin 3, 28049 Madrid, Spain.


In any metagenomic project, the coverage obtained for each particular species depends on its abundance. This makes it difficult to determine a priori the amount of DNA sequencing necessary to obtain a high coverage for the dominant genomes in an environment. To aid the design of metagenomic sequencing projects, we have developed COVER, a web-based tool that allows the estimation of the coverage achieved for each species in an environmental sample. COVER uses a set of 16S rRNA sequences to produce an estimate of the number of operational taxonomic units (OTUs) in the sample, provides a taxonomic assignment for them, estimates their genome sizes and, most critically, corrects for the number of unobserved OTUs. COVER then calculates the amount of sequencing needed to achieve a given goal. Our tests and simulations indicate that the results obtained through COVER are in very good agreement with the experimental results.

Fig. S1. The accuracy of the estimation of the fraction of 16S rRNA sequences belonging to unobserved OTUs (Good&aposs sample coverage). The results were obtained using a simulated data set composed of 16S rRNA sequences corresponding to 200 genomes, with abundances following a log-normal distribution (upper panel) or a broken-stick distribution (lower panel). Both distributions are used in ecology: the first is widely found in many natural communities, whereas the second is predicted for communities where the resources are partitioned into niches at random. Although microbial communities usually do not follow the broken-stick distribution, we wanted to test the performance of our calculation under this model of extremely high evenness. The insets show a rank-abundance graph showing the shapes of the respective distributions, with species ranked by abundance on the x-axis. The expected number of sequences is calculated using Good&aposs estimator, as described in the main text, whereas the real numbers are obtained by the random sampling of the number of sequences indicated by the x-axis.

Fig. S2. Accuracy of the estimation of unknown genome sizes. Upper: The difference in the genome size (expressed as |S1 − S2/max(S1, S2)|, with S1 and S2 representing the real sizes of the genomes) for pairs of genomes of known sizes, in relation to their taxonomic proximity. The relationship between the genome size and taxonomic relatedness is apparent. For instance, genomes related at the species level (i.e. different strains from the same species) usually have less than a 10% difference in genome size. If the genomes belong to the same genus, the difference can extend to 25%, although in most cases, it remains at 10% or less. Lower: Use of the genome sizes of sequenced species to infer the sizes for species currently being sequenced (species ‘in progress’ in the NCBI database,, whose size has been estimated, usually via PFGE). The plot shows the probability of inferring the size correctly using the sizes of other species at different taxonomic ranks. For instance, the case marked by a dashed line in the plot corresponds to the estimation of the size of some species using the known sizes of other species from the same genus. In that case, there is an approximate 75% probability that we can infer its genome size with less than 10% error.

Fig. S3. Accuracy of the estimation of the 16S rRNA copy number. Differences in copy number (expressed as |C1 − C2/max(C1, C2)|, with C1 and C2 representing the numbers of 16S copies in the genomes) for pairs of genomes of known copy number, in relation to their taxonomic proximity.

Fig. S4. Variation of the estimated coverage in relation to the number of 16S rRNA sequences provided. A community of 100 species was simulated, and the estimated coverage for the first 10 members was calculated by COVER using different initial numbers of 16S sequences, supposing a sequencing effort of 500 000 reads of 400 base pairs each. It can be seen that the estimates of coverage oscillate greatly when few sequences are provided, indicating that the community composition is still not well determined. When a substantial amount of 16S sequences is provided (between 2000 and 3000, in this case), the estimated coverage values stabilize and are very similar to the real coverage values (last point in the plot).

Fig. S5. Results of the estimation of coverage for a controlled data set composed of 100 genomes, with abundances following a log-normal distribution. The results are obtained by simulating the sequencing of 500 000 reads of 400 bp each. The plot shows the real coverage for each species (red line) and the obtained coverage predicted by COVER (green points). Species (genomes) are sorted according their abundances. Estimated coverage values match the real values very well. Some instances have no coverage estimated. These species have been merged with closely related ones because the 16S identity for the related species is 98% or more. For example, Burkholderia cenocepacia is given a coverage of zero because it was merged with Burkholderia pseudomallei, whose coverage is, thus, overestimated. Both species share 98% identity in their 16S rRNA. There was a similar occurrence for two more cases in this experiment: Bacillus anthracis was merged with Bacillus cereus, and Escherichia fergusonii was merged with Escherichia coli.

Table S1. Upper: Number of taxa for each rank, as listed in NCBI&aposs taxonomy database ( and the number of taxa containing at least one member with known size (from either complete genomes, genomes in progress or genomes with PFGE size estimates, Lower: Presence of families without any members of known genome size in the environmental samples ( In a set of 3035 samples, 810 contain a member from one of these families.

Table S2. Results obtained for the estimation of the number of reads needed for obtaining coverage 5× for the most represented genome in a controlled data set composed of 300 genomes, with abundances following a log-normal distribution. For studying the influence of inaccurate estimations of genomic sizes, we allowed these sizes to vary by some percentage of their original values. We draw a random value between 0 and a given percentage of the estimated genomic size, and added or subtracted that value to the estimation. The results obtained allowing 20% and 50% of variation are shown. The values change around 10% when allowing 20% of variation in the estimated sizes, and barely 25% when allowing 50% of variation.

Table S3. Comparison of the real and expected results for two metagenomic sequencing projects. The metagenomes were kindly provided by Dr Alejandro Mira (CSISP, Valencia, Spain), and they consist of two coupled sets of 16S and metagenomic sequences from oral samples. The first was obtained by sequencing amplicons from clone libraries. The contig length distributions for the real and expected instances were calculated as described in the text.

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EMI4_338_sm_FigS1.jpg165 KB Supporting info item
EMI4_338_sm_FigS2.jpg207.5 KB Supporting info item
EMI4_338_sm_FigS3.jpg61.4 KB Supporting info item
EMI4_338_sm_FigS4.jpg93.6 KB Supporting info item
EMI4_338_sm_FigS5.jpg46.1 KB Supporting info item
EMI4_338_sm_TabS1.doc23 KB Supporting info item
EMI4_338_sm_TabS2.doc27.5 KB Supporting info item
EMI4_338_sm_TabS3.doc33.5 KB Supporting info item

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