NEXTFLEX® 16S V1-V3 Amplicon-Seq Kit for Illumina® Platforms

  • Optimized protocol offers lower PCR bias and fewer off-target reads
  • Fast library prep protocol
  • Low input – As low as 1 ng of genomic DNA
  • Flexible barcode options– Up to 384 unique barcodes available for multiplexing of libraries
  • Does not require custom sequencing primers
  • Automation protocols are now available for the PerkinElmer Sciclone® NGS and NGSx Workstations to automate your 16S sequencing
  • Functionally validated on the Illumina® MiSeq® sequencer
Gut Microbiome Composition Predicts Infection Risk During Chemotherapy in Children With Acute Lymphoblastic Leukemia
Download Manual
Download Sciclone Automation Guide
For research use only. Not for use in diagnostic procedures.

  • NOVA-4202-02

    24 RXNS

  • NOVA-4202-03

    96 RXNS

  • NOVA-4202-04

    BARCODES 1-96
    192 RXNS

  • NOVA-4202-05

    BARCODES 97-192
    192 RXNS

  • NOVA-4202-06

    BARCODES 193-288
    192 RXNS

  • NOVA-4202-07

    BARCODES 289-384
    192 RXNS

Automated versions of the NEXTFLEX® 16S V1 – V3 Amplicon-Seq Library Prep kits are available by request. Please inquire for additional information at [email protected].

Preparation of Multiplexed Amplicon Libraries

The NEXTFLEX® 16S V1 – V3 Amplicon-Seq Library Prep Kit is designed for the preparation of multiplexed amplicon libraries that span the hypervariable domains one through three (V1-V3) of microbial 16S ribosomal RNA (rRNA) genes. These libraries are compatible with paired-end sequencing on the Illumina® sequencing platforms.

Fast Library Prep Protocol

There are two main steps involved in 16S V1-V3 amplicon processing: an initial PCR amplification using customized PCR primers that target the V1-V3 domains, and a subsequent PCR amplification that integrates relevant flow cell binding domains and unique 12 base pair sample indices. The limited number of cleanup steps ensures maximum recovery of amplicons for downstream sequencing.

Optimized Protocol Offers Lower PCR Bias and Fewer Off-target Reads

The protocol incorporated in the NEXTFLEX 16S V1 – V3 Amplicon-Seq Kit offers better sequencing results than can be obtained using traditional 16S sequencing protocols. The incorporation of the second PCR step in the protocol for the addition of the sample-specific index reduces the number of off-target reads typically encountered during amplicon sequencing.

Automate your 16S V1 – V3 Library Prep to Increase Throughput and Reduce Errors

A NEXTFLEX® automation protocol is now available on the Sciclone® NGS and NGSx workstations to help labs increase their throughput and reduce human errors. Automated versions of the NEXTFLEX® 16S V1 – V3 Amplicon-Seq Library Prep kits are available by request. Please inquire for additional information at [email protected]. Automation kits are custom items and require a minimum of 2 weeks lead time.


  • NEXTFLEX® PCR Master Mix
  • NEXTFLEX® 16S V1-V3 PCR I Primer Mix
  • NEXTFLEX® PCR II Barcoded Primer Mix
  • Resuspension Buffer
  • Nuclease-free Water


  • 1 ng – 50 ng high-quality genomic DNA in up to 36 µL nuclease-free water for each library
  • 96 well PCR Plate Non-skirted (Phenix Research, Cat # MPS-499) or similar
  • Adhesive PCR Plate Seal (Bio-Rad®, Cat # MSB1001)
  • Agencourt® AMPure® XP 5 mL (Beckman Coulter® Genomics, Cat # A63880)
  • Magnetic Stand – 96 (Thermo Fisher® Scientific, Cat # AM10027) or similar
  • Thermocycler
  • 2, 10, 20, 200 and 1000 µL pipettes / multichannel pipettes
  • Nuclease-free barrier pipette tips
  • Vortex
  • 80% Ethanol, freshly prepared (room temperature)


  • Sequences of NEXTFLEX 16S V1-V3 Amplicon-Seq Barcoded Primers Indexes – Excel / PDF


Selected Citations that Reference the Use of the NEXTFLEX 16S V1-V3 Amplicon-Seq Kit

  • Cortez, V., Canal, E., Dupont-Turkowsky, J. C., Quevedo, T., Albujar, C., Chang, T., . . . Bausch, D. G. (2018). Identification of Leptospira and Bartonella among rodents collected across a habitat disturbance gradient along the Inter-Oceanic Highway in the southern Amazon Basin of Peru. Plos One, 13(10). doi:10.1371/journal.pone.0205068.
  • Franzen, J., Zirkel, A., Blake, J., Rath, B., Benes, V., Papantonis, A., & Wagner, W. (2016). Senescence-associated DNA methylation is stochastically acquired in subpopulations of mesenchymal stem cells. Aging Cell, 16(1), 183-191. doi:10.1111/acel.12544.
  • Hakim, H., Dallas, R., Wolf, J., Tang, L., Schultz-Cherry, S., Darling, V., . . . Rosch, J. W. (2018). Gut Microbiome Composition Predicts Infection Risk During Chemotherapy in Children With Acute Lymphoblastic Leukemia. Clinical Infectious Diseases,67(4), 541-548. doi:10.1093/cid/ciy153.
  • Luna, R. A., Oezguen, N., Balderas, M., Venkatachalam, A., Runge, J. K., Versalovic, J., . . . Williams, K. C. (2017). Distinct Microbiome-Neuroimmune Signatures Correlate With Functional Abdominal Pain in Children With Autism Spectrum Disorder. Cellular and Molecular Gastroenterology and Hepatology, 3(2), 218-230. doi:10.1016/j.jcmgh.2016.11.008.
  • Maiuri, A. R., et al. (2017) Mismatch Repair Proteins Initiate Epigenetic Alterations during Inflammation-Driven Tumorigenesis. Cancer Research, 77(13), 3467-3478. doi:10.1158/0008-5472.can-17-0056.
  • Quereda, J. J., et al. (2016) Bacteriocin from epidemic Listeria strains alters the host intestinal microbiota to favor infection. PNAS. doi:10.1073/pnas.1523899113.
  • Pahwa, R., Balderas, M., Jialal, I., Chen, X., Luna, R. A., & Devaraj, S. (2017). Gut Microbiome and Inflammation: A Study of Diabetic Inflammasome-Knockout Mice. Journal of Diabetes Research,2017, 1-5. doi:10.1155/2017/6519785.
  • Ranjan, R., Rani, A., Metwally, A., McGee, H. S. and Perkins D. L. (2015) Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophy Res Com. doi:10.1016/j.bbrc.2015.12.083.
  • Su, A., Yang, W., Zhao, L., Pei, F., Yuan, B., Zhong, L., . . . Hu, Q. (2018). Flammulina velutipes polysaccharides improve scopolamine-induced learning and memory impairment in mice by modulating gut microbiota composition. Food & Function, 9(3), 1424-1432. doi:10.1039/c7fo01991b.
  • Whon, T. W., Chung, W., Lim, M. Y., Song, E., Kim, P. S., Hyun, D., . . . Nam, Y. (2018). The effects of sequencing platforms on phylogenetic resolution in 16 S rRNA gene profiling of human feces. Scientific Data, 5, 180068. doi:10.1038/sdata.2018.68.
  • Yao, J. et al. (2016) A Pathogen-Selective Antibiotic Minimizes Disturbance to the Microbiome. Antimicrob. Agents Chemother. 00535-16. doi:10.1128/AAC.00535-16.

The NEXTFLEX 16S V1 -V3 Amplicon-Seq Kit contains enough material to prepare 8, 24, 96 or 192 amplicon-seq libraries from genomic DNA for Illumina® sequencing. The shelf life of all reagents is 12 months when stored properly. All components can be safely stored at -20°C. This kit is shipped on dry ice.

Outside of novel sequencing technologies that emerge every few years, the ability to multiplex samples is the most critical and revolutionary aspect of next-generation sequencing. Multiplexing allows for acute control of throughput, amplifying the value of obtaining just enough data per sample.

To make multiplexing possible, small arbitrary sequences are incorporated into the sequencing adapters attached to all fragments of a particular sample. These sequences, known as barcodes, allow for post-sequencing processing to bin each fragment by its originating sample.

However, even high-fidelity polymerases used during sequencing reads are invariably prone to introducing errors. These errors are especially costly when landing during the barcode read, preventing proper binning and wasting associated sequencing reads. To alleviate this, the knowledge of bitwise error correction was extended to the base-wise language of sequencing.

The overall ability to correct barcode read errors stems from the differentiability between the entire set of barcodes. Differentiability can be called distance, or the number of single position changes that are required for one barcode sequence to become another. For example, the top sequence in the below figure has only one position change from the middle, while the middle has one position change from the bottom. Overall, the top to bottom sequence requires two position changes. This concept, known as the Hamming distance, is what powers barcode error correction and casual codebreaking games like Mastermind.


The greater the minimum distance separation across an entire barcode set, the stronger the differentiability. This in turn governs how many errors can be error-corrected across a barcode subset. Maximum error correction is governed by the following formula:


where d is the minimum distance across the entire set.

How does minimum distance affect generating barcode sets? By increasing the minimum distance across a subset, the overall maximum subset size decreases. One must set requirements so that sufficient barcodes are within a set of desired error correction.

We have expanded previously available barcode sequence sets in both set size and index lengths to accommodate higher levels of error correction. Also, other factors such as colorspace on Illumina instruments have been considered, leaving customers with a minimal amount of effort in selecting the best subsets for low-diversity sequencing runs.

Our new 12 nt barcode set, available with the NEXTFLEX® 16S V1 – V3 Amplicon-Seq Kit, allows for up to two error corrections and has multiple low-diversity pooling options. We will continue to develop new technologies to remain the leader in quality multiplexing options.

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