Library preparation and sequencing were carried out at the Core Facility Molecular Biology at the Center for Medical Research at the Medical University Graz, Austria. In brief, DNA concentrations were normalized using a SequalPrep™ normalization plate (Invitrogen), and each sample was indexed with a unique barcode sequence (eight cycles index PCR). After pooling of the indexed samples, a gel cut was carried out to purify the products of the index PCR. Sequencing was performed using the Illumina MiSeq device and MS-102-3003 MiSeq® Reagent Kit v3-600cycles (2 × 251 cycles).
Ms 102 3003 miseq reagent kit v3 600cycles
Manufactured by Illumina
The MS-102-3003 MiSeq® Reagent Kit v3-600cycles is a laboratory equipment product designed for use with the MiSeq® System. It provides the necessary reagents for sequencing up to 600 cycles on the MiSeq® platform.
Automatically generated - may contain errors
3 protocols using ms 102 3003 miseq reagent kit v3 600cycles
1
DNA Library Preparation and Illumina Sequencing
2
Amplicon Sequencing Protocol for Microbiome Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Library preparation and sequencing of the amplicons were carried out at the Core Facility Molecular Biology, Centre for Medical Research at the Medical University Graz, Austria. In brief, DNA concentrations were normalised using a SequalPrep™ normalisation plate (Invitrogen), and each sample was indexed with a unique barcode sequence (8 cycles index PCR). After pooling of the indexed samples, a gel cut was carried out to purify the products of the index PCR. Sequencing was performed using the Illumina MiSeq device and MS-102-3003 MiSeq® Reagent Kit v3-600cycles (2 × 150 cycles). The obtained fastq data is available in the European Nucleotide Archive under the study accession number: PRJEB41618.
The fastq data analysis was performed using QIIME277 (link),78 (link). After quality filtering, the DADA2 algorithm79 (link) was used to denoise truncated reads and generate amplicon sequence variants (ASVs). Taxonomic classification80 (link) was based on the SILVA v132 database81 (link) and the obtained feature table and taxonomy file were used for further analysis (Supplementary Table4 ). The overlapping features from negative controls (DNA extraction and PCR negative controls) were manually subtracted or removed from both the bacterial and archaeal dataset. The reads classified as chloroplast and mitochondria were also removed.
The fastq data analysis was performed using QIIME277 (link),78 (link). After quality filtering, the DADA2 algorithm79 (link) was used to denoise truncated reads and generate amplicon sequence variants (ASVs). Taxonomic classification80 (link) was based on the SILVA v132 database81 (link) and the obtained feature table and taxonomy file were used for further analysis (Supplementary Table
3
Amplicon Sequencing for Microbial Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Library preparation and sequencing of the amplicons were carried out at the Core Facility Molecular Biology, Center for Medical Research at the Medical University Graz, Austria. In brief, DNA concentrations were normalised using a SequalPrep™ normalisation plate (Invitrogen), and each sample was indexed with a unique barcode sequence (8 cycles index PCR). After pooling of the indexed samples, a gel cut was carried out to purify the products of the index PCR. Sequencing was performed using the Illumina MiSeq device and MS-102-3003 MiSeq® Reagent Kit v3-600cycles (2x150 cycles). The obtained fastq data is available in the European Nucleotide Archive under the study accession number: PRJEB41618.
The fastq data analysis was performed using QIIME2 [43] as described previously [44] . After quality filtering, the DADA2 algorithm [45] was used to denoise truncated reads and generate amplicon sequence variants (ASVs). Taxonomic classification [46] was based on the SILVA v132 database [47] and the obtained feature table and taxonomy file were used for further analysis (Supplementary Tab. 4). The overlapping features from negative controls (DNA extraction and PCR negative controls) were manually subtracted or removed from both the bacterial and archaeal dataset. The reads classified as chloroplast and mitochondria were also removed.
The fastq data analysis was performed using QIIME2 [43] as described previously [44] . After quality filtering, the DADA2 algorithm [45] was used to denoise truncated reads and generate amplicon sequence variants (ASVs). Taxonomic classification [46] was based on the SILVA v132 database [47] and the obtained feature table and taxonomy file were used for further analysis (Supplementary Tab. 4). The overlapping features from negative controls (DNA extraction and PCR negative controls) were manually subtracted or removed from both the bacterial and archaeal dataset. The reads classified as chloroplast and mitochondria were also removed.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!