Pre-made Library Sequencing

The Introduction of Pre-made Library Sequencing

CD Genomics accepts customers' prepared libraries for sequencing. You can submit the libraries for full QC test and use our sequencing-only service. We provide the most advanced and powerful sequencing platforms with different capacities and read lengths to fit any project scale, budget, and turnaround. Our QC library ensures optimal cluster generation and maximal data output for each run. Different samples can be multiplexed and sequenced together as long as they have the same read length. Our highly experienced team offers consultation on the best sequencers for different research goals. We also offer FREE PhiX spike-in, demultiplexing (FASTQ), and data transfer via FTP.

Prefabricated library sequencing is a high-throughput sequencing technique utilized for the systematic analysis of the genome, transcriptome, or other nucleic acid sequences of biological samples. It involves preparing nucleic acids from the sample into libraries, followed by sequencing using high-throughput sequencers, thereby generating a vast amount of sequence data for further analysis. Prefabricated libraries entail fragmenting the sample's nucleic acids before sequencing, and through a series of biochemical reactions, incorporating adapter sequences compatible with the sequencing platform, rendering them suitable for high-throughput sequencers.

If you want to learn more about pre-made library sequencing, you can refer to our article "The Introduction and Workflow of Pre-made Library Sequencing."

Advantages of Pre-made Library Sequencing

  • High-Throughput: Capable of generating a large amount of sequence data in a short timeframe.
  • Versatility: Applicable to various types of nucleic acid samples (DNA, RNA, etc.).
  • Flexibility: Enables research at different levels such as genomics, transcriptomics, epigenomics, etc.
  • Accuracy: High-throughput sequencing technology exhibits high accuracy and coverage.
  • Reliable Expertise: Proficient team expertise guides you through every step of sample processing.
  • Quick Turnaround Time: Provides the fastest turnaround time for sample processing, allowing quicker progression from quality control to data dissemination and expediting project timelines.

Application of Pre-made Library Sequencing

Pre-made Library Sequencing Workflow

The quality inspection method of the sizes and concentrations of the library is Qubit, Agilent bioanalyzer.

Workflow Diagram of Pre-made Library Sequencing.

Service Specifications

Sample Requirements

Platform Minimum Concentration Data Amount Volume Requirement
Novaseq-PE150 2 ng/μL X<30G
≥15 μL
≥25 μL
≥50 μL
≥70 μL
≥100 μL (additional 70μL for one more lane)
Nova- PE250 2 ng/μL X<30GM
≥15 μL
≥25 μL
≥50 μL
≥100 μL (additional 70μL for one more lane)
HiSeq-PE150 1 ng/μL 1 Lane ≥10 μL
MiSeq-PE300 1 ng/μL 1 Flowcell ≥10 μL

Note: Sample amounts are listed for reference only. For detailed information, please contact us with your customized requests.

Sequencing Strategy

Illumina Sequencing:

Platform Read Length (nt) Unit Unit Output (raw clusters in Millions)
NovaSeq 6000
PE50 Lane (SP) 375-400 M
PE50 Lane (S1) 650-800M
PE50 Lane (S2) 1,650-2,050 M
PE100 Lane (SP) 375-400 M
PE100 Lane (S1) 650-800M
PE100 Lane (S2) 1,650-2,050 M
PE100 Lane (S4) 4,000-5,000M
PE150 Lane (SP) 375-400 M
PE150 Lane (S1) 650-800M
PE150 Lane (S2) 1,650-2,050 M
PE150 Lane (S4) 4,000-5,0000M
PE250 Lane (SP) 375-400 M
HiSeq 4000 SE50 Lane 300-400 M
PE75 Lane 300-400 M
PE150 Lane 300-400 M
MiSeq PE150 Flowcell (Nano) 1 M
PE250 Flowcell (Nano) 1 M
PE150 Flowcell (Micro) 4 M
SE36 Flowcell (V2) 12-15 M
PE25 Flowcell (V2) 12-15 M
PE150 Flowcell (V2) 12-15 M
PE250 Flowcell (V2) 12-15 M
PE75 Flowcell (V3i) 22-25 M
PE300 Flowcell (V3) 22-25 M

Pacbio Sequencing

Platform Run Type
Pacbio Sequl II SMRT Cell 8M HiFi Sequencing
CLR Sequencing

Bioinformatics Analysis

  • Data Quality Control
  • Data Preprocessing
  • Alignment
  • Assembly
  • Variant Detection
  • Gene Expression Analysis
  • Functional Annotation
  • … and more

Note: Recommended data outputs and analysis contents displayed are for reference only. For detailed information, please contact us with your customized requests.

Analysis Pipeline

The Data Analysis Pipeline of Pre-made Library Sequencing.


  • The original sequencing data
  • Experimental results
  • Data analysis report
  • Details in Pre-made Library Sequencing for your writing (customization)

The Pre-made Library Sequencing Results Display Figure.

1. What types of samples can be used for pre-made library sequencing?

For pre-made library sequencing, various sample types are employed, encompassing:

2. How is the quality of the pre-made library assessed?

The quality of the pre-made library is assessed through:

  • Size Distribution: Analyzing the fragment size distribution using an instrument like the Agilent Bioanalyzer or TapeStation.
  • Concentration Measurement: Quantifying the library concentration using a Qubit fluorometer or quantitative PCR (qPCR).
  • Quality Control Metrics: Checking for adapter dimers, contamination, and overall quality using tools like FastQC for raw sequencing data.

3. What does the uniformity of library output refer to?

During sequencing, the output data volume of libraries in the same lane remains uniform relative to the amount of library input. For example, when mixing 5 libraries and obtaining a total of 50G data, there should not be a scenario where one library yields 30G of data while another only yields 5G; each library should ideally contribute equally at 10G.

4. Can pre-made library sequencing be utilized for single-cell analysis?

Certainly, pre-made library sequencing can be repurposed for single-cell analysis. Techniques like single-cell RNA sequencing (scRNA-seq) entail isolating individual cells, reverse transcribing RNA into cDNA, and creating sequencing libraries from the resulting single-cell cDNA. This methodology enables the investigation of gene expression at the single-cell level, unraveling insights into cellular heterogeneity and function.

Landscape of somatic mutations in 560 breast cancer whole genome sequences

Journal: Nature

Impact factor: 64.8001

Published: 2 May 2016


The mutational theory of cancer postulates that specific DNA sequence alterations, referred to as "driver" mutations, confer proliferative advantages to cells, fostering the emergence of malignant cell populations. These mutations can stem from diverse mechanisms, encompassing exposure to mutagens, errors in DNA repair mechanisms, and inaccuracies during DNA replication. Technological progress, spanning from karyotype analysis to high-throughput DNA sequencing, has substantially enhanced the delineation of cancer-associated mutations. Nonetheless, predominant attention has been directed towards protein-coding regions, leaving crucial inquiries unanswered regarding mutations within non-coding regions and the intrinsic mutagenic processes underlying breast cancer. To bridge these knowledge gaps, we conducted an in-depth analysis of whole-genome sequences from 560 breast cancer cases, striving for a comprehensive elucidation of somatic mutations in this context.


Sample Preparation:
  • 560 breast cancers
  • Normal tissue
  • DNA extraction
  • Total RNA extraction
Data Analysis:
  • Alignment
  • Processing of genomic data
  • Identification of novel breast cancer genes
  • Mutational signatures analysis
  • Rearrangement signatures
  • Individual patient whole genome profiles


Whole genomes of 560 breast cancers and matched non-neoplastic tissues were sequenced, detecting numerous somatic mutations. By combining data from various sources, new cancer genes were identified, and driver mutations were defined. Genomic rearrangements and copy number changes further contributed to the identification of driver mutations, with TP53, PIK3CA, and MYC being among the most frequently mutated genes.

Fig 1. Overview of the cohort and inventory of somatic mutations in 560 breast cancers. (Nik-Zainal et al., 2016)Fig 1. Cohort and catalogue of somatic mutations in 560 breast cancers.

We explored non-coding somatic mutations and identified recurring mutations in the PLEKHS1 promoter linked to mutational signatures 2 and 13. Analogous mutations were noted in the promoters of TBC1D12 and WDR74, suggesting potential hypermutability. Additionally, mutations were detected in long non-coding RNAs MALAT1 and NEAT1, though their significance as driver mutations remains uncertain.

Fig 2. Investigation of non-coding regions in breast cancer genomes. (Nik-Zainal et al., 2016)Fig 2. Non-coding analyses of breast cancer genomes.


Progress towards a comprehensive understanding of breast cancer genetics is underway, revealing multiple mutational signatures and implicating numerous cancer genes. However, the rarity of dominantly-acting fusion genes and non-coding driver mutations suggests additional complexities. Yet, many questions remain, including the identification of additional cancer genes and the roles of viruses or microbes. Further exploration of whole genome sequences from breast cancer patients is needed to fully elucidate the somatic mutational landscape of the disease.


  1. Nik-Zainal S, Davies H, Staaf J, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature, 2016, 534(7605): 47-54.
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