Bioinformatics Tools for PacBio Sequenced Amplicon Data Pre-processing and Target Sequence Extraction

  • Zeeshan AhmedEmail author
  • Justin Pranulis
  • Saman Zeeshan
  • Chew Yee Ngan
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


Modern high throughput sequencing technologies are enormously contributing to the generation of heterogeneous genomic data of different sizes and kinds. In most of the cases, NGS data is first produced in the raw form, which is then demultiplexed into text based formats, representing nucleotide sequences i.e. FASTA and FASTQ formats for secondary analysis. One of the major challenges for the downstream analysis of amplicon data is to first demultiplex FASTQ files based on the different oligonucleotides barcode combinations. Match & Scratch Barcodes (MSB) are a set of interactive bioinformatics tools that support the analysis of PacBio sequenced long read amplicon data by detecting multiple forward and reverse end adapter sequences, generic adapters attached to the region specific oligoes, multiple number of region specific oligos of variable length for the extraction of sequences of interest. These work with zero mismatch, retain only reads which map exactly to adapters and barcodes, report all sequences matched to both single and paired-end adapters and barcodes, and demultiplex FASTQ files based on the common and distinct barcodes combinations. The performance of MSB has been successfully tested using in-house sequenced non-published and external published datasets, which includes PacBio sequenced long read PDX (Patient-Derived Xenograft) amplicon data embedding multiple barcodes of variable lengths. MSB is user friendly and first interactively designed set of tools to empower non-computational scientists to demultiplex their own datasets and export results in different data formats (CSV, FASTA and FASTQ).


Bioinformatics PacBio Amplicon data Target sequences Software 



We thank Ahmed lab, Department of Genetics and Genome Sciences, Institute for Systems Genomics (ISG), School of Medicine, University of Connecticut Health Center (UConn Health), and The Jackson Laboratory for Genomics Medicine for their support to ZA, SZ and CYN. We also thank Partnership for Innovation and Education (PIE) and Technology Incubation Program (TIP) for supporting JP at UConn Health. We appreciate all colleagues, who have provided insight and expertise that greatly assisted the research and development.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zeeshan Ahmed
    • 1
    • 2
    Email author
  • Justin Pranulis
    • 1
  • Saman Zeeshan
    • 3
  • Chew Yee Ngan
    • 3
  1. 1.Genetics and Genome Science, School of MedicineUniversity of Connecticut Health Center (UConn Health)FarmingtonUSA
  2. 2.Institute for Systems Genomics, School of MedicineUniversity of Connecticut Health Center (UConn Health)FarmingtonUSA
  3. 3.The Jackson Laboratory for Genomic MedicineFarmingtonUSA

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