PANDAseq: paired-end assembler for illumina sequences

By A. Masella, Andrea K. Bartram, J. Truszkowski, Daniel G. Brown, Josh D. Neufeld. BMC Bioinformatics, 2012

2,021 citations recorded by the source corpus.

Illumina paired-end reads are used to analyse microbial communities by targeting amplicons of the 16S rRNA gene. Publicly available tools are needed to assemble overlapping paired-end reads while correcting mismatches and uncalled bases; many errors could be corrected to obtain higher sequence yields using quality information. PANDAseq assembles paired-end reads rapidly and with the correction of most errors. Uncertain error corrections come from reads with many low-quality bases identified by upstream processing. Benchmarks were done using real error masks on simulated data, a pure source template, and a pooled template of genomic DNA from known organisms. PANDAseq assembled reads more rapidly and with reduced error incorporation compared to alternative methods. PANDAseq rapidly assembles sequences and scales to billions of paired-end reads. Assembly of control libraries showed a 4-50% increase in the number of assembled sequences over naïve assembly with negligible loss of "good" sequence.

Subfields: Ecology, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/beaec7e89a861b140b7c0fd995e07f8fae9e99a1

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Citations in the corpus

The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression (2018, neutral): “Sequences with quality score of Q20 and higher were assembled for paired ends using default parameters of PANDASEQ with a minimum overlap of 25 nucleotides and maximum of 100 nucleotides between forward and reverse reads (45, 46).”
Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi (2015, methodology): “The remaining reads were paired using the PANDASEQ assembler (Masella et al. 2012) with minimum overlap of 25 bases and demultiplexed using mothur (no differences to primer+identifier allowed, minimum sequence length >200 bases).”
Biochar alters the soil microbiome and soil function: results of next‐generation amplicon sequencing across Europe (2017, methodology): “Using .fastq input, PandaSeq is able to determine the quality score of each base, and in cases where reads disagree on a basecall, the programme utilizes the base with the highest quality score.”
Falco: high-speed FastQC emulation for quality control of sequencing data (2019, neutral): “High-throughput sequencing is routinely used to profile copy number variations in cancers 1 , assemble genomes of microbial organisms 2,3 , quantify gene expression 4 , identify cell populations from single-cell transcriptomes in a variety of tissues 5 and track epigenetic changes in developing…”
Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform (2013, background): “Finally, the only published attempt to develop a method of curating paired sequence reads has suggested allowing various numbers of mismatches between the overlapping sequence reads; however, again, ﬁnal error rates were not provided (6, 17).”
A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology (2013, positive): “[8] recently developed a fast aligner (PANDAseq) for overlapping paired-end reads that employs Q-scores to solve disagreements between mismatches and retain a larger number of high-quality reads compared to naı̈ve approaches.”
Illumina metabarcoding of a soil fungal community (2013, methodology): “The computational time demand was w20 min for paired-end assembly (a straightforward procedure relying on an existing program, Masella et al., 2012), w1.”
High-fat diet alters gut microbiota physiology in mice (2013, methodology): “Reads were assembled using Pandaseq with a minimum overlap of 35bp (Masella et al., 2012).”
Ribosomal Database Project: data and tools for high throughput rRNA analysis (2013, methodology): “In both datasets, Assembler outperformed the original PANDAseq when scored in a similar manner (although such Q score based ﬁltering was not a goal of that implementation).”