Pheniqs: Fast and flexible quality-aware sequence demultiplexing

Abstract

Motivation: Output from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique "barcode" sequence and are then pooled, or "multiplexed", and sequenced together. Classifying, or "demultiplexing", the reads involves decoding the barcode sequence.Although instruments estimate the probability of incorrectly calling each nucleobase, available demultiplexers do not consult those estimates or report classification error probabilities. Results: We present Pheniqs, a fast and flexible sequence demultiplexer and quality analyzer. In addition to providing an efficient implementation of the widespread minimum distance decoder, Pheniqs introduces a novel Phred-adjusted maximum likelihood decoder that consults base calling quality scores and estimates the probability of a barcode decoding error. Setting an upper bound on the permissible error provides an intuitive way to control demultiplexing confidence and directly influence precision and recall. Pheniqs supports FASTQ and multiple Sequence Alignment/Map formats and uses auxiliary SAM tags to report both library classification and demultiplexing error probability. Evaluation on both real and semi-synthetic data indicates that Pheniqs is faster than existing demultiplexers, substantially when demultiplexing longer reads, and achieves greater accuracy by correctly reflecting quality measurements. Availability and Implementation: Implemented in multithreaded C++ and available under the terms of the AGPL-3.0 license agreement at

Host Genotype and Colonist Arrival Order Jointly Govern Plant Microbiome Composition and Function

“…Final PCR products were cleaned and normalized using Just-a-Plate, 96-well normalization and purification plates (Charm Biotech), then pooled and sent to the Oregon State University, Center for Genome Research and Biocomputing, for 250-bp, paired-end sequencing on the Illumina MiSeq platform. ll Current Biology 30, 3260-3266.e1-e5, August 17, 2020 e3 Report Fungal community bioinformatics Sequences were demultiplexed with Pheniqs v2.0.4 [61], using a phred-adjusted maximum likelihood confidence threshold of 0.995. Gene primers and length heterogeneity spacers were removed from the 5 0 ends of the paired reads using cutadapt v1.18 [60], discarding reads with gene primers not found in the expected positions.…”

Quantitative single-cell transcriptomics

“…For well-based methods, the number and sequence of barcodes in the library are known and usually designed with a maximal distance to each other to minimize the impact of sequencing errors. Such barcodes are fairly straightforward to demultiplex, and some methods provide a probabilistic assignment considering sequence quality, allowing for an unbiased and rigorous quality assessment [ 118 , 119 ].…”

The core seed mycobiome ofPseudotsuga menziesiivar.menziesiiacross provenances of the Pacific Northwest, USA

“…Processing of Illumina MiSeq library.-We used the following method to prepare our MiSeq library for analysis. Sequences were demultiplexed using Pheniqs (Galanti et al 2017) with a maximum likelihood confidence threshold of 0.995. Adaptors and the 5' gene primer were trimmed using Cutadapt 2.10 (Martin 2011).…”