Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing
Open Access
- 2 July 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 22 (1), 1-16
- https://doi.org/10.1186/s12859-021-04267-5
Abstract
Systems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation. We developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility. We introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.Keywords
Funding Information
- New York University (ADHPG-CGSB)
This publication has 22 references indexed in Scilit:
- LRSim: A Linked-Reads Simulator Generating Insights for Better Genome PartitioningComputational and Structural Biotechnology Journal, 2017
- Comprehensive single-cell transcriptional profiling of a multicellular organismScience, 2017
- Bayexer: an accurate and fast Bayesian demultiplexer for Illumina sequencesBioinformatics, 2015
- Error filtering, pair assembly and error correction for next-generation sequencing readsBioinformatics, 2015
- Large-scale contamination of microbial isolate genomes by Illumina PhiX controlStandards in Genomic Sciences, 2015
- deML: robust demultiplexing of Illumina sequences using a likelihood-based approachBioinformatics, 2014
- The Sequence Alignment/Map format and SAMtoolsBioinformatics, 2009
- Fast and accurate short read alignment with Burrows–Wheeler transformBioinformatics, 2009
- Nucleotide sequence of bacteriophage φX174 DNANature, 1977
- Information streams sharing a finite bufferInformation Processing Letters, 1972