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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Related Experiment Video

Updated: Oct 30, 2025

BEST: Barcode Enabled Sequencing of Tetrads

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Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode

Lior Galanti^1,2, Dennis Shasha³, Kristin C Gunsalus^4,5

¹Department of Biology, Center for Genomics and System Biology, New York University, New York, USA.

BMC Bioinformatics

|July 3, 2021

Summary

This summary is machine-generated.

Pheniqs software accurately decodes combinatorial barcodes in deep sequencing data by using probabilistic models. This flexible tool enhances data interpretation for complex experimental designs, improving reproducibility and scalability for genomics research.

Keywords:

Barcode decoding confidence Barcode noise filtering Barcode simulation Combinatorial indexing Sequence demultiplexing Single-cell split-pooling

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Last Updated: Oct 30, 2025

BEST: Barcode Enabled Sequencing of Tetrads

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Published on: May 1, 2014

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Sequencing of mRNA from Whole Blood using Nanopore Sequencing

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Published on: June 3, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Deep sequencing with combinatorial index tags is crucial for associating biological sequences with their origin.
Accurate interpretation of sequencing data relies on correct decoding of these combinatorial barcodes.
Increasing experimental complexity necessitates advanced models for barcode decoding.

Purpose of the Study:

To develop a robust software tool for accurate probabilistic decoding of combinatorial barcodes.
To provide a scalable and efficient solution for handling complex barcoding designs in deep sequencing.
To improve the accuracy and reliability of sequence classification in systems biology.

Main Methods:

Developed Pheniqs, a software implementing probabilistic decoding of combinatorial barcodes.
Incorporated basecalling quality scores and prior distributions to compute posterior decoding error probabilities.
Utilized a multithreaded implementation for parallelized processing and scalability.

Main Results:

Pheniqs achieves higher accuracy than traditional methods like minimum edit distance or maximum likelihood estimation.
The software computes combinatorial confidence scores for any barcoding strategy.
Pheniqs demonstrates linear scalability, classifying over 11 billion reads in under 2 hours with minimal memory usage.

Conclusions:

Pheniqs offers a flexible and accurate solution for decoding complex combinatorial barcodes in deep sequencing.
The software provides tunable sensitivity and supports integration into automated analysis pipelines.
Pheniqs has been successfully used in production for seven years in a genomics core facility, demonstrating its reliability and utility.