Dataset Information

PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment.

ABSTRACT:

Summary

Use of PacBio sequencing for characterizing barcoded libraries of genetic variants is on the rise. However, current approaches in resolving PacBio sequencing artifacts can result in a high number of incorrectly identified or unusable reads. Here, we developed a PacBio Read Alignment Tool (PacRAT) that improves the accuracy of barcode-variant mapping through several steps of read alignment and consensus calling. To quantify the performance of our approach, we simulated PacBio reads from eight variant libraries of various lengths and showed that PacRAT improves the accuracy in pairing barcodes and variants across these libraries. Analysis of real (non-simulated) libraries also showed an increase in the number of reads that can be used for downstream analyses when using PacRAT.

Availability and implementation

PacRAT is written in Python and is freely available (https://github.com/dunhamlab/PacRAT).

Supplementary information

Supplemental data are available at Bioinformatics online.

SUBMITTER: Yeh CC

PROVIDER: S-EPMC9306489 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment.

Yeh Chiann-Ling C CC Amorosi Clara J CJ Showman Soyeon S Dunham Maitreya J MJ

Bioinformatics (Oxford, England) 20220501 10

<h4>Summary</h4>Use of PacBio sequencing for characterizing barcoded libraries of genetic variants is on the rise. However, current approaches in resolving PacBio sequencing artifacts can result in a high number of incorrectly identified or unusable reads. Here, we developed a PacBio Read Alignment Tool (PacRAT) that improves the accuracy of barcode-variant mapping through several steps of read alignment and consensus calling. To quantify the performance of our approach, we simulated PacBio read ...[more]

PMID: 35561209

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Project description:BackgroundAlignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.ResultsTwo predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.ConclusionsPredicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Dataset Information

PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment.

Summary

Availability and implementation

Supplementary information

Publications

PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment.

Similar Datasets

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