Seqpac: A framework for sRNA-seq analysis in R using sequence-based counts.
Bioinformatics 2023;
39:7082956. [PMID:
36944267 PMCID:
PMC10085512 DOI:
10.1093/bioinformatics/btad144]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/24/2023] [Accepted: 03/20/2023] [Indexed: 03/23/2023] Open
Abstract
MOTIVATION
Feature-based counting is commonly used in RNA-sequencing (RNA-seq) analyses. Here, sequences must align to target features (like genes or non-coding RNAs) and related sequences with different compositions are counted into the same feature. Consequently, sequence integrity is lost, making results less traceable against raw data.
IMPLEMENTATION
Small RNA (sRNA) often maps to multiple features and shows an incredible diversity in form and function. Therefore, applying feature-based strategies may increase the risk of misinterpretation. We present a strategy for sRNA-seq analysis that preserves the integrity of the raw sequence making the data lineage fully traceable. We have consolidated this strategy into Seqpac: An R package that makes a complete sRNA analysis available on multiple platforms. Using published biological data, we show that Seqpac reveals hidden bias and adds new insights to studies that were previously analyzed using feature-based counting.
CONCLUSIONS
We have identified limitations in the concurrent analysis of RNA-seq data. We call it the traceability dilemma in alignment-based sequencing strategies. By building a flexible framework that preserves the integrity of the read sequence throughout the analysis, we demonstrate better interpretability in sRNA-seq experiments, which are particularly vulnerable to this problem. Applying similar strategies to other transcriptomic workflows may aid in resolving the replication crisis experienced by many fields that depends on transcriptome analyses.
AVAILABILITY
Seqpac is available on Bioconductor (https://bioconductor.org/packages/seqpac) and GitHub (https://github.com/danis102/seqpac).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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