Journal article
LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data
Computational and structural biotechnology journal, v 27, pp 556-563
01 Jan 2025
PMID: 39981293
Abstract
While several well-established quality control (QC) tools exist for short-read sequencing data, there is a general paucity of computational tools that efficiently deliver comprehensive metrics across a wide range of long-read sequencing data formats, such as Oxford Nanopore (ONT) POD5, ONT FAST5, ONT basecall summary, Pacific Biosciences (PacBio) unaligned BAM, and Illumina Complete Long Read (ICLR) FASTQ file formats. In addition to nucleotide sequence information, some file formats such as POD5 contain raw signal information used for base calling, while other file formats such as aligned BAM contain alignments to a linear reference genome or transcriptome and may also contain base modification information. There is currently no single available QC tool capable of summarizing each of these features. Furthermore, high-performance tools are required to efficiently process the growing data volumes from long-read sequencing platforms. To address these challenges, here we present LongReadSum, a high-performance tool for generating a summary QC report for major types of long-read sequencing data. We also demonstrate a few examples using LongReadSum to analyze cDNA sequencing, direct RNA sequencing, ONT reduced representation methylation sequencing (RRMS), and whole genome sequencing (WGS) data.
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Details
- Title
- LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data
- Creators
- Jonathan Elliot Perdomo - Children's Hospital of PhiladelphiaMian Umair Ahsan - Children's Hospital of PhiladelphiaQian Liu - Children's Hospital of PhiladelphiaLi Fang - Children's Hospital of PhiladelphiaKai Wang (Corresponding Author) - Children's Hospital of Philadelphia
- Publication Details
- Computational and structural biotechnology journal, v 27, pp 556-563
- Publisher
- Elsevier
- Number of pages
- 8
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering and Science
- Web of Science ID
- WOS:001420351700001
- Scopus ID
- 2-s2.0-85216578108
- Other Identifier
- 991022197395904721
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