Related Concept Videos
RNA-seq
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Genome Annotation and Assembly
Non-LTR Retrotransposons
Next-generation Sequencing
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Sanger Sequencing
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Pangenome-based human genome analysis improves trait association and genomic prediction.
CLASH (Chromatin Loop Across-sample Score Harmonizer) quantifies the relative contributions of genetic variation, methylation, and CTCF occupancy on chromatin loop strength across individuals.
A comprehensive assessment of tandem repeat genotyping methods for Nanopore long-read genomes.
Related Experiment Video
Updated: Nov 1, 2025

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
Published on: May 19, 2019
lra: A long read aligner for sequences and contigs.
Jingwen Ren1, Mark J P Chaisson1
1Department of Quantitative and Computational Biology (QCB), University of Southern California, Los Angeles, California, the United States of America.
Detecting genetic variation with long sequencing reads is challenging. A new method, lra, uses sparse dynamic programming with a concave gap penalty to improve structural variant discovery from PacBio and Oxford Nanopore (ONT) data.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Aligning single-molecule sequencing (SMS) reads for variation detection is computationally intensive.
- Standard sparse dynamic programming (SDP) uses linear gap penalties, which do not accurately model biological variation.
Purpose of the Study:
- To develop and evaluate a novel alignment method, lra, that utilizes SDP with a concave-cost gap penalty for improved structural variant (SV) discovery.
Main Methods:
- Implemented lra using SDP with a concave-cost gap penalty.
- Applied lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments.
- Assessed lra's performance in detecting structural variants (SVs) and calling variation from de novo assembly contigs.
Main Results:
- lra enhances sensitivity and specificity for SV discovery, especially for variants >1kb and from ONT reads.
- Runtime of lra is comparable to existing methods (1.05-3.76×).
- lra achieved a 3.2% increase in Truvari F1 score for variation calling from assembly contigs compared to minimap2+htsbox.
Conclusions:
- lra provides a more accurate and sensitive approach for structural variant detection using long-read sequencing data.
- The method demonstrates improved performance for challenging datasets, including Oxford Nanopore reads and de novo assemblies.
- lra is a valuable tool for genomic variation analysis, available via bioconda and GitHub.

