Biology subjects

Hu, J.

Publications and source records attributed to Hu, J..

38 records · Page 3Linked to original sources

bioRxiv2017

Interactive analysis of Long-read RNA isoforms with Iso-Seq Browser

BackgroundLong-read RNA sequencing, such as Pacific Biosciences Iso-Seq method, enables generation of sequencing reads that are 10 kilobases or even longer. These reads are ideal for discovering splice junctions and resolving full-length gene transcripts without time-consuming and error-prone techniques such as transcript assembly and junction inference.\n\nResultsIso-Seq Browser is a Web-based visual analytics tool for long-read RNA sequencing data produced by Pacific Biosciences isoform sequencing (Iso-Seq) techniques. Key features of the Iso-Seq Browser are: 1) an exon-only web-based interface with zooming and exon highlighting for exploring reference gene transcripts and novel gene isoforms, 2) automated grouping of transcripts and isoforms by similarity, 3) many customization features for data exploration and creating publication ready figures, and 4) exporting selected isoforms into fasta files for further analysis. Iso-Seq Browser is written in Python using several scientific libraries. The application and analyses described in this paper are freely available to both academic and commercial users at https://github.com/goeckslab/isoseq-browser\n\nConclusionsIso-Seq Browser enables interactive genome-wide visual analysis of long RNA sequence reads. Through visualization, highlighting, clustering, and filtering of gene isoforms, ISB makes it simple to identify novel isoforms and novel isoform features such as exons, introns and untranslated regions.

bioinformatics↗

bioRxiv2016

Evaluation on Efficient Detection of Structural Variants at Low Coverage by Long-Read Sequencing

BackgroundStructural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.\n\nResultsIn this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5% to 94.1% for deletions and 87.9% to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.\n\nConclusionsOur results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.

genomics↗