Opportunities and challenges in long-read sequencing data analysis

Keywords

PaceData scienceFocus (optics)Computer scienceGenomicsBiologyComputational biologyGenomeGeneticsGene

Affiliated Institutions

Related Publications

Algorithmic challenges in mammalian whole‐genome assembly

Serafim Batzoglou

Abstract Sequencing of the human, mouse, and rat genomes was accomplished more quickly than had seemed feasible a mere eight years ago. The accelerating pace of sequencing has b...

2005 Encyclopedia of Genetics, Genomics, P... 9 citations

De novo assembly of human genomes with massively parallel short read sequencing

Ruiqiang Li , Hongmei Zhu , Jue Ruan +11 more

Next-generation massively parallel DNA sequencing technologies provide ultrahigh throughput at a substantially lower unit data cost; however, the data are very short read length...

2009 Genome Research 2939 citations

Assembly of long, error-prone reads using repeat graphs

Mikhail Kolmogorov , Jeffrey Yuan , Yu Lin +1 more

2019 Nature Biotechnology 5451 citations

Whole-Genome Sequencing and Assembly with High-Throughput, Short-Read Technologies

Andreas Sundquist , Mostafa Ronaghi , Haixu Tang +2 more

While recently developed short-read sequencing technologies may dramatically reduce the sequencing cost and eventually achieve the $1000 goal for re-sequencing, their limitation...

2007 PLoS ONE 126 citations

Minimap2: pairwise alignment for nucleotide sequences

Heng Li

Abstract Motivation Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic cont...

2018 Bioinformatics 14625 citations

Publication Info

Year: 2020
Type: review
Volume: 21
Issue: 1
Pages: 30-30
Citations: 2449
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Opportunities and challenges in long-read sequencing data analysis

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2449

OpenAlex

Cite This

APA Style

                            
                                
                                    Shanika L. Amarasinghe, 
                                
                                    Shian Su, 
                                
                                    Xueyi Dong
                                
                                et al.
                            
                            (2020). 
                            Opportunities and challenges in long-read sequencing data analysis. 
                            Genome biology
                            , 21
                            (1)
                            , 30-30.
                            https://doi.org/10.1186/s13059-020-1935-5
                        

Identifiers

DOI: 10.1186/s13059-020-1935-5