PT  - JOURNAL ARTICLE
AU  - Li Deng
AU  - Lidong Guo
AU  - Mengyang Xu
AU  - Wenchao Wang
AU  - Shengqiang Gu
AU  - Xia Zhao
AU  - Fang Chen
AU  - Ou Wang
AU  - Xun Xu
AU  - Guangyi Fan
AU  - Xin Liu
TI  - SLR-superscaffolder: a &lt;em&gt;de novo&lt;/em&gt; scaffolding tool for synthetic long reads using a top-to-bottom scheme
AID  - 10.1101/762385
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 762385
4099  - http://biorxiv.org/content/early/2019/09/08/762385.short
4100  - http://biorxiv.org/content/early/2019/09/08/762385.full
AB  - Synthetic long read (SLR) sequencing technologies, such as stLFR co-barcoded reads and 10X genomics linked-reads, have recently been developed and widely applied in genomics research. Here, we developed the SLR-superscaffolder, a standalone scaffolding tool for general synthetic long reads, with a top-to-bottom scheme where long fragment reads information is firstly used in large-scale scaffolding and followed by the paired-end information used in local scaffolding, to effectively use the SLR information. We tested SLR-superscaffolder power to assemble the human genome from three data resources. For instance, using the draft assembly with contig NG50 of 13 kb generated from 64-fold stLFR co-barcoded reads, SLR-superscaffolder significantly improved its scaffold NG50 to 15 Mb. Based on the draft assembly with scaffold NG50 of 58kb using 20-fold PCR-free NGS data, its scaffold NG50 was also drastically increased to 8 Mb. For the draft assembly with contig NG50 of 6.6 Mb from about 30-fold Oxford Nanopore long reads, SLR-superscaffolder presented a notable improvement in scaffold polishing with a scaffold NG50 of 21 Mb. Furthermore, comparing with other available SLR scaffolding tools, SLR-superscaffolder could produce an assembly with the highest quality of the longest contiguity and the least errors. Thanks to the valuable long-range information provided by SLR, SLR-superscaffolder shows a broad range of applications in the genome assembly. The source code is accessible on GitHub (https://github.com/BGI-Qingdao/SLR-superscaffolder).