已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

WENGAN: Efficient and high quality hybrid de novo assembly of human genomes

作者
Alex Di Genova,Elena Buena‐Atienza,Stephan Ossowski,Marie-France Sagot
出处
期刊: [Cold Spring Harbor Laboratory]
被引量:8
标识
DOI:10.1101/840447
摘要

The continuous improvement of long-read sequencing technologies along with the development of ad-doc algorithms has launched a new de novo assembly era that promises high-quality genomes. However, it has proven difficult to use only long reads to generate accurate genome assemblies of large, repeat-rich human genomes. To date, most of the human genomes assembled from long error-prone reads add accurate short reads to further polish the consensus quality. Here, we report the development of a novel algorithm for hybrid assembly, W ENGAN , and the de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology. W ENGAN implements efficient algorithms that exploit the sequence information of short and long reads to tackle assembly contiguity as well as consensus quality. The resulting genome assemblies have high contiguity (contig NG50:16.67-62.06 Mb), few assembly errors (contig NGA50:10.9-45.91 Mb), good consensus quality (QV:27.79-33.61), and high gene completeness (B USCO complete: 94.6-95.1%), while consuming low computational resources (CPU hours:153-1027). In particular, the W ENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 62.06 Mb (NGA50:45.91 Mb), which surpasses the contiguity of the current human reference genome ( GRCh38 contig NG50:57.88 Mb). Providing highest quality at low computational cost, W ENGAN is an important step towards the democratization of the de novo assembly of human genomes. The W ENGAN assembler is available at https://github.com/adigenova/wengan

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
苗龙伟完成签到 ,获得积分10
1秒前
雾色笼晓树苍完成签到 ,获得积分10
1秒前
2秒前
2秒前
华仔应助眼泪划过面容采纳,获得10
2秒前
2秒前
gjww发布了新的文献求助10
3秒前
Mic应助aa采纳,获得10
4秒前
5秒前
花痴的沂发布了新的文献求助10
9秒前
9秒前
Komorebi完成签到 ,获得积分10
10秒前
mor完成签到 ,获得积分10
10秒前
linger完成签到 ,获得积分10
10秒前
RSU完成签到,获得积分10
11秒前
11秒前
Heike发布了新的文献求助10
12秒前
guoqing完成签到 ,获得积分10
12秒前
lzx完成签到,获得积分10
12秒前
lv发布了新的文献求助10
13秒前
研友_bZzO08完成签到,获得积分10
13秒前
LL完成签到 ,获得积分10
14秒前
leo0531完成签到 ,获得积分10
14秒前
辣辣啦完成签到 ,获得积分10
14秒前
14秒前
15秒前
彩色的易文完成签到,获得积分10
16秒前
16秒前
易寒完成签到,获得积分10
17秒前
江東完成签到 ,获得积分10
18秒前
活力鑫磊发布了新的文献求助10
18秒前
生动友容完成签到,获得积分10
18秒前
严钰佳发布了新的文献求助30
19秒前
StarTrr发布了新的文献求助10
20秒前
蹇蹇完成签到 ,获得积分10
21秒前
科研通AI6.4应助Heike采纳,获得10
23秒前
Liangccg完成签到 ,获得积分10
24秒前
24秒前
哇咔咔完成签到 ,获得积分10
25秒前
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Management and the Arts 310
Teaching Social and Emotional Learning in Physical Education 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7633095
求助须知:如何正确求助?哪些是违规求助? 9207493
关于积分的说明 19747443
捐赠科研通 7202089
什么是DOI,文献DOI怎么找? 3274916
关于科研通互助平台的介绍 2436834
邀请新用户注册赠送积分活动 2271744