National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Bioinformatics. 2018 Jun 15;34(12):2019-2028. doi: 10.1093/bioinformatics/bty020.
It is highly desirable to assemble genomes of high continuity and consistency at low cost. The current bottleneck of draft genome continuity using the second generation sequencing (SGS) reads is primarily caused by uncertainty among repetitive sequences. Even though the single-molecule real-time sequencing technology is very promising to overcome the uncertainty issue, its relatively high cost and error rate add burden on budget or computation. Many long-read assemblers take the overlap-layout-consensus (OLC) paradigm, which is less sensitive to sequencing errors, heterozygosity and variability of coverage. However, current assemblers of SGS data do not sufficiently take advantage of the OLC approach.
Aiming at minimizing uncertainty, the proposed method BAUM, breaks the whole genome into regions by adaptive unique mapping; then the local OLC is used to assemble each region in parallel. BAUM can (i) perform reference-assisted assembly based on the genome of a close species (ii) or improve the results of existing assemblies that are obtained based on short or long sequencing reads. The tests on two eukaryote genomes, a wild rice Oryza longistaminata and a parrot Melopsittacus undulatus, show that BAUM achieved substantial improvement on genome size and continuity. Besides, BAUM reconstructed a considerable amount of repetitive regions that failed to be assembled by existing short read assemblers. We also propose statistical approaches to control the uncertainty in different steps of BAUM.
http://www.zhanyuwang.xin/wordpress/index.php/2017/07/21/baum.
Supplementary data are available at Bioinformatics online.
以低成本组装具有高连续性和一致性的基因组是非常可取的。使用第二代测序（SGS）读取物进行草图基因组连续性的当前瓶颈主要是由于重复序列的不确定性引起的。尽管单分子实时测序技术非常有希望克服不确定性问题，但它相对较高的成本和错误率增加了预算或计算的负担。许多长读长组装器采用重叠布局共识（OLC）范式，该范式对测序错误、杂合性和覆盖度的变异性不太敏感。然而，当前的 SGS 数据组装器并没有充分利用 OLC 方法。
为了最小化不确定性，所提出的方法 BAUM 通过自适应唯一映射将整个基因组划分为区域；然后使用局部 OLC 并行组装每个区域。BAUM 可以（i）基于近缘物种的基因组进行参考辅助组装（ii）或改进基于短读或长读测序获得的现有组装结果。对两个真核生物基因组，野生稻 Oryza longistaminata 和鹦鹉 Melopsittacus undulatus 的测试表明，BAUM 在基因组大小和连续性方面取得了实质性的改进。此外，BAUM 重建了相当数量的重复区域，这些区域无法被现有的短读长组装器组装。我们还提出了统计方法来控制 BAUM 不同步骤中的不确定性。
http://www.zhanyuwang.xin/wordpress/index.php/2017/07/21/baum.
补充数据可在 Bioinformatics 在线获得。
