Genovo: De Novo Assembly for Metagenomes
Genovo: De Novo Assembly for Metagenomes
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DOI：
10.1089/cmb.2010.0244
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发表时间：
2011-03-01
影响因子：
1.7
通讯作者：
Koller, Daphne
中科院分区：
文献类型：
--
作者：
Laserson, Jonathan;Jojic, Vladimir;Koller, Daphne
下一代测序技术从样本中的DNA中产生大量的噪声读取。宏基因组学和种群测序旨在恢复样品中物种的基因组序列，这些物种可能具有高度的多样性。在这种情况下，用于单序列重建的方法不够灵敏。我们引入了一个从环境样本中生成读取的生成概率模型，并提出了Genovo，一个新的从头开始的序列组装器，可以在模型下发现可能的序列重构。非参数先验解释了样本中未知数量的基因组。推理是通过应用一系列爬坡步骤迭代直到收敛来完成的。我们在一系列合成实验中比较了Genovo与其他三个短读汇编程序的性能，并在使用454平台创建的9个宏基因组数据集上进行了比较，其中最大的数据集有311k个reads。Genovo的重建比其他方法覆盖了更多的碱基，恢复了更多的基因，即使对低丰度的序列也是如此，并且产生了更高的组装分数。补充材料可在www.liebertoinline.com/cmb上获得。
Next-generation sequencing technologies produce a large number of noisy reads from the DNA in a sample. Metagenomics and population sequencing aim to recover the genomic sequences of the species in the sample, which could be of high diversity. Methods geared towards single sequence reconstruction are not sensitive enough when applied in this setting. We introduce a generative probabilistic model of read generation from environmental samples and present Genovo, a novel de novo sequence assembler that discovers likely sequence reconstructions under the model. A nonparametric prior accounts for the unknown number of genomes in the sample. Inference is performed by applying a series of hill-climbing steps iteratively until convergence. We compare the performance of Genovo to three other short read assembly programs in a series of synthetic experiments and across nine metagenomic datasets created using the 454 platform, the largest of which has 311k reads. Genovo's reconstructions cover more bases and recover more genes than the other methods, even for low-abundance sequences, and yield a higher assembly score. Supplementary Material is available at www.liebertoinline.com/cmb.