细胞内大片段DNA数据存储的多RS码交织编码

doi:10.12211/2096-8280.2021-023

摘要/Abstract

摘要：

合成DNA作为潜在的数字信息存储介质，存储密度高，可用时间久，有望成为未来数据存储的重要选项。然而，DNA的合成与测序读出往往造成碱基的多种错误，无法满足数据存储的可靠性要求，而保证可靠性的编码方案往往效率较低。针对该问题，提出了一种面向酿酒酵母内大片段DNA数据存储的高效率编码方法。数据编码通过多个极高码率的里德-所罗门（RS）码的码字交织构建数据DNA单元，将其与酵母的自主复制序列（ARS）交替镶嵌，构成酵母人工染色体序列；数据读出时，利用二代高通量测序，组合了读段从头（de novo）组装、ARS导引例，用20×二代测序数据可无错恢复原始数据。该编码方法不仅能实现数据可靠存储，实现的DNA数据部分逻辑密度为1.973 bit/bp，即使考虑生物单元开销，总体逻辑密度仍达到1.947 bit/bp。该设计流程可支持Kb到Mb不同长度的DNA的编码，为大片段DNA数据存储的“湿”实验提供灵活的实验前验证与评估。

关键词: DNA数据存储, 里德-所罗门（RS）码, 交织, 自主复制序列, 重叠群

Abstract:

The synthetic DNA, as a potential digital data storage medium, has a high storage density and can be used for a very long period. It is expected to serve as an important option for future massive data storage. However, the synthesis, assembly and sequencing of DNA often introduce multiple types of base errors, which does not satisfy the reliability requirements of data storage, while reliability-enhanced coding schemes usually sacrifice the logical coding density by adding redundancy. To deal with this problem, an encoding process for DNA data storage using large synthetic DNA fragments in Saccharomyces Cerevisiae was proposed. Data writing into DNA chunks was constructed by interleaving multiple codewords of Reed Solomon (RS) codes with a very high code rate, embedded with autonomous replication sequences (ARSs) in alternation to form a yeast artificial chromosome. Utilizing the high-throughput sequencing, data readout combines short read assembly with the de Bruijn graphs, ARS guided contig combination and erasure/error correction to achieve reliable data recovery. The error correction capability has been fully exploited by interleaving the large missing fractions into random erasures across all the RS codewords and correcting more erasures than errors. We designed and simulated a 2.5 Mb ring chromosome and successfully recovered the original data from 20× high-throughput sequencing reads. The simulated sequencing data are generated using the ART simulation software, which has been trained using the real sequencing data from an artificial chromosome of 254 886 bp constructed for data storage previously. All the processes including the large DNA chunk assembly, DNA replication, extraction and high-throughput sequencing are viewed as the DNA storage channel in information theory community. We provided an efficient encoding scheme matching the codes and the DNA storage channel based on the information theory paradigm. The logical density of the data DNA chunks was 1.973 bit/bp, and the overall logical density still reached up to 1.947 bit/bp including the biological units (ARSs and vector backbones). The demonstrated design process can support DNA coding schemes with the different lengths from Kb up to Mb, which provides flexible verification and support for wet experiments in the synthesis and sequencing of large fragments of DNA for digital data storage.

Key words: DNA data storage, reed-solomon codes, interleaving, autonomously replicating sequence, contig

中图分类号:

Q819

陈为刚, 葛奇, 王盼盼, 韩明哲, 郭健. 细胞内大片段DNA数据存储的多RS码交织编码[J]. 合成生物学, 2021, 2(3): 428-443.

Weigang CHEN, Qi GE, Panpan WANG, Mingzhe HAN, Jian GUO. Multiple interleaved RS codes for data storage using up to Mb-scale synthetic DNA in living cells[J]. Synthetic Biology Journal, 2021, 2(3): 428-443.

图/表 9

图1 面向大片段DNA数据存储的高码率RS码编码方法[(a) Workflow of DNA data storage with large DNA fragments encoded by interleaved multiple RS codes. The workflow consists of three steps. First, digital data for a picture and a text file was converted into DNA sequences by interleaved multiple RS codes. Second, the artificial chromosome was assembled from multiple DNA sequences with ARS to stabilize the assembly and replication. Third, with the high-throughput sequencing, data readout uses short read assembly based on the de Bruijn graphs, ARS guided contig combination and RS erasure and error correction to achieve reliable data recovery. (b) Encoding procedures using high code rate RS codes. The encoding procedures include data scrambling, RS encoding, symbol interleaving, symbol-to-bit mapping, bit-to-base mapping and combining with ARSs to form a ring chromosome]

Fig. 1 Encoding scheme using a very high code rate RS codes for data storage with large DNA

图2 不同数量数据段组合构建不同长度的大片段DNA（Three examples are shown in details. In example 1, the original file was converted into the artificial chromosome with a length of 2 489 847 bp. In example 2, two picture files were converted into two smaller artificial chromosomes. In example 3, ten small files were converted into ten independent small artificial chromosomes）

Fig. 2 Building of variant-length large DNA integrating different number of data blocks

表1 不同编码方法的碱基逻辑密度比较

Tab. 1 Base logical density using different encoding schemes

主要结果	总体逻辑密度 (包含引物或载体骨架) /bit·bp^-1	数据部分逻辑密度(不包含引物或载体骨架) /bit·bp^-1
Church等^[1]	0.60	0.83
Goldman等^[2]	0.19	0.29
Grass等^[13]	0.83	1.16
Bornholt等^[9]	0.57	0.85
Erlich等^[10]	1.19	1.57
Blawat等^[14]	0.89	1.08
Organick等^[12]	0.81	1.10
Chen 等^[19]	1.19	1.24
Ping 等^[16]	1.32	1.88
本工作（单段DNA，2 489 847 p）	1.947	1.973

图3 基于短读段从头组装、ARS引导的多重叠群合并、RS码纠错纠删的数据恢复流程

Fig. 3 Data readout processes (The process includes de novo assembly from short reads using multiple programs with multiple k-mers, ARS navigated combination of multiple contigs, converting into symbol sequences of RS code, deinterleaving, and RS erasure and error correction)

图4 基于计算机仿真的编码大片段DNA体内存储验证流程(Sequencing reads from real large DNA chunks were used to train the parameters of high-throughput sequencing and end-to-end performance verification were performed. And reads assembly and decoding recovery schemes include contig assembly, ARS navigated combination of multiple contigs, deinterleaving and RS erasure and error correction)

Fig. 4 Verification procedures using computer simulation for proposed encoding method and construction scheme of large DNA chunks in living cells

图5 仿真读段与实际测序读段的碱基错误数量（编辑距离）分布(Red columns represent sequencing data from one end. Grey columns represent sequencing data from the other end. The probability of reads with errors from ART simulation is about 20%. And the quality of reads from ART simulation is slightly less good than real sequencing reads, which can well illustrate the error correction ability of the proposed method)

Fig. 5 Base error number (edit distance) distribution in simulation and real sequencing reads.

图6 仿真与实测读段的碱基错误随着位置变化情况(The second-generation sequencing reads contain insertion, deletion and substitution errors, and the error rate is about 10-4~10-3. Furthermore, the error rate of insertions and deletions is significantly lower than that of substitutions. Reads from ART simulation are very consistent with the characteristics of the real second-generation sequencing reads. It is verified that using simulation method to generate sequencing reads is feasible)

Fig. 6 Base error position distribution in simulation and real sequencing.

表2 采用交织多个RS码的数据恢复分析

Tab. 2 Data recovery analysis using interleaved multiple RS codes

测序覆盖度	组装软件	k-mer	非交织RS码不能恢复数据段数量以及缺失与错误（缺失数量+错误数量）	解交织后RS码译码
20×	Velvet	{25,27,29,31,33}	实验1：2个(2705+0,2450+7711)	成功
			实验2：3个(3525+0,673+1913,5194+0)	成功
			实验3：5个(6779+0,11003+0, 45+1855,9296+0,18612+0)	失败
			实验4：1个(5498+0)	成功
			实验5：1个(17306+0)	成功
			实验6：1个(8387+0)	成功
			实验7：1个(1134+0)	成功
			实验8：3个(2668+16,8320+0,9298+919)	成功
			实验9：1个(2466+1213)	成功
			实验10：2个(3657+0,0+4712)	成功
	Velvet+ABySS	V{25,27,29,31,33} A{25,27,29,31,33}	实验2(3525+1,1+1905)	成功
	Velvet+ABySS	V{25,27,29,31,33} A{25,27,29,31,33}	实验5(10041+0)	成功
25×	Velvet	25	实验6(937+0)	成功
		25	实验9(10620+1)	成功
		27	实验2(13604+1)	成功
		29	实验8(21488+0,3373+0)	成功
		29	实验10(13023+17)	成功
		31	实验2(1692+0)	成功
			实验7(1105+0)	成功
			实验8(3373+0)	成功
		33	实验2(1696+0)	成功
		33	实验10(1854+1986)	成功
		{25,27,29,31,33}	实验1～10均无大片段错误	成功
	ABySS	{25,27,29,31,33}	实验1(16950+0)	成功
	ABySS	{25,27,29,31,33}	实验8(2151+0)	成功
	Velvet+ ABySS	V{25,27,29,31,33} A{25,27,29,31,33}	实验1～10均无大片段错误	成功
30×	Velvet	27	实验1～7， 9,10均无大片段错误实验8(1292+0)	成功
	Velvet	{25,27,29,31,33}	实验1～10均无大片段错误	成功
	ABySS	{25,27,29,31,33}	实验1～10均无大片段错误	成功
	Velvet+ABySS	V{25,27,29,31,33} A{25,27,29,31,33}	实验1～10均无大片段错误	成功

图7 不同测序覆盖度与组装方法，在ARS识别与重叠群合并后，数据DNA部分错误分布('×' and '□' represent base erasure probability and base error probability of 10 experiments respectively. The histogram represents the average of ten experiments. When the sequencing depth increases, the performance of different assembly methods improves, and the number of residual errors and erasures decreases rapidly)

Fig. 7 Base error distribution of the data DNA after ARS identification and contig merging using different assembly methods and sequencing coverage

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