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标题：

UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon reads

摘要：

Amplicon sequencing of tags such as 16S and ITS ribosomal RNA is a popular method for investigating microbial populations. In such experiments, sequence errors caused by PCR and sequencing are difficult to distinguish from true biological variation. I describe UNOISE2, an updated version of the UNOISE algorithm for denoising (error-correcting) Illumina amplicon reads and show that it has comparable or better accuracy than DADA2.

地址：

http://biorxiv.org/content/early/2016/10/15/081257

软件：

http://www.drive5.com/usearch/

导读：

Usearch 先前在微博上介绍了很多次，主要三点： 1. 序列相似性比对， 2. 微生物多样性数据处理，逐渐构成了小生态，3. 序列处理瑞士军刀， 三点上竞争对手都很多， 第一点上 diamond 、 RAPSearch等都是竞争对手， 第二点 vsearch 紧随其后，另外还有 [QIIME 、Mothur 等老牌工具， 第三个问题太多了，主要有 seqtk 、 seqkit 等。 不过这个帖子提到的是新出炉的 UNOISE2，就是错误纠正（这类工具也很多）， 包括了去除测序错误的序列，嵌合体序列，Phix 污染序列以及低复杂度序列等， 然后就可以直接构建 OTU表了， UNOISE2 流程推荐直接从最原始的序列开始， 合并双端序列、过滤、去冗余、错误纠正、序列比对、构建OTU表、一气呵成。 另外：可以增加调整序列方向这一步，需要参考序列库，比如 RDP 的序列库，或者使用 Silva 的库。

不等不提 Usearch 工具使用序列： 32位版本不管是工业界还是学术界随便用，免费， 64位版本需要进行收费了，学术界要比工业界便宜不少，现在刚进入 9.0版本，销售策略也进行了调整，从先前按年订阅， 变成现在 按大版本号订阅 ，更人性化了。

官网的主要介绍：

-. UNOISE algorithm

The UNOISE algorithm performs error-correction (denoising) on amplicon reads. It is implemented in the unoise command. UNOISE is designed for Illumina reads, not earlier technologies such as 454 pyrosequencing.

Correct biological sequences are recovered from the reads, resolving distinct sequences down to a single difference (sometimes) or two or more differences (almost always). I consider this approach superior to traditional OTU clustering at 97% identity because OTUs may merge different species (or more generally, different phenotypes) with distinct sequences while denoising gives the best possible resolution.

Errors are corrected as follows: – Reads with sequencing error are identified and removed. – Abundances are corrected (when the OTU table is generated). – Chimeras are removed. – PhiX sequences are removed. – Low-complexity sequences due to Illumina artifacts are removed.

Using denoised sequences as OTUs has two possible drawbacks: a single species may be split into two OTUs due to different strains or paralogs, and the sensitivity is slightly lower because UPARSE can make robust OTUs from unique sequences with abundance as low as 2 while the minimum abundance for UNOISE is around 4. I consider splitting of strains to be a good thing, because they may have different phonotypes and hence different ecological roles. Splitting due to paralogs is relatively benign (what does it matter?), and is not solved by clustering at 97% identity because paralogs have identities <97% in some cases. Splitting or lumping is unadvoidable regardless of whether the clustering identity is 97% or 100% so I would argue that it is better to resolve as many distinct biological sequences as possible. Sensitivity to unique sequences with abundance <8 (summed over all samples) is rarely important in practice.

Denoised sequences are valid OTUs (the clustering identity is 100%, if you like) and can be used to generate an OTU table in just the same way as 97% OTUs.

-. UNOISE pipeline

A UNOISE pipeline recovers biological sequences from an amplicon sequencing experiment by performing error-correction (denoising) of Illumina reads. UNOISE is not designed for other sequencing technologies, e.g. 454 pyrosequencing reads. The UNOISE algorithm is implemented in the unoise command.

See Tutorials for example scripts & data.

Reads in FASTQ format I strongly recommended starting from “raw” reads, i.e. the reads originally provided by the sequencing machine base-calling software. You should do quality filtering with USEARCH rather than using reads that have already been filtered by third-party software.

Reads in FASTA format The unoise command supports reads in FASTA format. You may need to do this if your reads have already been quality filtered by some other method and you don’t have access to the original FASTQ reads.

Sample pooling I recommend combining reads from as many samples as possible. See sample pooling for discussion.

Read quality filtering Quality filtering of the reads should be done using USEARCH because maximum expected error filtering method is much more effective at suppressing reads with high error rates than other filters, e.g. those based on average Q scores. Using a maximum expected errors of 1.0 is a good default choice (-fastq_maxee 1.0 option to fastq_filter or fastq_merge_maxee 1.0 option of fastq_mergepairs). You can use fastx_learn to estimate the error rate after filtering.

Global trimming You should trim reads to a fixed length unless the sequences are contigs generated by a paired read assembler, in which case it may not be necessary. You should also trim any primer-binding sequences at the ends of the reads. See global trimming for discussion.

Unique sequences Get the set of unique sequences with abundances using the fastx_uniques command with the -sizeout option. This will be the input file for the unoise command.

Creating an OTU table Denoised sequences are valid OTUs (the clustering identity is 100%, if you like) and can be used to generate an OTU table in just the same way as 97% OTUs. Reads must have sample identifiers for this to work. The simplest way to do this is usually to use the -relabel @ option of fastq_filter or fastq_mergepairs.

Example commands For typical Illumina reads with one pair of FASTQ files (R1 and R2) per sample.

usearch -fastq_mergepairs _R1.fastq -relabel @ -fastaout reads.fq

usearch -fastq_filter reads.fq -fastq_maxee 1.0 -fastaout filtered.fa

usearch -fastx_uniques filtered.fa -fastaout uniques.fa -sizeout

usearch -unoise uniques.fa -tabbedout out.txt -fastaout denoised.fa

usearch -usearch_global reads.fq -db denoised.fa -strand plus -id 0.97 -otutabout otu_table.txt

版本：

2016-11-17.v1