演讲人：李欢

What is Bioinformatics? Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.

Introduction to Bioinformatics

Development of methods & algorithms to organize, integrate, analyze and interpret biological and biomedical data

Study of the inherent structure & flow of biological information

Goals of bioinformatics: – Identify patterns – Classify – Make predictions – Create models – Better utilize existing knowledge

Biodiversity

Biologists most often define biodiversity as the "totality of genes, species, and ecosystems of a region".An advantage of this definition is that it seems to describe most circumstances and presents a unified view of the traditional three levels at which biological variety has been identified:

species diversity ecosystem diversity genetic diversity

species diversity

species richness

常用分析软件平台

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• A single piece of open-source, expandable software • Offers a suite of tools from processing raw reads to

community analysis • Includes many popular software algorithms • Command-line based

QIIME takes users from their raw sequencing output through initial

analyses such as ：

OTU picking, taxonomic assignment

construction of phylogenetic trees

through downstream statistical analysis visualization

production of publication-quality graphics.

Quantitative Insights Into Microbial Ecology

comparison and analysis of microbial communities high-throughput amplicon sequencing data ~~ a variety of platforms

基础分析流程 Raw Data

Trim

Align

Screen Precluster Chimeric

Distance

Unique

Cluster

Classify

Taxonomy OTU

OTU table

数据预处理

OUT聚类

分 类

结果呈现

Rarefaction

Index Estimator

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一、数据统计与优化

454 Data Format -- sff

sff - Standard Flowgram Format (binary file)

fasta - sequence file

>HN9H46I01CTGSI CTACTCGTAGTTACCGCGGCTGCTGGCTCCCGTAGGATCAAAACTACTTAGTCGACTGCCCAAAGGGCACCACAGGGGAT

Raw Data数据提取与转换

• sffinfo -s file.sff > file.fna 转化为相应的序列文件 • sffinfo -q file.sff >file.qual 转化为相应的格式文件

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有效数据统计分析

barcode Primer F Primer R Adaptor

Barcode完全匹配 Primer最大错配数=2 —— mothur

处理前：

处理后：

Primer F Primer R Adaptor

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barcode Primer F Primer R Adaptor

去除序列末端的后引物和接头序列（Adaptor）、多碱基N、polyA/T尾巴及低质量碱基； —— seqclean 舍弃长度短于200bp、模糊碱基数大于0、序列平均质量低于25的序列

处理前：

处理后：

Primer F

优化数据统计分析 为了得到更高质量及更精准的生物信息分析结果，我们还可以对数据进行优化处理：

Trim.seqs命令

Denoise

Salt and pepper noise, pixels in the image are very different in color or intensity from their surrounding

Gaussian noise--Amplifier noise each pixel in the image will be changed from its original value by a small amount.

Quantization noise (uniform noise) caused by quantizing the pixels of a sensed image to a number of discrete levels is known as quantization noise.

Denoise--Mothur

uses a expectation-maximization algorithm to correct flowgrams to identify the idealized form of each flowgram and translate that flowgram to a DNA sequence.

Chris Quince's PyroNoise algorithm shhh.flows

shhh.seqs SeqNoise

The AmpliconNoise

SeqNoise does not operate on the underlying flowgrams but the DNA sequence.

- Run AmpliconNoise

– Run phase of denoiser algorithm: prefix clustering

– Remove noise from 454 sequencing data

– Denoise a flowgram file

Denoise--Qiime

split_libraries.py – Split libraries according to barcodes specified in mapping file

mothur > sff.multiple(file=sfffiles.txt)

minflows & maxflows bdiffs & pdiffs & ldiffs & sdiffs & tdiffs signal & noise maxhomop lookup minlength & maxlength keepfirst & removelast

sequence lengths end-trimming minimum quality score

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二、OUT聚类分析

根据“物以类聚”的道理，对样品或指标进行分类的一种多元统计分析方法。

聚类分析（Cluster Analysis）

聚类方法 • 对样品分类常用距离 • 对指标分类常用相似系数

聚类结果 聚类将数据分类到不同的类或者簇 • 同一个簇中的对象------相似性很大 • 不同的簇间的对象------相异性很大

对象----大量的样品 特点----没有先验知识的情况下进行

OTU聚类 OTU（Operational Taxonomic Units）指的是分类

操作单元，是在系统发生学研究或群体遗传学研究中，为

了便于进行分析，人为给某一个分类单元（品系，种，属，

分组等）设置的同一标志。

微生物群落

OTU3

OTU1 OTU2

一般情况下，如果序列之间，比如不同的 16S

rRNA序列的相似性高于97%就可以把它定义为一

个OTU，每个OTU对应于一种不同的16S rRNA序

列，也就是每个OTU对应于一种不同的微生物。

通过OTU分析，就可以知道样品中的微生物多样性和不

同微生物的丰度。同时通过OTU的稀有度分析，还可以

看出测序量是否足够反应样品中的大部分微生物种。

聚类方法

距离

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OTU分析主要步骤:

1. 提取非重复序列，碱基完全一致序列为重复序列，降低分析中间过程冗余计算量； 2. 与silva库（http://www.arb-silva.de/）中的aligned(16S/18S, SSU)核糖体序列数据库比对； 3. 使用UCHIME方法检测并去除Chimeric 序列； 4. 计算比对对齐后的序列间uncorrected pairwise距离 5. 使用最远邻近法聚类生成OTU。

Unique Align Chimeric remove

Distance calculate

OTU cluster

基于软件--mothur

Unique -- 提取非重复序列 提取非重复序列，碱基完全一致序列为重复序列，降低中间过程冗余计算量。

names文件

Align -- 序列比对

序列比对是指为确定两个或多个序列之间的相似性以至于同源性，而将它们按照一定的规律排列。

序列比对的基本思想，是找出检测序列和目标序列的相似性。

比对过程中需要在检测序列或目标序列中引入空位，以表示插入或删除。

Align--序列比对

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SILVA provides aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya).

The greengenes web application provides access to the current and comprehensive 16S rRNA gene sequence alignment.

Database Project (RDP) provides ribosome related data and services to the scientific community, including online data analysis and aligned and annotated Bacterial and Archaeal small-subunit 16S rRNA sequences.

数据库Database

kmer

a. 查找最相似模版序列

Kmer: 具有指定长度为K (such as K=17)的DNA序列。将原始reads转换成Kmer, 每条read(长度为L)上可取出L-K+1个Kmer; 则每个Kmer的出现次数符合泊松分布。

blastn

suffix tree

blast：Basic Local Alignment Search Tool，采用统计学记分系统局部对准算法，找出两个被比较序列的“最类似”片断，并得出可能只包含两个序列的某个部分的对准结果。

Suffix tree：是一种数据结构，用来支持有效的字符串匹配和查询。

Align--序列比对

序列比对算法-- Needleman-Wunsch -- 基于最长公共子串的文本比较算法

A=GGATCGA，B=GAATTCAGTTA G A A T T C A G T T A

0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 3 4 5 5 5 5

A 0 1 2 3 3 3 3 4 5 5 5 6

对于1≤i≤N，1≤j≤M 若ai=bj，则 LCS(i,j)=LCS(i-1,j-1)+1 若ai≠bj，则 LCS(i,j)= Max(LCS(i-1,j-1),LCS(i-1,j),LCS(i,j-1))

Align--序列比对

b. 序列联配-- Needleman-Wunsch -- 基于最长公共子串的文本比较算法

A=GGATCGA，B=GAATTCAGTTA

G A A T T C A G T T A

0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 3 4 5 5 5 5

A 0 1 2 3 3 3 3 4 5 5 5 6

I. 定位在矩阵的右下角 II. 回溯单元格，至矩阵的左上角 若ai=bj，则回溯到左上角单元格 若ai≠bj，回溯到左上角、上边、左边中值最大的单元格，若有相同最大值的单元格，优先级按照左上角、上边、左边的顺序 III.根据回溯路径，写出匹配字串

A：GGA_TC_G_ _A B：GAATTCAGTTA

Align--序列比对

c. 空位填补

NAST

Nearest Alignment Space Termination

Align--序列比对

重新在候选和模板序列对之间插入空位，采用NAST算法，使候选序列与原始模板序列兼容。

Algorithm

Summary--序列统计 summary.seqs

对比对整齐之后的序列做一个统计，可以查看序列的基本信息，从而决定后续序列截取的位置。

Screen--序列筛选 trim-seq.pl

screen.seqs

filter.seqs

根据summary结果，对序列去头截尾

去掉不符合要求的“bad”序列

去掉所有序列中的同一位置的空位字符

选项： minlength maxhomop

Bad accnos

Chimera--去嵌合

UCHIME can detect chimeras using a reference database or de novo using abundance information on the assumption that chimeras are less abundant than their parents because they must have undergone fewer rounds of amplification.

Chimera.uchime remove.seqs

查找嵌合序列 移除嵌合序列

Pre.cluster--预聚类

a pseudo-single linkage algorithm

The basic idea —— abundant sequences are more likely to generate erroneous sequences than rare sequences.

removing sequences of pyrosequencing errors

Sue Huse

优点：这个算法根据它们的丰富度给序列排名,在排列的序列上而不是距离矩阵将某个阈值以内的序列归类，从而移除了大量的序列使距离计算更为快速。

Distance--计算距离 dist.seqs ：calculate uncorrected pairwise distances between aligned DNA sequences.

Method: onegap

countends

nogaps eachgap

ATCATCATCG AGC- - -TTG - 4/10=0.4

calc

T

F

Nearest neighbor

Cluster --聚类分析

cluster method：

Cluster

Furthest neighbor

Average neighbor

Each of the sequences within an OTU are at most X% distant from the most similar sequence in the OTU.

All of the sequences within an OTU are at most X% distant from all of the other sequences within the OTU.

This method is a middle ground between the other two algorithms.

QIIME—去嵌合序列

filter_fasta.py

Algorithm

Blast_fragments approach

ChimeraSlayer

usearch61

remove sequences from a fasta or fastq file based on input criteria

identify_chimeric_seqs.py

pick_otus.py QIIME—OUT聚类

cd-hit ----Li & Godzik, 2006; Li, Jaroszewski, & Godzik, 2001.

Blast----Altschul, Gish, Miller, Myers, & Lipman, 1990.

Mothur----Schloss et al., 2009.

prefix/suffix ----Qiime team, unpublished.

Trie----Qiime team, unpublished.

uclust ----Edgar, RC 2010

uclust_ref ----Edgar, RC 2010.

usearch61 ----Edgar, RC 2010, version v6.1.544

usearch61_ref ----Edgar, RC 2010, version v6.1.544

Methods

OTU聚类结果 $1是OTU中seqs之间的距离；$2表示OTU总数； $3往后列出的是每个OTU所含的序列的名字， 每两个OTU用空格隔开， 每一个OTU里的序列名用“，”隔开列名称

list文件

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三、分类学分析

分类学分析（Taxonomy） -- --根据silva库中的参考序列对OTU进行种属鉴定

classify.seqs

method=wang

method=knn

The bayesian method looks at the query sequence kmer by kmer.

The k-Nearest Neighbor algorithm involves identifying the k-most similar sequences in a database that are similar to your sequence.

This is the method that is implemented by the RDP and is described by Wang et al.

template

taxonomy

calculates the probability a sequence from a given taxonomy would contain a specific kmer

Query sequence

calculates the probability a query sequence would be in a given taxonomy based on the kmers it contains

assign

highest probability

method=wang

RDP naïve Bayesian Classifier

method=knn

use the taxonomy information for each of the k-most similar sequences to determine the consensus taxonomy

numwanted search=kmer and ksize search=blast, match, mismatch, gapopen, and gapextend search=suffix search=distance

find the 10 most similar sequences in the database

HHBK7OF02GRRUS Bacteria{superkingdom}(100);Bacteroidetes{phylum}(100);Bacteroidia{class}(100);Bacteroidales{order}(100);Bacteroidaceae{family}(100);Bacteroides{genus}(100);unclassified; HHBK7OF02FVMOK Bacteria{superkingdom}(100);Firmicutes{phylum}(100);Clostridia{class}(100);Clostridiales{order}(100);Peptostreptococcaceae{family}(100);Peptostreptococcaceae_Incertae_Sedis(100);Peptostreptococcaceae_Incertae_Sedis_uncultured_Clostridium_sp.{species}(94);unclassified;

置信界限（bootstrap ）

Classif--序列分类 runs a bootstrapping algorithm to find the confidence limit of the assignment by randomly choosing with replacement 1/8 of the kmers in the query.

bootstrap

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四、结果统计

分类学统计

每条序列的分类 每个OTU的分类

superkingdom Kingdom phylum class order family genus species

Classify.otu get a consensus taxonomy for each otu

提取OUT代表序列 get.oturep

Taxlevel：分类学等级编号(按顺序编号，与固定水平不是对应的)； rankID：分类等级编号；例如，0.1.1与0.1.2平级且皆为0.1的子级。 taxon ：分类学名称； daughterlevels：该级分类学下所含子级的数目； total：在该分类学单元下所包含的序列数或OTU数；如：0.1.1.1.1为1；

0.1.1.1.2为2；0.1.1.1.3为3；此后并无0.1.1.1.n（n>=4）出现，则，0.1.1.1为1+2+3=6

taxonomy.seq.summary.0.03.xls

taxonomy.otu.summary.0.03.xls Output：

otu.taxonomy.0.03.xls Output：

Output：

纲水平分类表

OUT代表序列names文件

Estimator –多样性指数

估计群落中含有OTU数目

Simpson值越大，说明群落多样性越低

Shannon值越大，说明群落多样性越高

其数值越高，则样本中序列没有被测出的概率越低

http://www.mothur.org/wiki/Calculators

summary.single

菌群丰度（Community richness）

Chao - the Chao1 estimator

Ace - the ACE estimator

测序深度

Coverage - the Good’s coverage

Shannon - the Shannon index

Simpson - the Simpson index

菌群多样性（Community diversity）

calc=ace-chao-shannon-simpson-coverage

Rarefaction –稀释性曲线

曲线趋向平坦时，说明取样的数量合理

Sn = Average number of OTUs observed after drawing n individuals.

St = Total number of OTUs in sample of N total individuals

re-sampling without replacement approach rarefaction.single

Shannon-Wiener曲线

趋向平坦时，说明测序量能反应曲线样品中绝大多数微生物信息

Shannon-Wiener 是反映样品中微生物多样性的指数，利用各样品的测序量在不同测序深度时的微生物多样性指数构建曲线，以此反映各样本在不同测序数量时的微生物多样性。

rarefaction.single

calc=shannon

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Rank abundance 等级曲线

统计每一个OTU所含的序列数，将OTUs按丰度由大到小的等级排序，再以OTU的等级为横坐标，以每个OTU等级所含的序列数（也可用序列数的相对百分含量）为纵坐标

物种的丰度越高，曲线在横轴上的范围越大

曲线越平缓，物种分布越均匀

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