基因组测序模拟

杨利霞

基因组测序模拟
, N* S  U+ s, M; q1 Q基因组测序模拟

一、摘要

1 n* `/ j, d0 X8 S! j( X% ~7 e通过熟悉已有的基因组测序模拟和评估程序，加深全基因组鸟枪法测序原理的理解，并且能够编写程序模拟全基因组鸟枪法测序，理解覆盖度、测序深度、拷贝数等概念，设置测序相关参数，生成单端/双端测序结果文件

! f* A9 M0 ^( D0 b6 s二、材料和方法

1、硬件平台

. V0 B1 u: }) Q. G$ Q5 B处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
* y1 N; G% h, ?( V# ~安装内存(RAM)：16.0GB
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/ t. m/ L9 Y& {' D* H2、系统平台
$ A6 P4 L' P3 x& P9 c# jWindows 8.1，Ubuntu
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3、软件平台
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art_454
GenomeABC http://crdd.osdd.net/raghava/genomeabc/
/ L$ M8 q: i4 }4 S& uPython3.5
Biopython
4、数据库资源

NCBI数据库：https://www.ncbi.nlm.nih.gov/
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5、研究对象
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酵母基因组Saccharomyces cerevisiae S288c (assembly R64)
ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_genomic.fna.gz

6、方法

! ~0 T0 u( W! @8 g- r7 h: v, }art_454的使用
2 K3 o6 q+ b$ G+ _首先至art系列软件的官网，下载软件，在ubuntu系统安装，然后阅读相关参数设置的帮助文档，运行程序。
GenomeABC
- W! `* d- ]0 L5 V6 [6 [进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
9 h1 m: a" ^. V+ g0 B) k编程模拟测序
下载安装python，并且安装biopython扩展模块，编写程序，模拟单端/双端测序。
三、结果
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1、art_454的运行结果
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无参数art_454运行，阅读帮助文档
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  \" A9 V5 r0 ^) s& o图表 1无参数art_454运行
- e+ y( W7 V  [" V. x1 N2 m对酵母基因组进行基因组单端测序模拟，FOLD_COVERAGE设为20，即覆盖度为20.
; ]8 s+ P( Z( }! d下图为模拟单端测序，程序运行过程及结果
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1 }' t- [' Q! {& _, b( E" D( u图表 2 art454单端测序

图表 3 art454单端模拟结果
2 M7 D* N. ?& K1 r4 z双端测序模拟，FOLD_COVERAGE设为20，即覆盖度为20；MEAN_FRAG_LEN设为1500，即平均片段长度为1500；STD_DEV设为20，即长度的标准差为20
下图为模拟双端测序，程序运行过程及结果
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图表 4 art454双端测序
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8 I4 ~' p& `# f7 W图表 5 art454双端模拟结果
; p- u! P* }7 R: n  s2、GenomeABC
3 W6 K8 @. x5 Z$ E# Y/ X下图为设置参数页面

下图为结果下载页面
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图表 6 结果下载页面
3、编程模拟测序结果
" ^# P9 x% f3 H' B% ]拷贝数是这里的N值；覆盖度是m，测序深度是宏观的量，在这里与覆盖度意思相同，就是测序仪10X，20X。
8 V# ]8 N1 V2 y. Q; \  ?- S单端测序
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图表 7 程序模拟单端测序
双端测序

8 W  t8 \& J2 y2 f图表 8 程序模拟双端测序
0 S+ _% [! L* f. t/ B测序结果
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图表 9 结果文件

* a" |5 X2 J9 O; u1 S. u5 e' V因为期望片段长度是600bp，在片段长度区间200-1000bp内，所以大部分的片段都没有删除。
) w- c8 m6 F9 f) d3 N$ t9 `" Z7 @! w测序结果统计表
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' [* s, U4 \4 {! h测序方式        基因组大小(bp)        片段长度区间 (bp)        N值        期望片段长度        克隆保留率        片段数量        Reads长度范围（bp）        Reads总数量        Reads总长度        覆盖度(m值)        理论丢失率(e-m)        覆盖率(1-e-m)
单端        12157kb        200-1000        10        600        0.95        107378        50-100        101968        7645.541kb        0.62889        0.53318        0.46682
单端        12157kb        200-1000        20        600        0.95        213722        50-100        202996        15227.882kb        1.25259        0.28576        0.71424
; ~4 _( _; ?8 I4 y! {* B双端        12157kb        200-1000        10        600        0.95        106704        50-100        202770        15212.662kb        1.25134        0.28612        0.71388
" z; U- p& [3 E  s% P2 p双端        12157kb        200-1000        20        600        0.95        214212        50-100        407186        30534.265kb        2.51164        0.08114        0.91886
四、讨论和结论
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程序运行方法
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6 m3 [' ^/ g9 Q2 r$ g2 l在类的构造方法init()中，调整参数。
Averagefragmentlength为片段平均的长度；
; b" P, M' ]3 ]: k8 [) nminfragmentlength和maxfragmentlength是保留片段的范围；
cloneRetainprobability是克隆的保留率；
minreadslength和maxreadslength是测序reads的长度范围

模拟测序的诸多方法都封装成了Sequencing类，只需要创建类，并调用singlereadsequencing()和pairreadsequencing()方法，传入文件名的参数即可。

附录
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from Bio import SeqIO
) g/ S* w, ^% Q' T+ i6 o8 Wfrom math import exp
* v7 z; f; X& {! U! S/ Q3 Z7 ~( k4 Zimport random
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class Sequencing:
, a" ]) a( m2 u    # N代表拷贝份数
" V0 B: @, B5 ]- n- r    def __init__(self)
        self.fragmentList = []
2 u2 \! {9 _8 a9 [$ |. ]* e; e        self.readsID = 1
        self.readsList = []
        self.averagefragmentlength = 650
        self.minfragmentlength = 500
4 q/ O" Z2 w+ y, _$ M        self.maxfragmentlength = 800
        self.cloneRetainprobability = 1
: j2 q0 S6 R: K: s        self.minreadslength = 50
        self.maxreadslength = 150
% W+ x- X! V5 F- K: A        self.N = 10
        self.genomeLength = 0
' w! V3 M$ _; ^9 }. l        self.allreadslength = 0

    # 生成断裂点
' Y. C7 o6 u. H6 z4 g- M+ M    def generatebreakpoint(self, seqlen, averageLength):
, R/ P' G/ `6 ]        # 假设平均每500bp 产生一个断裂点(averageLength = 500),通过随机函数生成seqlen/500个随机断裂点(1到seqlen之间的随机整数)
4 i4 C/ j2 C" K( ^+ t% ?, b7 h5 e        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
        breakpoint.append(seqlen)
        breakpoint.append(0)
        # 把随机断裂点从小到大排序
# R3 m" q; R) C( |; K4 ^        breakpoint.sort()
        return breakpoint

    # 沿断裂点打断基因组,并删除不符合长度要求的序列片段,定义片段范围:200-1000bp
    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
& p# ?  Q( j4 |            fragment = seq[breakpoint:breakpoint[i + 1]]
            if maxfragmentlength > len(fragment) > minfragmentlength:
                self.fragmentList.append(fragment)
        return self.fragmentList
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    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
    def clonefragment(self, fragmentList, cloneRetainprobability):
" }/ @, i3 y0 g$ n$ G        clonedfragmentList = []
        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
3 O) {8 \( A, J& Z2 H1 e            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
        return clonedfragmentList

    # 模拟单端测序,并修改reads的ID号
8 q9 D5 `2 z8 Y, n1 z/ b' i    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
% n1 L  d! {; e            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
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8 l2 O* f' Y7 S) _; C3 H( H    def singlereadsequencing(self, genomedata, sequencingResult):
3 i7 `% m# @; T3 r$ t& m        for seq_record in SeqIO.parse(genomedata, "fasta"):
, j( F3 k) I2 n            seqlen = len(seq_record)
+ H3 p0 T4 Q, ]2 Y! m! ^, N, Q: h            self.genomeLength += seqlen
3 j# \* Z8 H1 ?% I3 v. m            for i in range(self.N):
                # 生成断裂点
. e: C4 r" \" X4 Q- {  t# j2 S                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
' F+ x* }# y8 p! {# H2 U. O  J/ }                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
        # 模拟克隆时的随机丢失情况
& p( U$ }3 W; k  n0 Q; k) F) e        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
0 |" W4 c7 r3 X8 z% D& g3 F9 p8 {        # 模拟单端测序
        self.singleread(clonedfragmentList)
2 @5 o* [) e8 l. c5 _        SeqIO.write(self.readsList, sequencingResult, "fasta")
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    def pairread(self, clonedfragmentList):
$ `# W: N( _, E% s! V& m! {+ r        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
. g! t8 A7 x$ d7 O4 Q( d( I            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
1 W0 d! E, Y5 Y# I* h: X; {            readslength = random.randint(self.minreadslength, self.maxreadslength)
. z$ D) J3 w2 R7 J, H6 L0 D            self.allreadslength += readslength
, H9 s6 p! y3 t            self.readsList.append(fragment[:readslength])

7 X7 l# ~6 x% }, z            readslength = random.randint(self.minreadslength, self.maxreadslength)
% A( |! a# I+ {/ Y            self.allreadslength += readslength
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+ J9 }3 T0 O. \/ h7 `0 _            fragmentcomplement = fragment.reverse_complement()
% P6 F2 f4 t: r( ]9 _* r$ d            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
5 q: i4 M* l8 I2 q9 f1 F- Z! j8 P            fragmentcomplement.description = str(self.readsID) + "." + description
( Y8 s# T  A* j! _            self.readsList.append(fragmentcomplement[:readslength])
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% n( o% b! e% D' i3 M) i1 [9 S: ?            self.readsID += 1
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# L6 d0 `% s  l! C: T2 a3 X    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
: o2 z6 \( \* @        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
            self.genomeLength += seqlen
- v6 Z/ w- }1 Y8 \            for i in range(self.N):
5 ^1 N, H* l/ s$ u7 E                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
% Z& w; F' l5 x7 p* \        # 模拟克隆时的随机丢失情况
5 k, ^0 T/ r4 t+ W: ]* k1 O  N! b        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
8 x" z6 w  Y: U: ]! E) R        self.pairread(clonedfragmentList)
* ~5 u. t$ I" A        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
2 o! I; I: C( y+ b        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
% H; ~* A1 R1 x- d        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
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$ J6 B. l* A0 D    def resultsummary(self):
" D& A: F1 v0 f1 ~$ [        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
1 h& x9 ~( c* g! n( t) i7 x        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
: J7 ?0 W# ]' t; \; P: ^        print("期望片段长度:" + str(self.averagefragmentlength))
8 r, `5 S9 @3 t4 D; b8 R        print("克隆保留率:" + str(self.cloneRetainprobability))
, s+ m/ J! k8 p        print("片段数量:" + str(len(self.fragmentList)))
        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
; t0 l4 l4 F! ]& T        print("reads总数量:" + str(len(self.readsList)))
& ~5 K; C# g0 k        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
8 Z" ?7 P0 M( x2 c+ d2 l/ }- V        m = self.allreadslength / self.genomeLength
        print("覆盖度(m值):" + str(round(m, 5)))
: z. Q/ Z/ b' M, _7 n& j        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
! b7 H3 W' P- f$ _0 N, K        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
# -------------------------------------------主程序-------------------------------------------
, {& A* M# c! h: E# 模拟单端测序
% o6 j4 h! D+ u5 L5 O: tsequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
sequencingObj.resultsummary()

, |; D9 l, e! L# \7 Q$ W& _% ?# 模拟双端测序
sequencingObj = Sequencing()
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
- y* i" |, L2 `1 ]sequencingObj.resultsummary()
* J2 X4 H- F* h( F1 dfrom Bio import SeqIO
0 k: q; x/ n  q  Nfrom math import exp
import random
4 C8 o5 F0 q  N0 V% g
3 C6 u; K' u1 n/ Uclass Sequencing:
    # N代表拷贝份数
/ P! G7 ^4 z( d# e! h0 k    def __init__(self):
        self.fragmentList = []
        self.readsID = 1
        self.readsList = []
, m; d" B6 \2 l+ y6 u2 H        self.averagefragmentlength = 650
        self.minfragmentlength = 500
2 S# w+ O: a# l5 f, S        self.maxfragmentlength = 800
        self.cloneRetainprobability = 1
        self.minreadslength = 50
/ o5 g! ~9 v, j        self.maxreadslength = 150
  M  k5 L4 e4 o4 W$ N        self.N = 10
' [5 ^: F7 g% S$ J% g$ ?        self.genomeLength = 0
        self.allreadslength = 0
* m# u$ Q1 [( q+ p$ `" U) n# z
    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
* c7 R& [6 t2 r: [        # 假设平均每500bp 产生一个断裂点(averageLength = 500),通过随机函数生成seqlen/500个随机断裂点(1到seqlen之间的随机整数)
& W1 Z8 [2 a/ d' A        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
7 [" P/ F! o4 X/ a/ ^" D        breakpoint.append(seqlen)
        breakpoint.append(0)
        # 把随机断裂点从小到大排序
        breakpoint.sort()
4 m0 M, t$ ~/ K3 `( k" p+ y! [! A        return breakpoint

: n3 L# Q( j8 k2 h) O    # 沿断裂点打断基因组,并删除不符合长度要求的序列片段,定义片段范围:200-1000bp
    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
3 X' {# G2 a/ I3 M1 G8 t# |9 |; {            fragment = seq[breakpoint:breakpoint[i + 1]]
            if maxfragmentlength > len(fragment) > minfragmentlength:
                self.fragmentList.append(fragment)
        return self.fragmentList

    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
6 h- p# e8 U# F- @/ L    def clonefragment(self, fragmentList, cloneRetainprobability):
) G' N! D9 m. T- Q5 }1 P8 S' G        clonedfragmentList = []
        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
            if Lossprobability <= cloneRetainprobability:
* L1 k7 y& _$ _; [; N* O( u0 O9 O                clonedfragmentList.append(fragmentList)
        return clonedfragmentList
3 ^! T( }7 t, ]. n% `
5 Z. k7 }  }! k    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
6 N4 k+ B5 n' G- }- Y5 v        for fragment in clonedfragmentList:
! ^/ g/ O, m1 \, }$ ], t& x& B            fragment.id = ""
$ g- L( g5 Y! ?2 X6 O            fragment.name = ""
+ b$ ?4 d& j7 w& J            fragment.description = fragment.description[12:].split(",")[0]
- }' e) B/ ~5 o) P$ V            fragment.description = str(self.readsID) + "." + fragment.description
) @/ N: a7 f7 e7 d, J9 T            self.readsID += 1
/ V0 z% K$ [- w; \& _7 U            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

2 N6 S" v7 e7 F7 K* L+ t4 l! Z    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
& I4 k1 z2 k2 m" q  v8 S1 j4 `7 a0 X            self.genomeLength += seqlen
            for i in range(self.N):
                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
- B; z6 v' x0 O0 x8 D& f: Q                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
        # 模拟克隆时的随机丢失情况
2 Q/ j( {# C2 |        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟单端测序
        self.singleread(clonedfragmentList)
        SeqIO.write(self.readsList, sequencingResult, "fasta")

* h7 F; r, P/ }2 J: {0 W    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
6 `  c$ B" Z/ T+ Y' @& T            fragment.id = ""
5 e' Q% ]6 l8 l            fragment.name = ""
, H/ u. B# j2 i& j            description = fragment.description[12:].split(",")[0]
8 g, {' J3 E$ b+ p            fragment.description = str(self.readsID) + "." + description
6 |+ U4 j, n1 I6 d# X) u            readslength = random.randint(self.minreadslength, self.maxreadslength)
; g5 v- ]! e" c( s7 i! ?+ n            self.allreadslength += readslength
' j. M! t$ z$ N. H            self.readsList.append(fragment[:readslength])
" l7 |  v: ~5 T! v& S
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength

. w! i; Y! G4 H* @7 w            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
8 A7 [  X' l! u0 D9 [& x' j5 A            fragmentcomplement.description = str(self.readsID) + "." + description
/ d& z  l" O# L& ?6 Q            self.readsList.append(fragmentcomplement[:readslength])

/ s+ p, H2 |- p0 v# L3 q            self.readsID += 1
1 y0 u$ G' }1 Q  g8 r9 m! f
) T$ g2 `* G  m9 ^) d) k" @' J# j    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
! W" i0 O0 e$ n5 a            seqlen = len(seq_record)
/ F2 F7 y/ k+ r1 r) x            self.genomeLength += seqlen
: |: j8 B% S+ A8 j- P- H+ }            for i in range(self.N):
                # 生成断裂点
+ t2 |, h" |5 O8 m* N+ J, b8 J                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
( J5 I9 F0 ^4 g: @# ~                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
( u  B% V, H6 B7 N! Q/ K        # 模拟克隆时的随机丢失情况
) v5 p& {& N$ w- N* ]; i        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
( D% f0 j' I" M& o, h+ h        # 模拟双端测序
        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
5 v  b) c. B: j        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
: k9 U1 G( X" i0 L  V6 \2 K& u        SeqIO.write(readsList_2, sequencingResult_2, "fasta")

* Q% u$ r# P2 m* |    def resultsummary(self):
+ j5 j. b: q5 w8 e, r2 d  n6 E! Z        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
0 r5 D' F" Y5 n( j0 p0 [) d7 j        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
/ H0 H& x3 J" u. ]& G1 f* \$ o8 O        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
$ u/ H$ a) [% x' O7 _0 }" ?        print("片段数量:" + str(len(self.fragmentList)))
: f3 T5 r" h: I7 n3 y1 V  z        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
( O. K6 F. P& T1 b/ L        print("reads总数量:" + str(len(self.readsList)))
        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
$ u% V) Y* E; D! o        print("覆盖度(m值):" + str(round(m, 5)))
        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
6 O; c, @% C- [        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
; g, t& z0 w2 D* a# -------------------------------------------主程序-------------------------------------------
$ o- G7 ^( Y% C7 q# 模拟单端测序
sequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
, L& ^8 C5 S$ F8 P# J9 s4 d5 }sequencingObj.resultsummary()

$ S9 Q  O5 V1 _# 模拟双端测序
, n; M; r& k; z+ e- PsequencingObj = Sequencing()
2 V8 S. `$ H) d4 [0 a% D" ]sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
/ q) M0 v" x4 P9 x% d# SsequencingObj.resultsummary()


( y! J" Y6 j# h: ~) {

3477959497

不错。。。。。。。。。。。。。。。。。
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