基因组测序模拟

杨利霞

基因组测序模拟
基因组测序模拟
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6 R, l7 {  s) z( T5 P6 ~: z一、摘要
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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
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# T4 n! @  i$ W0 {3 e9 x3 h# ~+ q二、材料和方法
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5 Z" a& ^7 W3 |& [" I2 C. W9 {1、硬件平台

# d* p5 h! r4 X2 d$ \8 B( \处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
; H+ W# P& E; k安装内存(RAM)：16.0GB
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* e6 `  E2 a; _  O3 s! U! N2、系统平台
: W% ]8 [! s" M) {( RWindows 8.1，Ubuntu
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  N% j1 z0 ]' H3、软件平台

4 G/ @- g8 r7 y9 H, L  oart_454
3 E. d5 O& x& ?. a. }/ [. hGenomeABC http://crdd.osdd.net/raghava/genomeabc/
Python3.5
' \0 `& P( {. K3 z2 f" G. B; f  [Biopython
4、数据库资源
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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

. C; \; J# z( j6、方法

" ^" V# b( r6 U+ Gart_454的使用
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
进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
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

4 |7 K/ B" E$ w$ T  p/ q1、art_454的运行结果

/ z9 @3 o  N9 k9 T) `& d' r无参数art_454运行，阅读帮助文档

* R4 D4 P$ S8 e图表 1无参数art_454运行
' {4 J; z8 b9 N4 O8 j; U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.
下图为模拟单端测序，程序运行过程及结果

图表 2 art454单端测序

图表 3 art454单端模拟结果
1 m9 N$ R. c4 l$ R# C0 G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
! f! x2 N; H1 q& q& z4 ?下图为模拟双端测序，程序运行过程及结果
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) P5 r5 ^/ [, Q$ H/ c, a图表 4 art454双端测序

) B" M' ^, o, f( d图表 5 art454双端模拟结果
2、GenomeABC
下图为设置参数页面

1 t$ z% W5 i# n/ X2 y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
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。
( N' c/ f; G* u单端测序

5 t$ f' i) E7 w0 E- F+ z图表 7 程序模拟单端测序
双端测序
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图表 8 程序模拟双端测序
( j7 M3 M! ]' D$ @7 ]8 p测序结果

! D+ v8 K! a0 F/ Z图表 9 结果文件

' G7 I! x5 W% f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。
测序结果统计表

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)
/ Q5 a! @( Z8 Z, B2 R) l单端        12157kb        200-1000        10        600        0.95        107378        50-100        101968        7645.541kb        0.62889        0.53318        0.46682
( C2 t7 D, u/ U7 i单端        12157kb        200-1000        20        600        0.95        213722        50-100        202996        15227.882kb        1.25259        0.28576        0.71424
: W( e" H% S" S4 a+ v) m双端        12157kb        200-1000        10        600        0.95        106704        50-100        202770        15212.662kb        1.25134        0.28612        0.71388
双端        12157kb        200-1000        20        600        0.95        214212        50-100        407186        30534.265kb        2.51164        0.08114        0.91886
3 o) d% p6 K) o) N1 C! p  A* M8 G四、讨论和结论
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  S: S( h" o' q0 c' S% S程序运行方法
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" O- ]4 X: h$ S4 b1 H在类的构造方法init()中，调整参数。
Averagefragmentlength为片段平均的长度；
+ I3 r3 ^; x. J9 @* wminfragmentlength和maxfragmentlength是保留片段的范围；
cloneRetainprobability是克隆的保留率；
) Y4 C- r' E: Cminreadslength和maxreadslength是测序reads的长度范围
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* E- Z3 L( }+ x2 M( A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。
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' d# z7 F( y% B) Q( q3 q附录
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* J; q- j$ @9 r. B, D6 a/ `8 @% cfrom Bio import SeqIO
7 [: K" o( Z9 w8 zfrom math import exp
import random

, }3 u' w6 d# x/ lclass Sequencing:
2 l) S, y' Y1 l. T4 e    # N代表拷贝份数
5 @7 d3 O! x4 B: E4 u    def __init__(self)
        self.fragmentList = []
/ n9 S  a# s. x2 x4 l4 q: f& I, g        self.readsID = 1
        self.readsList = []
        self.averagefragmentlength = 650
8 z. {( c- z" y        self.minfragmentlength = 500
        self.maxfragmentlength = 800
3 P! E: c" d+ X        self.cloneRetainprobability = 1
        self.minreadslength = 50
; j' M% U' h/ h; y$ c9 T        self.maxreadslength = 150
" L9 b6 L; {+ @; i& a        self.N = 10
        self.genomeLength = 0
, V$ y/ V* r  @6 R" m6 \        self.allreadslength = 0

    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
        # 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)
( A, a2 D, m6 i5 U2 N        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
4 j5 {1 {% ~$ Y        breakpoint.append(seqlen)
8 H4 j& e  c- }) R- Q3 h        breakpoint.append(0)
; I$ W. v- D* [+ H5 N        # 把随机断裂点从小到大排序
        breakpoint.sort()
        return breakpoint
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4 C4 ^" r# J& p0 a# i3 ]    # 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):
5 Z+ e1 f6 ]9 Y        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
            if maxfragmentlength > len(fragment) > minfragmentlength:
* [$ Y2 T" v6 K  v% @1 K                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 W9 f" u7 n6 R" G+ f. d. ^        clonedfragmentList = []
5 a. V0 `/ o* t* X7 \  K/ V        Lossprobability = [random.random() for _ in range(len(fragmentList))]
8 @: _+ k0 A) d: A4 w/ u# G) Q        for i in range(len(fragmentList)):
" e2 t+ h; h! H0 |, B- U# A, Q            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
' f8 q1 b4 l( `" |, ]  k; L        return clonedfragmentList

    # 模拟单端测序,并修改reads的ID号
8 R. R7 k' N: r. R/ U- u    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
/ ^5 a* I  L" K1 b' Y            fragment.id = ""
4 n7 G" x% _  B5 N            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
1 [, i9 S: x- O# N+ A. i2 o! X4 K            readslength = random.randint(self.minreadslength, self.maxreadslength)
' e' y% j* `7 s$ A4 q( x& ~5 h            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
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    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
7 w/ t8 |# b% s0 L5 i, j            self.genomeLength += seqlen
6 n- b+ c2 ^5 r            for i in range(self.N):
0 S, w1 Y5 l( K                # 生成断裂点
  O- L2 c5 Y+ @! z3 p1 W2 ~1 t& _' K                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
0 H0 b& ?3 S) R) }. `        # 模拟克隆时的随机丢失情况
$ ]+ A% d2 o- R2 n$ `# ^        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟单端测序
2 l8 }6 X! c3 j3 V* }; D        self.singleread(clonedfragmentList)
' D" M0 j' w& X2 e& w" Z        SeqIO.write(self.readsList, sequencingResult, "fasta")

" S; D1 K1 V( ^$ P+ j& N! j    def pairread(self, clonedfragmentList):
# m' f% |" n7 B$ b" b        for fragment in clonedfragmentList:
& t( x1 Q1 O: A% j3 W            fragment.id = ""
1 w9 t; G( T5 w            fragment.name = ""
. `: T$ N  e, c4 |+ W9 E8 N" q            description = fragment.description[12:].split(",")[0]
0 T7 ?8 X, e; S, E7 ^2 k" v* Z            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
+ ~# A9 k/ H4 @            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
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, p2 M: Y6 @5 u- r! L            readslength = random.randint(self.minreadslength, self.maxreadslength)
1 _- f/ M. K; d& J3 g. S2 S            self.allreadslength += readslength

; }. ^+ r7 O- S1 Y6 H            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])
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            self.readsID += 1

    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
8 R- Z7 L2 Z1 m3 T. _, k6 O/ v) z) I6 m        for seq_record in SeqIO.parse(genomedata, "fasta"):
% l" L9 h' u1 ^* Z            seqlen = len(seq_record)
            self.genomeLength += seqlen
            for i in range(self.N):
, G  d' S. ?, }  c) [                # 生成断裂点
: ?7 _5 {5 K: n$ |- Q                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
9 p4 I$ x6 j/ @4 p! q3 Y                # 沿断裂点打断基因组
. V8 Y4 r3 Z& q( K6 t3 {                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
        # 模拟克隆时的随机丢失情况
  y) G/ R! t) Z$ l        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
$ N' g9 D9 Z! @/ b, _        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
' p& y- S7 E; \% Y( q        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
$ V( ^& F6 Q4 M        SeqIO.write(readsList_2, sequencingResult_2, "fasta")

- J) Z, ]5 ~9 r" j. `    def resultsummary(self):
+ e4 k3 \( x' a5 J% A        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
  }) i1 H; a7 R; k8 U        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
( r! @0 A- ~4 D1 v$ \7 a( F. [$ {        print("N值:" + str(self.N))
        print("期望片段长度:" + str(self.averagefragmentlength))
0 V0 T# F7 [7 m# H* W8 }* R        print("克隆保留率:" + str(self.cloneRetainprobability))
        print("片段数量:" + str(len(self.fragmentList)))
. H. a- d6 g0 Z& }        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
7 {  v. x  g6 m! `9 v' U$ f8 I        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
7 d1 G4 A: H$ H( M% s$ Z8 [' ?        print("覆盖度(m值):" + str(round(m, 5)))
8 h7 x6 V* X" e! W3 {        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
* \. O! D$ B5 J# D- v$ h5 R/ e# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
4 @9 r. M- l& p( Z) n5 psequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
* [" o* J( ~- {6 E" \sequencingObj.resultsummary()

# 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")
( f0 \9 o9 O2 H$ w* |sequencingObj.resultsummary()
from Bio import SeqIO
from math import exp
1 z! K; S( Q( m) }4 ]' Z4 d4 c' ^4 jimport random

- J% M, L$ X. u! O9 h) Hclass Sequencing:
3 ]# l6 u$ ?7 B  W1 `! _    # N代表拷贝份数
% k. u" f4 d4 x, _# H, N    def __init__(self):
        self.fragmentList = []
2 c6 r2 U3 |, d) V* X        self.readsID = 1
% u: G0 W  ]% @0 ^9 A% S7 D, _3 [        self.readsList = []
8 [3 Y, g" r2 X        self.averagefragmentlength = 650
        self.minfragmentlength = 500
        self.maxfragmentlength = 800
        self.cloneRetainprobability = 1
        self.minreadslength = 50
4 U7 S  O$ A$ Y# }  n' x( r/ r        self.maxreadslength = 150
        self.N = 10
        self.genomeLength = 0
7 @+ n2 _; g' H% }" b/ m        self.allreadslength = 0

    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
$ `1 ^0 _5 }/ `# M; x. h        # 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)
; S# }0 i) k' {        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
; l4 G5 Q9 e6 Y" b        breakpoint.append(seqlen)
        breakpoint.append(0)
- Z, R& w: N" |! `" G- u        # 把随机断裂点从小到大排序
7 _( T7 T& v, k) j4 v        breakpoint.sort()
        return breakpoint

) D# e- p, e9 B( T% a3 n    # 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
5 x5 ^+ b1 u: h/ k    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
            if maxfragmentlength > len(fragment) > minfragmentlength:
                self.fragmentList.append(fragment)
        return self.fragmentList

9 S/ t/ }. G* L  Q    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
" L7 h6 C7 B- b& _4 r9 [    def clonefragment(self, fragmentList, cloneRetainprobability):
, l! }0 r8 Y7 L        clonedfragmentList = []
$ ^* W8 U# ]( h) O8 H        Lossprobability = [random.random() for _ in range(len(fragmentList))]
, J/ Q2 U. R$ y* U9 N        for i in range(len(fragmentList)):
* M- ~4 W7 N! h! K            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
        return clonedfragmentList

& L) u, P: x9 x% d! y    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
/ p: L& a/ i9 g3 h7 ~9 w3 Q2 B- a# o: _5 g            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
' E; R8 E3 ]* v( [, u
    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
6 w. I9 z. Y: q" a! q& [+ f            seqlen = len(seq_record)
" k/ U5 ?, m5 T7 J# ?8 P9 ]1 W            self.genomeLength += seqlen
            for i in range(self.N):
8 @; O! J$ l! L6 `& o/ p" s                # 生成断裂点
# }% t; ]" [! o" r$ e% \1 Q& [                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
2 n' x1 Y/ a  U6 ~$ f/ |% N5 ~                # 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
3 r; L6 j2 Q+ B' L3 M* r        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟单端测序
        self.singleread(clonedfragmentList)
        SeqIO.write(self.readsList, sequencingResult, "fasta")
8 Z; K* o* F) b1 `5 M# V  |' ^# I
. x: u3 l. {( O) V: p$ n5 f1 U    def pairread(self, clonedfragmentList):
8 Q! F3 f9 V% ^* ~2 @        for fragment in clonedfragmentList:
            fragment.id = ""
7 P6 W+ w+ W" O/ B3 U" |7 G            fragment.name = ""
- q4 V8 m/ v8 e, N+ L2 ?' ?4 R% B8 m            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
/ z3 n- ?, i1 u0 y0 f2 I0 u8 E7 \            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

            readslength = random.randint(self.minreadslength, self.maxreadslength)
  n, }  ^- U2 r9 B            self.allreadslength += readslength
) x4 P) c4 s' {. C  F1 }7 ]* J) ~
1 ~# d) s0 J  K$ r! p            fragmentcomplement = fragment.reverse_complement()
$ Q2 j+ w! G' Z            fragmentcomplement.id = ""
" Q7 d# g5 Q6 x4 Q' T            fragmentcomplement.name = ""
5 j  q: u4 H+ X            fragmentcomplement.description = str(self.readsID) + "." + description
( h: b) S3 S3 {" `6 ~            self.readsList.append(fragmentcomplement[:readslength])

$ e: R* f) C* @$ u$ g            self.readsID += 1
% @2 W6 z8 z! l
( x- Z( Z+ L+ ~+ {7 c    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
$ u6 J6 B  h6 C5 P4 b5 V! T            seqlen = len(seq_record)
0 v1 E. X8 u/ L, F            self.genomeLength += seqlen
            for i in range(self.N):
                # 生成断裂点
, c2 f. K, a$ e2 d3 x                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 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
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
  ~! V0 }. c- y2 {0 S        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
) U- ?) ]7 T) n. Q; ?# i
    def resultsummary(self):
        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
8 l7 m! n3 m' H' X% c  H        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
        print("片段数量:" + str(len(self.fragmentList)))
% }( ^- M( p  c/ O' i* b        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
% q9 k( V; _' `  m        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
        print("覆盖度(m值):" + str(round(m, 5)))
2 v0 C9 ^5 K; y4 @- }6 N0 O        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
% n" ?5 G6 h7 C5 j- h1 L        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
# G- U+ M9 A" R! ~# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
$ m- T, b) O3 b: [$ }: X0 H8 ^* @sequencingObj = Sequencing()
$ T) D5 \! P: l: ^sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
( d  J- E) m, _0 S5 f, ksequencingObj.resultsummary()
# e0 g) c* a; X" z+ x/ s
2 U: }; D1 m, Q# @* F+ N, U. {+ M# 模拟双端测序
( V+ z* w$ m7 hsequencingObj = Sequencing()
" B8 G% k4 V( r9 l7 [; jsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()

3477959497

不错。。。。。。。。。。。。。。。。。