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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一、摘要
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. V0 b; q, J9 M- `! l7 n* F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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二、材料和方法
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! Z4 \, u( h0 Z$ M/ j& P2 B# Q1、硬件平台
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处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
( Y* f5 x% B2 H/ b/ @$ Y0 Y2 a安装内存(RAM)：16.0GB

& ^+ d1 G* k4 N7 `) v, r2、系统平台
. i& e9 D5 L& `9 J" \3 sWindows 8.1，Ubuntu
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) Y5 |' x; e3 }- D% Z6 X1 w" t3、软件平台
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7 Z8 ?0 P. h0 f9 T$ V- eart_454
4 ^& _% ~* E/ r* P' ]# jGenomeABC http://crdd.osdd.net/raghava/genomeabc/
Python3.5
6 Z1 o3 i; q% ~9 s# k1 j5 vBiopython
4、数据库资源
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NCBI数据库：https://www.ncbi.nlm.nih.gov/

5、研究对象

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

3 g3 m) W+ o: V/ [4 \. U  ~3 Xart_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
" l' s+ v" K5 o! i' h7 ~& B5 g6 k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

1、art_454的运行结果
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) ^2 z! e; p' g7 ~无参数art_454运行，阅读帮助文档
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, }) @# b' Q7 S+ ^  [图表 1无参数art_454运行
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
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图表 2 art454单端测序
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8 `( ~8 g4 b2 y3 [: q6 `) B图表 3 art454单端模拟结果
$ w6 W" {0 z7 V+ w+ N# a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
7 M1 R+ r8 R' P- D! q# }& N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

% C# r0 B% i) v0 c4 o, _, x' }图表 5 art454双端模拟结果
2 U- }5 p" P3 r8 }) |$ J' `4 t  ]2、GenomeABC
下图为设置参数页面

3 L: F% y) n! b9 G! d) t* f下图为结果下载页面
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图表 6 结果下载页面
) r: L, u' E; H+ }3、编程模拟测序结果
8 U7 r4 D6 ]$ l$ z3 N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。
- S/ r: K( \. l" @! s单端测序
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图表 7 程序模拟单端测序
双端测序
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+ u& ]5 B7 V9 t) F9 R1 j图表 8 程序模拟双端测序
4 p0 [+ c0 [* ~" p测序结果
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图表 9 结果文件

2 d$ s) ~& \0 l$ K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。
) M  y- V( N% a  @6 l* k测序结果统计表
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; |& v0 B5 F6 b9 i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)
1 T$ \9 f: k, V' v单端        12157kb        200-1000        10        600        0.95        107378        50-100        101968        7645.541kb        0.62889        0.53318        0.46682
5 g$ l& g# |3 H单端        12157kb        200-1000        20        600        0.95        213722        50-100        202996        15227.882kb        1.25259        0.28576        0.71424
双端        12157kb        200-1000        10        600        0.95        106704        50-100        202770        15212.662kb        1.25134        0.28612        0.71388
5 r0 q8 B- V" f! S: a) I: D$ m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

程序运行方法
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在类的构造方法init()中，调整参数。
# T- O' A9 D0 @0 w3 U6 y$ Y  fAveragefragmentlength为片段平均的长度；
minfragmentlength和maxfragmentlength是保留片段的范围；
) B8 H9 x4 `! O0 @+ z2 B2 J* \( FcloneRetainprobability是克隆的保留率；
+ C- S0 n* f: x" }4 uminreadslength和maxreadslength是测序reads的长度范围
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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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from Bio import SeqIO
from math import exp
import random
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class Sequencing:
  K; ~; I$ I8 R4 @* l; q/ K  V    # N代表拷贝份数
: L5 U9 G: }! \* c    def __init__(self)
( q0 X" A& X8 R: d2 `( C5 I! O+ @        self.fragmentList = []
5 t, M3 h6 _: c9 f) G+ S        self.readsID = 1
        self.readsList = []
( \% u) e  l; O& m        self.averagefragmentlength = 650
        self.minfragmentlength = 500
- X1 ?$ C0 i. F( G# n5 K        self.maxfragmentlength = 800
        self.cloneRetainprobability = 1
/ c( B0 O1 ]6 |: ?0 W. V        self.minreadslength = 50
        self.maxreadslength = 150
9 {. q( j! U3 `/ a& l: `        self.N = 10
        self.genomeLength = 0
        self.allreadslength = 0

    # 生成断裂点
7 {! B0 n6 ?0 a3 P3 T    def generatebreakpoint(self, seqlen, averageLength):
  G6 d* F6 k) W# ~$ X        # 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)
( n5 q$ s8 T, v3 r8 W        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
9 H7 @0 V/ J' k, J; `        breakpoint.append(seqlen)
        breakpoint.append(0)
0 |. o! \' U: S! T" b+ H& s9 i" P        # 把随机断裂点从小到大排序
        breakpoint.sort()
        return breakpoint
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    # 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):
! ~4 R( d! I+ x3 D, |  x        for i in range(len(breakpoint) - 1):
% v/ E5 t, ]% t6 O6 X. q- M% u            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):
        clonedfragmentList = []
6 c: C3 G# r! q# g& s1 L8 I" c        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
        return clonedfragmentList
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  [% G6 {( e) M9 Y    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
, e( Z& u' [; v" r8 @- y* M6 ^6 V        for fragment in clonedfragmentList:
            fragment.id = ""
/ Z2 Q9 u2 U/ k5 U2 {; R% {            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
5 A/ Y5 A& u1 s  `. p, k            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
/ n0 g+ S1 S4 y; S. o' Y            readslength = random.randint(self.minreadslength, self.maxreadslength)
& f. H7 g; P. J0 v            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
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& E1 _: Z6 n4 p7 x# e0 }6 a; g    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
            self.genomeLength += seqlen
            for i in range(self.N):
                # 生成断裂点
! i& w. |! \- k                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
+ O$ {: H' F. \* A, c: M                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)
        # 模拟单端测序
5 p. J0 y' D3 W9 ?- I1 D        self.singleread(clonedfragmentList)
        SeqIO.write(self.readsList, sequencingResult, "fasta")

; i. B& y% d. l    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
7 P5 G% y& d% c6 i6 j1 T3 o. T4 s            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
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+ C9 v. ]7 n1 @1 C* P            fragmentcomplement = fragment.reverse_complement()
% n6 x* d3 a, }1 S( d5 [            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
3 u2 z! h5 D  t/ e, w1 I7 O            fragmentcomplement.description = str(self.readsID) + "." + description
# M! O( m& t9 R1 N+ z            self.readsList.append(fragmentcomplement[:readslength])

# V# v# w# |  N+ `0 f* a5 k, a: d* a            self.readsID += 1
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    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
0 m) a. l7 S. a& M3 f- J) P        for seq_record in SeqIO.parse(genomedata, "fasta"):
& ~# B* \( n) T# T2 [* d            seqlen = len(seq_record)
# B' `' Q2 l' z" j5 }, G            self.genomeLength += seqlen
            for i in range(self.N):
* Y6 T+ U5 r, v# u1 F" J                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
" C( y' J. P7 t# a% l  b. w+ p                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
' F. x8 U% K8 @# p' h2 V        # 模拟克隆时的随机丢失情况
7 B$ N; r9 o: V" j        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
/ H# W/ w5 ~* a; R        # 模拟双端测序
+ f( U) k% }/ f9 f9 J' N3 W        self.pairread(clonedfragmentList)
5 `; C1 o2 _9 N        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
7 v5 @; h7 V6 y/ g; c2 l( b        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
8 V3 b/ u9 T9 t        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
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, c2 p; B, g. }/ L/ x; `    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))
        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
# [3 g( j5 Y! u7 h, {2 w2 v        print("片段数量:" + str(len(self.fragmentList)))
        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
" i: Z0 U: K" A1 Q) D; U5 x        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
+ R, F5 w: ^; X$ T1 o. ]        m = self.allreadslength / self.genomeLength
: G9 C4 X: I' k; a& p, z; Q        print("覆盖度(m值):" + str(round(m, 5)))
        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)))
# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
sequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
, |" J$ Y4 ^) g0 x  F4 dsequencingObj.resultsummary()
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# 模拟双端测序
2 P# `% y+ r0 W- MsequencingObj = Sequencing()
; l) \1 K2 E, D  nsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
: [. m' U" B" ~* {# isequencingObj.resultsummary()
from Bio import SeqIO
- ~2 I# r* c# }from math import exp
import random
% g$ A8 P! x7 q8 ^8 L, c5 o
class Sequencing:
    # N代表拷贝份数
2 l9 P" q, F) p( x( H    def __init__(self):
1 [1 a; o" V! u5 u& }% F" D) l" d+ v        self.fragmentList = []
        self.readsID = 1
  F/ F% T& a9 w9 j( }" D: {        self.readsList = []
7 g# ]# G2 X( }3 ]7 F        self.averagefragmentlength = 650
        self.minfragmentlength = 500
        self.maxfragmentlength = 800
$ g$ d3 I0 {, }  I4 k+ @3 w        self.cloneRetainprobability = 1
, W  A: S. D$ {- g) ]        self.minreadslength = 50
' L  S( X$ Z/ [+ n1 k        self.maxreadslength = 150
        self.N = 10
        self.genomeLength = 0
, y! V3 Q& O, b1 Q& \, ]        self.allreadslength = 0

; }# Q  c5 e9 a* r4 L& a7 B7 t" o    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
, ]. t4 s# ~6 G/ y; v7 F4 {, L5 r* x        # 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)
        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
4 U$ g9 V' K9 Y7 R        breakpoint.append(seqlen)
        breakpoint.append(0)
        # 把随机断裂点从小到大排序
3 R7 t( Q3 w0 P$ B& y& {. H        breakpoint.sort()
- L+ O# t! g! {+ O/ w# m        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):
            fragment = seq[breakpoint:breakpoint[i + 1]]
* y; g& Y; S0 ~; z! z8 g  A7 G            if maxfragmentlength > len(fragment) > minfragmentlength:
5 f0 S/ X1 S  Y! a                self.fragmentList.append(fragment)
        return self.fragmentList

& L: g+ d  t2 S2 }    # 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):
8 H$ x; ~3 F. i, g0 G        clonedfragmentList = []
1 R  P4 m9 `& Y/ f        Lossprobability = [random.random() for _ in range(len(fragmentList))]
: m$ J3 B. P0 P( r        for i in range(len(fragmentList)):
, p3 @% ]$ h+ U3 o2 {4 b$ h- V9 W            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
        return clonedfragmentList
/ i# V  x; l; V$ c. v- V: S
$ K! q' `, ^2 c9 O    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
* k' s. _& z# k        for fragment in clonedfragmentList:
4 ^) y7 g/ S* g9 n2 T# _            fragment.id = ""
            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
& t1 q3 v: v/ F, ^            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
' q( Y' j% S, K9 D2 K" R2 _            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
, ^4 u0 ^$ j2 O            self.readsList.append(fragment[:readslength])
  I$ E. a4 \+ n( T2 p/ s7 j0 q% ~
; ^3 m$ M7 D4 S1 S% o/ Z- \+ {. z4 H    def singlereadsequencing(self, genomedata, sequencingResult):
% g8 ~2 b: m7 Z; a9 Y        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
  R& m0 B/ }: B$ U- \            self.genomeLength += seqlen
4 [' u! w: h  F2 A0 h            for i in range(self.N):
- n8 t  l* g5 I1 l$ k, [0 f                # 生成断裂点
6 J( M( G" O- ?& J" M# O                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
( x- |+ W% P+ ^" b* B/ o8 U$ v        # 模拟克隆时的随机丢失情况
* t+ l" T! ?5 q8 I/ V/ i7 j        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟单端测序
3 ^# Q, i* }" \# Q! O        self.singleread(clonedfragmentList)
8 ^' i6 A5 ~/ c. ^/ o  k  ^        SeqIO.write(self.readsList, sequencingResult, "fasta")

" W! y/ T) Y; }! K$ s4 }- X    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
) t  l  L6 [/ j2 K& B: A1 H& D            fragment.id = ""
            fragment.name = ""
            description = fragment.description[12:].split(",")[0]
, O5 P# H/ y; r8 D% ^            fragment.description = str(self.readsID) + "." + description
3 M3 L& p( r7 U/ Y. m9 A            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
6 r( f9 ?9 ~- y% e. V            self.readsList.append(fragment[:readslength])
7 D+ Z9 E( `4 d
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
( i) b- z5 T" I6 f$ Q0 a
            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
9 ~' I4 e2 c7 y            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])

% U4 [3 a# ?# B: ^8 x1 u/ r            self.readsID += 1

( f& u- E, S7 I" z1 k+ d9 j    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
- K9 _7 C4 N& [* \" D% c% F; C6 I        for seq_record in SeqIO.parse(genomedata, "fasta"):
& o& c, G: K) V7 l6 B            seqlen = len(seq_record)
            self.genomeLength += seqlen
0 H0 d1 q3 p0 {            for i in range(self.N):
                # 生成断裂点
7 Y9 y; d! X! O                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
7 R6 S- a6 X! D7 a: t4 S7 i                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
1 {# n" W5 f! o7 u        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
3 m% m& Q* H* \8 a2 A9 N% o: I        # 模拟双端测序
        self.pairread(clonedfragmentList)
- E+ T0 |/ K( S        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")
. r& K; c1 z* K6 G) D" Z        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
! l: f* W; [0 d
    def resultsummary(self):
        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
) M6 Z& U0 R0 E' ~7 J# ~        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
- q1 e) x; V4 m8 z& v2 l( j6 C8 U) s        print("片段数量:" + str(len(self.fragmentList)))
' p% h- e7 l* X5 A" [# R        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
, N+ u& P0 s* ]        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)))
- G& ~0 p9 ~6 B* w4 s' x, ?, j. N% `        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)))
# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
; }6 _7 u. T7 O$ i$ N6 _# O5 p+ UsequencingObj = Sequencing()
( l3 H! E1 m0 l- M# |sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
sequencingObj.resultsummary()
1 [, _- B6 ^: M* Z0 ^) O
8 P. |8 U. C) s8 ^3 ?7 |* l# 模拟双端测序
  E: J) y3 G; r$ {5 tsequencingObj = Sequencing()
! k* j, c! r1 h5 V8 I2 CsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()
, T! w; _+ k! a
9 r, A4 F' d4 M
) k, p1 R9 b. C! N0 p1 f

3477959497

不错。。。。。。。。。。。。。。。。。
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