基因组测序模拟

杨利霞

基因组测序模拟
' ^5 x9 n0 D7 {+ q$ n基因组测序模拟
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$ ~! t# x  ]- @$ C) T* s一、摘要
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' K! O: h' r1 G  ?! c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

二、材料和方法
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1、硬件平台

4 `5 u' R9 }7 h7 T/ x处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
; E5 k, C/ ~5 ]. _- f6 w安装内存(RAM)：16.0GB
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' j; I6 B- Q6 H! \7 ~2、系统平台
# o, a' N8 _9 g& ]8 W. GWindows 8.1，Ubuntu
$ j  w0 v6 ^1 n% I# O$ l
3、软件平台
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6 e7 h# j- A/ {2 i* x! Q4 ~art_454
+ O* j0 Y+ N& D' r; pGenomeABC http://crdd.osdd.net/raghava/genomeabc/
) @) G$ {6 ~2 Z" g; _3 PPython3.5
2 r- ~) p$ T3 E; m' d5 BBiopython
0 [% W: l) ~1 N' M8 F9 E, x" b" @0 B4、数据库资源

NCBI数据库：https://www.ncbi.nlm.nih.gov/
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+ j7 m( W* H' f, b: z  @( T! z" F% i5、研究对象
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酵母基因组Saccharomyces cerevisiae S288c (assembly R64)
4 i0 T2 k: T& [0 Cftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_genomic.fna.gz
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5 W. u- L. R& }5 O" U/ l& G6、方法

+ T/ V* I2 P$ w- U8 _! iart_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。
# y5 ?( M( Q9 ?9 N0 T; x, aGenomeABC
4 b) v2 x9 ^$ R' R: N% w4 f进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
! ~+ U2 m" ~! c$ @+ I& F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

无参数art_454运行，阅读帮助文档
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1 T  w( v" L' A9 A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

! N+ b! I. {3 ?$ s. c图表 3 art454单端模拟结果
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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" o* b2 h2 h9 U/ h& r: b( `图表 4 art454双端测序

$ u9 I7 O- i/ {8 l( K  q' Z图表 5 art454双端模拟结果
2、GenomeABC
7 s( Q* U6 J0 F下图为设置参数页面
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, ?! V& k1 {  Q3 G/ n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。
; U0 P4 I. f7 B单端测序

( {5 b! z3 B, s/ P1 w/ M图表 7 程序模拟单端测序
双端测序

8 T; B& P& t- M4 ?图表 8 程序模拟双端测序
测序结果
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图表 9 结果文件

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
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$ L8 A: o5 B/ E; |. `5 }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
% [) A- x: l8 g3 Q$ j2 U  \$ l- F( C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
双端        12157kb        200-1000        20        600        0.95        214212        50-100        407186        30534.265kb        2.51164        0.08114        0.91886
. T) q: ?; e) `& P四、讨论和结论

程序运行方法

( x( \- X, A5 |在类的构造方法init()中，调整参数。
Averagefragmentlength为片段平均的长度；
! ?  D! A+ _$ z6 W) r- s, O" fminfragmentlength和maxfragmentlength是保留片段的范围；
cloneRetainprobability是克隆的保留率；
* L0 Y# Z8 U0 l  c. ?3 R5 e5 Dminreadslength和maxreadslength是测序reads的长度范围
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3 v, o% \+ ?. {; n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。

( e; F: z; L! l0 P! ?5 \! F8 E# {附录

from Bio import SeqIO
from math import exp
. [# R7 u  W% j  |% Kimport random
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* n! ~- o8 Z- |3 pclass Sequencing:
    # N代表拷贝份数
    def __init__(self)
% e' T9 y2 X( _9 b2 d( C1 o        self.fragmentList = []
        self.readsID = 1
        self.readsList = []
        self.averagefragmentlength = 650
$ l( r' f; N1 E' i- L* b& n# _        self.minfragmentlength = 500
" g5 ~7 e2 ]# a( ?- H        self.maxfragmentlength = 800
/ p% A: P- i  C* s" x        self.cloneRetainprobability = 1
9 I- h7 R* h. M3 T# q+ [/ j        self.minreadslength = 50
$ l; h" m: W; t( z  H        self.maxreadslength = 150
2 }- v  z& o, p4 x' D        self.N = 10
+ u) {- q& ~  U9 ~) P7 N        self.genomeLength = 0
, ]5 H4 G" K3 M: [$ s: j) x        self.allreadslength = 0

. `$ q1 q) b8 D* h3 t( t    # 生成断裂点
% a) T5 E, j2 N6 n- C9 G    def generatebreakpoint(self, seqlen, averageLength):
7 {- [7 s' ?; Q, I& e        # 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)
! ~3 _9 u1 p! d3 N        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
3 \. ^4 j+ x" E2 m+ g7 D8 \8 Y, O8 Y: t        breakpoint.append(seqlen)
; a' `8 `- x; n2 W        breakpoint.append(0)
3 i/ C1 X" }! D; r: Z( x        # 把随机断裂点从小到大排序
  u  j; S8 w& ~        breakpoint.sort()
6 B: y0 a4 Q! x        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
6 _# C3 X9 Y1 W- r# m    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:
) M( O- r: R  I2 E- c- A                self.fragmentList.append(fragment)
        return self.fragmentList
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7 j, h) _. T* D    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
3 [' V& E8 s# u( J    def clonefragment(self, fragmentList, cloneRetainprobability):
' Q) C0 P" N& d# E( Q$ O        clonedfragmentList = []
2 ?  Q2 y) U: u3 p        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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0 v8 l1 _# X" _" C6 O7 k    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
  I: Q% P- z( }! d        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
. a( O  G# S8 A0 n# l7 D! u0 g3 q            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
4 ?! M7 ~3 D$ n2 F9 G+ E5 K            self.readsID += 1
% P2 P7 M* b; b' q8 ~3 M) w            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
  m0 U( i- Q1 w2 b2 D$ a  {
1 J: D: _% s4 x9 Q    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
9 O8 f) i, K* \2 E0 E; d6 e            seqlen = len(seq_record)
            self.genomeLength += seqlen
( Q0 ?5 Z! Y. k! g; o            for i in range(self.N):
                # 生成断裂点
8 l- W4 t( I) C' @. C' f5 t                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
( c$ j* X: s8 p% n- X                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
2 g4 ~; Y+ `/ D3 o6 W( K4 C% z. A        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
( O1 y+ {8 k8 a  C* T, ~' [. w        # 模拟单端测序
        self.singleread(clonedfragmentList)
; g' F* ^. @9 M4 B        SeqIO.write(self.readsList, sequencingResult, "fasta")

    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
6 R& S( v) v1 m6 ]            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
( I: P% l& k: T9 N5 G/ E            self.readsList.append(fragment[:readslength])

            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength

  B9 j' D! `6 U* S( O            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])

            self.readsID += 1

' S7 O. }% B! P1 Y    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
6 F" @; t$ N. k            self.genomeLength += seqlen
8 s/ ]( Q) ^/ u$ r0 F            for i in range(self.N):
# ]& Q0 |: C& d! X; i# U                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
' [1 q! Q+ ~( G/ z1 j5 z8 X9 F' h                # 沿断裂点打断基因组
4 B% h' [9 R) u                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
* f, i, W6 t) W" s; @. f- n        # 模拟克隆时的随机丢失情况
* e4 h3 U. L! a3 Y! ^        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
$ Q. i- z. S2 y" M3 T$ L. j( ~        # 模拟双端测序
9 t3 V' ~" w& ]- a8 v+ M& o        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
8 H5 M. I) i) U2 ]" ?6 B/ r" B3 b        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")

    def resultsummary(self):
- ]7 H% n5 i' R* t9 _2 f6 R        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
; N8 R( n3 p. h0 M        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))
        print("片段数量:" + str(len(self.fragmentList)))
+ \  @5 d4 a8 z/ U+ e        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
) N. Z% f8 k8 N: Y        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
% f! x3 Z: s4 T' j        m = self.allreadslength / self.genomeLength
0 {) T# l$ O- G/ X+ c! f8 w        print("覆盖度(m值):" + str(round(m, 5)))
% m6 K: n" Y% l7 ]7 f        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
# G* G5 v% _2 V$ D9 s$ a# A2 d5 ~        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
, X" [& v  v/ u& n# -------------------------------------------主程序-------------------------------------------
" Z& @! K9 j- O. s! _. M5 m  C5 b# 模拟单端测序
sequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
sequencingObj.resultsummary()
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& H% J1 [! E% ?4 n# 模拟双端测序
0 g; [/ n$ R8 o* `% P7 K$ KsequencingObj = Sequencing()
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
* w( }0 o9 |; Q: x; z3 p) B# GsequencingObj.resultsummary()
from Bio import SeqIO
7 y1 `1 K$ j& D3 Wfrom math import exp
. ], G2 A) P- \+ i' ?1 n8 s8 C$ dimport random
$ ?/ h) \1 T6 n- e
0 J6 x! o5 W- _/ S4 j, _5 K( iclass Sequencing:
    # N代表拷贝份数
0 v) b' E% a- |5 ~! d6 m" s    def __init__(self):
* G( `/ |8 H! [# W        self.fragmentList = []
$ v4 R4 J7 l* S- H        self.readsID = 1
6 I5 D1 c; l5 G$ ~3 n        self.readsList = []
0 d" E& K" [! _2 W- D. F0 W; ?# _        self.averagefragmentlength = 650
        self.minfragmentlength = 500
        self.maxfragmentlength = 800
% C0 t; [& `. a$ J& b$ b        self.cloneRetainprobability = 1
        self.minreadslength = 50
        self.maxreadslength = 150
        self.N = 10
& R4 N9 L' |3 Y8 W' C- w        self.genomeLength = 0
        self.allreadslength = 0

% g% s  d3 T  R. n; U1 \: e. t    # 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)
8 c* s; |7 j5 I) s0 g- A        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
        breakpoint.append(seqlen)
- b4 D7 Q& O9 G. E" D/ n        breakpoint.append(0)
1 H# S3 @) [- s6 P7 P' n( x        # 把随机断裂点从小到大排序
( c. I7 J( x" B$ u        breakpoint.sort()
9 O, B' K/ `9 Z3 L( ~) u, q        return breakpoint
% J* A: O7 @, [$ d# w' J
    # 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):
" K+ h8 M" Y" f            fragment = seq[breakpoint:breakpoint[i + 1]]
  c9 s  F6 U! {            if maxfragmentlength > len(fragment) > minfragmentlength:
                self.fragmentList.append(fragment)
( a& B& s  K$ B: ~9 [        return self.fragmentList
& Q0 ?9 d1 z+ p
    # 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):
; z1 P# x% \; T" S        clonedfragmentList = []
        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
# o3 u' ~2 Y4 x            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
        return clonedfragmentList
5 ~& Y% _. X: R, y
5 y9 O6 j5 p# r- _) }% s    # 模拟单端测序,并修改reads的ID号
# k8 Z2 n: c$ ]1 j, C! M    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
2 {  D# b: g/ c* o  U            fragment.id = ""
; [' P5 }* w: m0 L7 M            fragment.name = ""
            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
$ o* Q. p  I0 _, U' a- H, n            self.readsList.append(fragment[:readslength])

    def singlereadsequencing(self, genomedata, sequencingResult):
5 |/ B# n% p0 n1 o& r        for seq_record in SeqIO.parse(genomedata, "fasta"):
5 ^7 S' B0 r/ q! l1 P' g( X            seqlen = len(seq_record)
            self.genomeLength += seqlen
3 D7 a; `3 H  @5 T$ c6 ~            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
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
& ?! u2 C/ @8 i: y6 `% `        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
3 P! r! y' G/ b( }' N" v        # 模拟单端测序
; L. R. a/ z: e6 H/ E( ?        self.singleread(clonedfragmentList)
& A" a& d0 c* O( v' \4 D        SeqIO.write(self.readsList, sequencingResult, "fasta")

. r. y4 w5 f' D- M8 J    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
4 H/ ?: c- I' x( S            description = fragment.description[12:].split(",")[0]
% K; ?0 v; a+ X# Y" e5 g2 G            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
7 Q# `2 @8 `" i( |5 N! Z5 ?' v6 |- }
            readslength = random.randint(self.minreadslength, self.maxreadslength)
! g# V. ~$ s5 y" j$ T# A3 o2 K            self.allreadslength += readslength
# [' N, x1 e( [( Z  o# M
; y7 {6 b9 d' g            fragmentcomplement = fragment.reverse_complement()
% _  t4 k6 e; J6 O& T            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
2 E- b, w2 B# n2 [' e* S3 ^            fragmentcomplement.description = str(self.readsID) + "." + description
0 V# |$ M3 L4 s, ^  \  L4 T/ p! a            self.readsList.append(fragmentcomplement[:readslength])

- X9 s( s* |8 \6 X) E! m2 J, P            self.readsID += 1
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# I! R" K8 Y2 t$ b9 `, K1 r  ]    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
# Z" j) {  h* |1 C$ H        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
            self.genomeLength += seqlen
6 l4 T7 c) r1 M8 i            for i in range(self.N):
                # 生成断裂点
3 c- L, }8 K6 K6 Z                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
  f1 B; U+ h% o9 q1 u                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
3 n; r+ [  E( B0 V4 m        # 模拟克隆时的随机丢失情况
+ {7 G" P6 Z# s( `$ o. R        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
. r# B2 Z6 e! w: O: Q. i        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]
, u" P1 m9 n$ I: s( i1 i( {        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
        SeqIO.write(readsList_2, sequencingResult_2, "fasta")

5 R7 Y; t1 C: }# h    def resultsummary(self):
1 I  i; f/ W# q$ }, h' w        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
( _  t( Q8 P1 P3 @: D        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))
        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)))
        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)))
0 q& Q9 \+ T5 _1 I( p  O' D        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
- h" f" J" S4 A+ O        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
7 o9 j* E7 O  z- O6 T: l# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
' M) f7 b4 e; d$ x6 W4 G" t) T4 PsequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
! ^' O) p8 Y/ gsequencingObj.resultsummary()

# 模拟双端测序
sequencingObj = Sequencing()
& x& m0 O2 M; N; A& h7 t9 Z4 HsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()

3 U- U8 {9 v; O
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3477959497

不错。。。。。。。。。。。。。。。。。
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