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基因组测序模拟

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基因组测序模拟
基因组测序模拟

一、摘要

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

1、硬件平台

处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
安装内存(RAM)：16.0GB

2、系统平台
Windows 8.1，Ubuntu

3、软件平台

art_454
GenomeABC http://crdd.osdd.net/raghava/genomeabc/
Python3.5
Biopython
4、数据库资源

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

art_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

1、art_454的运行结果

无参数art_454运行，阅读帮助文档

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

图表 2 art454单端测序

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

图表 4 art454双端测序

图表 5 art454双端模拟结果
2、GenomeABC
下图为设置参数页面

下图为结果下载页面

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。
单端测序

图表 7 程序模拟单端测序
双端测序

图表 8 程序模拟双端测序
测序结果

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

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
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
四、讨论和结论

程序运行方法

在类的构造方法init()中，调整参数。
Averagefragmentlength为片段平均的长度；
minfragmentlength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

from Bio import SeqIO
from math import exp
import random

class Sequencing:
# N代表拷贝份数
def __init__(self)
      self.fragmentList = []
      self.readsID = 1
      self.readsList = []
      self.averagefragmentlength = 650
      self.minfragmentlength = 500
      self.maxfragmentlength = 800
      self.cloneRetainprobability = 1
      self.minreadslength = 50
      self.maxreadslength = 150
      self.N = 10
      self.genomeLength = 0
      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)
      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
      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):
         fragment = seq[breakpoint:breakpoint[i + 1]]# z, N) n  y9 o/ A5 I' _
         if maxfragmentlength > len(fragment) > minfragmentlength:8 P6 @% ?5 B* X" P
            self.fragmentList.append(fragment)
2 R+ S3 B+ r! m8 u       return self.fragmentList
. k4 d: F( H% R9 P' f' M, w0 I0 Q; O8 B  @
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率, T' y$ g6 s. {/ e& u$ j; B
def clonefragment(self, fragmentList, cloneRetainprobability):: Y  H6 d% `2 F& i% ?6 [; c2 k
      clonedfragmentList = []
* D* M( }, j1 A. V9 E       Lossprobability = [random.random() for _ in range(len(fragmentList))]0 @- [: {  F8 Z- z
      for i in range(len(fragmentList)):) i' y/ l, B0 D
         if Lossprobability <= cloneRetainprobability:  q$ L" H0 y: _* ?& _
            clonedfragmentList.append(fragmentList)+ f) D/ L, K- f# y$ C$ f0 i
      return clonedfragmentList
0 F% z5 h7 B6 f. T3 G) b$ d) j# H; E
# 模拟单端测序,并修改reads的ID号
, |' Z. T# B0 o' J) a def singleread(self, clonedfragmentList):
& k% e" ^) L4 l+ t       for fragment in clonedfragmentList:5 j, h6 U; q  X) X3 K
         fragment.id = ""
8 ?8 W" C$ v' m5 z% Q5 f          fragment.name = ""
' D) `0 S% Z: Q          fragment.description = fragment.description[12:].split(",")[0]+ k* c# m5 m3 j5 k6 m/ C
         fragment.description = str(self.readsID) + "." + fragment.description3 N5 Z' j" T% I
         self.readsID += 1
$ h& M! d$ I& z7 S          readslength = random.randint(self.minreadslength, self.maxreadslength)
! R9 q- ]+ ]( k% \          self.allreadslength += readslength
' F/ Y7 B* D# s$ }* H  G          self.readsList.append(fragment[:readslength])
2 C  A. M) c  t" l8 D* D' ?# E) ], M6 f7 _8 w9 a- B# k2 W/ [3 Y' T; _
def singlereadsequencing(self, genomedata, sequencingResult):. K: Y/ [0 W+ D' l* C* A
      for seq_record in SeqIO.parse(genomedata, "fasta"):
3 N0 H2 g" d$ b          seqlen = len(seq_record)
" D& L7 C3 C6 X9 G, H, I2 R+ ?. J  \          self.genomeLength += seqlen
' k9 i6 U+ q! X1 L* k0 L          for i in range(self.N):
3 X2 i; d# T# i& T& Y1 Z5 f; }' k             # 生成断裂点1 T& f5 j6 g, Z) p
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)% u, ]- G9 `9 K- J
            # 沿断裂点打断基因组$ ?: l) h! _8 X, ]) z/ F4 {/ x7 m! x
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)! i( M9 j* Y' F! I" n1 i, R6 Q# p
      # 模拟克隆时的随机丢失情况
  L2 p4 @3 \: C% `7 X' B/ b$ a       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)3 u! Z2 _( s5 y6 Z9 Z" a; m
      # 模拟单端测序' N2 j$ S. ?3 u$ h0 {
      self.singleread(clonedfragmentList)
- G4 i9 p/ C' l+ r1 a* l       SeqIO.write(self.readsList, sequencingResult, "fasta")
0 d2 w" N  ]1 Y6 k$ ]7 [  r" N4 e, h0 `- j
def pairread(self, clonedfragmentList):
+ F7 I. v/ d6 H) @* z       for fragment in clonedfragmentList:
; B5 q: N6 }1 ~4 c* D/ v! ~          fragment.id = ""# n/ H! S4 R. i, R9 ^* D
         fragment.name = ""& Q1 u/ F  s: o6 V# w7 [8 o5 L0 ]4 ]
         description = fragment.description[12:].split(",")[0]9 s  J8 j! D$ T  T  y$ a* \7 T
         fragment.description = str(self.readsID) + "." + description* }  B/ W& ]2 _  D# ^. p! A3 j
         readslength = random.randint(self.minreadslength, self.maxreadslength)5 P$ O9 U8 y: ]1 R
         self.allreadslength += readslength
  E% S# |1 @2 r* O, y          self.readsList.append(fragment[:readslength])+ t2 B8 r9 C; X& ?. u) D

3 i: `* |' H5 v9 f1 N          readslength = random.randint(self.minreadslength, self.maxreadslength)
7 N/ \& Y# \& K! z          self.allreadslength += readslength  V# e# I6 G2 d2 T# b0 s+ K

+ ]% j% H8 x& S" J% }: ^          fragmentcomplement = fragment.reverse_complement()/ O- u0 b5 v& W* M
         fragmentcomplement.id = ""7 Q6 n) Y; l* T4 P1 B1 g, y' t3 w
         fragmentcomplement.name = ""+ W' a$ L# H: W3 @4 B& J; D
         fragmentcomplement.description = str(self.readsID) + "." + description
! d% y+ k. ~+ d6 W          self.readsList.append(fragmentcomplement[:readslength])
7 `8 D; }& _  d: X6 p6 T: Q, V* X5 ^  o; R- u' z
         self.readsID += 14 @- L3 K  _4 }4 T+ D/ g/ d5 b

" \$ V4 P' V1 P def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):+ M3 v& i$ L0 R
      for seq_record in SeqIO.parse(genomedata, "fasta"):# }9 o4 h1 I* }8 j  n) z6 l
         seqlen = len(seq_record), C& K) T, c3 `% K* K
         self.genomeLength += seqlen- N% q$ V+ C* M& b. t2 @/ U
         for i in range(self.N):
+ b5 f' ^- H) i* C             # 生成断裂点
. d0 `5 u; t7 h' t- }/ c  i             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
  F7 o# e0 D( @. I# b6 A: t             # 沿断裂点打断基因组4 }- w  Y+ K0 c
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
) Q& b2 L4 f7 d( I% Z/ F, S       # 模拟克隆时的随机丢失情况
* D1 ^! _8 f5 G$ J; X) m- v8 J       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)6 W: X$ ]' B- t- ]8 G
      # 模拟双端测序, G: W1 M9 O  E2 p9 i
      self.pairread(clonedfragmentList)
1 O% p" U- E4 ~) ]7 A- f1 G- d       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]" s5 S4 o* c3 Q/ V7 o
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
: N7 a5 U/ ?- s9 c       SeqIO.write(readsList_1, sequencingResult_1, "fasta")& {1 K6 a( h+ |/ |
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")) O4 a; ^. x4 f2 l" c- ^7 v# @2 A
/ ~8 g, N2 Y9 p$ X' q- G6 ]. V
def resultsummary(self):: F5 f% W- j. k7 a) k9 T
      print("基因组长度:" + str(self.genomeLength / 1000) + "kb")/ t- X3 v2 C- ~6 [  h
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
: m4 r3 \8 a2 s6 @$ c       print("N值:" + str(self.N))) V% ^" ?9 k' W# h" F
      print("期望片段长度:" + str(self.averagefragmentlength))
+ ?, |, z" ?; l2 ~. f+ f, V       print("克隆保留率:" + str(self.cloneRetainprobability))
- n! o0 U8 y; ?/ [) R. p4 B$ h       print("片段数量:" + str(len(self.fragmentList)))8 `5 z- i# v' D. @  F
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))6 s4 e& o) a0 h6 K; W
      print("reads总数量:" + str(len(self.readsList)))6 A/ C% ~% x/ G0 H: m( x5 i- e" [
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")+ @6 I/ d6 {$ x  I2 c9 g
      m = self.allreadslength / self.genomeLength
/ }8 F  @7 w$ v8 }/ J% @* V) N; T       print("覆盖度(m值):" + str(round(m, 5)))
/ q6 Q$ T# E, b1 B+ e  B' s       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))( p; P1 @. `6 B' W$ S
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))$ [7 A4 o/ b  J' U3 ?  Y
# -------------------------------------------主程序-------------------------------------------
# I8 {. c: @5 D' ~: j$ T) Z& z# 模拟单端测序
* X7 j% x) D! @, HsequencingObj = Sequencing()
0 k3 x3 R% d# esequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
) G5 f  r# v0 {( MsequencingObj.resultsummary()3 k' C6 w/ e+ Z$ G5 N0 p8 q

# F4 d$ O: L5 V; ~' D# 模拟双端测序, Q1 i4 [/ `; I7 ^" b
sequencingObj = Sequencing()
  {, z3 u1 d9 j  {) V1 K+ jsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")  F! s: b" I8 Q6 J, e
sequencingObj.resultsummary()
' z, w6 M  L0 v( \7 }* \3 L2 ifrom Bio import SeqIO' ]7 x0 [) f8 j9 E1 U' ~9 P
from math import exp+ m, C9 j0 O( W0 |# a
import random
# Q) Y- f/ K$ M7 ?5 g; K
% q4 o/ W9 e; O, o. v$ Yclass Sequencing:
+ q6 e( I+ b) K8 a! I # N代表拷贝份数  C! K/ J* P9 A  e5 y
def __init__(self):
/ \% `% X" m' v/ |4 Z1 A% J5 W       self.fragmentList = []7 s, |( j; Y- J- l
      self.readsID = 1
0 E! v! _0 i) ^       self.readsList = []
8 B* l3 h8 n4 g$ X# l       self.averagefragmentlength = 650
9 w; y5 Y3 `2 b& ^8 o       self.minfragmentlength = 500
0 X) f) S; I4 d5 n; t/ @       self.maxfragmentlength = 800
) Y6 Z" {5 A" ]5 W: g       self.cloneRetainprobability = 1- G4 l, z6 |% I  d9 @
      self.minreadslength = 50
( }. j* W5 {% T% s& C# z       self.maxreadslength = 150
4 A4 P+ x. V% X- i( R, E9 S! P       self.N = 10
5 L2 Z4 k) E1 I' B       self.genomeLength = 0/ u6 t% J) ^+ l8 h
      self.allreadslength = 0
; v, l  I. P% p5 Y& r2 z2 M3 B$ Y2 V2 M
# 生成断裂点
: K( q) R' S9 z3 |) B def generatebreakpoint(self, seqlen, averageLength):
- N" K& S+ z: p! r& s9 Y5 U       # 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)$ ~, K  L( m- p, K: n
      breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]4 ~' l) _, Z- z' a3 L0 C1 U2 U9 g: q; `
      breakpoint.append(seqlen)6 n1 s" y9 x# }
      breakpoint.append(0)% {- a2 d  W) B) l( ?; a
      # 把随机断裂点从小到大排序
4 W5 b" I" g' M. c* H3 v       breakpoint.sort()
: h5 o1 N8 e7 b* V1 o+ v4 ^       return breakpoint# B5 n1 E% K( p, s; s0 `- W
! N( F: k0 D; H( T- W( g; W  W/ Y7 `
# 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
! p$ m9 I. l. i" N def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
) E/ R6 m! M: w) Z+ E( m       for i in range(len(breakpoint) - 1):
" P4 j6 w1 @. h3 M8 s, y' C$ v0 ?          fragment = seq[breakpoint:breakpoint[i + 1]]% D+ n  r3 i% x
         if maxfragmentlength > len(fragment) > minfragmentlength:
/ z3 P$ n. d3 U) b) F2 n$ a             self.fragmentList.append(fragment)* n$ n3 H  n+ q, k9 m) t% Q
      return self.fragmentList! K" Y9 ?+ u$ P* [

7 B" c' ~9 ~3 d! B7 t) ?& q0 X # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率( ?+ n5 F6 h& P; r1 x" a( J; G
def clonefragment(self, fragmentList, cloneRetainprobability):9 J1 b; Z5 S2 x' P
      clonedfragmentList = []
" ?5 I* [2 G+ O' U       Lossprobability = [random.random() for _ in range(len(fragmentList))]
8 S! q/ }3 Y' v) L) x' A% j       for i in range(len(fragmentList)):- b' @6 w6 a4 l. h' J% V
         if Lossprobability <= cloneRetainprobability:
) V) ~1 _& t6 g% {& y, `9 u             clonedfragmentList.append(fragmentList)
' c1 [) R1 z$ P2 R$ a' i1 U( `       return clonedfragmentList% h" b. e+ |- Z
- o* e$ r' x6 [9 {6 d
# 模拟单端测序,并修改reads的ID号" d1 m& v% u  K: a
def singleread(self, clonedfragmentList):
" V+ h0 F2 G" {* R       for fragment in clonedfragmentList:' b7 R$ Z2 k7 R/ Z9 b
         fragment.id = ""
" ^( i  x( ]0 m" ?          fragment.name = ""
$ k/ [6 O4 s6 c6 [% h9 R. Q          fragment.description = fragment.description[12:].split(",")[0]
& b+ V/ U, _! O' c0 g( }9 B4 u          fragment.description = str(self.readsID) + "." + fragment.description  d( G& H8 o1 t: j. r; Q
         self.readsID += 1+ z3 |: [" Y+ O0 n' E
         readslength = random.randint(self.minreadslength, self.maxreadslength)
8 _2 |! }  c% l! Q/ h; @          self.allreadslength += readslength6 a2 R! o# b$ b8 @7 V  h
         self.readsList.append(fragment[:readslength])1 o5 c; D) ]6 s0 ^* g/ |" T" j

! F4 m& ^. A7 j. ^6 ` def singlereadsequencing(self, genomedata, sequencingResult):3 u3 r3 X3 I6 `" Q" A/ O% K5 d% D% s
      for seq_record in SeqIO.parse(genomedata, "fasta"):
1 C2 @: w) J0 \/ k$ l) ~6 }          seqlen = len(seq_record)( x. l+ |6 M) @; m  C! c, |4 z7 y
         self.genomeLength += seqlen
+ I  F& T+ q% N" Y          for i in range(self.N):4 b  W5 H* A% C5 U- [' N
            # 生成断裂点
8 u+ p8 S% l$ e6 I5 c+ n             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
, n1 f# j/ B0 W- N% X, k7 b: z* R$ a             # 沿断裂点打断基因组
" P* L( j/ M) A( Q" e1 ^" S% ]             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
% ]+ T% w/ E+ j! Q! L       # 模拟克隆时的随机丢失情况
. m0 K: P# D6 f) {& E' x+ W       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
7 i  s1 ~  {% |+ i7 F       # 模拟单端测序% _( }, Y+ f$ ?* b1 I- e5 L" _
      self.singleread(clonedfragmentList)0 I1 l' U8 J3 z+ {# r% Q5 \1 F
      SeqIO.write(self.readsList, sequencingResult, "fasta")
. w+ W; S& Z# G% a) G, {
5 ?5 T  o, T" ^5 `: r def pairread(self, clonedfragmentList):
* [$ n7 H: L7 i* j       for fragment in clonedfragmentList:
& |1 p: C+ K. o" ^/ B1 E& w: p          fragment.id = ""7 o( [$ w. h, t2 x$ R: r1 m( ?. d! p
         fragment.name = ""
# _3 {* E" y0 c0 G          description = fragment.description[12:].split(",")[0]
. G5 T3 b$ m$ A$ t. r( M8 A          fragment.description = str(self.readsID) + "." + description5 m3 _4 e3 S0 m/ O. ^
         readslength = random.randint(self.minreadslength, self.maxreadslength)
$ P6 _6 R$ c$ f1 w          self.allreadslength += readslength' @: @4 y- Q6 k# G7 N  V, u* w. o
         self.readsList.append(fragment[:readslength])+ ~9 ?0 j9 d1 T+ G9 F0 Q

$ a, g# w( ^" P1 e, N! ^          readslength = random.randint(self.minreadslength, self.maxreadslength)- {1 X% L( ^) \. F: h9 z
         self.allreadslength += readslength# q  w3 c9 U  b! M, N4 w

3 s% @0 w6 O4 Y* g          fragmentcomplement = fragment.reverse_complement()
6 e% g$ p  e, c8 z2 h/ \          fragmentcomplement.id = "") A9 G1 B3 a7 o9 `: W, W; N& D/ A
         fragmentcomplement.name = ""
7 ]' j) f% N3 j" p: t. C          fragmentcomplement.description = str(self.readsID) + "." + description9 T; |* L4 @8 O$ R4 \6 I
         self.readsList.append(fragmentcomplement[:readslength]). W$ f5 ]; `% f' J9 l5 Y) x
4 W5 g) I. i! b8 X2 B, V
         self.readsID += 14 J; p1 u" c9 g

/ ~5 E8 i  b8 C) p; ^ def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):1 w/ X1 l7 c0 y) k8 v# m0 f
      for seq_record in SeqIO.parse(genomedata, "fasta"):
( P7 g- n# v4 F% ~          seqlen = len(seq_record)
8 [% P8 v% i. N5 Q6 A6 V* |          self.genomeLength += seqlen
6 Z6 K% X0 b( t9 B8 o          for i in range(self.N):& O/ @# P" ^( r* @& O" X
            # 生成断裂点* F. s1 u  Y2 H; z: d
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
$ X/ d# ~% J1 M( p( l             # 沿断裂点打断基因组, D/ I. U0 r' J" [: F- U
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength): {1 \4 h# a% s; K0 F) j5 u
      # 模拟克隆时的随机丢失情况
* e0 V* z, w2 I2 V1 V, a       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)( r7 {4 X9 V" a) h
      # 模拟双端测序, d  Y$ Y* i2 a" J
      self.pairread(clonedfragmentList)
6 s$ ?0 a8 L( r9 v4 V- i# _       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
* L! k5 x/ J# e% M       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
8 l4 ~; D& U: n! A; f       SeqIO.write(readsList_1, sequencingResult_1, "fasta")
" H  K4 H4 ~, x, n+ R       SeqIO.write(readsList_2, sequencingResult_2, "fasta")% W- E/ J: ~8 X6 S1 }( P
. Z6 u% c) Z2 o
def resultsummary(self):/ X, g  E; l/ p& K
      print("基因组长度:" + str(self.genomeLength / 1000) + "kb")# t. M! J: `# `
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
+ N- U+ K  L/ ?! `       print("N值:" + str(self.N)): K) }/ A5 e, p& h
      print("期望片段长度:" + str(self.averagefragmentlength))
7 S* W9 ]/ O8 P  T4 E5 h       print("克隆保留率:" + str(self.cloneRetainprobability))
- m+ w. N  Q; h% `6 {7 V1 @       print("片段数量:" + str(len(self.fragmentList))): L" ~  L; e' n* p2 U$ R% o
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))! J* w* r  j" D
      print("reads总数量:" + str(len(self.readsList)))9 t7 z" K. q- M3 \
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
0 r) t# E. m& B       m = self.allreadslength / self.genomeLength
! e4 ~) |2 S* E# b. u" R       print("覆盖度(m值):" + str(round(m, 5)))
  |9 x  V; |4 r       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
( s- n: u2 c$ s       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5))), m# V1 s, a2 O0 q8 E
# -------------------------------------------主程序-------------------------------------------
% x9 ^3 g2 X3 K8 J9 v  Q6 Q# 模拟单端测序2 A5 X* j. ^4 M! ~
sequencingObj = Sequencing()
, A6 T& j; d% N+ J- `: nsequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")) U( r$ P! g$ f8 S/ t. g1 {- _( \
sequencingObj.resultsummary()! \% y+ D" ~) ~
1 i3 p2 }" A* g+ u6 Y0 P( y
# 模拟双端测序
5 O" K2 {$ [) {4 o5 P4 |5 j' lsequencingObj = Sequencing()
' k& `' N% S7 R% LsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
, Z1 y9 s, r: dsequencingObj.resultsummary()0 R/ S- ?- c4 l, y' \& d1 K( f
7 E: I$ `/ p0 S, H& i( T! o

8 v5 O6 N3 L' Y! W7 o. |. @5 @+ K$ b" q

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