基因组测序模拟

杨利霞

基因组测序模拟
3 z- g$ ?5 z, d) \基因组测序模拟

+ `' N. V" q( G& [1 \: t, l1 A% p2 y一、摘要

0 A* G" O+ P  E$ Q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

" w3 |' }% j+ @8 A9 m1、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
* F( {. B& ?; K- E2 \( L1 ?- |- t& S: LWindows 8.1，Ubuntu

3、软件平台

: }# t9 x' |. k2 O# ^2 R3 yart_454
GenomeABC http://crdd.osdd.net/raghava/genomeabc/
# f# i; r! B, D+ Y, [# Q$ I# NPython3.5
Biopython
4、数据库资源
% y" Z6 k; h( s# E' U
- U( E& R1 C3 g8 b5 `7 l' ENCBI数据库：https://www.ncbi.nlm.nih.gov/

- Z8 \4 C) C; \; {2 [+ Z  p5、研究对象

5 Q7 R. @: ?% [1 ~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
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0 I" X. i/ D3 h6、方法
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/ s) z& p# G0 r  t+ w0 Dart_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。
# m. o. S. N1 J6 X9 T' K1 Q  yGenomeABC
进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
/ N+ _0 g2 @) u/ s: F3 v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
5 |4 b+ a8 J' T$ f* y0 n9 Q! P& O
& g' V# ]* K5 v1 `9 {! t$ z% ^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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; }" u* v2 Y5 n2 K+ q- V# e: P图表 1无参数art_454运行
2 L# f; @! ?( _- \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.
: K! h' D  O- A  Y# y0 Q2 R. c下图为模拟单端测序，程序运行过程及结果
' U1 p- E- L: B* F" J% B4 e% m
- k" i+ M* l$ R; u3 Z图表 2 art454单端测序
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0 c( {2 j( `# Z* r: c图表 3 art454单端模拟结果
% B( j8 P5 d5 e9 i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

. P) S, A% z% t/ S9 R! W/ Q& y图表 4 art454双端测序

2 t5 b9 A( s8 _9 r图表 5 art454双端模拟结果
2、GenomeABC
6 h! [. B# T: D: ]下图为设置参数页面

/ z  U! |  G' z0 }下图为结果下载页面

图表 6 结果下载页面
0 u) r+ z" f/ _! F9 ?+ v4 C% p/ P% N3、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。
9 ]! Y. x: [+ i. c4 d单端测序

图表 7 程序模拟单端测序
% M0 d1 |; a' u6 T; C- {! q; t双端测序

图表 8 程序模拟双端测序
: w3 |8 `# m1 ]测序结果

图表 9 结果文件
  m5 _: z  q+ \6 ^
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
" W) ]) a; B+ y" V) b  Z/ x+ E
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
# U( @, K, L; s. |+ v7 D, m双端        12157kb        200-1000        20        600        0.95        214212        50-100        407186        30534.265kb        2.51164        0.08114        0.91886
$ J' ~. x; k+ ]  ?四、讨论和结论
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程序运行方法

! h+ |/ Z# g; h9 F6 j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；
0 B5 v5 d8 G! d7 e# Iminreadslength和maxreadslength是测序reads的长度范围
' u* L1 ^) Y  N
8 A& B6 E/ i! {6 W* l, 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。
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附录

from Bio import SeqIO
$ s1 S* F) L. ^from math import exp
import random

class Sequencing:
+ }+ t3 D5 r) w$ B9 H$ Y0 R    # N代表拷贝份数
    def __init__(self)
        self.fragmentList = []
        self.readsID = 1
0 a7 K& J3 l/ x  o& p$ t        self.readsList = []
        self.averagefragmentlength = 650
- Q$ A3 {" H! w$ n  ]        self.minfragmentlength = 500
  e: ?! [& n" T4 |7 k        self.maxfragmentlength = 800
7 N/ [! t# m& Z7 n) K        self.cloneRetainprobability = 1
9 x: q. t. j2 @$ R        self.minreadslength = 50
        self.maxreadslength = 150
        self.N = 10
2 H& `3 }& m, C4 I( N3 A3 h        self.genomeLength = 0
$ h- |; ?& i$ Y# {9 ?6 N        self.allreadslength = 0
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    # 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)
9 j9 _9 S* D$ @# r$ A1 v. G/ Y; r6 J        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
5 B' X' h/ n- P    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
" B& a$ H/ w! ]1 J1 t2 |: ~* ^- n        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
/ |2 C1 I* z" l$ G/ ?8 g            if maxfragmentlength > len(fragment) > minfragmentlength:
% o; \% A4 ~) I$ S7 X  W                self.fragmentList.append(fragment)
4 Z7 B, M  p3 M& h% X% V        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 = []
        Lossprobability = [random.random() for _ in range(len(fragmentList))]
. n: @0 F- {5 j/ [( a% M        for i in range(len(fragmentList)):
+ S8 o& X0 F1 [% U            if Lossprobability <= cloneRetainprobability:
' Y) z8 ~7 e; d% i: R3 N                clonedfragmentList.append(fragmentList)
        return clonedfragmentList
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+ W& P: W2 j9 f1 G    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
: U" ?. U" {8 f/ Z6 S        for fragment in clonedfragmentList:
# A  j, s" ^2 F7 Y- H/ [7 w/ T            fragment.id = ""
+ e. D4 F1 \: z0 b            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
  O1 K: r- g# E3 t) ^0 k) q- r  o            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
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, i3 O" M3 _/ R) i& S' ~1 m1 M) D6 A2 B    def singlereadsequencing(self, genomedata, sequencingResult):
- {, B& |; Q4 H# t        for seq_record in SeqIO.parse(genomedata, "fasta"):
5 f& W; P! `$ L$ G            seqlen = len(seq_record)
            self.genomeLength += seqlen
6 P/ j6 r, H' G6 i2 o! M            for i in range(self.N):
+ b( f( f! V0 I4 i4 m                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
: S7 f  ?- j( z$ k3 _- r                # 沿断裂点打断基因组
. Q2 O' Y3 t1 w* i1 `2 @                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
( w, X" K8 \+ {; w. \' A2 J        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
% J% j" R! c' C9 {        # 模拟单端测序
6 U- A3 W3 J# ~+ `! f- m' i        self.singleread(clonedfragmentList)
7 N9 m' ^% G; n, e3 \        SeqIO.write(self.readsList, sequencingResult, "fasta")

    def pairread(self, clonedfragmentList):
% V# e' U2 L+ {6 O        for fragment in clonedfragmentList:
' p. z5 K4 X+ z2 D7 g            fragment.id = ""
            fragment.name = ""
            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
4 A; ^! a2 t& y( J- n            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
. a- S. v. g% f4 [, s            self.readsList.append(fragment[:readslength])

+ `- F( L8 h* P) k7 D' M6 U! W2 [            readslength = random.randint(self.minreadslength, self.maxreadslength)
3 B1 y0 M7 P9 {; V# G, g; C( K2 o            self.allreadslength += readslength

            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
6 [7 x  Q/ A# h1 u. w1 J+ V            fragmentcomplement.name = ""
2 x. y( k, \2 Q2 P. V: z7 e            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])
% x7 g  q8 o" W2 L' q
+ F: E& f/ O' }( N- k1 ]+ d7 w$ K6 v            self.readsID += 1

% ]8 {8 r3 D  l# k# a    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
            self.genomeLength += seqlen
$ {9 c" i6 s- P$ B+ N            for i in range(self.N):
                # 生成断裂点
6 C7 a& d7 C) Q                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
- K" Q: s6 s. K                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
5 s) \  I, {( i7 d7 U( l) A! w        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
% |: ]8 }% t8 N7 f$ d6 V        # 模拟双端测序
" p" k0 A+ k8 O  c        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
" A4 b/ W' M* [8 T$ N        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")
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( G: t1 \0 B! @* z; a# v    def resultsummary(self):
        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
3 v3 ^' p' X' Y" n8 o        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
6 b% E) D4 f3 g% @3 ^7 g        print("期望片段长度:" + str(self.averagefragmentlength))
! l( Q# H9 ^) y; w. ]        print("克隆保留率:" + str(self.cloneRetainprobability))
, j9 G- P8 Z* Z9 h& l        print("片段数量:" + str(len(self.fragmentList)))
% G5 x& o( R( j# ?1 ^0 P  x" m" {        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)))
        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
, C4 X' p7 L! c1 K+ h- b3 k7 msequencingObj = Sequencing()
2 r7 {# l  _% NsequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
4 A" [' y% \0 `; m9 A; TsequencingObj.resultsummary()

# 模拟双端测序
sequencingObj = Sequencing()
; W+ Z! k: h+ esequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
. p) y  B" @. G" L5 `4 CsequencingObj.resultsummary()
: u) d& w5 u' gfrom Bio import SeqIO
/ s: @. h0 P' E4 ]: Nfrom math import exp
import random
- g, z8 M5 a3 W+ s4 p
class Sequencing:
    # N代表拷贝份数
    def __init__(self):
        self.fragmentList = []
+ ^0 f5 v1 j, N7 T8 H        self.readsID = 1
. d+ y# p; C9 u* J  f        self.readsList = []
8 R8 k& \% ~4 @3 a        self.averagefragmentlength = 650
* ?# y! P; H- A6 e+ x' z        self.minfragmentlength = 500
        self.maxfragmentlength = 800
7 g+ v$ ~+ o% J# A0 r        self.cloneRetainprobability = 1
6 Q! A2 Q$ m6 n* J6 T3 z        self.minreadslength = 50
        self.maxreadslength = 150
        self.N = 10
2 o# S2 ~0 I; d# }) v        self.genomeLength = 0
        self.allreadslength = 0

! K7 r/ p2 N- }    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
" ?+ O; d  P: e  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)
        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
; z6 o+ q2 V3 P& ^$ L# }4 q        breakpoint.sort()
        return breakpoint
: s& S  ?! i7 w% J" f3 S
3 e( l- O4 W8 |0 K( C8 H! V5 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
    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
- G9 T( A1 r5 b# F) @; M        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
7 }# R/ k6 Y! X7 K- V2 ^            if maxfragmentlength > len(fragment) > minfragmentlength:
8 h, P1 p' r  q$ T                self.fragmentList.append(fragment)
- p; Y) j/ t. \; e% D" X3 x        return self.fragmentList
0 @/ o* j8 A' C2 H: L
    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
* q2 _7 u: ?8 A. `( N4 T    def clonefragment(self, fragmentList, cloneRetainprobability):
        clonedfragmentList = []
' m7 N9 E4 u+ e1 ?2 u, n& }        Lossprobability = [random.random() for _ in range(len(fragmentList))]
! B" ?+ T! `7 q. g3 ?& o        for i in range(len(fragmentList)):
' I; o$ B3 T, j4 A: v# F1 F0 S. A, w/ F/ k: z            if Lossprobability <= cloneRetainprobability:
+ R. X- u, X6 b5 P+ }8 p                clonedfragmentList.append(fragmentList)
9 i% z" A) t/ @1 K3 r0 S* ~        return clonedfragmentList
& \/ p; a7 w( N" ~" j3 m# o
5 o% {5 u9 [5 K7 T" P0 A    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
% B% d5 X/ r3 ?            fragment.id = ""
' C! j& Z: b  v! k; }' h            fragment.name = ""
. A3 W4 X* U8 d6 a/ B/ R0 [4 ~8 N; @            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
0 u( \% A; \: z; N3 Q            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
' M; M8 P' j# I8 B            self.allreadslength += readslength
, w7 y; w; h: v  A            self.readsList.append(fragment[:readslength])

    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
, o9 X! b4 m  X7 p( z' y% m9 b                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
# v' {$ _9 w- E                # 沿断裂点打断基因组
+ O  l5 j' Z$ q  z+ Y                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
0 r! M  r& M8 A+ t8 X        # 模拟克隆时的随机丢失情况
/ |- \0 A( J/ A4 U# v7 y        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟单端测序
+ [, e; C  S8 {$ L4 n3 K        self.singleread(clonedfragmentList)
8 l9 z8 W4 ^7 f" q4 _        SeqIO.write(self.readsList, sequencingResult, "fasta")

, I2 L6 s! q; d    def pairread(self, clonedfragmentList):
: w4 _, z. K- q  Y/ }        for fragment in clonedfragmentList:
            fragment.id = ""
0 z) c9 f1 q* C+ x! p7 m2 H            fragment.name = ""
0 g4 B! _+ Q& P            description = fragment.description[12:].split(",")[0]
7 q& ]9 A, X9 G            fragment.description = str(self.readsID) + "." + description
! ^& J# s. Y8 T7 p3 R+ c4 ]            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
$ }( |3 _5 h$ f6 @            self.readsList.append(fragment[:readslength])
; @; ]7 l! z1 a. c4 c
            readslength = random.randint(self.minreadslength, self.maxreadslength)
" i4 m- K1 {/ \+ g% `+ {& Q$ p0 S" |' e            self.allreadslength += readslength
% ~6 o1 f: E; X( K9 e4 U' a- f
            fragmentcomplement = fragment.reverse_complement()
! A5 d* \3 ?7 B6 G& k  F+ }9 f            fragmentcomplement.id = ""
5 ?3 h3 X7 M' T/ o( q            fragmentcomplement.name = ""
9 A# o/ M' \: g' K0 T0 R            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])

; z% u* a% g! X7 n0 g9 z" P) D            self.readsID += 1

    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
( _  G1 v+ k/ \0 r4 g            seqlen = len(seq_record)
            self.genomeLength += seqlen
            for i in range(self.N):
- f9 H9 e$ t  q4 _8 I" o                # 生成断裂点
% p. @$ R% C5 L1 ^- n' _: h& S                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)
$ C" c/ _" _( I' D+ |        # 模拟双端测序
9 K, F* b: s6 R0 r* {        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]
$ L9 r, z' |3 P        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
: }2 y" ~3 H( ?% ~        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
% t2 b( p1 Y  N3 y* d2 U, N
, W9 \/ ?+ M- _# w8 Z) R# e/ d1 S) Y    def resultsummary(self):
5 j* H/ _! @! A( j6 s5 _        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
. w$ `1 \& J& K3 [        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
. v$ i. L6 o6 G' s! w$ }$ B( T, ?        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
. V$ u+ K  W* w+ q( B% A        print("片段数量:" + str(len(self.fragmentList)))
9 g& J! d( [7 C        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
+ [& g# E% L  L5 W) m        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
( c6 X4 Y: t4 b" b3 V4 N% d        print("覆盖度(m值):" + str(round(m, 5)))
/ `% w/ I! P8 u2 g        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
$ Y9 `. g9 x$ w5 V        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
) n: e- l9 t$ Y! u# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
6 i- k. w8 {2 vsequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
3 c7 t: x# {3 g" hsequencingObj.resultsummary()
. h4 J' r+ z0 e; K6 O4 A+ c, p
# 模拟双端测序
( O( X+ R1 C! j8 osequencingObj = Sequencing()
- }: I& C# K" o! {. I6 ^# HsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()
& {  Z9 b* k$ c* y( k7 U

- k3 g* M1 `# [9 X3 q, x

1 U! ]7 ^$ e3 [8 e9 L9 Y

3477959497

不错。。。。。。。。。。。。。。。。。