基因组测序模拟

杨利霞

基因组测序模拟
& k( o; `0 I. G: Z0 ?% G& q! D" p基因组测序模拟

/ d, S# t% k) m' D5 N0 `一、摘要
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4 W* k& e1 G: U( e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

( t( p+ y, g  _4 g二、材料和方法
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1、硬件平台

处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
安装内存(RAM)：16.0GB
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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
5 C1 K/ h: k7 ~! G0 P+ ?* x. lBiopython
. ?9 R& j: h: r: t" O" y  y4、数据库资源
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: \/ f/ i7 p% S& N# f) x) ]NCBI数据库：https://www.ncbi.nlm.nih.gov/

6 X: ^, \2 O8 l' @7 p& v5、研究对象
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. z1 F% J9 Z! u/ E* ?* v酵母基因组Saccharomyces cerevisiae S288c (assembly R64)
& s" V* Z& }- a3 ^4 [ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_genomic.fna.gz

0 H$ V- g* F3 S0 E9 p4 V: ]6、方法

art_454的使用
0 B% J! k- x$ X6 A' p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。
( U5 D3 q* X# J( LGenomeABC
3 ]/ B0 g* a6 R5 h4 B% q进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
  i3 V7 M6 k2 G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。
; z7 N$ Y" I+ a! P) t三、结果
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+ K7 Q; |' @: g& U+ L  a# d9 z1、art_454的运行结果
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) g2 Z. o1 y8 e2 e% ?+ C7 {) |无参数art_454运行，阅读帮助文档
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图表 1无参数art_454运行
* Y) m8 u& U" H5 F4 b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.
& O( g/ @8 _  I" P6 V下图为模拟单端测序，程序运行过程及结果
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$ a  `* U' e5 z图表 2 art454单端测序
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! Z+ A' l$ j) h1 z. u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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6 d2 Q  }! ?. z. K1 i' `& k& R- Y0 A图表 4 art454双端测序
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! e5 D; {/ u  w) l2 J. y图表 5 art454双端模拟结果
2、GenomeABC
- q) ?- i% A; L下图为设置参数页面

下图为结果下载页面
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图表 6 结果下载页面
  N5 I& c; w# j2 y+ l/ S3、编程模拟测序结果
) ]# i2 K& \. l, E- _4 H# @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。
! x& S: }2 S+ Y# l单端测序
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& a, K- l6 N2 Q) n+ Q3 r* k- l图表 7 程序模拟单端测序
双端测序
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1 r; L3 ]! H! y) ]$ \4 b8 F图表 8 程序模拟双端测序
0 F+ U2 V& M8 ^1 z( ~* _测序结果
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图表 9 结果文件
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: O" m& A8 n/ G) \& J6 r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。
4 t: e3 J. M9 q. u  u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
( I5 Z/ f9 G6 a( K双端        12157kb        200-1000        10        600        0.95        106704        50-100        202770        15212.662kb        1.25134        0.28612        0.71388
: K/ N- e" Y5 G$ q9 c. K% B双端        12157kb        200-1000        20        600        0.95        214212        50-100        407186        30534.265kb        2.51164        0.08114        0.91886
$ ]5 d$ D* d9 @2 Y1 P四、讨论和结论
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* k# {/ ]2 v, ^3 B  _* z程序运行方法

在类的构造方法init()中，调整参数。
6 O, {) p! V1 c: y0 t8 P' i3 EAveragefragmentlength为片段平均的长度；
1 u1 ^6 H( ^% X4 h- tminfragmentlength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
% E: [5 X  W6 w) e3 d; }import random
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class Sequencing:
# a6 U  g6 _' u    # N代表拷贝份数
* v) i6 a  d" s: X% V& k# S5 F$ U    def __init__(self)
" [. c* h* Z  G        self.fragmentList = []
        self.readsID = 1
        self.readsList = []
        self.averagefragmentlength = 650
        self.minfragmentlength = 500
        self.maxfragmentlength = 800
: R( g# n/ U+ l; L, H! v9 Y        self.cloneRetainprobability = 1
        self.minreadslength = 50
        self.maxreadslength = 150
- I  n  P8 i# s  k7 @% A        self.N = 10
        self.genomeLength = 0
        self.allreadslength = 0

& {8 X! b' \( R) {6 _7 u    # 生成断裂点
" L' `% }) r: {" x" \5 s8 {# h    def generatebreakpoint(self, seqlen, averageLength):
: A; T, Z5 G( q! r4 y8 r        # 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)
2 X, c& {& b$ X: F        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
        breakpoint.append(seqlen)
; }( Z$ E0 P! m1 u3 W. j& o        breakpoint.append(0)
        # 把随机断裂点从小到大排序
, u  e3 c& Z; ]# n2 y! `        breakpoint.sort()
0 E* B% |# @) G* ?% s: m; N$ F        return breakpoint
: z. O% x* v4 t/ r' s/ \  x) S$ w
) G$ Q+ j; t. Q  M% l6 `' g    # 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
9 J7 K6 v5 M6 ?) b+ f    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
8 v8 r# d' g. m  c, V! Y            if maxfragmentlength > len(fragment) > minfragmentlength:
8 t( ]& z/ m; X9 ~" H                self.fragmentList.append(fragment)
        return self.fragmentList

2 J: `0 n# {; r7 ^& J# F' q( X6 d    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
  i. ]  V! r0 q. T    def clonefragment(self, fragmentList, cloneRetainprobability):
        clonedfragmentList = []
0 Q, y) \- O9 e( w- n; y        Lossprobability = [random.random() for _ in range(len(fragmentList))]
5 y; N6 S/ t0 [0 _  h7 o        for i in range(len(fragmentList)):
: F  B0 [: \6 o) _            if Lossprobability <= cloneRetainprobability:
$ T# j. v/ I8 e6 v' }9 o                clonedfragmentList.append(fragmentList)
' x$ Y" j5 p+ [1 Q: y        return clonedfragmentList
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    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
3 P0 n( S  O- b. R# A9 G4 T        for fragment in clonedfragmentList:
0 b. K8 Y7 B. g1 T4 ]. [            fragment.id = ""
            fragment.name = ""
$ H9 `" a2 n0 {9 M: o4 m            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
, N/ D7 z1 l4 m5 J            self.allreadslength += readslength
6 D, q* G6 q0 g0 w            self.readsList.append(fragment[:readslength])

* R; u* e6 A9 W) n: w/ M    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
3 H7 B- R" S' ]4 Q. n- x9 n            seqlen = len(seq_record)
! [  w: V# c: R) m+ z* F            self.genomeLength += seqlen
' B. g% e6 p" }1 X& ^; s            for i in range(self.N):
                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
$ c: S- W, m! f3 t& O                # 沿断裂点打断基因组
8 y# N, i! Q# }$ F' d                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
& T: t, U  c- y+ Y' w! @        self.singleread(clonedfragmentList)
        SeqIO.write(self.readsList, sequencingResult, "fasta")

" l$ d% M5 c% w9 m+ g# ^    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
            description = fragment.description[12:].split(",")[0]
, e/ [% U  L+ p( Y2 h9 L+ O            fragment.description = str(self.readsID) + "." + description
+ F! Q9 n/ ?4 c            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

            readslength = random.randint(self.minreadslength, self.maxreadslength)
' x9 k0 [# ?1 ^$ T) {: i3 {- ?. s; ]6 }/ Y            self.allreadslength += readslength
* B$ v" H! V$ X- n2 K$ ^% e
1 [' S2 r" Y# }: ^            fragmentcomplement = fragment.reverse_complement()
6 o3 \' d' w: L9 x' E$ s2 Z            fragmentcomplement.id = ""
0 m+ R: _$ f! {" b# R) t, i; O; a            fragmentcomplement.name = ""
            fragmentcomplement.description = str(self.readsID) + "." + description
' C3 \- N$ o. S! p4 k7 l            self.readsList.append(fragmentcomplement[:readslength])
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0 ]& @) ?# E' q6 J$ B            self.readsID += 1
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& m5 p# t+ V2 h. i    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
. K4 H4 z- H2 X; i  h% E: O            seqlen = len(seq_record)
1 y$ Q  \5 Z5 K+ z- L            self.genomeLength += seqlen
1 L. |2 D# v# Y) ~4 g$ G7 [9 U" c            for i in range(self.N):
                # 生成断裂点
7 ^" ?+ O) g4 c9 E' S, E                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
, R/ a# D% W1 f( y        # 模拟克隆时的随机丢失情况
! G5 L2 W2 ?* u1 I  n        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
8 \; Y: m; w2 x        # 模拟双端测序
        self.pairread(clonedfragmentList)
2 p! B7 o0 h  Z3 Z        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]
% y1 p: O4 n( l# d$ q        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
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6 x6 u5 J9 x2 p/ C" q" u( Q" |    def resultsummary(self):
) X. K) B6 U7 ~6 o1 {5 W) n        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))
/ }- E' v9 Z# J9 x8 p2 b        print("期望片段长度:" + str(self.averagefragmentlength))
8 k$ U3 g( Z( Z5 G        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
+ d7 P/ b" L6 e$ \9 p0 `        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
. R2 {1 G. |6 `sequencingObj = Sequencing()
/ S) I$ E- h7 Y7 O; h: v; n1 asequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
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")
sequencingObj.resultsummary()
  q" j1 t4 m9 c- e- P5 R, Bfrom Bio import SeqIO
from math import exp
+ b( J' c6 B) v" S$ |% `7 Y; Z6 Mimport random

class Sequencing:
    # N代表拷贝份数
! j$ X& V! X! h& d2 i* y( f* m. i7 Q    def __init__(self):
        self.fragmentList = []
- D* y' O# @2 @$ X3 n        self.readsID = 1
) C# C3 H' A# F' Z        self.readsList = []
        self.averagefragmentlength = 650
8 Q8 H) L- A! z4 g7 h        self.minfragmentlength = 500
        self.maxfragmentlength = 800
* k5 a; l" y4 n+ {# @        self.cloneRetainprobability = 1
; ?& I) b) g" Y1 d4 u% j; v' N6 r        self.minreadslength = 50
        self.maxreadslength = 150
        self.N = 10
3 P+ ?& Z! j' x% t  N7 n/ B3 ^) o        self.genomeLength = 0
        self.allreadslength = 0
* U) ]% F  x) e  K( F- y
    # 生成断裂点
9 N/ n6 u! S/ |3 |" @2 e    def generatebreakpoint(self, seqlen, averageLength):
' J5 Z# F( e! [. r- y; w4 @5 V        # 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)
- v: L3 y1 P( C. O, {; e$ _        breakpoint.append(0)
        # 把随机断裂点从小到大排序
1 C3 s( m9 ^% [. a8 i6 J        breakpoint.sort()
        return breakpoint
) P; }6 }1 X* \2 t% p9 p4 P
    # 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
% T' p4 k" k" _1 w6 F+ v) F$ B    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
+ V( ^& P  n5 c3 E! i            if maxfragmentlength > len(fragment) > minfragmentlength:
                self.fragmentList.append(fragment)
7 Y0 s9 \: Z3 ]9 z% h/ r. Y# M        return self.fragmentList
/ {9 e: ^1 C' {% J* K- e
6 _1 \! ~" |" w    # 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):
; a2 B& M' P6 }2 \6 n        clonedfragmentList = []
; p% e8 \+ |5 D3 W3 R- B        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
+ S; h( V2 V# {! W            if Lossprobability <= cloneRetainprobability:
4 W# ~$ e8 E& V6 K! X3 l                clonedfragmentList.append(fragmentList)
        return clonedfragmentList

' n4 s3 ]3 _) t& F# |! S1 p0 C    # 模拟单端测序,并修改reads的ID号
8 c5 s* ?1 K! [  W/ ^6 P1 }    def singleread(self, clonedfragmentList):
2 o- W+ C2 h1 H8 Z# k, u        for fragment in clonedfragmentList:
0 w7 L; m6 G2 `3 v7 [* _% }            fragment.id = ""
            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
4 e$ a+ E- {, q( q! w3 ]' j8 R            fragment.description = str(self.readsID) + "." + fragment.description
            self.readsID += 1
$ k% A( y) ^* l* A            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
: J% Q/ }. b5 ~8 h& K
    def singlereadsequencing(self, genomedata, sequencingResult):
. G  w6 P- [4 O+ p6 z/ r+ e1 v- o; F        for seq_record in SeqIO.parse(genomedata, "fasta"):
0 t  ?4 O: R9 |7 K( h+ r4 _            seqlen = len(seq_record)
% L& N: t- R" l+ V3 v            self.genomeLength += seqlen
            for i in range(self.N):
2 ~$ m9 E8 B* x8 I* |* ~5 z                # 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)
7 [5 Y6 d! q+ |4 W0 X        # 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
+ }4 U" k- D, W        self.singleread(clonedfragmentList)
5 N- D8 m% ^+ E        SeqIO.write(self.readsList, sequencingResult, "fasta")

* m0 b1 C, m. K+ p5 Z- b6 h3 S    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
* b# u9 g9 S) {7 \. @; P            fragment.name = ""
            description = fragment.description[12:].split(",")[0]
/ w( t- l0 K( @4 m            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
# N, |  H/ ]# n9 E9 N) W+ i
: [- X6 B* ~0 f5 R  i& m            readslength = random.randint(self.minreadslength, self.maxreadslength)
$ i; R- i& J/ q/ ]3 G$ j            self.allreadslength += readslength
0 Z, \5 o1 I1 l  {
/ [) d- v" B2 Y) j            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
  ^" K9 w$ ~* H/ m2 `7 \4 C! S: A            fragmentcomplement.name = ""
8 J( M) f) H& M7 P; I! b) O: [5 U            fragmentcomplement.description = str(self.readsID) + "." + description
7 \! A5 W* K2 Z" g            self.readsList.append(fragmentcomplement[:readslength])

& s! R0 j: _. w2 B+ i. y            self.readsID += 1

    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
0 \) E7 s8 }2 y+ ?        for seq_record in SeqIO.parse(genomedata, "fasta"):
% v% W) |. w4 f* a# v1 z            seqlen = len(seq_record)
            self.genomeLength += seqlen
) k% n& ?2 x, ]% [. ]7 ~4 {            for i in range(self.N):
* j5 o+ Q/ t9 n) [2 |* ?7 G8 d1 x+ L                # 生成断裂点
4 U% G  b0 C. T8 V$ Q2 n6 w                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
3 s6 s6 l8 u" n. N" E4 p        # 模拟克隆时的随机丢失情况
5 w$ s2 v1 x1 r! u$ T! g7 {        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
        self.pairread(clonedfragmentList)
1 p. k; o1 ~7 Z* k! F% q' Q2 ]        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
+ j1 L1 U; m& Y8 h# i: x! i        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")
3 }% s7 V6 p2 C0 I  E6 }. z, C
    def resultsummary(self):
& T. _  o8 X6 M9 c# U4 r. u        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
$ Z7 Y) F) m; V6 M+ ]        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
7 }& M" x$ Z# K        print("N值:" + str(self.N))
# [* l3 T- z" T        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)))
) `% D3 D6 ^2 n0 Z0 ?+ a. t        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
( @5 s" v$ x! v! J4 J        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)))
# Q1 G. X4 W, Y2 e* c# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
sequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
sequencingObj.resultsummary()
& W4 o6 f, z5 Q/ o/ P& ?
# 模拟双端测序
- F$ f5 g3 {5 hsequencingObj = Sequencing()
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
0 I) D% e4 o# H- @! ysequencingObj.resultsummary()
0 c9 u2 m$ ^5 `$ i+ G
& P2 c1 W% X6 a' Q9 W
/ @+ n, f; i# P+ [7 i: q

% t3 N& U3 u, k( O* x) }8 [6 K6 x# q

3477959497

不错。。。。。。。。。。。。。。。。。