genome analysis toolkit

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Jellyfish K-mer analysis and genome size estimate

much. Of course, this method was subjective because calling peaks is subjective.Please note-i think 17-mers is too short for this kind of analysis. I prefer 31-mers because they is the longest computationally-efficient kmers. Also, FYI, Bbnorm is faster than jellyfish and can Also generate kmer-frequency histograms:khist.sh IN=READS.FQ Hist=khist.txtAlso, it makes more sense to plot these things as log-log rather than linear-linear;And the y-axis sho

The Manhattan map (manhattan Plot) in R language Painting Genome-wide Association analysis

1. Install R in Linux2, ready to draw the Manhattan map of the R script is MANHATTAN.R,MANHATTAN.R content as Follows:#!/usr/bin/rscript#example:rscript PLOT_MANHATOM.R xxx.assoc xxx.pdfargv 3, ready to Plink run out Of. assoc.linear files, such as Mydata.assoc.linear4. Enter one of the following commands in Linux:Among them, Mydata.png is the Manhattan map we want (manhattan Plot)Rscript MANHATTAN.R mydata.assoc.linear Mydata.pngThe Manhattan map (manhattan Plot) in R language Painting

Analysis on the composition and use of the hacker toolkit-forwarding

Analysis on the composition and use of the hacker Toolkit Responsible editor: Admin updated on: 2005-8-6 After a host is attacked, hackers usually upload a specially configured hacker toolkit that contains various common hacker tools. With the help of these tools, hackers transform bots into their "workstation" to hide their tracks or

Analysis on the composition and use of the hacker toolkit --- post

After a host is attacked, hackers usually upload a specially configured hacker toolkit that contains various common hacker tools. With the help of these tools, hackers transform bots into their "workstation" to hide their tracks or attack other hosts as a springboard. Every good hacker has his own unique set of tools. As a beginner who just entered the hacker Hall, it is especially important to choose and purchase a suitable

DEBIAN8 installation Percona-toolkit detection of MySQL index and slow query analysis

';# ######################################################################### forge.erp_community_education# ######################################################################### Community_education_community_id_index is a left-prefix of PRIMARY# Key Definitions:# KEY ' Community_education_community_id_index ' (' community_id '),# PRIMARY KEY (' community_id ', ' education_id '),# Column Types:# ' community_id ' int (ten) unsigned NOT NULL# ' education_id ' int (ten) unsigned NOT NULL# To r

MySQL Slow scan log analysis tool Percona-toolkit

-query-digest--user=root–password=abc123--review h=localhost,d=test,t=query_history--create-review-table slow.log_20140402(9)throughtcpdumpCrawlMySQLof theTCPprotocol data, and then analyzeTcpdump-s 65535-x-nn-q-tttt-i any-c-Port 3306 > Mysql.tcp.txtPt-query-digest--type tcpdump mysql.tcp.txt> slow_report9.log(Ten)AnalysisBinlogMysqlbinlog mysql-bin.000093 > Mysql-bin000093.sqlPt-query-digest--type=binlog mysql-bin000093.sql > Slow_report10.log(one)AnalysisGeneral LogPt-query-digest--type=genlog

Python Data Analysis Toolkit (1)--numpy (i)

]: B=np.ones ([3,4])#generate all 1 arrays - +in [5]: b -Out[5]: +Array ([[1., 1., 1., 1.], A[1., 1., 1., 1.], at[1., 1., 1., 1.]]) - -In [6]: C=np.random.rand (3,4)#generating a random array - -in [7]: C -Out[7]: inArray ([[[0.36417168, 0.24336724, 0.78826727, 0.42894367], -[0.77198615, 0.95897315, 0.25628233, 0.53995372], to[0.02777746, 0.25093856, 0.14544893, 0.10475779]]) + -In [8]: D=np.eye (3)#Generating a unit array the *in [9]: D $Out[9]:Panax NotoginsengArray ([[1., 0., 0.], -[0.,

Windows Flex + GCC lexical analysis experiment Toolkit

Lexical analysis is an essential part of compilation principles. This part of the experiment generally uses flex and GCC tools. I like protable apps. After reading some documents, I sorted out this Toolkit myself. In fact, this Toolkit only collects and organizes some existing programs, and only has one original BAT file. If the entire

Python Data Analysis Toolkit (4)--matplotlib (ii)

0.18Explode = (0, 0.1, 0, 0)9 Ten #Specify Canvas 1, instantiate plt.subplots () OneFIG1, ax1 =plt.subplots () A - #autopct is the specified percentage display specification, which retains one decimal place - #the last two keyword values are added shadows, and the starting angle is determined theAx1.pie (sizes, Explode=explode, Labels=labels, autopct='%1.1

Recurrent Neural Network Language Modeling Toolkit Source analysis (three)

lead to the movement of data, this is the way to see the source side to review some knowledge of C vocab= (struct Vocab_word *) realloc (vocab, vocab_max_size * sizeof (struct Vocab_word)); } The hash value of Word is used as the subscript for Vocab_hash, and the integer value corresponding to the subscript is the index hash=getwordhash (word) for that word in vocab; vocab_hash[hash]=vocab_size-1; return vocab_size-1;}here is an algorithm for selecting sorting, vocab[1] to

Recurrent Neural Network Language Modeling Toolkit source analysis (iv)

following code constructs a structure such as:Double DF, DD; int i; df=0; Dd=0; a=0; b=0;//Note here vocab is from big to small row good order//The following is the word classification, classification is based on their one Yuan word//classification of the end result is that the closer to the previous category is very small, they appear a high frequency// The closer to the next category is the more word it contains, the more sparse if (old_classes) {//Old classes for (i=0; iThe a

Python Data Analysis Toolkit (3)--matplotlib (i)

follows:It is worth noting that matplotlib in the display of Chinese, there will be some problems, to solve this problem, please see the Portal: https://www.cnblogs.com/shikaihong/p/7717741.htmlLet's look at an example from an official document. (such as infringement contact deletion)1 #Import this package2 ImportMatplotlib.pyplot as Plt3 ImportNumPy as NP4 5 #define a curve-generated function6 deff (t):7 returnNp.exp (-T) *np.cos (2*np.pi*t)8 9 TenT1=np.arange (0.0,5.0,0.1) OneT2=np.arange

Analysis of large. NET ERP system Enterprise information Implementation Personnel Toolkit

1 First Implementation Meeting When the contract is signed, the software company and the customer agreed to the meeting time, the two sides held the first implementation of the meeting, to determine the implementation of the team list, project implementation cycle, training schedule, implementation master plan, System preparation master file data preparation, software company technical support contact. 2 Project Implementation Master plan System installation and testing: Business Research, deman

pandas:powerful Python Data Analysis Toolkit

)Parameters:filepath_or_buffer : str, pathlib. Path, Py._path.local.localpath or any object with a read () method (such as a file handle or Stringio)header : int or List of ints, default ' infer ' Row number (s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no names passed, otherwise None. usecols : array-like, default None Return a subset of the columns. All elements in this array must either is positional (i.e. integer indic

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