Whole Genome Alignment Sequence Level Alignment Protein Gene Finding

新一代测序数据分析软件汇总Integrated solutionsCLCbio Genomics Workbench - de novo and reference assembly of Sanger, Roche FLX, Illumina, Helicos, and SOLiD data. Commercial next-gen-seq software that extends the CLCbio Main Workbench software. Includes SNP detection, CHiP-seq, browser and other features. Commercial. Windows, Mac OS X and Linux.Galaxy - Galaxy = interactive and reproducible genomics. A job webportal.Genomatix - Integrated Solutions for Next Generation Sequencing data analysis.JMP Genomics - Next gen visualization and statistics tool from SAS. They are working with NCGR to refine this tool and produce others.NextGENe - de novo and reference assembly of Illumina, SOLiD and Roche FLX data. Uses a novel Condensation Assembly Tool approach where reads are joined via "anchors" into mini-contigs before assembly. Includes SNP detection, CHiP-seq, browser and other features. Commercial. Win or MacOS.SeqMan Genome Analyser - Software for NextGeneration sequence assembly of Illumina, Roche FLX and Sanger data integrating with Lasergene Sequence Analysis software for additional analysis and visualization capabilities. Can use a hybrid templated/de novo approach. Commercial. Win or Mac OS X.SHORE - SHORE, for Short Read, is a mapping and analysis pipeline for short DNA sequences produced on a Illumina Genome Analyzer. A suite created by the 1001 Genomes project. Source for POSIX.SlimSearch - Fledgling commercial product.Align/Assemble to a referenceABySS - Assembly By Short Sequences. ABySS is a de novo sequence assembler that is designed for very short reads. The single-processor version is useful for assembling genomes up to0-50 Mbases in size. The parallel version is implemented using MPI and is capable of assembling larger genomes. By Simpson JT and others at the Canadas Michael Smith Genome Sciences Centre. C++ as source.BFAST - Blat-like Fast Accurate Search Tool. Written by Nils Homer, Stanley F. Nelson and BarryMerriman at UCLA.Bowtie - Ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate ofmillion reads per hour on a typical workstation with gigabytes of memory. Uses a Burrows-Wheeler-Transformed (BWT) index. Link to discussion thread here. Written by Ben Langmead and Cole Trapnell. Linux, Windows, and Mac OS X.BWA - Heng Lees BWT Alignment program - a progression from Maq. BWA is a fast light-weighted tool that aligns short sequences to a sequence database, such as the human reference genome. By default, BWA finds an alignment within edit distance to the query sequence. C++ source.ELAND - Efficient Large-Scale Alignment of Nucleotide Databases. Whole genome alignments to a reference genome. Written by Illumina author Anthony J. Cox for the Solexa 1G machine.Exonerate - Various forms of pairwise alignment (including Smith-Waterman-Gotoh) of DNA/protein against a reference. Authors are Guy St C Slater and Ewan Birney from EMBL. C for POSIX.GenomeMapper - GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. A tool created by the 1001 Genomes project. Source for POSIX.GMAP - GMAP (Genomic Mapping and Alignment Program) for mRNA and EST Sequences. Developed by Thomas Wu and Colin Watanabe at Genentec. C/Perl for Unix.gnumap - The Genomic Next-generation Universal MAPper (gnumap) is a program designed to accurately map sequence data obtained from next-generation sequencing machines (specifically that of Solexa/Illumina) back to a genome of any size. It seeks to align reads from nonunique repeats using statistics. From authors at Brigham Young University. C source/Unix.MAQ - Mapping and Assembly with Qualities (renamed from MAPASS2). Particularly designed for Illumina with preliminary functions to handle ABI SOLiD data. Written by Heng Li from the Sanger Centre. Features extensive supporting tools for DIP/SNP detection, etc. C++ source MOSAIK - MOSAIK produces gapped alignments using the Smith-Waterman algorithm. Features a number ofsupport tools. Support for Roche FLX, Illumina, SOLiD, and Helicos. Written by Michael Str枚rg at Boston College. Win/Linux/MacOSXMrFAST and MrsFAST - mrFAST & mrsFAST are designed to map short reads generated with the Illumina platform to reference genome assemblies; in a fast and memory-efficient manner. Robust to INDELs and MrsFAST has a bisulphite mode. Authors are from the University of Washington. C as source.MUMmer - MUMmer is a modular system for the rapid whole genome alignment of finished or draft sequence. Released as a package providing an efficient suffix tree library, seed-and-extend alignment, SNP detection, repeat detection, and visualization tools. Version.0 was developed by Stefan Kurtz, Adam Phillippy, Arthur L Delcher, Michael Smoot, Martin Shumway, Corina Antonescu and Steven L Salzberg - most of whom are at The Institute for Genomic Research in Maryland, USA. POSIX OS required.Novocraft - Tools for reference alignment of paired-end and single-end Illumina reads. Uses a Needleman-Wunsch algorithm. Can support Bis-Seq.Commercial. Available free for evaluation, educational use and for use on open not-for-profit projects. Requires Linux or Mac OS X.PASS - It supports Illumina, SOLiD and Roche-FLX data formats and allows the user to modulate very finely the sensitivity of the alignments. Spaced seed intial filter, then NW dynamic algorithm to a SW(like) local alignment. Authors are from CRIBI in Italy. Win/Linux. RMAP - Assembles0 -bp Illumina reads to a FASTA reference genome. By Andrew D. Smith and Zhenyu Xuan at CSHL. (published in BMC Bioinformatics). POSIX OS required.SeqMap - Supports up to or more bp mismatches/INDELs. Highly tunable. Written by Hui Jiang from the Wong lab at Stanford. Builds available for most OSs.SHRiMP - Assembles to a reference sequence. Developed with Applied Biosystems colourspace genomic representation in mind. Authors are Michael Brudno and Stephen Rumble at the University of Toronto. POSIX. Slider- An application for the Illumina Sequence Analyzer output that uses the probability files insteadof the sequence files as an input for alignment to a reference sequence or a set of reference sequences. Authors are from BCGSC. Paper is here.SOAP - SOAP (Short Oligonucleotide Alignment Program). A program for efficient gapped and ungapped alignment of short oligonucleotides onto reference sequences. The updated version uses a BWT. Can call SNPs and INDELs. Author is Ruiqiang Li at the Beijing Genomics Institute. C++, POSIX.SSAHA - SSAHA (Sequence Search and Alignment by Hashing Algorithm) is a tool for rapidly finding near exact matches in DNA or protein databases using a hash table. Developed at the Sanger Centre by Zemin Ning, Anthony Cox and James Mullikin. C++ for Linux/Alpha. SOCS - Aligns SOLiD data. SOCS is built on an iterative variation of the Rabin-Karp string search algorithm, which uses hashing to reduce the set of possible matches, drastically increasing search speed. Authors are Ondov B, Varadarajan A, Passalacqua KD and Bergman NH.SWIFT - The SWIFT suit is a software collection for fast index-based sequence comparison. It contains:SWIFT fast local alignment search, guaranteeing to find epsilon-matches between two sequences. SWIFT BALSAM a very fast program to find semiglobal non-gapped alignments based on k-mer seeds. Authors are Kim Rasmussen (SWIFT) and Wolfgang Gerlach (SWIFT BALSAM) SXOligoSearch - SXOligoSearch is a commercial platform offered by the Malaysian based Synamatix. Will align Illumina reads against a range of Refseq RNA or NCBI genome builds for a number of organisms. Web Portal. OS independent.Vmatch - A versatile software tool f美丽的花虽然会凋谢，可是盛开的时刻值得欣赏。

全基因组扩增——微量DNA分析的金钥匙来源：青年人() 更新时间：2010/3/8 19:51:27 【字体：小大】聚合酶链反应(PCR)技术的发展和应用使得微量甚至单个细胞DNA的分析成为可能，极大地促进了法医学、古生物学、分子诊断学和分子病理学的发展。

但即便是对于非常敏感的PCR技术，许多材料所能提供的DNA量也只能用于一次或几次PC R反应。

为能从少量细胞中最大限度的获取信息，全基因组扩增技术应运而生。

一、全基因组扩增的概念全基因组扩增（whole genome amplification, WGA）是一组对全部基因组序列进行非选择性扩增的技术，其目的是在没有序列倾向性的前提下大幅度增加DNA的总量。

常用的方法有IRS-PCR（interspersed repeat sequence PCR）［1］、LA-PCR（linker ada ptor PCR）［2］、T-PCR（Tagged-PCR）［3］、DOP-PCR（degenerate oligonucleotide pr imed PCR）［4］、PEP-PCR（primer extension preamplification PCR）［5］等。

其中最具代表性方法是DOP-PCR和PEP-PCR。

DOP-PCR和PEP-PCR的基本原理都是通过其随机引物与基因组DNA多处退火从而使大部分基因组序列得到扩增。

DOP-PCR的引物是由其3′和5′端的特异核苷酸序列和中间的6个核苷酸构成的随机引物组成。

其PCR程序是先在低退火温度下进行几个循环的低严谨扩增，然后再提高退火温度，进行几十个循环的严谨扩增。

由于DOP-PCR的引物3′端设计的是在基因组中高频出现的序列，因此，在首轮的低严谨扩增条件下能与基因组多处退火，从而将基因组普遍扩增。

然后下一轮的严谨扩增中又将低严谨扩增的产物再次放大［4］。

与DOP-PCR不同的是，PEP-PCR的引物是由15个随机核苷酸组成的完全随机引物。

BRIG0.95ManualNabil AlikhanJune27,2011/projects/brig/1CONTENTS2 Contents1Introduction3 2Licence9 3Installation93.1Installing BLAST (9)4Warning when using BLAST114.1Low complexityfiltering (11)4.2Expected values(e-values)and bit scores (11)5Visualising whole genome comparisons135.1Step1:Load in sequences (13)5.2Step2:Configure rings (14)5.3Step3:Review and submit (16)6Working with a Multi-FASTA reference186.1Step1:Load in sequences (18)6.2Step2:Configure rings,annotations and spacer value (19)6.3Step3:Configure image settings and submit (23)7Visualising graphs and genome assemblies257.1Walkthrough for visualising SAMfile mapping coverage (26)7.2Walk through for visualising acefile assembly coverage (31)8Walkthroughs on creating custom annotations398.1Adding custom annotations from a tab-delimitedfile,GenBank orEMBLfile (39)8.1.1Step1:Load in sequences (39)8.1.2Step2:Configure rings (40)8.1.3Step3:Adding annotations (41)8.1.4Step4:Review and submit (43)8.2How to create tab-delimitedfiles for BRIG (45)9Configuration options469.1Saving and reopening your work (46)9.2BLAST options (46)9.3Setting BRIG options (48)9.4Setting Image options (50)9.5Loading a preset image template (52)1INTRODUCTION3 1IntroductionThe BLAST Ring Image Generator(BRIG)is a cross-platform desktop applica-tion written in Java1.6.It uses CGView[5]for image rendering and the Basic Local Alignment Search Tool(BLAST)for genome comparisons.It has a graph-ical user interface programmed on the Swing framework,which takes the user step-by-step through the configuration of a circular image generation.Figure1is an example of an image BRIG can create.Figure1:BRIG example output image of a simulated draft E.coli O157:H7 genome.Thefigure show BLAST comparisons against28published E.coli and Salmonella genomes against the simulated draft genome.1INTRODUCTION4 Figure2shows a magnified view of the same example image showing similar-ity between a central reference genome in the centre against other query sequences as a set of concentric rings,where colour indicates a BLAST match of a particular percentage identity.BRIG does not represent sequences that are not present in the reference genome The image shows:•GC skew,•GC content,•Genome coverage and contig boundaries(calculated from an assemblyfile),•Genome alignment results,customs annotations.Figure2:A magnified view of BRIG example image1INTRODUCTION5 How to use this manualThis manual contains a set of detailed walk throughs where readers are taken step by step through a worked example.Each walkthrough highlights different features of BRIG and users should work through each one.If you are interested in a particular aspect of BRIG,please turn to the relevant walkthrough:•Whole genome comparisons,including how to load in coverage graphs,e.g.Figures1&3,see Section5on page13.•Using a user-defined list of genes as a reference(in Multi-FASTA),e.g Fig-ure4,see Section6on page18.•Creating and visualising graphs generated from assembly(.ace)or read mapping coverage(.SAM),e.g Figure5,see Section7on page25.•Labeling images with information from GenBank,Tab-delimited or Multi-FASTAfiles,like those seen in Figure3,4&5,see Section8on page39.The manual also has detailed instructions for how to install and configure BRIG:•For instructions on how to install BRIG,see Section3on page9.•For instructions on how to configure BRIG and save BRIG settings,see Section9on46.1INTRODUCTION6Figure3:Reference:Published E.coli O157:H7Sakai genome.Query:Com-plete genome sequences of related strains,listed in the key.The prophage regions from the Sakai genome are marked in alternating black&blue.To make an image like this please refer to Section5on page13.1INTRODUCTION7Figure4:Reference:A list of translated genes that make up the Locus of Entero-cyte Effacement(LEE),which encodes a Type III secretion system.Query:Raw sequencing reads simulated from several complete LEE+published genomes(nu-cleotide sequence)and E.coli K12,(negative control;LEE-).You can clearly see gene presence/absence,and divergence(the colour represents sequence identity on a sliding scale,the greyer it gets;the lower the percentage identity).To make an image like this please refer to Section6on page18.1INTRODUCTION8Figure5:Reference:Published E.coli O157:H7Sakai genome.Query:Read mapping coverage of sequencing reads simulated from complete genomes,indi-cated in the key.Simulated sequencing reads were mapped onto the published complete Sakai genome using BWA.The read coverage for each genome was generated from the resulting SAMfipare this with Figure3,which is based on the original published genome sequences.To make an image like this please refer to Section7on page25.2LICENCE9 2LicenceThis program is free software:you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foun-dation,either version3of the License,or(at your option)any later version.This program is distributed in the hope that it will be useful,but without any warranty;without even the implied warranty of merchantability orfitness for a particular purpose.See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program.If not,see</licenses/>.Please note that these restrictions do not apply to the third party libraries bundled with this software.3InstallationThere’s no real”Installation”process for BRIG itself.However,BLAST+[2]or BLAST legacy[1]must already be installed and BRIG needs to be able to locate the BLAST executables(See Section3.1).To run BRIG users need to:1.Download the latest version(BRIG-x.xx-dist.zip)from/projects/brig/2.Unzip BRIG-x.xx-dist.zip to a desired location.3.Run BRIG.jar,by double clicking.Users who wish to run BRIG from the command-line need to:1.Navigate to the unpacked BRIG folder in a command-line interface(termi-nal,console,command prompt).2.Run“java-Xmx1500M-jar BRIG.jar”.Where-Xmx specifies the amountof memory allocated to BRIG.3.1Installing BLASTThe latest version of BLAST+[2]can be downloaded from:ftp:///blast/executables/blast+/LATEST/ BLAST+offers a number of improvements on the original BLAST implementa-tion and comes as a bundled installer,which will walk users through the installa-tion process.Please read the published paper on BLAST+:3INSTALLATION10 Camacho,C.,G.Coulouris,et al.(2009).“BLAST+:architecture and appli-cations.”BMC Bioinformatics10(1):421Available online at:http://www. /1471-2105/10/421The latest version of BLAST legacy[1]can be downloaded from:ftp:///blast/executables/release/LATEST/ BLAST legacy comes as a compressed package,which will unzip the BLAST binaries where ever the package is.We advise users tofirst create a BLAST direc-tory(in either the home or applications directory),copy the downloaded BLAST package to that directory and unzip the package.BRIG supports both BLAST+&BLAST ers can specify the loca-tion of their BLAST installation in the BRIG options menu which is: Main window>Preferences>BRIG options.The window is shown in Figure6.If BRIG cannotfind BLAST it will prompt users at runtime.PRO TIP1:BRIG uses BLAST,do not use wwwblast or netblast with BRIG. PRO TIP2:If BOTH BLAST+and legacy versions are in the same location, BRIG will prefer BLAST+.Figure6:You can change where BRIG looks for BLAST in the BRIG options window.For more information about BRIG options see Section9.2on page48.4W ARNING WHEN USING BLAST11 4Warning when using BLASTBRIG relies on the Basic Local Alignment Search Tool(BLAST)for genome comparisons.BLAST has a number of behaviours that may seem counterintuitive and we encourage users to learn about local alignment and the BLAST algorithm to fully understand the images that BRIG produces.There are a few concepts to keep in mind when using BRIG:4.1Low complexityfilteringPRO TIP3:BLASTfilters may cause gaps in alignments,which will show up as blank regions in BRIG images.BLASTfilters(BLAST legacy-Fflag or BLAST+-dust/-seg noflag)filter the query sequence for low-complexity sequences by default.This includes sequences that are highly repetitive or contain the same nucleotide for long lengths of the sequences.Low-complexityfiltering is generally a good idea,but it may break long matches into several smaller matches.This is often shown in BRIG images as truncations or gaps in alignments,it is particularly obvious in very small reference sequences where alignments are shown on a gene-by-gene level.To prevent this,either turn offfiltering or use soft masking.4.2Expected values(e-values)and bit scoresPRO TIP4:BLAST’s bitscorefiltering may cause different results in BRIG if users swap the query and reference sequences,particularly if these are very different sizes.BLAST uses statistical thresholds tofilter out“bad alignments”;alignment matches that appear random to BLAST.One of these thresholds is the e-value,which is the probability of the alignment occurring by chance,given the complexity of the match,sequence composition and the size of the database.It is more likely in a larger sequence that an alignment could occur by chance,so BLAST is more critical of these matches.This can create different expected values if BLAST is used with the same reference sequence against databases of different sizes and may potentiallyfilter out significant matches or include poor scoring ones.4W ARNING WHEN USING BLAST12 Because of this,users might notice different results in BRIG images if they swap the order of the database and reference sequences around in the BLAST, especially if the two sequences are quite different in size.The differences are often due to a few very low-scoring hits.Users should consider what an appropriate e-value threshold is for the compar-isons that they run.Remember,that BLAST runs with an e-value of10by default, we recommend that users change this ers can set thefinal threshold (e-value)with the-eflag in BLAST legacy or-evalueflag in BLAST+.PRO TIP5:BLAST does not handle spaces infilenames,BRIG will prompt users if they have spaces infile locations.5VISUALISING WHOLE GENOME COMPARISONS135Visualising whole genome comparisonsIn this section we will walk through the basics of generating an image.This walk through will be comparing an E.coli genome withfive other E.coli genomes and mapping the read coverage from the underlying genome assembly onto the same image.For this walk through,users will need BRIG examples.zip,which is avail-able from the BRIG website(/projects/brig/ files/).This contains all the genomes andfiles needed to follow along with this walk through.Unzip it somewhere easily accessible,like the home directory or desktop.About the reference genomeThe reference genome used in this walk through is a simulated E.coli genome assembly.We took the published E.coli O157:H7Sakai genome(Accession num-ber BA000007)sequence and had assembly reads simulated by METASIM[4]and then assembled these using Newbler version2.3.The resulting contiguous se-quences were ordered using Mauve[3]against the published Sakai genome.This simulated E.coli is useful for illustrating some of BRIG’s graphing features for assembly read coverage.Enterohemorrhagic E.coli are gram-negative,enteric bacterial pathogens.They can cause diarrhea,hemorrhagic colitis,and hemolytic uremic syndrome.This particular genome we are using in this example was based on an E.coli O157:H7 isolated from the Sakai,Japan outbreak.5.1Step1:Load in sequencesThe walk through will work out of the unzipped BRIG examples.zip in the Chap-ter568wholeGenomeExamples folder.The walk through and relatedfigureswill use C:\BRIG examples\Chapter568wholeGenomeExamples as that loca-tion.To keep thefinal image consistent with the walk through,please open”Exam-pleProfile.xml”from the Chapter568wholeGenomeExamples folder.Thisfile configures BRIG to the same image settings in the walk through.1.First,set BRIGExample.fna as the reference sequence.2.Set<unzipped BRIG examples folder>\Chapter568wholeGenomeExamplesas the query sequence folder.3.Press“add to data pool”,this should load several items into the pool list,there should be ninefiles.5VISUALISING WHOLE GENOME COMPARISONS144.Set the Chapter568wholeGenomeExamples as the output folder.5.The BLAST options box should be left blank.6.Click nextPRO TIP6:Users can add individualfiles to the data pool too.5.2Step2:Configure ringsThe next step is to configure what information is shown on each of the concentric rings in BRIG.Create six rings,for each ring:1.Set the legend text for each ring2.Select the required sequences from the data pool and click on“add data”toadd to the ring list.3.Choose a colour4.Set the upper(90)and lower(70)identity threshold.5VISUALISING WHOLE GENOME COMPARISONS155.Click on“add new ring”and repeat steps for each new ring required.The values required for each ring are detailed in the table below.Notice that thatsequences can be collated into a single ring,like the example of K12&HS.The ring will show BLAST matches from both HS and K12.Legend text Required sequences ColourGC Content GC Content IgnoreGC Skew GC Skew IgnoreCoverage BRIGExample.graph153,0,0O157:H7E coli O157H7Sakai.gbk0,0,153HS and K12E coli HS.fna0,153,0E coli K12MG1655.fnaCFT073and UTI89E coli CFT073.fna153,0,153E coli UTI89.fna5VISUALISING WHOLE GENOME COMPARISONS16 PRO TIP7:Rings can be reordered by dragging them in the Ring List pane. PRO TIP8:You can set default threshold values in“BRIG options”.See section9.2(page48)for more details.PRO TIP9:When using a Genbank/EMBLfile as a reference,users can choose whether to use the protein or nucleotide sequence.5.3Step3:Review and submitThe last window allows us to change the BLAST options,the location of the imagefile and set the image title,which will appear in the centre of the ring.For the walkthrough configure the third window as:1.Set the image title as“BRIG example image”.2.Hit submit.3.The image will be created in the specified output directory and should looksomething like Figure7.BRIG will format Genbankfiles,run BLAST,parse the results and render the im-age.Thefinal image(Figure7)shows GC Content and Skew,the Genome cover-age,contig boundaries,and the BLAST results against the other E.coli genomes. The results for HS and K12have been collated into a single ring,likewise for UTI89and CFT073.5VISUALISING WHOLE GENOME COMPARISONS17Figure7:Thefinal BRIG imagePRO TIP10:Image settings,like size,fonts,etc can be configured in:Main window>Preferences>Image options..6WORKING WITH A MULTI-FASTA REFERENCE186Working with a Multi-FASTA reference6.1Step1:Load in sequencesThis section is a walk through of how to use BRIG to generate an image using a listof genes in Multi-FASTA format as a reference.The multi-FASTAfile in this ex-ample is a number of virulence genes from enterohemorrhagic and uropathogenic E.coli,which includes EHEC polarfimbraie(ecpA to ecpR),EHEC Locus of En-terocyte Effacement(espF to espG)and the UPEC F1C Fimbraie(focA to focI), which will be compared against the whole genome seqeuences of E.coli strainsO157:H7Sakai,K12MG1665,O126:H7and CFT073.Start a new session in BRIG and load in thefiles from the Chapter568wholeGenomeExamples folderin the unzipped BRIG-Example folder:1.Set the reference sequence as“Ecoli vir.fna”.Users can use the browsebutton to traverse thefile system.2.Set<unzipped BRIG examples folder>/Chapter568wholeGenomeExamplesas the query sequence folder.3.Press“add to data pool”,this should load several items into the pool list.4.Set the output folder as unzipped BRIG-Example folder.5.Make sure the BLAST options box is blank.6.Click“Next”.6WORKING WITH A MULTI-FASTA REFERENCE196.2Step2:Configure rings,annotations and spacer valueThe next step is to configure what information is shown on each concentric ring in BRIG.Figure8is an example of how one of the windows should be set up.There should befive rings.Do the follow for each ring,according to the table below:1.Set legend text for each ring.2.Select the required sequences from the data pool and click“add data”toadd.3.Choose a colour4.Set upper(90)and lower(70)identity thresholds.5.Click“add new ring”and repeat these steps for each new ring.Legend text Required sequences ColourO157:H7E coli O157H7Sakai.gbk172,14,225O126:H7E coli O126.fna255,0,51CFT073E coli CFT073.fna0,0,102K12E coli K12MG1655.fna161,221,231null none ignoreAfter each ring is configured,users need to make the following changes:1.Set the spacerfield to50base pairs.2.Set the ring size of ring5as“2”.PRO TIP11:The Spacerfield determines the number of base pairs to leave between FASTA sequences.The next step is to add the gene annotations,which will be fetched from the Multi-FASTA headers:1.Click Add custom features in the second BRIG window to bring up customannotation window(Figure9).2.Double click“Ring5”.3.Set“input data”as Multi-FASTA.4.Set“colour”as alternating red-blue5.Click add.6WORKING WITH A MULTI-FASTA REFERENCE20Figure8:Ring set-up for Multi-FASTAfile6WORKING WITH A MULTI-FASTA REFERENCE21Figure9:Custom annotation window-adding gene annotations6WORKING WITH A MULTI-FASTA REFERENCE22 This step colours the gaps between FASTA entries,the gaps are calculated from the Multi-FASTAfile(Figure10).For each genome ring,do the following:1.Set“input data”as Multi-FASTA.2.Set“colour”as black3.Check“load gaps only”.4.Click add.The results should be similar to Figure10in the left hand pane.Close the window when this is done.PRO TIP12:A spacer value can be set when using protein sequences from a Genbank/EMBLfile.Figure10:Custom annotation window-adding spacers6WORKING WITH A MULTI-FASTA REFERENCE236.3Step3:Configure image settings and submitThere are a few more steps to complete and then the image isfinished.In the customize ring window:1.Make the following changes in Preferences>Image options(a)Set“show shading”in“Global settings”as false.(b)Set“featureSlot”spacing in“Feature settings”as x-small.2.Return to customize ring window,click“Next”to go to thefinal BRIGconfirmation window3.Set the image title as“Various E.coli virulence genes”and press submit. The output image should be something like Figure11.The alternating red-blue op-tion has automatically alternated the red and blue colours for the gene labels.This option is available whenever a multi-FASTAfile is used as a reference sequence. This same option could be used to show contig or genome scaffold boundaries. This image shows some real biological information very clearly.1.CFT073(UPEC)and K12MG1655(Commensal)do not carry the Locus ofEnterocyte Effacement.These virulence factors are specific to EHEC and EPEC.2.All E.coli shown carry the common pilus(ecpA-R).3.Only CFT073carries the F1Cfimbriae.PRO TIP13:You use protein sequences as a multi-FASTA reference and use blastx to improve alignment accuracy for divergent sequences.6WORKING WITH A MULTI-FASTA REFERENCE24Figure11:Output image from Multi-FASTA walkthrough.This was generated using BLAST+[2],BLAST legacy[1]will produce slightly different results.7VISUALISING GRAPHS AND GENOME ASSEMBLIES25 7Visualising graphs and genome assembliesBRIG can produce any user-specified graph e.g coverage,read mapping,expres-sion data etc.For example,the coverage graph in Figure2was produced from a tab-delimited textfile,with a start,stop and value for that range.BRIG supports.acefiles(produced by Newbler,454/Roche’s propriety as-sembler,and used by PHRAP/Consed)or SAMfiles(used for read mapping and some de-novo assemblers).BRIG has a number of modules for handling assembly information.These tools are:•Contig mapping:BRIG will use BLAST to try and map contigs from an .ace or Multi-FASTAfile onto a reference genome and produce a.graphfile that can show frequency of BLAST hits and the best BLAST hit position of contigs.It will then produce a.graphfile of the frequency of BLAST hits and the best BLAST hit position of contigs and another.graphfile,with the suffix”rep.graph”showing all the other BLAST hits.•Coverage graph:BRIG requires an.ace or.samfile and an output loca-tion(Figure7.1).BRIG will calculate coverage values over a user-defined window and produce a.graphfile in the output folder.This will create a tab-delimited.graphfile,which can be loaded into back BRIG.•Convert graph:A draft genome is usually modified post assembly;adding spacers,reordering contigs,etc.These changes are often not reflected in the original.acefiles BRIG uses to generate coverage graphs.BRIG can use BLAST to align the original assembly output with the newer sequence and map the coverage information to the new sequence.BRIG requires:–Original454AllContigs.fna produced by Newbler.–Graphfile created by BRIG’s“Coverage graph”module,based onNewbler’s acefile.–The modified sequence or another suitable reference genome.BRIG will produce a new.graphfile in the output folder,using thefilename of the originalfile,with“new.graph”appended to the end.To create work with graphfiles:Main window>Modules>Create graphfiles Using graphfiles in BRIG images.graphfiles should visible when users load a directory into the query sequence pool(Figure7.1).Graphs can be treated like any other sequencefile in BRIG;the example from Figure7.1shows a graphfile loaded into thefirst ring of a particular BRIG session.7VISUALISING GRAPHS AND GENOME ASSEMBLIES26 PRO TIP14:Graphfiles cannot be shown on same ring as sequencefiles (protein or nucleotide).7.1Walkthrough for visualising SAMfile mapping coverage. This section will give a worked example of producing a BRIG image show-ing mapping reads coverage from a SAM inputfile.Thefinal image will look like Figure12.This walkthrough requires BRIG examples.zip from the BRIG website(/projects/brig/files/).Unzip this somewhere convenient.The general procedure is tofirst generate the graphfiles from the SAMfile,add additionalfiles to data pool,edit the rings and annotation, then render the image.1.Open a new BRIG session.2.Create the graphfile from the graphfiles modules:Main window>Mod-ules>Create graphfiles.(a)Set drop down to coverage graph,fill infields(Figure7.1).(b)Set Assemblyfile as“Mu50.sam”from the BRIG examples/Chapter7-sam-examples folder.(c)Set Output folder as the location of the Chapter7-sam-examples folder.(d)Window size as“1”..(e)Click Create Graph.This will add the graphfile to the data pool whenit hasfinished.7VISUALISING GRAPHS AND GENOME ASSEMBLIES27Close the coverage graph window and return to thefirst main window.1.Set referencefile as“S.aureus.Mu50-plasmid-AP003367.gbk”from the Chapter7-sam-examples ers can use the browse button to traverse thefilesystem.2.Set<unzipped BRIG examples folder>/Chapter7-sam-examples as the querysequence folder.3.Press“add to data pool”,this should load several items into the pool list.4.Set the output folder as unzipped Chapter7-sam-examples folder.5.Make sure the BLAST options box is blank.Click next to move to the next window to configure the rings and add in annota-tions.1.Create4rings,name them“Mapping Coverage”,“pSK57”,“SAP014A”,“CDS”.2.Ring1Settings:(a)Add“Mu50.sam.graph”from data pool to Ring1.(b)Set graph maximum value as“10”.(c)Set colour as rgb(204,0,0).(d)Set legend title as“Mapping coverage”.(e)Check show red/blue.7VISUALISING GRAPHS AND GENOME ASSEMBLIES283.Ring2Settings:(a)Add“S.aureus.pSK57-plasmid-GQ900493.gbk”from data pool to Ring2.(b)Set colour as rgb(0,0,102).(c)Set legend title as“pSK57”.4.Ring3Settings:(a)Add“S.aureus.SAP014A-plasmid-GQ900379”from data pool to Ring3.(b)Set colour as rgb(102,0,102).(c)Set legend title as“SAP014A”.5.Ring4Settings:(a)Set colour as rgb(0,0,0).(b)Set legend title as“CDS”.7VISUALISING GRAPHS AND GENOME ASSEMBLIES29Click“Add Custom features”.1.Double-click Ring4.2.Set Input data to“Genbank”.3.Set colour to“black”.4.Set Draw feature as“default”.5.Set Genbankfile location to the location of“S.aureus.Mu50-plasmid-AP003367.gbk”.6.Set Feature as“CDS”7.Click add.This will load all the coding sequences from the Genbankfile.These annotations will be drawn as arrows,indicating orientation.Close this window and click next on the second window.1.Set title as“S.aureus Mu50plasmid”.2.Click Submit.This will generate thefinal image,it should look like Figure12.7VISUALISING GRAPHS AND GENOME ASSEMBLIES30Figure12:S.aureus Mu50plasmid,showing read mapping from simulated454 reads,CDSs,and genome comparisons to other S.aureus plasmids,pSK57& SAP014A.Alignments were performed with BLAST+7VISUALISING GRAPHS AND GENOME ASSEMBLIES317.2Walk through for visualising acefile assembly coverage. This section will give a worked example of producing a BRIG image showing assembly coverage read from an acefile.Thefinal image will look like Fig-ure13.This walk through requires BRIG examples.zip from the BRIG website (/projects/brig/files/).Unzip this some-where convenient.The general procedure is tofirst generate the graphfiles from the acefile,con-vert the coverage information to reference sequence if necessary,add additional files to data pool,edit rings and annotation,then render the image.Draft genome sequences are often modified to be consistant with other infor-mation(e.g genome scaffolding,PCR sequencing of gaps)after being initially assembled.This may change the order and size of thefinal genome sequence compared to the original assembly.To show the read coverage from the assembly on thefinal sequence correctly the“Convert graph”module within BRIG can be used to map the coverage infor-mation from the acefile onto the new sequence.This module can also be used to map read coverage from an assembly onto a closely-related reference genome.7VISUALISING GRAPHS AND GENOME ASSEMBLIES32First,produce the coverage graphfile based off the assembly(acefile):1.Open a new BRIG session.2.Create the graphfile from the graphfiles module:Main window>Modules>Create graphfiles.3.Set drop down to coverage graph,fill infields(Figure7.2).(a)Set Assemblyfile as“454-S.aureus.Mu30.ace”from the BRIGEXAMPLE2-ace folder.(b)Set Output folder as the location of the BRIGEXAMPLE2-ace folder.(c)Set window size as“1”.4.Click“Create Graph”.This will add the graphfile to the data pool when ithasfinished.7VISUALISING GRAPHS AND GENOME ASSEMBLIES33 Next,map the coverage generated in the previous graphfile to the modified genome sequence.1.Remain in the“Create custom graph”window:Main window>Modules>Create graphfiles.2.Set drop down to convert graph,fill infields as below.(a)Set Original sequence as“454AllContigs-S.aureus.Mu50.fna”.(b)Set New sequence as“S.aureus.Mu50-plasmid-AP003367.fna”.(c)Set graphfile as“454-S.aureus.Mu50.ace.graph”.(d)Set Output folder as the location of the BRIGEXAMPLE2-ace folder.(e)Window size as“1”.3.Click“Create Graph”.This will add the graphfile to the data pool when ishasfinished.7VISUALISING GRAPHS AND GENOME ASSEMBLIES34Close the“Create custom graph”window and return to the main window.1.Set referencefile as“S.aureus.Mu50-plasmid-AP003367.gbk”from theBRIGEXAMPLE2-ace ers can use the browse button to traverse thefile system.2.Set<unzipped BRIGEXAMPLE2-ace folder>/genomes as the query se-quence folder.3.Press“add to data pool”,this should load several items into the pool list.4.Set the output folder as unzipped BRIGEXAMPLE2-ace folder.5.Make sure the BLAST options box is blank.。

生物信息学主要英文术语及释义生物信息学主要英文术语及释义Abstract Abstract Syntax Syntax Syntax Notation Notation Notation (ASN.l)(ASN.l)（NCBI 发展的许多程序，如显示蛋白质三维结构的Cn3D等所使用的内部格式）等所使用的内部格式） A A language language language that that that is is is used used used to to to describe describe describe structured structured structured data data data types types types formally, formally, formally, Within Within Within bioinformatits,it bioinformatits,it bioinformatits,it has has been been used used used by by by the the the National National National Center Center Center for for for Biotechnology Biotechnology Biotechnology Information Information Information to to to encode encode encode sequences, sequences, sequences, maps, maps, taxonomic information, molecular structures, and biographical information in such a way that it can be easily accessed and exchanged by computer software. Accession number （记录号）（记录号）A unique identifier that is assigned to a single database entry for a DNA or protein sequence. Affine gap penalty （一种设置空位罚分策略）（一种设置空位罚分策略）（一种设置空位罚分策略） A gap penalty score that is a linear function of gap length, consisting of a gap opening penalty and a a gap gap gap extension extension extension penalty penalty penalty multiplied multiplied multiplied by by by the the the length length length of of of the the the gap. gap. gap. Using Using Using this this this penalty penalty penalty scheme scheme scheme greatly greatly enhances enhances the the the performance performance performance of of of dynamic dynamic dynamic programming programming programming methods methods methods for for for sequence sequence sequence alignment. alignment. alignment. See See See also also Gap penalty. Algorithm （算法）（算法）A A systematic systematic systematic procedure procedure procedure for solving for solving a a problem problem problem in in in a a a finite finite finite number number number of of of steps, steps, steps, typically typically typically involving involving involving a a repetition of operations. Once specified, an algorithm can be written in a computer language and run as a program. Alignment （联配/比对/联配）联配）Refers to the procedure of comparing two or more sequences by looking for a series of individual characters or character patterns that are in the same order in the sequences. Of the two types of alignment, alignment, local local local and and and global, global, global, a a a local local local alignment alignment alignment is is is generally generally generally the the the most most most useful. useful. useful. See See See also also also Local Local Local and and Global alignments. Alignment score （联配/比对/联配值）联配值）An algorithmically computed score based on the number of matches, substitutions, insertions, and deletions deletions (gaps) (gaps) (gaps) within within within an an an alignment. alignment. alignment. Scores Scores Scores for for for matches matches matches and and and substitutions substitutions substitutions Are Are Are derived derived derived from from from a a scoring scoring matrix matrix matrix such such such as as as the the the BLOSUM BLOSUM BLOSUM and and and P AM P AM matrices matrices matrices for for for proteins, proteins, proteins, and and and aftine aftine aftine gap gap gap penalties penalties suitable for the matrix are chosen. Alignment scores are in log odds units, often bit units (log to the the base base base 2). 2). 2). Higher Higher Higher scores scores scores denote denote denote better better better alignments. alignments. alignments. See See See also also also Similarity Similarity Similarity score, score, score, Distance Distance Distance in in sequence analysis. Alphabet （字母表）（字母表）The total number of symbols in a sequence-4 for DNA sequences and 20 for protein sequences. Annotation （注释）（注释）The The prediction prediction prediction of of of genes genes genes in in in a a a genome, genome, genome, including including including the the the location location location of of of protein-encoding protein-encoding protein-encoding genes, genes, genes, the the sequence of the encoded proteins, anysignificantmatches to other Proteins of known function, and the location of RNA-encoding genes. Predictions are based on gene models; e.g., hidden Markov models models of of of introns introns introns and and and exons exons exons in in in proteins proteins proteins encoding encoding encoding genes, genes, genes, and and and models models models of of of secondary secondary secondary structure structure structure in in RNA. Anonymous FTP （匿名FTP ）When a FTP service allows anyone to log in, it is said to provide anonymous FTP ser-vice. A user can can log log log in in in to to to an an an anonymous anonymous anonymous FTP FTP FTP server server server by by by typing typing typing anonymous anonymous anonymous as as the the user name user name and and his E-mail his E-mail address as a password. Most Web browsers now negotiate anonymous FTP logon without asking the user for a user name and password. See also FTP. ASCII The American Standard Code for Information Interchange (ASCII) encodes unaccented letters a-z, A-Z, A-Z, the the the numbers numbers numbers O-9, O-9, O-9, most most most punctuation punctuation punctuation marks, marks, marks, space, space, space, and and and a a a set set set of of of control control control characters characters characters such such such as as carriage carriage return return return and and and tab. tab. tab. ASCII ASCII ASCII specifies specifies specifies 128 128 128 characters characters characters that that that are are are mapped mapped mapped to to to the the the values values values O-127. O-127. ASCII ASCII tiles tiles tiles are are are commonly commonly commonly called called called plain plain plain text, text, text, meaning meaning meaning that that that they they they only only only encode encode encode text text text without without without extra extra markup. BAC clone （细菌人工染色体克隆）（细菌人工染色体克隆）Bacterial Bacterial artificial artificial artificial chromosome chromosome chromosome vector vector vector carrying carrying carrying a a a genomic genomic genomic DNA DNA DNA insert, insert, insert, typically typically typically 100100100––200 200 kb. kb. Most of the large-insert clones sequenced in the project were BAC clones. Back-propagation （反向传输）（反向传输）When training feed-forward neural networks, a back-propagation algorithm can be used to modify the network weights. After each training input pattern is fed through the network, the network’s output output is is is compared compared compared with with with the the the desired desired desired output output output and and and the the the amount amount amount of of of error error error is is is calculated. calculated. calculated. This This This error error error is is back-propagated through the network by using an error function to correct the network weights. See also Feed-forward neural network. Baum-Welch algorithm （Baum-Welch 算法）算法）An expectation maximization algorithm that is used to train hidden Markov models. Baye ’s rule （贝叶斯法则）（贝叶斯法则）Forms Forms the the the basis basis basis of of of conditional conditional conditional probability probability probability by by by calculating calculating calculating the the the likelihood likelihood likelihood of of of an an an event event event occurring occurring based on the history of the event and relevant background information. In terms of two parameters A and B, the theorem is stated in an equation: The condition-al probability of A, given B, P(AIB), is is equal equal equal to to to the the the probability probability probability of of of A, P(A), A, P(A), times times the the the conditional conditional conditional probability probability probability of of of B, B, B, given given given A, P(BIA), A, P(BIA), divided by the probability of B, P(B). P(A) is the historical or prior distribution value of A, P(BIA) is a new prediction for B for a particular value of A, and P(B) is the sum of the newly predicted values for B. P(AIB) is a posterior probability, representing a new prediction for A given the prior knowledge of A and the newly discovered relationships between A and B. Bayesian analysis （贝叶斯分析）（贝叶斯分析）A A statistical statistical statistical procedure procedure procedure used used used to to to estimate estimate estimate parameters parameters parameters of of of an an an underlyingdistribution underlyingdistribution underlyingdistribution based based based on on on an an observed distribution. See a lso Baye’s rule.Biochips （生物芯片）（生物芯片）Miniaturized arrays of large numbers of molecular substrates, often oligonucleotides, in a defined pattern. They are also called DNA microarrays and microchips. Bioinformatics （生物信息学）（生物信息学）The merger of biotechnology and information technology with the goal of revealing new insights and and principles principles principles in in in biology. biology. biology. /The /The /The discipline discipline discipline of of of obtaining obtaining obtaining information information information about about about genomic genomic genomic or or or protein protein sequence sequence data. data. data. This This This may may may involve involve involve similarity similarity similarity searches searches searches of of of databases, databases, databases, comparing comparing comparing your your your unidentified unidentified sequence sequence to to to the the the sequences sequences sequences in in in a a a database, database, database, or or or making making making predictions predictions predictions about about about the the the sequence sequence sequence based based based on on current current knowledge knowledge knowledge of of of similar similar similar sequences. sequences. sequences. Databases Databases Databases are are are frequently frequently frequently made made made publically publically publically available available through the Internet, or locally at your institution. Bit score （二进制值/ Bit 值）值）The value S' is derived from the raw alignment score S in which the statistical properties of the scoring system used have been taken into account. Because bit scores have been normalized with respect respect to to to the the the scoring scoring scoring system, system, system, they they they can can can be be be used used used to to to compare compare compare alignment alignment alignment scores scores scores from from from different different searches. Bit units From information theory, a bit denotes the amount of information required to distinguish between two two equally equally equally likely likely likely possibilities. possibilities. possibilities. The The The number number number of of of bits bits bits of of of information, information, information, AJ, AJ, AJ, required required required to to to convey convey convey a a message that has A4 possibilities is log2 M = N bits. BLAST （基本局部联配搜索工具，一种主要数据库搜索程序）（基本局部联配搜索工具，一种主要数据库搜索程序）Basic Local Alignment Search Tool. A set of programs, used to perform fast similarity searches. Nucleotide sequences can be compared with nucleotide sequences in a database using BLASTN, for for example. example. example. Complex Complex Complex statistics statistics statistics are are are applied applied applied to to to judge judge judge the the the significance significance significance of of of each each each match. match. match. Reported Reported sequences may be homologous to, or related to the query sequence. The BLASTP program is used to search a protein database for a match against a query protein sequence. There are several other flavours of BLAST. BLAST2 is a newer release of BLAST. Allows for insertions or deletions in the sequences being aligned. Gapped alignments may be more biologically significant. Block （蛋白质家族中保守区域的组块）（蛋白质家族中保守区域的组块）Conserved Conserved ungapped ungapped ungapped patterns patterns patterns approximately approximately approximately 3-60 3-60 3-60 amino amino amino acids acids acids in in in length length length in in in a a a set set set of of of related related proteins. BLOSUM matrices （模块替换矩阵，一种主要替换矩阵）（模块替换矩阵，一种主要替换矩阵）An alternative to PAM tables, BLOSUM tables were derived using local multiple alignments of more distantly related sequences than were used for the PAM matrix. These are used to assess the similarity of sequences when performing alignments. Boltzmann distribution （Boltzmann 分布）分布）Describes Describes the the the number number number of of of molecules molecules molecules that that that have have have energies energies energies above above above a a a certain certain certain level, level, level, based based based on on on the the Boltzmann gas constant and the absolute temperature.Boltzmann probability function(Boltzmann 概率函数) See Boltzmann distribution. Bootstrap analysis A method for testing how well a particular data set fits a model. For example, the validity of the branch branch arrangement arrangement arrangement in in in a a a predicted predicted predicted phylogenetic phylogenetic phylogenetic tree tree tree can can can be be be tested tested tested by by by resampling resampling resampling columns columns columns in in in a a multiple multiple sequence sequence sequence alignment alignment alignment to to to create create create many many many new new new alignments. alignments. alignments. The The The appearance appearance appearance of of of a a a particular particular branch in trees generated from these resampled sequences can then be measured. Alternatively, a sequence sequence may may may be be be left left left out out out of of of an an an analysis analysis analysis to to to deter-mine deter-mine deter-mine how how how much much much the the the sequence sequence sequence influences influences influences the the results of an analysis. Branch length （分支长度）（分支长度）In sequence analysis, the number of sequence changes along a particular branch of a phylogenetic tree. CDS or cds （编码序列）（编码序列）（编码序列） Coding sequence. Chebyshe, d inequality The probability that a random variable exceeds its mean is less than or equal to the square of 1 over the number of standard deviations from the mean. Clone （克隆）（克隆）Population of identical cells or molecules (e.g. DNA), derived from a single ancestor. Cloning V ector （克隆载体）（克隆载体）A molecule that carries a foreign gene into a host, and allows/facilitates the multiplication of that gene in a host. When sequencing a gene that has been cloned using a cloning vector (rather than by by PCR), PCR), PCR), care care care should should should be be be taken taken taken not not not to to to include include include the the the cloning cloning cloning vector vector vector sequence sequence sequence when when when performing performing similarity searches. Plasmids, cosmids, phagemids, Y ACs and PACs are example types of cloning vectors. Cluster analysis （聚类分析）（聚类分析） A method for grouping together a set of objects that are most similar from a larger group of related objects. The relationships are based on some criterion of similarity or difference. For sequences, a similarity or distance score or a statistical evaluation of those scores is used. Cobbler A single sequence that represents the most conserved regions in a multiple sequence alignment. The BLOCKS server uses the cobbler sequence to perform a database similarity search as a way to reach sequences that are more divergent than would be found using the single sequences in the alignment for searches. Coding system (neural networks) Regarding Regarding neural neural neural networks, networks, networks, a a a coding coding coding system system system needs needs needs to to to be be be designed designed designed for for for representing representing representing input input input and and output. output. The The The level level level of of of success success success found found found when when when training training training the the the model model model will will will be be be partially partially partially dependent dependent dependent on on on the the quality of the coding system chosen. Codon usageAnalysis of the codons used in a particular gene or organism. COG （直系同源簇）（直系同源簇）Clusters of orthologous groups in a set of groups of related sequences in microorganism and yeast (S. cerevisiae). These groups are found by whole proteome comparisons and include orthologs and paralogs. See also Orthologs and Paralogs. Comparative genomics （比较基因组学）（比较基因组学）A comparison of gene numbers, gene locations, and biological functions of genes in the genomes of diverse organisms, one objective being to identify groups of genes that play a unique biological role in a particular organism. Complexity (of an algorithm)（算法的复杂性）（算法的复杂性）Describes the number of steps required by the algorithm to solve a problem as a function of the amount of data; for example, the length of sequences to be aligned. Conditional probability （条件概率）（条件概率）The The probability probability probability of of of a a a particular particular particular result result result (or (or (or of of of a a a particular particular particular value value value of of of a a a variable) variable) variable) given given given one one one or or or more more events or conditions (or values of other variables). Conservation （保守）（保守）Changes at a specific position of an amino acid or (less commonly, DNA) sequence that preserve the physico-chemical properties of the original residue. Consensus （一致序列）（一致序列）A A single single single sequence sequence sequence that that that represents, represents, represents, at at at each each each subsequent subsequent subsequent position, position, position, the the the variation variation variation found found found within within corresponding columns of a multiple sequence alignment. Context-free grammars A A recursive recursive recursive set set set of of of production production production rules rules rules for for for generating generating generating patterns patterns patterns of of of strings. strings. strings. These These These consist consist consist of of of a a a set set set of of terminal characters that are used to create strings, a set of nonterminal symbols that correspond to rules and act as placeholders for patterns that can be generated using terminal characters, a set of rules for replacing nonterminal symbols with terminal characters, and a start symbol. Contig （序列重叠群/拼接序列）拼接序列）A A set set set of of of clones clones clones that that that can can can be be be assembled assembled assembled into into into a a a linear linear linear order. order. order. A A A DNA DNA DNA sequence sequence sequence that that that overlaps overlaps overlaps with with another contig. The full set of overlapping sequences (contigs) can be put together to obtain the sequence for a long region of DNA that cannot be sequenced in one run in a sequencing assay. Important in genetic mapping at the molecular level. CORBA （国际对象管理协作组制定的使OOP 对象与网络接口统一起来的一套跨计算机、操作系统、程序语言和网络的共同标准）作系统、程序语言和网络的共同标准）The The Common Common Common Object Object Object Request Request Request Broker Broker Broker Architecture Architecture Architecture (CORBA) (CORBA) (CORBA) is is is an an an open open open industry industry industry standard standard standard for for working working with with with distributed distributed distributed objects, objects, objects, developed developed developed by by by the the the Object Object Object Management Management Management Group. CORBA allows Group. CORBA allows the interconnection of objects and applications regardless of computer language, machine architecture, or geographic location of the computers. Correlation coefficient （相关系数）（相关系数）A numerical measure, falling between - 1 and 1, of the degree of the linear relationship between two variables. A positive value indicates a direct relationship, a negative negative value value value indicates indicates indicates an an an inverse inverse inverse relationship, relationship, relationship, and and and the the the distance distance distance of of of the the the value value value away away away from from from zero zero indicates the strength of the relationship. A value near zero indicates no relationship between the variables. Covariation (in sequences)（共变）（共变）Coincident change at two or more sequence positions in related sequences that may influence the secondary structures of RNA or protein molecules. Coverage (or depth) （覆盖率（覆盖率/厚度）厚度）The average number of times a nucleotide is represented by a high-quality base in a collection of random raw sequence. Operationally, a 'high-quality base' is defined as one with an accuracy of at least 99% (corresponding to a PHRED score of at least 20). Database （数据库）（数据库）A A computerized computerized computerized storehouse storehouse storehouse of of of data data data that that that provides provides provides a a a standardized standardized standardized way way way for for for locating, locating, locating, adding, adding, removing, and changing data. See also Object-oriented database, Relational database. Dendogram A form of a tree that lists the compared objects (e.g., sequences or genes in a microarray analysis) in a vertical order and joins related ones by levels of branches extending to one side of the list. Depth （厚度）（厚度）See coverage Dirichlet mixtures Defined Defined as as as the the the conjugational conjugational conjugational prior prior prior of of of a a a multinomial multinomial multinomial distribution. distribution. distribution. One One One use use use is is is for for for predicting predicting predicting the the expected expected pattern pattern pattern of of of amino amino amino acid acid acid variation variation variation found found found in in in the the the match match match state state state of of of a a a hid-den hid-den hid-den Markov Markov Markov model model (representing one column of a multiple sequence alignment of proteins), based on prior distributions found in conserved protein domains (blocks). Distance in sequence analysis （序列距离）（序列距离）The number of observed changes in an optimal alignment of two sequences, usually not counting gaps. DNA Sequencing （DNA 测序）测序）The The experimental experimental experimental process process process of of of determining determining determining the the the nucleotide nucleotide nucleotide sequence sequence sequence of of of a a a region region region of of of DNA. DNA. DNA. This This This is is done by labelling each nucleotide (A, C, G or T) with either a radioactive or fluorescent marker which which identifies identifies identifies it. it. it. There There There are are are several several several methods methods methods of of of applying applying applying this this this technology, technology, each each with with with their their advantages and disadvantages. For more information, refer to a current text book. High throughput laboratories laboratories frequently frequently frequently use use use automated automated automated sequencers, sequencers, sequencers, which which which are are are capable capable capable of of of rapidly rapidly rapidly reading reading reading large large numbers of templates. Sometimes, the sequences may be generated more quickly than they can be characterised. Domain （功能域）（功能域）A A discrete discrete discrete portion portion portion of of of a a a protein protein protein assumed assumed assumed to to to fold fold fold independently independently independently of of of the the the rest rest rest of of of the the the protein protein protein and and possessing its own function.Dot matrix （点标矩阵图）（点标矩阵图）Dot matrix diagrams provide a graphical method for comparing two sequences. One sequence is written horizontally across the top of the graph and the other along the left-hand side. Dots are placed within the graph at the intersection of the same letter appearing in both sequences. A series of diagonal lines in the graph indicate regions of alignment. The matrix may be filtered to reveal the the most-alike most-alike most-alike regions regions regions by by by scoring scoring scoring a a a minimal minimal minimal threshold threshold threshold number number number of of of matches matches matches within within within a a a sequence sequence window. Draft genome sequence （基因组序列草图）（基因组序列草图）（基因组序列草图） The sequence produced by combining the information from the individual sequenced clones (by creating merged sequence contigs and then employing linking information to create scaffolds) and positioning the sequence along the physical map of the chromosomes. DUST （一种低复杂性区段过滤程序）（一种低复杂性区段过滤程序）A program for filtering low complexity regions from nucleic acid sequences. Dynamic programming （动态规划法）（动态规划法）A dynamic programming algorithm solves a problem by combining solutions to sub-problems that are computed once and saved in a table or matrix. Dynamic programming is typically used when a problem has many possible solutions and an optimal one needs to be found. This algorithm is used for producing sequence alignments, given a scoring system for sequence comparisons. EMBL （欧洲分子生物学实验室，EMBL 数据库是主要公共核酸序列数据库之一）数据库是主要公共核酸序列数据库之一）European Molecular Biology Laboratories. Maintain the EMBL database, one of the major public sequence databases. EMBnet （欧洲分子生物学网络）（欧洲分子生物学网络）European European Molecular Molecular Molecular Biology Biology Biology Network: Network: Network: / / was was established established established in in in 1988, 1988, 1988, and and provides provides services services services including including including local local local molecular molecular molecular databases databases databases and and and software software software for for for molecular molecular molecular biologists biologists biologists in in Europe. There are several large outposts of EMBnet, including EXPASY . Entropy （熵）（熵）From information theory, a measure of the unpredictable nature of a set of possible elements. The higher the level of variation within the set, the higher the entropy. Erdos and Renyi law In a toss of a “fair” coin, the number of heads in a row that can be expected is the logarithm of the number of tosses to the base 2. The law may be generalized for more than two possible outcomes by changing the base of the logarithm to the number of out-comes. This law was used to analyze the number of matches and mismatches that can be expected between random sequences as a basis for scoring the statistical significance of a sequence alignment. EST （表达序列标签的缩写）（表达序列标签的缩写）See Expressed Sequence Tag Expect value (E)（E 值）值）E E value. value. value. The The The number number number of of of different different different alignents alignents alignents with with with scores scores scores equivalent equivalent equivalent to to to or or or better better better than than than S S S that that that are are expected to occur in a database search by chance. The lower the E value, the more significant the score. In a database similarity search, the probability that an alignment score as good as the one found between a query sequence and a database sequence would be found in as many comparisons between random sequences as was done to find the matching sequence. In other types of sequence analysis, E has a similar meaning. Expectation maximization (sequence analysis) An algorithm for locating similar sequence patterns in a set of sequences. A guessed alignment of the sequences is first used to generate an expected scoring matrix representing the distribution of sequence characters in each column of the alignment, this pattern is matched to each sequence, and and the the the scoring scoring scoring matrix matrix matrix values values values are are are then then then updated updated updated to to to maximize maximize maximize the the the alignment alignment alignment of of of the the the matrix matrix matrix to to to the the sequences. The procedure is repeated until there is no further improvement. Exon （外显子）（外显子）。

"NTM" 通常指的是非结核分枝杆菌（Non-tuberculous Mycobacteria），这是一类与结核分枝杆菌（Mycobacterium tuberculosis）在分类学上相似但在疾病表现和治疗方案上有所不同的细菌。

当谈到寻找NTM 的直接同源基因或进行序列比较时，通常涉及到生物信息学和分子生物学的方法。

直接同源基因的比较1.双向最佳匹配（BBH, Bidirectional Best Hits）: 这是一种常用的方法来识别两个物种之间的直接同源基因。

BBH 方法基于两个物种之间的双向最佳BLAST比对结果，即如果一个物种中的基因A是另一个物种中基因B的最佳比对结果，并且基因B也是基因A的最佳比对结果，那么可以认为基因A和基因B是同源的。

2.基因家族和进化分析: 通过构建基因家族并进行进化分析，可以确定NTM与其他物种之间的同源关系。

这可以通过使用如OrthoMCL, MAFFT,FastTree 等工具来完成。

3.保守基因和保守区域: 通过比较多个物种的基因组，可以确定一组保守的基因或保守的DNA区域，这些通常具有功能重要性。

序列比较方法1.全基因组比对（Whole Genome Alignment）: 通过比对两个物种的整个基因组，可以识别同源区域和可能的重组事件。

常用的工具有Mauve,BRIG, MUMmer 等。

2.局部比对工具（Local Alignment Tools）: 如BLAST, BLAT, LAST等，这些工具用于比较两个序列之间的局部相似性。

3.多重序列比对（Multiple Sequence Alignment）: 当涉及到多个物种或序列时，可以使用多重序列比对方法，如Clustal, MUSCLE, MAFFT等。

金标准"金标准" 在生物信息学中通常指的是一种被广泛接受且可靠的方法或数据集，用于评估其他方法或数据集的准确性和可靠性。

园艺学报，2015，42 (11)：2183–2196.Acta Horticulturae Sinicadoi：10.16420/j.issn.0513-353x.2015-0436；http：//www. ahs. ac. cn 2183辣椒全基因组WRKY转录因子的分析刁卫平，王述彬*，刘金兵，潘宝贵，郭广君，戈伟（江苏省农业科学院蔬菜研究所，江苏省高效园艺作物遗传改良重点实验室，南京 210014）摘 要：基于已公布的辣椒全基因组数据，利用生物信息学方法对辣椒WRKY转录因子家族进行全面鉴定和系统命名，并在此基础上对基因分类、染色体定位、系统进化关系和结构域序列保守性进行了研究。

结果表明：辣椒CaWRKY家族包含71个基因，根据WRKY结构域的数量及锌指结构的特征可将其分为GroupⅠ、GroupⅡ和GroupⅢ等3大类，GroupⅡ又可分为Ⅱ（a）、Ⅱ（b）、Ⅱ（c）、Ⅱ（d）和Ⅱ（e）等5个亚类。

辣椒12条染色体上均有WRKY转录因子分布，其中第1号染色体上分布最多，共有10个，第4号染色体上分布最少，仅有2个。

辣椒每类/亚类WRKY几乎含有相同的保守基序。

辣椒WRKY编码的蛋白在132 ~ 869个氨基酸范围内，平均氨基酸数量为373个。

关键词：辣椒；WRKY；转录因子；生物信息学中图分类号：S 641.3 文献标志码：A 文章编号：0513-353X（2015）11-2183-14 Genome-wide Analysis of the WRKY Transcription Factor Family in PepperDIAO Wei-ping，WANG Shu-bin*，LIU Jin-bing，PAN Bao-gui，GUO Guang-jun，and GE Wei （Institute of Vegetable Crops，Jiangsu Academy of Agricultural Sciences，Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement，Nanjing 210014，China）Abstract：In the present study，based on the recently released pepper whole-genome sequences，CaWRKY gene family identification，gene classification，chromosome location，sequence alignment and conserved structure domains of CaWRKY proteins were predicted and analyzed with bioinformatics methods. The results showed that 71 CaWRKY genes were identified which were classified into three main groups（Ⅰ，Ⅱand Ⅲ），with the second group further divided five subgroups[Ⅱ（a），Ⅱ（b），Ⅱ（c），Ⅱ（d）and Ⅱ（e）]. A total of 70 CaWRKY genes were mapped to 12 chromosomes，whereas only CaWRKY70 was not mapped to any particular chromosome. Genome mapping analysis revealed that pepper WRKY genes were enriched on several chromosomes（1，2，3 and 7），especially on chromosome 1 which encompasses the largest number of 10 CaWRKY genes，while chromosome 4 only contained 2 CaWRKY genes. The pepper WRKYs from each group or subgroup were shown to share similar motif compositions，and CaWRKY proteins contained 132–869 amino acids and 373 in average. Our results will provide a platform for functional identification and molecular breeding tudy of WRKY genes in pepper.Key words：pepper；WRKY；transcription factor；bioinformatic收稿日期：2015–08–03；修回日期：2015–11–09基金项目：国家高技术研究发展计划（‘863’计划）项目（2012AA100103）；江苏省自然科学基金项目（2014 BK1380）；国家现代农业产业技术体系建设专项资金项目（CARS-25）；江苏省农业科技自主创新资金体系类项目[CX（12）1004]* 通信作者Author for correspondence（E-mail：wangsbpep@）Diao Wei-ping，Wang Shu-bin，Liu Jin-bing，Pan Bao-gui，Guo Guang-jun，Ge Wei.Genome-wide analysis of the WRKY transcription factor family in pepper. 2184Acta Horticulturae Sinica，2015，42 (11)：2183–2196.转录因子（transcription factor）是指能够结合在基因上游特异核苷酸序列上的蛋白质，以特定的强度在特定的时间与空间调控基因的转录。

F a e M D s p eG ih]C t k(1/3 - 3/3)p e s G NSC 91-2213-E-002-129G898192731G x W j T u tD H G x W j T u t[Z(Kun-Mao Chao)(email: kmchao@.tw)K nw Aw wA b AA…¡C o[ R T]C A¬O§Ú-̲{¶¥¬q«E»Ý¤ÀªR»PÂk¯Çªº¸êC z L h C A i HC O u B M w]W h t L C M A o¦]²Õ§Ç¦C«Ü¤jªº¯S¦â´N¬O¥¦-Ì«D±`A u O@q A]O H U p P A pG H n u A¦b-pºâ®É¶¡¤ÎªÅ¶¡¤W¡A³£¬O¦æ¤£³qC p eD n N O]p@M i h]C n u Az L h]C AU a]cCc O H]C A N L]C(©C q)«t a P CA b o A i H o Bw C M A N o C PC o w A p@A N o F@Wh C C w N os@n A i C]]p F@h C Ru A i T a p h CC A]R F lb@q A H K oCG C R B p]BpAbstractDue to the advancement of genome sequencing technology, more and more genomic sequences have been determined. In the near future, the draft of human genomic sequence will be finished. World-widesequencing capacity is ramping up to the level of one vertebrate genome per year, and after the human and mouse genomes are completed it will turn to chicken,fish, rat, etc. These data, which essentially encode all the genetic information in life, will soon need to be analyzed and classified. By multiple sequence comparison, we are able to locate the conserved regions in the biological sequences. It can also be used to study gene regulation or even infer evolutionary trees. However, these genomic sequences are usuallyvery long. As the sequences are getting longer and longer, there is no doubt that time-efficient and space-saving strategies for multiple sequence alignments will become more and more important in the near future. The purpose of this project is to design a software tool for aligning multiple genomic sequences. It will be used to explore the structure and function of a whole genome sequence.Our idea is based on a given genomic sequence. We first use a very fast method to compare other sequences with the base sequence. Then we roughly determine their relative location. By pasting these sequences according totheir relativity, a simple multiple sequence alignment can be derived. We have implemented a simple multiplealignment program. We have also implemented an efficient algorithm that can accurately compute the score of a multiple sequence alignment. We haveadjusted the bias of the base sequence by extending the segments which were aligned together in the crude alignment. KeywordsSequence analysis,computational genomics, computational biology.We have surveyed the literatures relevant to the multiple sequence alignment problem. In particular, weare interested in the alignment methods dealing with long sequences. In large-scale sequencing projects, the task of converting experimental data into biologically relevant information requires a higher level of abstraction in sequence analysis. Therefore, we have also developed a prototype for genomicsequence visualization tools. A graphic interface allows the user to zoom into any specific area of the resulting alignment.We first compare the selected genomic sequence with all other given sequences. Then we develop a simple pasting program for converting these pairwise alignments into a tentativemultiple sequence alignment. Thepairwise alignments provide theinformation about the possible coherent multiple alignment columns in sequences. What we do here is more or less a pile-up procedure for aligning all sequences together. We first use a very fast method to compare other sequences with the base sequence. Then we roughly determine their relative location. By pasting these sequences according to their relativity, a crude multiple sequence alignment can be derived.To improve the quality of the multiple sequence alignment, a round-robin iterative improvement of a multiple alignment will be initiated in the next year. The improved alignment tool will be used to test some real-world data.We comprise software dedicated to the visualization of resulting alignments so that more biological meaningful information can be extracted. It will provide users a reliable data management system which allows the user to manipulate both the sequences as well as the resulting alignment. It will be a framework that allows several toolsto work together in a cooperative way under the user’s control. Automatic annotation of the alignment will give the users more valuable information.To improve the quality of the multiple sequence alignment, a round-robin iterative improvement of a multiple alignment is initiated. We start by pasting the alignments together, then repeatedly (1) delete an aligned fragment and (2) align that fragment with the remainder of the multiple alignment (using a variant of our yama2 procedure where we need to optimize based on the fact that one of the two alignments must be a single sequence). The improved alignment tool will be used to test some real-world data.We continue improving the alignment tool by other approaches. Specifically, we adjust the bias of the base sequence by extending the segments which were aligned together in the crude alignment. That way, we are able to compensate the situations where the segments are more similar to each other (longer local alignments) than they are to the base genomic sequence. The local alignments we find by iteratively improving the crude alignment created from the pairwise alignments with the base genomic sequence encompass these longer alignments in some way.m[1] Altschul, S., Gish, W., Miller, W., Myers,E. and Lipman, D. (1990) A basiclocal alignment search tool. J. Mol.Biol. 215, 403-410.[2] Altschul, S. and Lipman, D. (1989)Trees, stars, and multiple biologicalsequence alignment. SIAM J. Appl. Math. 49, 197-209.[3] Altschul, S., Madden, T. L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389-3402. [4] Bassett, Jr. D.E., Eisen, M.B. andBoguski, M. S. (1999) Gene expression informatics – it’s all in your mine. Nature Genetics Supplement 21, 51-55. [5] Chao, K. -M. (1999) Calign: aligningsequences with restricted affine gap penalties. Bioinformatics, 15, 298-304. [6] Ephremides, A. and Hajek, B. (1998)Information theory and communication networks: an unconsummated union. IEEE Transactions on Information Theory 44, 2416-2434.[7] Eppstein, D., Gaili, Z., Giancarlo, R. andItaliano, G . (1992) Sparse dynamic programming I: linear cost functions. Journal of the ACM 39, 519-545.[8] Feng, D. and Doolittle, R. (1987)Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol. 25, 351-360.[9] Gusfield, D. (1997) Algorithms onstrings, trees, and sequences: computer science and computational biology. Cambridge University Press .[10] Lenhof, H. Morgenstern, B. andReinert, K. (1999) An exact solution for the segment-to-segment multiplesequence alignment problem. Bioinformatics 15, 203-210.[11] Medigue, C., Rechenmann, F.,Danchin, A. and Viari, A. (1999) Imagene: an integrated computer environment for sequence annotation and analysis. Bioinformatics 15, 2-15. [12] Morgenstern, B., Dress, A., andWerner, T. (1996) Multiple DNA and protein sequence alignment based on segment-to-segment comparison. Proc. Natl. Acad. Sci. 93, 12098-12103. [13] Morgenstern, B., Frech, K., Dress, A.and Werner, T. (1998) DIALIGN: finding local similarities by multiple sequence alignment. Bioinformatics 14, 290-294.[14] Mott, R. (1999) Local sequencealignments with monotonic gap penalties. Bioinformatics 15, 455-462. [15] Setubal, J. and Meidanis, J. (1997)Introduction to computational molecular biology. PWS Publishing Company . [16] Thompson, J. D., Higgins, D. G . andGibson, T. J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22, 4673-4680. [17] Z. Zhang, P. Berman and W. Miller(1998) Alignments without low-scoring regions. J. Computational Biology 5, 197-210.。

sequence的用法和搭配作为名词，sequence可以用于描述生物学、数学、计算机科学等领域中的序列或顺序。

以下是一些常见的用法和搭配：1. DNA sequence：DNA序列，指一段DNA分子中的碱基顺序。

2. protein sequence：蛋白质序列，指蛋白质分子中的氨基酸顺序。

3. nucleotide sequence：核苷酸序列，指核酸分子中的碱基顺序。

4. amino acid sequence：氨基酸序列，指多肽或蛋白质分子中的氨基酸顺序。

5. sequence alignment：序列比对，指将两个或多个序列在相似性的基础上进行比较和对齐的过程。

6. sequence analysis：序列分析，指通过计算、统计和比对等方法研究序列的性质和功能。

7. sequence generator：序列发生器，指能够按照一定规则生成特定顺序序列的程序或设备。

除了科学领域，sequence还可以用于描述时间上的顺序或顺序的发展。

以下是一些常见的用法和搭配：1. chronological sequence：按时间顺序，指按事物发生的时间顺序排列。

2. sequence of events：一系列事件，指按照事件发生的顺序排列的一系列事情。

3. sequence of actions：一系列行动，指按照行动发生的顺序排列的一系列动作。

4. sequence of numbers：一系列数，指按照数字大小或特定规则排列的一系列数值。

5. sequence of steps：一系列步骤，指按特定顺序进行的一系列操作或行为。

作为动词，sequence可以用于描述按照特定的顺序安排或发生。

以下是一些常见的用法和搭配：1. sequence the genome：对基因组进行测序，指按照特定的方法和顺序确定基因组的核酸序列。

2. sequence the steps：按序安排步骤，指按照特定的顺序安排操作或行为的步骤。