The Sol Genomics Network (SGN)—from genotype to phenotype to breeding

The Sol Genomics Network (SGN)—from genotype to phenotype to breeding
The Sol Genomics Network (SGN)—from genotype to phenotype to breeding

D1036–D1041Nucleic Acids Research,2015,Vol.43,Database issue Published online26November2014 doi:10.1093/nar/gku1195

The Sol Genomics Network(SGN)––from genotype to phenotype to breeding

Noe Fernandez-Pozo1,Naama Menda1,Jeremy D.Edwards2,Surya Saha1,3,Isaak Y.Tecle1, Susan R.Strickler1,Aureliano Bombarely4,Thomas Fisher-York1,Anuradha Pujar1, Hartmut Foerster1,Aimin Yan1and Lukas A.Mueller1,5,*

1Boyce Thompson Institute for Plant Research,Ithaca,NY14853,USA,2Dale Bumpers National Rice Research Center,Stuttgart,AR72160,USA,3Department of Plant Pathology and Plant-Microbe Biology,Cornell University, Ithaca,NY14853,USA,4Department of Horticulture,Virginia Polytechnic Institute and State University,Blacksburg, VA24061–0002,USA and5Department of Plant Breeding,Cornell University,Ithaca,NY14853,USA

Received October01,2014;Revised November01,2014;Accepted November03,2014

ABSTRACT

The Sol Genomics Network(SGN,http: //https://www.360docs.net/doc/6e10436416.html,)is a web portal with genomic and phenotypic data,and analysis tools for the Solanaceae family and close relatives.SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato,pepper,eggplant,tobacco and Nicotiana benthamiana.The database also stores loci and phenotype data,which researchers can upload and edit with user-friendly web interfaces.Tools such as BLAST,GBrowse and JBrowse for browsing genomes,expression and map data viewers,a locus community annotation system and a QTL analysis tools are available.A new tool was recently implemented to improve Virus-Induced Gene Silenc-ing(VIGS)constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database,SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-speci?c databases.

INTRODUCTION

In recent years,high-quality reference genomes have been generated for many plant genomes including Arabidopsis, rice,maize,poplar,grapevine and tomato.The Sol Ge-nomics Network(SGN,https://www.360docs.net/doc/6e10436416.html,)was orig-inally created to store genetic and genomic information, mainly genetic mapping and expressed sequence tags data for Solanaceous species,such as tomato,potato,pepper, eggplant,petunia and tobacco.SGN is the hub for the tomato genome sequencing project and hosts whole genome sequences of tomato wild accessions,potato,pepper,Nico-tiana benthamiana,tobacco and eggplant.It also hosts non-Solanaceae reference genomes,such as Arabidopsis thaliana (1),Vitis vinifera(2)and rice(3–6)for comparative analysis. However,reference genomes by themselves,although use-ful for addressing many questions,are of limited usefulness for understanding a key question of biology––how pheno-types result from genotypes.To address this question,also sometimes referred to as‘G2P’,information beyond a sin-gle reference genome is needed.To address G2P questions,a genome database must not only store the reference genome information(such as gene models,expression data and re-peat information),but also include two additional impor-tant data types:sequence variation data and phenotypic data.Based on these data,analyses such as Genome-Wide Association Studies are possible that can identify genomic regions contributing to a trait of interest if necessary crite-ria are met.Since genotyping has become cheaper to per-form,it is the phenotyping that presents a bottleneck(7), therefore,new phenotyping systems are needed.A priority for the SGN database has been to provide a comprehensive module for storing genotypic and phenotypic information as databases that integrate genotypes and phenotypes seam-lessly are required for breeding.

An important application of the growing genomic and phenotypic data is crop improvement.What roles can genome databases play in breeding better crops?Genomic data and breeding data need to be tightly integrated to inform breeding.Breeding data are a new dimension for genome databases,mainly because breeding is a complex process that entails crossing,planting and selecting plants, all of which should ideally be managed from within the genome database.The SGN platform has recently been supplemented with a breeding management system that is tailored to genomic-based breeding approaches,such as Genomic Selection(8).It is currently implemented in a database called Cassavabase(https://www.360docs.net/doc/6e10436416.html,/)that is based on the SGN platform.

*To whom correspondence should be addressed.Tel:+6072556557;Fax:+6072541242;Email:lam87@https://www.360docs.net/doc/6e10436416.html,

C The Author(s)2014.Published by Oxford University Press on behalf of Nucleic Acids Research.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License(https://www.360docs.net/doc/6e10436416.html,/licenses/by/4.0/),which permits unrestricted reuse,distribution,and reproduction in any medium,provided the original work is properly cited.

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THE SGN GENOME DATABASE

Genome sequences form the backbone of the genomics ap-proach and a significant component of the SGN database is devoted to the storage of genome information.The genome database implements functionality that is typical for model organism databases(MODs),such as genome viewers and a locus database,but there are some notable differences to other MODs.SGN is a community-driven database,in which data is under the direct control of users who can up-date or delete the information he or she submitted at any time through easy-to-use web interfaces.Such community-curated databases are still rare among MODs,most of which are being maintained exclusively by in-house cura-tors.While community-based systems cannot replace pro-fessional curation,in-house curator-based models are much more expensive to operate and are not scalable for large re-search communities.The problem with community-based systems is the potential lack of community involvement and interest.Our experience is that community involvement re-quires active outreach and will not come about sponta-neously.Currently,SGN has132active community cura-tors,who are responsible for editing of570distinct loci,with a total of661edits and data contributions.The majority of these curators(>90%)were approached by an SGN curator (email or in person at conferences and meetings).Another feature of the SGN system is its support for multiple species, with the comparative viewer,an SGN custom application

(9).

A well-curated genome provides a list of annotated loci and SGN has a comprehensive system for dealing with loci. Each locus has a locus detail page that contains all the in-formation about it in the database.The page is divided into sections organized by data type.The first section,‘Locus details’,contains basic information about a locus,such as its name,symbol and textual description.This section is editable only by a specially assigned user called the‘locus editor’and SGN-based curators.The locus editor is a sci-entist who is considered an expert on the locus,preferably someone involved in its characterization.The locus editor is assigned by an SGN curator after contacting authors of publications involving the https://www.360docs.net/doc/6e10436416.html,ers from the community can also request locus editor privileges through a link on the each locus page.SGN curators will then review the request and grant it if the user fulfills locus editor criteria,which are affiliation with research institute and proven work related to the gene.While only the locus editors can edit the gene name,symbol and function,other SGN users with‘submit-ter’privileges can contribute to all the other sections on the page,such as associated publications,GenBank sequences, images and ontology annotations.Each submitted item is owned by the respective submitter,who has the right to fur-ther edit or delete the item.A full description of the inter-face is presented in Menda et al.(6),with newly added fea-tures of linking the locus with a gene model,which provides cross-links between the genome and the experimentally de-scribed genetic locus and manual curation of gene families (10).The locus editing is also supported by an SGN cus-tom tool called solQTL(11),which allows users to upload raw phenotype and genotype data from QTL studies to the SGN https://www.360docs.net/doc/6e10436416.html,ers can use the tool to run QTL anal-ysis for their traits and link predicted QTLs to annotated genomic regions in the database.This cross-linking enables them to exploit the rich genomic data annotations and per-haps identify candidate genes underlying the phenotype of their traits.

Interacting with users is a critical part of a web por-tal,such as SGN.We have implemented a comprehensive tracking system for managing user feedback and our in-ternal software development https://www.360docs.net/doc/6e10436416.html,ers submit feed-back and feature requests via the web contact form or email to sgn-feedback@https://www.360docs.net/doc/6e10436416.html,.We use the Trac ticket management system(https://www.360docs.net/doc/6e10436416.html,/)to track all user interactions.Each ticket is assigned to an SGN de-veloper for follow-up and resolution in a timely manner. The source code for SGN and for all the custom tools de-veloped by the SGN programmers is maintained publicly in the Github open source repository(https://https://www.360docs.net/doc/6e10436416.html,/ solgenomics/sgn).A detailed description of the technology behind the SGN platform is available in previous publica-tions of the SGN database(5).The progress of website fea-ture requests by users as well as internal software develop-ment tasks is managed with the Github Issues system. Since the last publication of SGN in the Nucleic Acids Research Database issue(5),15Solanaceae genome se-quences and draft genomes have been published(12–20), including the closely related species Coffea canephora(21) (Table1)while other genomes,such as Petunia axillaris and Petunia inflata,will be published soon(22).All the public data from these species,including different genome builds and their annotations are stored and shared on the SGN FTP site(ftp://https://www.360docs.net/doc/6e10436416.html,/genomes/).Annotations include the gene(transcripts and proteins)sequences and their coordinates in the genome,in FASTA and GFF for-mats,respectively.The information of the finished genomes, i.e.the ones assembled to the level of pseudo-molecules,is incorporated into the SGN database and tools.The draft genomes data are only added to the SGN tools including Basic Local Alignment Search Tool(BLAST)(23),genome browsers and SolCyc biochemical pathways,with some up-dates on-going or imminent.SGN BLAST,has a new inter-face to find sequences based on sequence similarity.Now it is easier to access the most popular BLAST databases from Solanaceae model plants.It includes genome data sets (chromosomes or scaffolds),gene and protein sequences, transcriptome contigs,markers,organelles and other data sets for Solanaceae species.

SGN also provides SolCyc,a set of Pathway/Genome Databases(PGDB)based on MetaCyc information that provide an encyclopedia of metabolic pathways and en-zymatic reactions for the Solanaceae species(24).SolCyc was created using MetaCyc as a reference database and the Pathologic component of Pathway Tools software(25).The Metabolic networks were predicted based on the annota-tions of the genes from the Solanaceae species.Previous SolCyc PGDBs(5)were based on the annotations from contigs from transcriptome assemblies(unigenes).Current pathways were updated using the annotations from the gene models predicted for the reference genome sequence of tomato(LycoCyc),potato(PotatoCyc),N.benthamiana (BenthamianaCyc)and pepper(CapCyc).Other species will be updated soon.

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Table1.List of published genome sequences and draft genomes from Solanaceae and close relative species

Published genome Date of publication Journal Reference Potato(Solanum tuberosum)July2011Nature(12) Tomato(Solanum lycopersicum‘Heinz1706 )May2012Nature(13) Solanum pimpinellifolium Draft May2012Nature(13) Nicotiana benthamiana Draft July2012MPMI(14) Nicotiana sylvestris Draft June2013Genome Biol.(15) Nicotiana tomentosiformis Draft June2013Genome Biol.(15) Pepper(Capsicum annuum)CM334Jan.2014Nat.genet.(16) Capsicum annuum Zunla-1Jan.2014PNAS(17) Capsicum annuum glabriusculum Jan.2014PNAS(17) Tobacco(Nicotiana tabacum)TN90Draft https://www.360docs.net/doc/6e10436416.html,mun.(18) Nicotiana tabacum K326Draft https://www.360docs.net/doc/6e10436416.html,mun.(18) Nicotiana tabacum BX Draft https://www.360docs.net/doc/6e10436416.html,mun.(18) Eggplant(Solanum melongena)Draft Aug.2014DNA Res.(19) Solanum pennellii Draft Sep.2014Nat.Genet.(20) Coffee(Coffea canephora)Sep.2014Science(21)

Another important feature of genome databases are genome viewers.Genome viewers enable users to explore a genome or genomic region using an intuitive and graph-ical display of the genome.They also present the users with informative tracks like expression data,gene mod-els,markers,SNPs and many other data that allows users to analyze all this information visually to facilitate the study of a genomic region or feature.SGN has provided this functionality in the past using the GBrowse system (26),but recently added the newer JBrowse(27),which of-fers a more modern and fluid user experience.All SGN genomes are being migrated to this new browser.As of October2014,tomato genome version2.50and annota-tion ITAG2.4,tomato genome version2.40and annota-tion ITAG2.3,tomato accessions,Solanum pennellii,Nico-tiana benthamiana draft genome,pepper genome and Nico-tiana tabacum genome are available on JBrowse.Eggplant genome will be added in the next months.

To take advantage of the newly available genome se-quences,new web tools have been developed at SGN.One example is the SGN Virus-Induced Gene Silencing(VIGS) tool(https://www.360docs.net/doc/6e10436416.html,/tools/vigs),created to help re-searchers design VIGS constructs(Fernandez-Pozo et al., submitted).This tool was developed to be highly interactive using AJAX(28)for the communications between the front end and the back end,allowing fast calculations without reloading the website.It also uses HTML5to draw graph-ical elements,which allows the reorganization and redraw-ing of elements on the fly.JQuery(29)(https://www.360docs.net/doc/6e10436416.html,/) and other Javascript libraries are used to improve the user experience.In recent years,SGN has made use of these web languages and libraries on the new breeding tools and other software,making them more user-friendly and interactive. Another popular tool for SGN users is the tomato unigene converter,which can translate old unigenes names used in publications,such as microarray experiments,to the newest unigene version or the gene name assigned in the reference genome.

THE SGN PHENOTYPE DATABASE

For genotype to phenotype questions,phenotypic informa-tion is of obvious importance and SGN has a suite of tools to deal with phenotypic information.Phenotypes are stored in the Chado Natural Diversity schema(30)that was co-developed by a group of MODs,including SGN and the Genome Database for Rosaceae(31).Each database de-veloped their own user interface around the core database schema.The central concept in the Chado schema is the plant line or stock,which can be characterized with pheno-typic data and where the traits are stored using ontologies and other metadata.

There are different types of stocks,including‘accession’and‘plot’.Accessions are plant lines that are available as germplasm,whereas plots are specific accessions that have been planted in the field and which are associated with a trial and other metadata,such as the field location and the field layout.The distinction between accessions and plots is important in a breeding program because selection de-cisions are made at the level of accessions based on phe-notypic data from(sometimes multiple)plots derived from that https://www.360docs.net/doc/6e10436416.html,rmation about the experimental design of the field layout(consisting of plots)is necessary to make assessments of an accession from raw plot data.Pheno-types are usually associated with plots to provide all the necessary metadata for the phenotyping experiment.In a phenotyping experiment,the actual phenotypic values are recorded for each plot,but also includes the date of the ex-periment,the operator,notes and other conditions.For the Solanaceae,a phenotype ontology was developed based on previous large-scale phenotyping experiments(5).The on-tology was mapped to the standard Plant Ontology for com-patibility with other projects.For each group of species,it is possible to load custom ontologies into the database that describe the specific traits of interest for that species.For example,the SGN system has been applied for cassava in Cassavabase,in which a cassava-specific ontology is used to describe the cassava traits.This ontology was developed by the Crop Ontology Consortium(https://www.360docs.net/doc/6e10436416.html,/) (32,33),who have also developed ontologies for a wide va-riety of other plants.It is recommended to use one of these standard ontologies if they are suitable for a given project. THE SGN BREEDING FUNCTIONS

The ultimate promise of plant genomics is the improvement of crops.However,until now,the effect of genomics on crop improvement has lagged.How can genomics achieve this

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promise?The answer is in the application of new breeding paradigms,such as Genomic Selection(8).Genomic Selec-tion was developed for animal breeding and is now being ap-plied to plants.In a nutshell,Genomic Selection uses pheno-typic and genotypic information to correlate genotypes with phenotypes,aiming to predict phenotypes from genotypic information.Since genotyping is now fast,relatively cheap and can be done at the seedling stage,the breeding cycles be-come cheaper and faster.However,these strategies require robust databases of vast amounts of genotypic and pheno-typic information,as well as algorithms and interfaces to perform the model building and breeding value prediction. The SGN system was a good starting point for the imple-mentation of a genomic-based breeding system as it already incorporated comprehensive tools to accommodate pheno-typic and genotypic information.The system was applied to a cassava breeding project by creating a separate website called Cassavabase.Detailed functionality of Cassavabase will be described in a separate publication. CROSSES,PEDIGREES AND FIELD LAYOUTS Tracking genotype and phenotype within a breeding pro-gram presents a number of challenges for a database because the data are more complicated than a simple genotype-phenotype matrix.Breeding lines can be related to or derived from other lines in complex ways.Phenotypes need to be assessed with multiple replicates and sometimes multiple locations.Because breeding lines are sometimes heterogenous mixtures or segregating populations,the iden-tity and source of material needs to be tracked down to the level of the individual plot or plant.The system captures this information by providing breeders with tools for managing their breeding program and thereby automatically populat-ing the database.Important activities in breeding are cross-ing lines,tracking pedigrees,designing field trials and col-lecting data.The system provides functionality for all these use cases.It has an easy-to-use cross-function that allows existing accessions to be crossed with each other,selfed or open pollinated.The function is implemented in a simple dialog window in which the parents and type of cross can be selected and other metadata can be provided,such as number of flowers involved in the cross,operator informa-tion and number of seeds obtained.The resulting accessions from a cross can be generated in the database when enter-ing the cross,or added later.The newly generated accessions from a cross will automatically have the correct pedigree in-formation in the database and will appear in the pedigree viewer.

Field layouts can be calculated based on several meth-ods,including complete and incomplete block designs(e.g. augmented).The field layout dialog provides a simple way to create the layouts from a list of accessions.The acces-sions will be distributed in the layout by creating plot entries linked to the accessions.The plot entries then have meta-data associated with them,such as the breeding program, the field layout type and the location and ultimately also the phenotypic scores recorded on the field.The field lay-outs and trait information can be downloaded to a tablet running the Android Field book application(34)for the collection of phenotypes and collected phenotypes can be uploaded to the database.

As a recurring theme,the breeding process requires the creation and maintenance of lists of different types of items, such as accessions or plots,which are needed for different purposes,for example,field layout generation or crosses.To simplify the management of lists,a list manager was imple-mented and integrated into the website.Lists can store any number of text elements that can designate,for example, accessions or plots.Each list has a name and also has an assigned type;a list of type accession should only contain valid accession identifiers.Each list type has a validation function,which can check every element in the list and will report elements that are invalid.The list interface is tightly integrated with the tools on the website,with many tools re-quiring the input of a list.To create lists,users can type the list elements one by one in the list creation dialog,but this can be tedious for long lists.Therefore,a number of tools are available to automatically create larger lists.One such tool is the search wizard,which can select a large number of acces-sions or plots based on the any combination of trial name, year or location.Also,the results of an accession search can be fed directly into a list.

FUTURE DIRECTIONS

The SGN code is currently used and will be used as the core for new species-specific breeding databases,such as the pre-viously mentioned CassavaBase.The huge increase of SGN data produced in recent years for experiments like RNA-seq and Genotyping-by-Sequencing(35)is a challenge to store as this can not be properly handled by relational databases. The new SGN applications will use indexed files for fast and easy storage as well as retrieval of these data.As a result, SGN will be a mixed database that combines a traditional relational database with NoSQL methods(36)like indexed files for big data.More features for breeders will be added and a Tomato Expression Atlas for several developmental stages and tissues isolated using laser dissection will be de-veloped(already in progress).In general,SGN tools will be focused on integrating the genomic and phenotypic data with expression data and incorporating new tools useful for researchers and breeders.Specifically,a Genomic Selection tool,already implemented on https://www.360docs.net/doc/6e10436416.html, will be avail-able on SGN.The tool will enable Genomic Selection by breeders who will be able to build prediction models to es-timate genomic estimated breeding values of selection can-didates.Data analysis and visualization will be made more interactive using modern web programming libraries such as jQuery.

SUMMARY AND CONCLUSIONS

Advances in genomics have enabled researchers to more ef-ficiently identify and characterize genes.This has been pos-sible in part because of the MODs that make the genome in-formation easily accessible to researchers.The SGN system is a flexible,community-curated,clade-oriented system that integrates a number of Solanaceae species genomes,includ-ing tomato,potato and pepper,with comparative features and graphical tools to support genomics researches.The

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next step in the genomics advances is to transform breed-ing.This requires novel systems that combine traditional genomics databases with breeding applications.The SGN platform has been expanded in this direction in recent years. The system has been applied to crops outside of SGN itself and is currently used for cassava and sweet potato.

FUNDING

National Science Foundation(NSF);United States Depart-ment of Agriculture(USDA);Cornell University;Boyce Thompson Institute.Funding for open access charge:NSF [IOS#0820612].

Conflict of interest statement.None declared.

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m a t l a b符号运算函数大 全 The Standardization Office was revised on the afternoon of December 13, 2020

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基本初等函数图像及性质大全(初中高中)

一、一次函数与二次函数 (1)二次函数解析式的三种形式 ①一般式:2 ()(0)f x ax bx c a =++≠ ②顶点式:2 ()()(0)f x a x h k a =-+≠ ③两根式:12()()()(0)f x a x x x x a =--≠ (2)求二次函数解析式的方法 ①已知三个点坐标时,宜用一般式. ②已知抛物线的顶点坐标或与对称轴有关或与最大(小)值有关时,常使用顶点式. ③若已知抛物线与x 轴有两个交点,且横线坐标已知时,选用两根式求()f x 更方便. (3

①.二次函数2 ()(0)f x ax bx c a =++≠的图象是一条抛物线,对称轴方程为,2b x a =- 顶点坐标是2 4(,)24b ac b a a -- ②当0a >时,抛物线开口向上,函数在(,]2b a -∞- 上递减,在[,)2b a -+∞上递增,当2b x a =- 时,2 min 4()4ac b f x a -=;当0a <时,抛物线开口向下,函数在(,]2b a -∞-上递增,在[,) 2b a -+∞上递减,当2b x a =-时,2 max 4()4ac b f x a -=. 二、幂函数 (1)幂函数的定义 叫做幂函数,其中x 为自变量,α是常数. 过定点:所有的幂函数在(0,)+∞都有定义,并且图象都通过点(1,1).

(1)根式的概念:如果,,,1n x a a R x R n =∈∈>,且n N +∈,那么x 叫做a 的n 次方根. (2)分数指数幂的概念 ①正数的正分数指数幂 的意义是:0,,,m n a a m n N +=>∈且1)n >.0的正分数指数幂 等于0. ②正数的负分数指数幂 的意义是: 1()0,,,m m n n a a m n N a -+==>∈且1)n >.0的负分数指数幂没有意义. (3)运算性质 ①(0,,)r s r s a a a a r s R +?=>∈ ②()(0,,)r s rs a a a r s R =>∈ ③()(0,0,)r r r ab a b a b r R =>>∈

世界港口代码

南美航线 港口代码港口中文名称港口英文名称所在国家ACADH英雄港ANGRA DO HEROISMO 大西洋群岛 ARBBL布兰卡港BAHIA BLANCA 阿根廷 ARCAM坎帕纳CAMPANA 阿根廷 ARCDR里瓦达维亚CORNODORO RIVADAVIA 阿根廷 ARCON康塞普西翁CONCEPCION 阿根廷 ARDIA迪亚曼泰DIAMANTE 阿根廷 ARENA布宜诺斯艾利斯BUENOS AIRES 阿根廷 ARIBI伊比奎IBICUY 阿根廷 ARLPI拉普拉塔LA PLATA 阿根廷 ARMDP马德普拉塔MAR DEL PLATA 阿根廷 ARMEN门多萨MONDOZA 阿根廷 ARNEC内利切阿NOCOCHEA 阿根廷 ARPAC阿塞维多港PUERTO ACEVEDO 阿根廷 ARPCO科罗拉多角PUNTA COLORADA 阿根廷 ARPDE德塞阿多港PUERTO DESEADO 阿根廷 ARPMA马德林港PUERTO MADRYN 阿根廷 ARPQU蓬塔基利亚PUNTA QUILLA 阿根廷 ARQUE克肯QUEQUEN 阿根廷 ARRAM拉马约RAMALLO 阿根廷 ARRCA雷卡拉达RECALADA 阿根廷 ARRGA里奥加耶戈斯RIO GALLEGOS 阿根廷 ARRGR里奥格兰德RIO GRANDE,AR 阿根廷 ARROS罗萨里奥ROSARIO 阿根廷 ARSAE圣安东尼奥SAN ANTONIO ESTE 阿根廷 ARSCR圣克鲁斯SANTA CRUZ(ARG.) 阿根廷 ARSFE圣菲SANTA FE 阿根廷 ARSFO圣弗尔南多SAN FERNANDO,AR 阿根廷 ARSLO圣洛伦索SAN LPRENZ0 阿根廷 ARSNS圣尼古拉斯SAN NICOLAS 阿根廷 ARSPE圣佩德罗SAN PEDRO,AR 阿根廷 ARSSB圣塞瓦斯蒂安SAN SEBASTIAN BAY 阿根廷 ARUSH乌斯怀亚USHUAIA 阿根廷 ARVCO孔斯蒂图西翁镇VILLA CONSTITUCION 阿根廷 ARZAR萨拉特ZARATE 阿根廷 BRABA阿里亚布兰卡AREIA BRANCA 巴西

Excel函数符号表

Excel函数用途、参数、用法速查表(按Ctrl+F搜索) 一、数据库函数(13条) DAVERAGE DCOUNT DCOUNTA DGET DMAX DMIN DPRODUCT DSTDEV DSTDEVP DSUM DVAR DVARP GETPIVOTDATA 二、日期与时间函数(20条) DATE DATEVaLUE DAY DAYS360 EDATE EOMONTH HOUR MINUTE MONTH NETWORKDAYS NOW SECOND TIME TIMEVaLUE TODAY WEEKDAY WEEKNUM WORKDAY YEAR YEARFRAC 三、外部函数(2条) EUROCONVERT SQL、REQUEST 四、工程函数(39条) BESSELI BESSELJ BESSELK BESSELY BIN2DEC BIN2HEX BIN2OCT COMPLEX CONVERT DEC2BIN DEC2HEX DEC2OCT DELTA ERF ERFC GESTEP HEX2BIN HEX2DEC HEX2OCT IMABS IMAGINARY IMARGUMENT MCONJUGATE IMCOS IMDIV IMEXP IMLN IMLOG10 IMLOG2 IMPOWER IMPRODUCT IMREAL IMSIN IMSQRT IMSUB IMSUM OCT2BIN OCT2DEC OCT2HEX 五、财务函数(52条) ACCRINT ACCRINTM AMORDEGRC AMORLINC COUPDAYBS COUPDAYS COUPDAYSNC COUPNUM COUPPCD CUMIPMT CUMPRINC DB DDB DISC DOLLARDE DOLLARFR DURATION EFFECT FV FVSCHEDULE INTRATE IPMT IRR ISPMT MDURATION MIRR NOMINAL NPER NPV ODDFPRICE ODDFYIELD ODDLPRICE ODDLYIELD PMT PPMT PRICE PRICEDISC PRICEMAT PV RATE RECEIVED SLN SYD TBILLEQ TBILLPRICE TBILLYIELD VDB XIRR XNPV YIELD YIELDDISC YIELDMAT 六、信息函数(9条) CELL ERROR、TYPE INFO IS 类ISEVEN ISODD N NA TYPE

电子表格函数中的符号是什么意思

类别是什么意思电子表格函数中的符号是什么意思? 运算符类型 计算运算符分为四种不同类型:算术、比较、文本连接和引用。运算符 说明 :(冒号) (单个空格) ,(逗号) 引用运算符 - 负数(如 -1) % 百分比 ^ 乘方 * 和 / 乘和除 + 和 - 加和减 & 连接两个文本字符串(串连) ==

比较运算符 算术运算符 若要完成基本的数**算(如加法、减法或乘法)、合并数字以及生成数值结果,请使用以下算术运算符。 算术运算符 含义 示例 +(加号) 加法 3+3 -(减号) 减法负数 3-1 -1 *(星号) 乘法 3*3 /(正斜杠) 除法 3/3 %(百分号) 百分比 20% ^(脱字号)

乘方 3^2 比较运算符 可以使用下列运算符比较两个值。当用运算符比较两个值时,结果为逻辑值:TRUE 或 FALSE。比较运算符 含义 示例 =(等号) 等于 A1=B1 >(大于号) 大于 A1>B1 =(大于等于号) 大于等于 A1>=B1 (不等号) 不等于 A1B1 文本连接运算符 可以使用与号 (&) 联接或连接一个或多个文本字符串,以生成一段文本。 文本运算符

含义 示例 &(与号) 将两个文本值连接或串起来产生一个连续的文本值 ("North"&"wind") 引用运算符 可以使用以下运算符对单元格区域进行合并计算。 引用运算符 含义 示例 :(冒号) 区域运算符,生成对两个引用之间的所有单元格的引用,包括这两个引用 B5:B15 ,(逗号) 联合运算符,将多个引用合并为一个引用 SUM(B5:B15,D5:D15) (空格) 交叉运算符,生成对两个引用共同的单元格的引用 B7:D7 C6:C8 Excel 执行公式运算的次序 在某些情况中,执行计算的次序会影响公式的返回值,因此,了解如何确定计算次序以及如何更改次序以获得所需结果非常重要。

函数公式大全

三角函数诱导公式 常用的诱导公式有以下几组: 公式一: 设α为任意角,终边相同的角的同一三角函数的值相等: sin(2kπ+α)=sinα cos(2kπ+α)=cosα tan(2kπ+α)=tanα cot(2kπ+α)=cotα 公式二: 设α为任意角,π+α的三角函数值与α的三角函数值之间的关系:sin(π+α)=-sinα cos(π+α)=-cosα tan(π+α)=tanα cot(π+α)=cotα 公式三: 任意角α与-α的三角函数值之间的关系: sin(-α)=-sinα cos(-α)=cosα tan(-α)=-tanα cot(-α)=-cotα 公式四: 利用公式二和公式三可以得到π-α与α的三角函数值之间的关系:sin(π-α)=sinα cos(π-α)=-cosα tan(π-α)=-tanα cot(π-α)=-cotα 公式五: 利用公式一和公式三可以得到2π-α与α的三角函数值之间的关系:sin(2π-α)=-sinα cos(2π-α)=cosα

tan(2π-α)=-tanα cot(2π-α)=-cotα 公式六: π/2±α与α的三角函数值之间的关系: sin(π/2+α)=cosα cos(π/2+α)=-sinα tan(π/2+α)=-cotα cot(π/2+α)=-tanα sin(π/2-α)=cosα cos(π/2-α)=sinα tan(π/2-α)=cotα cot(π/2-α)=tanα 诱导公式记忆口诀 ※规律总结※ 上面这些诱导公式可以概括为: 对于k·π/2±α(k∈Z)的个三角函数值, ①当k是偶数时,得到α的同名函数值,即函数名不改变; ②当k是奇数时,得到α相应的余函数值,即 sin→cos;cos→sin;tan→cot,cot→tan. (奇变偶不变) 然后在前面加上把α看成锐角时原函数值的符号。 (符号看象限) 例如: sin(2π-α)=sin(4·π/2-α),k=4为偶数,所以取sinα。 当α是锐角时,2π-α∈(270°,360°),sin(2π-α)<0,符号为“-”。 所以sin(2π-α)=-sinα 上述的记忆口诀是: 奇变偶不变,符号看象限。 公式右边的符号为把α视为锐角时,角k·360°+α(k∈Z),-α、180°±α,360°-α所在象限的原三角函数值的符号可记忆 水平诱导名不变;符号看象限。 各种三角函数在四个象限的符号如何判断,也可以记住口诀“一全正;二正弦;三为切;四余弦”. 这十二字口诀的意思就是说: 第一象限内任何一个角的四种三角函数值都是“+”; 第二象限内只有正弦是“+”,其余全部是“-”; 第三象限内切函数是“+”,弦函数是“-”; 第四象限内只有余弦是“+”,其余全部是“-”.

matlab符号运算函数大全

3.1 算术符号操作 命令+、-、*、.*、\、.\、/、./、^、.^、’、.’ 功能符号矩阵的算术操作 用法如下: A+B、A-B 符号阵列的加法与减法。 若A与B为同型阵列时,A+B、A-B分别对对应分量进行加减;若A与B中至少有一个为标量,则把标量扩大为与另外一个同型的阵列,再按对应的分量进行加减。 A*B 符号矩阵乘法。 A*B为线性代数中定义的矩阵乘法。按乘法定义要求必须有矩阵A 的列数等于矩阵B的行数。即:若 A n*k* B k*m=(a ij)n*k.*(b ij)k*m= C n*m=(c ij)n*m,则,i=1,2,…,n;j=1,2,…,m。 或者至少有一个为标量时,方可进行乘法操作,否则将返回一出错 信息。 A.*B 符号数组的乘法。 A.*B为按参量A与B对应的分量进行相乘。A与B必须为同型阵 列,或至少有一个为标量。即: A n*m.* B n*m=(a ij)n*m.*(b ij)n*m= C n*m=(c ij)n*m,则c ij= a ij* b ij,i=1,2,…,n; j=1,2,…,m。 A\B 矩阵的左除法。 X=A\B为符号线性方程组A*X=B的解。我们指出的是,A\B近似地 等于inv(A)*B。若X不存在或者不唯一,则产生一警告信息。矩阵 A可以是矩形矩阵(即非正方形矩阵),但此时要求方程组必须是 相容的。 A.\B 数组的左除法。 A.\B为按对应的分量进行相除。若A与B为同型阵列时, A n*m.\ B n*m=(a ij)n*m.\(b ij)n*m= C n*m=(c ij)n*m,则c ij= a ij\ b ij,i=1,2,…,n; j=1,2,…,m。若若A与B中至少有一个为标量,则把标量扩大为与另 外一个同型的阵列,再按对应的分量进行操作。 A/B 矩阵的右除法。 X=B/A为符号线性方程组X*A=B的解。我们指出的是,B/A粗略地 等于B*inv(A)。若X不存在或者不唯一,则产生一警告信息。矩阵 A可以是矩形矩阵(即非正方形矩阵),但此时要求方程组必须是 相容的。 A./B 数组的右除法。 A./B为按对应的分量进行相除。若A与B为同型阵列时, A n*m./ B n*m=(a ij)n*m./(b ij)n*m= C n*m=(c ij)n*m,则c ij= a ij/b ij,i=1,2,…,n; j=1,2,…,m。若A与B中至少有一个为标量,则把标量扩大为与另外 一个同型的阵列,再按对应的分量进行操作。 A^B 矩阵的方幂。

全球主要港口及代码

地区代码港口代码英文名中文名航线ACFUN FUNCHAL 丰沙尔东西非 ACLPA LASPALMAS 拉斯帕马斯东西非 阿联酋AE AEABD ABU DHABI 阿布扎比波红 AEDAS DAS ISLAND 达斯岛波红 AEDUB DUBAI 迪拜波红 AEFUJ FUJARAH 富查伊拉波红 AEJAL JEBEL ALI 阿里山波红 AEKFA KHOR FAKKAN 豪尔费坝波红 AESJH SHARJAH 沙迦波红 ANKRA KRALENDIJK 克拉伦代克中南美 ANORA ORANJESTAD 奥拉涅斯塔德中南美 ANWIL WILLEMSTAD 威廉斯塔德中南美 安哥拉AO AOLUA LUANDA 罗安达东西非 阿根廷AR ARBNA BUENOS AIRES 布宜诺斯艾利斯南非南美 ASPGO PAGO PAGO 帕里帕果澳新 澳大利亚AU AUADE ADELAIDE 阿德莱德澳新 AUBLB BELL BAY 贝尔贝澳新 AUBNE BURNIE 伯尼澳新 AUBRI BRISBANE 布里斯班澳新 AUFRE FREMANTLE 弗里曼特尔澳新 AUHBT HOBART 霍巴特澳新 AUMEL MELBOURNE 墨尔本澳新 AUPTH PERTH 佩斯澳新 AUSYD SYDNEY 悉尼澳新 巴巴多斯BB BBBTN BRIDGETOWN 布里奇敦中南美 孟加拉BD BDCTG CHITTAGONG 吉大港东南亚 比利时BE BEANT ANTWERPEN 安特卫普欧洲 BEBRU BRUSSELS 布鲁塞尔欧洲 BEZEE ZEEBRUGGE 泽布吕赫欧洲 保加利亚BG BGVAR VARNA 瓦尔纳地中海 巴林BH BHRAN BAHRAIN 巴林东南亚 贝宁BJ BJCOT COTONOU 科托努东西非 文莱BN BNMUH MUARA HARBOUR 穆阿拉港东南亚 巴西BR BRBLM BELEM 贝伦南非南美 BRIJI ITAJAI 伊塔雅伊南非南美 BRMAN MANAUS 马瑙斯南非南美 BRPAR PARANAGUA 巴拉那瓜南非南美 BRRDJ RIO DE JANEIRO 里约热内卢南非南美 BRREC RECIFE 累西腓南非南美 BRRGR RIO GRANDE 里奥格兰德南非南美 BRSAL SALVADOR 萨尔瓦多南非南美 BRSTS SANTOS 桑托斯南非南美 BRVIT VICTORIA 维多利亚南非南美

中国海关之世界各港口代码

中国海关之世界各港口代码(海港、空港)(只透露部分) ( Sun, 14 Mar 2010 21:10:20 +0800 ) Description: 港口中文名港口英文名航线国别代码港口代码 阿富汗Afghanistan 3 101 101 巴林Bahrian 3 102 102 孟加拉国Bangladesh 3 103 103 不丹Bhutan 3 104 104 文莱Brunei 2 105 105 缅甸Burma 2 106 106 柬埔寨Democratic Kampuchea 2 107 107 塞浦路斯Cyprus 6 108 108 朝鲜民主主义 人民共和 Korea,DPR 1 109 109 香港Hong Kong 1 110 110 印度India 3 111 111 印度尼西亚Indonesia 3 112 112 伊朗Iran 6 113 113 伊拉克Iraq 3 114 114 以色列IARAEL 3 115 115 日本Japan 16 116 116 约旦Jordan 5 117 117 科威特Kuwait 3 118 118 老挝Laos,PDR 2 119 119 黎巴嫩Lebanon 6 120 120 澳门Macau 1 121 121 马来西亚Malaysia 2 122 122 马尔代夫Maldives 3 123 123 蒙古Mongolia 7 124 124 尼泊尔Nepal 3 125 125 阿曼Oman 3 126 126 巴基斯坦Pakistan 3 127 127 巴勒斯坦Palestine 3 128 128 菲律宾Philippines 2 129 129 卡塔尔Qatar 3 130 130 沙特阿拉 3 131 131

世界主要港口国家及首都对照表(中英文)

世界主要国家及首都对照表(中英文)

世界著名城市中英文 [美国] Honolulu 夏威夷檀香山 [美国] Anchorage 阿拉斯加安克雷奇[加拿大] Vancouver 温哥华 [美国] San Francisco 旧金山 [美国] Seattle 西雅图 [美国] Los Angeles 洛杉矶 [加拿大] Aklavik 阿克拉维克 [加拿大] Edmonton 艾德蒙顿 [美国] Phoenix 凤凰城 [美国] Denver 丹佛 [墨西哥] Mexico City 墨西哥城 [加拿大] Winnipeg 温尼伯 [美国] Houston 休斯敦 [美国] Minneapolis 明尼亚波利斯[美国] St. Paul 圣保罗 [美国] New Orleans 新奥尔良 [美国] Chicago 芝加哥 [美国] Montgomery 蒙哥马利 [危地马拉] Guatemala 危地马拉 [萨尔瓦多] San Salvador 圣萨尔瓦多[洪都拉斯] Tegucigalpa 特古西加尔巴[尼加拉瓜] Managua 马那瓜 [古巴] Havana 哈瓦那 [美国] Indianapolis 印地安纳波利斯[美国] Atlanta 亚特兰大 [美国] Detroit 底特律 [美国] Washington DC 华盛顿哥伦比亚特区 [美国] Philadelphia 费城 [加拿大] Toronto 多伦多 [加拿大] Ottawa 渥太华 [巴哈马] Nassau 拿骚 [秘鲁] Lima 利马 [牙买加] Kingston 金斯敦 [柬埔寨] Bogota 波哥大 [美国] New York 纽约 [加拿大] Montreal 蒙特利尔 [美国] Boston 波士顿 [多米尼加] Santo Domingo 圣多明各[玻利维亚] La Paz 拉帕兹 [委内瑞拉] Caracas 加拉加斯 [波多黎各] San Juan 圣胡安 [加拿大] Halifax 哈里法克斯 [智利] Santiago 圣地亚哥[巴拉圭] Asuncion 亚松森 [加拿大] St\. John\'s 圣约翰斯 [阿根廷] Buenos Aires 布宜诺斯艾利 斯 [乌拉圭] Montevideo 蒙特维的亚 [巴西] Brasilia 巴西利亚 [巴西] Sao Paulo 圣保罗 [巴西] Rio de Janeiro 里约热内卢 [冰岛] Reykjavik 雷克雅未克 [葡萄牙] Lisbon 里斯本 [摩洛哥] Casablanca 卡萨布兰卡 [爱尔兰] Dublin 都柏林 [英国] London 伦敦 [西班牙] Madrid 马德里 [西班牙] Barcelona 巴塞罗那 [法国] Paris 巴黎 [尼日利亚] Lagos 拉各斯 [阿尔及利亚] Algiers 阿尔及尔 [比利时] Brussels 布鲁塞尔 [荷兰] Amsterdam 阿姆斯特丹 [瑞士] Geneva 日内瓦 [瑞士] Zurich 苏黎世 [德国] Frankfurt 法兰克福 [挪威] Oslo 奥斯陆 [丹麦] Copenhagen 哥本哈根 [意大利] Rome 罗马 [德国] Berlin 柏林 [捷克] Prague 布拉格 [克罗地亚] Zagreb 萨格雷布 [奥地利] Vienna 维也纳 [瑞典] Stockholm 斯德哥尔摩 [匈牙利] Budapest 布达佩斯 [塞尔维亚与蒙特内哥罗] Belgrade 贝 尔格莱德 [波兰] Warsaw 华沙 [南非] Cape Town 开普敦 [保加利亚] Sofia 索非亚 [希腊] Athens 雅典城 [爱沙尼亚] Tallinn 塔林 [芬兰] Helsinki 赫尔辛基 [罗马尼亚] Bucharest 布加勒斯特 [白俄罗斯] Minsk 明斯克 [南非] Johannesburg 约翰尼斯堡 [土耳其] Istanbul 伊斯坦布尔 [乌克兰] Kyiv 基辅 [乌克兰] Odesa 敖德萨 [津巴布韦] Harare 哈拉雷 [埃及] Cairo 开罗 [土耳其] Ankara 安卡拉 [以色列] Jerusalem 耶路撒冷 [黎巴嫩] Beirut 贝鲁特 [约旦] Amman 安曼 [苏丹] Khartoum 喀土穆 [肯尼亚] Nairobi 内罗毕 [俄罗斯] Moscow 莫斯科 [埃塞俄比亚] Addis Ababa 亚的斯亚贝 巴 [伊拉克] Baghdad 巴格达 [也门] Aden 亚丁 [沙特阿拉伯] Riyadh 利雅得 [马达加斯加] Antananarivo 安塔那那 利佛 [科威特] Kuwait City 科威特城 [伊朗] Tehran 德黑兰 [阿拉伯联合酋长国] Abu Dhabi 阿布 扎比 [阿富汗] Kabul 喀布尔 [巴基斯坦] Karachi 卡拉奇 [乌兹别克斯坦] Tashkent 塔什干 [巴基斯坦] Islamabad 伊斯兰堡 [巴基斯坦] Lahore 拉合尔 [印度] Mumbai 孟买 [印度] New Delhi 新德里 [印度] Kolkata 柯尔喀塔 [尼泊尔] Kathmandu 加德满都 [孟加拉] Dhaka 达卡 [缅甸] Yangon 仰光 [泰国] Bangkok 曼谷 [越南] Hanoi 河内 [印尼] Jakarta 雅加达 [马来西亚] Kuala Lumpur 吉隆坡 [新加坡] Singapore 新加坡 [中国] Hong Kong 香港 [澳大利亚] Perth 珀斯 [中国] Beijing 北京 [菲律宾] Manila 马尼拉 [中国] Shanghai 上海 [中国] Taipei 台北

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