生物信息课程-06.Annotation
生物信息学英文术语及释义总汇
Abstract Syntax Notation (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）A language that is used to describe structured data types formally, Within bioinformatits,it has been used by the National Center for Biotechnology Information to encode sequences, 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）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）A gap penalty score that is a linear function of gap length, consisting of a gap opening penalty and a gap extension penalty multiplied by the length of the gap. Using this penalty scheme greatly enhances the performance of dynamic programming methods for sequence alignment. See also Gap penalty.Algorithm（算法）A systematic procedure for solving a problem in a finite number of steps, typically involving 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）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, local and global, a local alignment is generally the most useful. See also Local and Global alignments.Alignment score（联配/比对/联配值）An algorithmically computed score based on the number of matches, substitutions, insertions, and deletions (gaps) within an alignment. Scores for matches and substitutions Are derived from a scoring matrix such as the BLOSUM and PAM matrices for proteins, and aftine gap penalties suitable for the matrix are chosen. Alignment scores are in log odds units, often bit units (log to the base 2). Higher scores denote better alignments. See also Similarity score, Distance in sequence analysis.Alphabet（字母表）The total number of symbols in a sequence-4 for DNA sequences and 20 for protein sequences.Annotation（注释）The prediction of genes in a genome, including the location of protein-encoding genes, 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 of introns and exons in proteins encoding genes, and models of secondary structure 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 log in to an anonymous FTP server by typing anonymous as the user name and 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.ASCIIThe American Standard Code for Information Interchange (ASCII) encodes unaccented letters a-z, A-Z, the numbers O-9, most punctuation marks, space, and a set of control characters such as carriage return and tab. ASCII specifies 128 characters that are mapped to the values O-127. ASCII tiles are commonly called plain text, meaning that they only encode text without extra markup.BAC clone（细菌人工染色体克隆）Bacterial artificial chromosome vector carrying a genomic DNA insert, typically 100–200 kb. Most of the large-insert clones sequenced in the project were BAC clones.Back-propagation（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 is compared with the desired output and the amount of error is calculated. This error 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）An expectation maximization algorithm that is used to train hidden Markov models.Baye’s rule（贝叶斯法则）Forms the basis of conditional probability by calculating the likelihood of an event 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 equal to the probability of A, P(A), times the conditional probability of B, given 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）A statistical procedure used to estimate parameters of an underlyingdistribution based on an observed distribution. See also Baye’s rule.Biochips（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）The merger of biotechnology and information technology with the goal of revealing new insights and principles in biology. /The discipline of obtaining information about genomic or protein sequence data. This may involve similarity searches of databases, comparing your unidentified sequence to the sequences in a database, or making predictions about the sequence based on current knowledge of similar sequences. Databases are frequently made publically available through the Internet, or locally at your institution.Bit score （二进制值/ Bit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 to the scoring system, they can be used to compare alignment scores from different searches.Bit unitsFrom information theory, a bit denotes the amount of information required to distinguish between two equally likely possibilities. The number of bits of information, AJ, required to convey 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）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 example. Complex statistics are applied to judge the significance of each match. 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）Conserved ungapped patterns approximately 3-60 amino acids in length in a set of related proteins.BLOSUM matrices（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 thesimilarity of sequences when performing alignments.Boltzmann distribution（Boltzmann 分布）Describes the number of molecules that have energies above a certain level, based on the Boltzmann gas constant and the absolute temperature.Boltzmann probability function(Boltzmann 概率函数)See Boltzmann distribution.Bootstrap analysisA method for testing how well a particular data set fits a model. For example, the validity of the branch arrangement in a predicted phylogenetic tree can be tested by resampling columns in a multiple sequence alignment to create many new alignments. The appearance of a particular branch in trees generated from these resampled sequences can then be measured. Alternatively, a sequence may be left out of an analysis to deter-mine how much the sequence influences the results of an analysis.Branch length（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）Coding sequence.Chebyshe, d inequalityThe 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）Population of identical cells or molecules (e.g. DNA), derived from a single ancestor.Cloning Vector （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 PCR), care should be taken not to include the cloning vector sequence when performing similarity searches. Plasmids, cosmids, phagemids, YACs and PACs are example types of cloning vectors.Cluster analysis（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.CobblerA 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 neural networks, a coding system needs to be designed for representing input and output. The level of success found when training the model will be partially dependent on 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）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）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）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）The probability of a particular result (or of a particular value of a variable) given one or more events or conditions (or values of other variables).Conservation （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）A single sequence that represents, at each subsequent position, the variation found within corresponding columns of a multiple sequence alignment.Context-free grammarsA recursive set of production rules for generating patterns of strings. These consist of a set 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）A set of clones that can be assembled into a linear order. A DNA sequence that overlaps 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）The Common Object Request Broker Architecture (CORBA) is an open industry standard for working with distributed objects, developed by the Object Management 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）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 value indicates an inverse relationship, and the distance of the value away from zero indicates the strength of the relationship. A value near zero indicates no relationship between the variables.Covariation (in sequences)（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/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）A computerized storehouse of data that provides a standardized way for locating, adding, removing, and changing data. See also Object-oriented database, Relational database.DendogramA 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）See coverageDirichlet mixturesDefined as the conjugational prior of a multinomial distribution. One use is for predicting the expected pattern of amino acid variation found in the match state of a hid-den Markov 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）The number of observed changes in an optimal alignment of two sequences, usually not counting gaps.DNA Sequencing （DNA测序）The experimental process of determining the nucleotide sequence of a region of DNA. This is done by labelling each nucleotide (A, C, G or T) with either a radioactive or fluorescent marker which identifies it. There are several methods of applying this technology, each with their advantages and disadvantages. For more information, refer to a current text book. High throughput laboratories frequently use automated sequencers, which are capable of rapidly reading large numbers of templates. Sometimes, the sequences may be generated more quickly than they can be characterised.Domain （功能域）A discrete portion of a protein assumed to fold independently of the rest of the protein andpossessing its own function.Dot matrix（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 most-alike regions by scoring a minimal threshold number of matches within a sequence window.Draft genome sequence （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）A program for filtering low complexity regions from nucleic acid sequences.Dynamic programming（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）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）European Molecular Biology Network: /was established in 1988, and provides services including local molecular databases and software for molecular biologists in Europe. There are several large outposts of EMBnet, including EXPASY.Entropy（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 lawIn 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）See Expressed Sequence TagExpect value (E)（E值）E value. The number of different alignents with scores equivalent to or better than S that 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 the scoring matrix values are then updated to maximize the alignment of the matrix to the sequences. The procedure is repeated until there is no further improvement.Exon （外显子）Coding region of DNA. See CDS.Expressed Sequence Tag (EST) （表达序列标签）Randomly selected, partial cDNA sequence; represents it's corresponding mRNA. dbEST is a large database of ESTs at GenBank, NCBI.FASTA （一种主要数据库搜索程序）The first widely used algorithm for database similarity searching. The program looks for optimal local alignments by scanning the sequence for small matches called "words". Initially, the scores of segments in which there are multiple word hits are calculated ("init1"). Later the scores of several segments may be summed to generate an "initn" score. An optimized alignment that includes gaps is shown in the output as "opt". The sensitivity and speed of the search are inversely related and controlled by the "k-tup" variable which specifies the size of a "word". (Pearson andLipman)Extreme value distribution（极值分布）Some measurements are found to follow a distribution that has a long tail which decays at high values much more slowly than that found in a normal distribution. This slow-falling type is called the extreme value distribution. The alignment scores between unrelated or random sequences are an example. These scores can reach very high values, particularly when a large number of comparisons are made, as in a database similarity search. The probability of a particular score may be accurately predicted by the extreme value distribution, which follows a double negative exponential function after Gumbel.False negative（假阴性）A negative data point collected in a data set that was incorrectly reported due to a failure of the test in avoiding negative results.False positive （假阳性）A positive data point collected in a data set that was incorrectly reported due to a failure of the test. If the test had correctly measured the data point, the data would have been recorded as negative.Feed-forward neural network （反向传输神经网络）Organizes nodes into sequence layers in which the nodes in each layer are fully connected with the nodes in the next layer, except for the final output layer. Input is fed from the input layer through the layers in sequence in a “feed-forward” direction, resulting in output at the final layer. See also Neural network.Filtering (window size)During pair-wise sequence alignment using the dot matrix method, random matches can be filtered out by using a sliding window to compare the two sequences. Rather than comparing a single sequence position at a time, a window of adjacent positions in the two sequences is compared and a dot, indicating a match, is generated only if a certain minimal number of matches occur.Filtering （过滤）Also known as Masking. The process of hiding regions of (nucleic acid or amino acid) sequence having characteristics that frequently lead to spurious high scores. See SEG and DUST.Finished sequence（完成序列）Complete sequence of a clone or genome, with an accuracy of at least 99.99% and no gaps.Fourier analysisStudies the approximations and decomposition of functions using trigonometric polynomials.Format (file)（格式）Different programs require that information be specified to them in a formal manner, using particular keywords and ordering. This specification is a file format.Forward-backward algorithmUsed to train a hidden Markov model by aligning the model with training sequences. The algorithm then refines the model to reduce the error when fitted to the given data using a gradient descent approach.FTP (File Transfer Protocol)（文件传输协议）Allows a person to transfer files from one computer to another across a network using an FTP-capable client program. The FTP client program can only communicate with machines that run an FTP server. The server, in turn, will make a specific portion of its tile system available for FTP access, providing that the client is able to supply a recognized user name and password to the server.Full shotgun clone （鸟枪法克隆）A large-insert clone for which full shotgun sequence has been produced.Functional genomics（功能基因组学）Assessment of the function of genes identified by between-genome comparisons. The function of a newly identified gene is tested by introducing mutations into the gene and then examining the resultant mutant organism for an altered phenotype.gap （空位/间隙/缺口）A space introduced into an alignment to compensate for insertions and deletions in one sequence relative to another. To prevent the accumulation of too many gaps in an alignment, introduction of a gap causes the deduction of a fixed amount (the gap score) from the alignment score. Extension of the gap to encompass additional nucleotides or amino acid is also penalized in the scoring of an alignment.Gap penalty（空位罚分）A numeric score used in sequence alignment programs to penalize the presence of gaps within an alignment. The value of a gap penalty affects how often gaps appear in alignments produced by the algorithm. Most alignment programs suggest gap penalties that are appropriate for particular scoring matrices.Genetic algorithm（遗传算法）A kind of search algorithm that was inspired by the principles of evolution. A population of initial solutions is encoded and the algorithm searches through these by applying a pre-defined fitness measurement to each solution, selecting those with the highest fitness for reproduction. New solutions can be generated during this phase by crossover and mutation operations, defined in the encoded solutions.Genetic map （遗传图谱）A genome map in which polymorphic loci are positioned relative to one another on the basis of the frequency with which they recombine during meiosis. The unit of distance is centimorgans (cM), denoting a 1% chance of recombination.Genome（基因组）The genetic material of an organism, contained in one haploid set of chromosomes.Gibbs sampling methodAn algorithm for finding conserved patterns within a set of related sequences. A guessed alignment of all but one sequence is made and used to generate a scoring matrix that represents the alignment. The matrix is then matched to the left-out sequence, and a probable location of the corresponding pattern is found. This prediction is then input into a new alignment and another scoring matrix is produced and tested on a new left-out sequence. The process is repeated until there is no further improvement in the matrix.Global alignment（整体联配）Attempts to match as many characters as possible, from end to end, in a set of twomore sequences.Gopher (一个文档发布系统，允许检索和显示文本文件)Graph theory（图论）A branch of mathematics which deals with problems that involve a graph or network structure. A graph is defined by a set of nodes (or points) and a set of arcs (lines or edges) joining the nodes. In sequence and genome analysis, graph theory is used for sequence alignments and clustering alike genes.GSS（基因综述序列）Genome survey sequence.GUI（图形用户界面）Graphical user interface.H （相对熵值）H is the relative entropy of the target and background residue frequencies. (Karlin and Altschul, 1990). H can be thought of as a measure of the average information (in bits) available per position that distinguishes an alignment from chance. At high values of H, short alignments can be distinguished by chance, whereas at lower H values, a longer alignment may be necessary. (Altschul, 1991)Half-bitsSome scoring matrices are in half-bit units. These units are logarithms to the base 2 of odds scores times 2.Heuristic（启发式方法）A procedure that progresses along empirical lines by using rules of thumb to reach a solution. The solution is not guaranteed to be optimal.Hexadecimal system（16制系统）The base 16 counting system that uses the digits O-9 followed by the letters A-F.HGMP （人类基因组图谱计划）Human Genome Mapping Project.Hidden Markov Model (HMM)（隐马尔可夫模型）In sequence analysis, a HMM is usually a probabilistic model of a multiple sequence alignment, but can also be a model of periodic patterns in a single sequence, representing, for example, patterns found in the exons of a gene. In a model of multiple sequence alignments, each column of symbols in the alignment is represented by a frequency distribution of the symbols called a state, and insertions and deletions by other states. One then moves through the model along a particular path from state to state trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to thatparticular state from a previous one (the transition probability). State and transition probabilities are then multiplied to obtain a probability of the given sequence. Generally speaking, a HMM is a statistical model for an ordered sequence of symbols, acting as a stochastic state machine that generates a symbol each time a transition is made from one state to the next. Transitions betweenstates are specified by transition probabilities.Hidden layer（隐藏层）An inner layer within a neural network that receives its input and sends its output to other layers within the network. One function of the hidden layer is to detect covariation within the input data, such as patterns of amino acid covariation that are associated with a particular type of secondary structure in proteins.Hierarchical clustering（分级聚类）The clustering or grouping of objects based on some single criterion of similarity or difference.An example is the clustering of genes in a microarray experiment based on the correlation between their expression patterns. The distance method used in phylogenetic analysis is another example.Hill climbingA nonoptimal search algorithm that selects the singular best possible solution at a given state or step. The solution may result in a locally best solution that is not a globally best solution.Homology（同源性）A similar component in two organisms (e.g., genes with strongly similar sequences) that can be attributed to a common ancestor of the two organisms during evolution.Horizontal transfer（水平转移）The transfer of genetic material between two distinct species that do not ordinarily exchange genetic material. The transferred DNA becomes established in the recipient genome and can be detected by a novel phylogenetic history and codon content com-pared to the rest of the genome.HSP （高比值片段对）High-scoring segment pair. Local alignments with no gaps that achieve one of the top alignment scores in a given search.HTGS/HGT（高通量基因组序列）High-throughout genome sequences。
生物信息-名词解释
逐个克隆法：对连续克隆系中排定的BAC克隆逐个进行亚克隆测序并进行组装（公共领域测序计划）。
全基因组鸟枪法：在一定作图信息基础上，绕过大片段连续克隆系的构建而直接将基因组分解成小片段随机测序，利用超级计算机进行组装。
单核苷酸多态性(SNP),主要是指在基因组水平上由单个核苷酸的变异所引起的DNA序列多态性。
遗传图谱又称连锁图谱，它是以具有遗传多态性（在一个遗传位点上具有一个以上的等位基因，在群体中的出现频率皆高于1%）的遗传标记为“路标”，以遗传学距离（在减数分裂事件中两个位点之间进行交换、重组的百分率，1%的重组率称为1cM）为图距的基因组图。
遗传图谱的建立为基因识别和完成基因定位创造了条件。
物理图谱是指有关构成基因组的全部基因的排列和间距的信息，它是通过对构成基因组的DNA分子进行测定而绘制的。
绘制物理图谱的目的是把有关基因的遗传信息及其在每条染色体上的相对位置线性而系统地排列出来。
转录图谱是在识别基因组所包含的蛋白质编码序列的基础上绘制的结合有关基因序列、位置及表达模式等信息的图谱。
比较基因组学：全基因组核苷酸序列的整体比较的研究。
特点是在整个基因组的层次上比较基因组的大小及基因数目、位置、顺序、特定基因的缺失等。
环境基因组学：研究基因多态性与环境之间的关系，建立环境反应基因多态性的目录，确定引起人类疾病的环境因素的科学。
宏基因组是特定环境全部生物遗传物质总和，决定生物群体生命现象。
转录组即一个活细胞所能转录出来的所有mRNA。
研究转录组的一个重要方法就是利用DNA芯片技术检测有机体基因组中基因的表达。
而研究生物细胞中转录组的发生和变化规律的科学就称为转录组学。
蛋白质组学：研究不同时相细胞内蛋白质的变化，揭示正常和疾病状态下，蛋白质表达的规律，从而研究疾病发生机理并发现新药。
蛋白组：基因组表达的全部蛋白质，是一个动态的概念，指的是某种细胞或组织中，基因组表达的所有蛋白质。
代谢组是指是指某个时间点上一个细胞所有代谢物的集合，尤其指在不同代谢过程中充当底物和产物的小分子物质，如脂质，糖，氨基酸等，可以揭示取样时该细胞的生理状态。
生物信息教案高中生物
生物信息教案高中生物
年级：高中
课时：1
教学目标：
1. 了解生物信息的概念和应用领域。
2. 掌握生物信息学中常用的工具和技术。
3. 能够解释生物信息学对生物研究和医学领域的重要意义。
教学内容：
1. 什么是生物信息学
2. 生物信息学的应用领域
3. 常用的生物信息学工具和技术
4. 生物信息学在生物研究和医学领域的应用
教学过程：
1. 导入（5分钟）：通过提问引入生物信息学的概念和重要性，激发学生的兴趣。
2. 授课（15分钟）：讲解生物信息学的定义、应用领域以及常用的工具和技术，介绍生物信息学在生物研究和医学领域的作用。
3. 案例分析（20分钟）：通过实际案例分析生物信息学在生物领域和医学领域的具体应用，让学生更加直观地了解生物信息学的重要性。
4. 讨论与总结（10分钟）：与学生讨论生物信息学在解决生物问题和推动医学进步中的作用，总结学生的学习收获和认识。
5. 作业布置（5分钟）：布置作业，让学生进一步加深对生物信息学的理解和掌握。
教学反馈：通过课堂讨论、作业批改等方式，及时了解学生对生物信息学的掌握情况，帮助学生提高学习效果。
教学资源：
1. 教科书
2. 计算机、网络资源
3. 生物信息学相关文献资料
教学评估：
1. 学生的课堂表现
2. 学生的作业完成情况
3. 学生对生物信息学的理解和运用能力
教学延伸：
1. 生物信息学的最新进展和发展趋势
2. 组织学生开展生物信息学项目实践活动
备注：根据学生的具体情况和实际需求，可以适当调整教案内容和教学方法，以达到更好的教学效果。
生物信息学英文介绍
生物信息学英文介绍Introduction to Bioinformatics.Bioinformatics is an interdisciplinary field that combines biology, computer science, mathematics, statistics, and other disciplines to analyze and interpret biological data. At its core, bioinformatics leverages computational tools and algorithms to process, manage, and minebiological information, enabling a deeper understanding of the molecular basis of life and its diverse phenomena.The field of bioinformatics has exploded in recent years, driven by the exponential growth of biological data generated by high-throughput sequencing technologies, proteomics, genomics, and other omics approaches. This data deluge has presented both challenges and opportunities for researchers. On one hand, the sheer volume and complexityof the data require sophisticated computational methods for analysis. On the other hand, the wealth of information contained within these data holds the promise oftransformative insights into the functions, interactions, and evolution of biological systems.The core tasks of bioinformatics encompass genome annotation, sequence alignment and comparison, gene expression analysis, protein structure prediction and function annotation, and the integration of multi-omic data. These tasks require a range of computational tools and algorithms, often developed by bioinformatics experts in collaboration with biologists and other researchers.Genome annotation, for example, involves the identification of genes and other genetic elements within a genome and the prediction of their functions. This process involves the use of bioinformatics algorithms to identify protein-coding genes, non-coding RNAs, and regulatory elements based on sequence patterns and other features. The resulting annotations provide a foundation forunderstanding the genetic basis of traits and diseases.Sequence alignment and comparison are crucial for understanding the evolutionary relationships betweenspecies and for identifying conserved regions within genomes. Bioinformatics algorithms, such as BLAST and multiple sequence alignment tools, are widely used for these purposes. These algorithms enable researchers to compare sequences quickly and accurately, revealing patterns of conservation and divergence that inform our understanding of biological diversity and function.Gene expression analysis is another key area of bioinformatics. It involves the quantification of thelevels of mRNAs, proteins, and other molecules within cells and tissues, and the interpretation of these data to understand the regulation of gene expression and its impact on cellular phenotypes. Bioinformatics tools and algorithms are essential for processing and analyzing the vast amounts of data generated by high-throughput sequencing and other experimental techniques.Protein structure prediction and function annotation are also important areas of bioinformatics. The structure of a protein determines its function, and bioinformatics methods can help predict the three-dimensional structure ofa protein based on its amino acid sequence. These predictions can then be used to infer the protein'sfunction and to understand how it interacts with other molecules within the cell.The integration of multi-omic data is a rapidly emerging area of bioinformatics. It involves theintegration and analysis of data from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics. This approach enables researchers to understand the interconnectedness of different biological processes and to identify complex relationships between genes, proteins, and metabolites.In addition to these core tasks, bioinformatics also plays a crucial role in translational research and personalized medicine. It enables the identification of disease-associated genes and the development of targeted therapeutics. By analyzing genetic and other biological data from patients, bioinformatics can help predict disease outcomes and guide treatment decisions.The future of bioinformatics is bright. With the continued development of high-throughput sequencing technologies and other omics approaches, the amount of biological data available for analysis will continue to grow. This will drive the need for more sophisticated computational methods and algorithms to process and interpret these data. At the same time, the integration of bioinformatics with other disciplines, such as artificial intelligence and machine learning, will open up new possibilities for understanding the complex systems that underlie life.In conclusion, bioinformatics is an essential field for understanding the molecular basis of life and its diverse phenomena. It leverages computational tools and algorithms to process, manage, and mine biological information, enabling a deeper understanding of the functions, interactions, and evolution of biological systems. As the amount of biological data continues to grow, the role of bioinformatics in research and medicine will become increasingly important.。
《生物信息学(A类)》课程教学大纲
（More）
备注 （Notes）
本课程的考试，注重对学生综合运用所学知识解决问题能力的考核，考试 成绩包括三个方面：
（1）期末考试，占总成绩的60％。 （2）平时成绩，占总成绩的40％，包括上机实验，占25％；课堂报告＋出勤， 占15％。 《生物信息学》，陈铭主编，第一主编非我校教师，科学出版社，2015年2月， 第二版，ISBN: 9787030432872，采用五届，非外文教材，十三五国家规划教材
生物化学，遗传学，分子生物学
张利达
课程网址
无
(Course Webpage)
《生物信息学》是一门面向生物学相关专业的选修课程，主要讲授生物信息学 的概念和方法，以及如何应用生物信息学手段解决生命科学问题。授课内容包 括生物信息学数据库、序列比对、基因预测、分子进化、生物网络建模、新一 代测序及应用等内容。在讲解基本原理同时，介绍相应的生物信息分析软件， *课程简介（Description） 并通过实例使大家熟悉如何使用这些软件来分析生物数据。此外，进一步通过 讲解具体的研究案例，使大家了解如何用生物信息学的方法及研究思路来解决 生命科学中的问题。本课程不仅为学生提供必要的基础理论知识的同时，重点 培养学生利用专业技能分析解决问题的能力，为学生从事与生物学相关专业技 术工作、科学研究工作等打下坚实的基础。
授课对象 （Audience）
授课语言 (Language of Instruction)
*开课院系 （School） 先修课程 （Prerequisite） 授课教师 （Instructor）
专业选修课
主要面向植物科学与技术专业本科生、也向动物科学、生物学等相关专业本科 生开放 中文
农业与生物学院
生物信息学PPT课件
生物信息学在农业研究中的应用
1 2 3
作物育种
生物信息学可以通过基因组学手段分析作物的遗 传变异，为作物育种提供重要的遗传资源。
转基因作物研究
通过生物信息学分析，可以了解转基因作物的基 因表达和性状变化，为转基因作物的研发和应用 提供支持。
农业环境监测
生物信息学可以帮助研究人员监测农业环境中的 微生物群落、土壤质量等指标，为农业生产提供 科学依据。
特点
生物信息学具有数据密集、技术依赖、多学科交叉、应用广泛等特点。
生物信息学的重要性
促进生命科学研究
提高疾病诊断和治疗水平
生物信息学为生命科学研究提供了强 大的数据分析和挖掘工具，有助于深 入揭示生命现象的本质和规律。
生物信息学在疾病诊断和治疗方面具 有重要作用，通过对基因组、蛋白质 组等数据的分析，有助于实现个体化 精准医疗。
03 生物信息学技术与方法
基因组测序技术
基因组测序技术概述
基因组测序是生物信息学中的一项关键技术，它能够测定生物体的 全部基因序列，为后续的基因组学研究提供基础数据。
测序原理
基因组测序主要基于下一代测序技术，如高通量测序和单分子测序， 通过这些技术可以快速、准确地测定生物体的基因序列。
测序应用
基因组测序在医学、农业、生物多样性等多个领域都有广泛应用，如 疾病诊断、药物研发、作物育种等。
生物信息学ppt课件
目录
• 生物信息学概述 • 生物信息学的主要研究领域 • 生物信息学技术与方法 • 生物信息学的应用前景 • 生物信息学的挑战与展望 • 案例分析
01 生物信息学概述
定义与特点
定义
生物信息学是一门跨学科的学科，它利用计算机科学、数学和工程学的原理、 技术和方法，对生物学数据进行分析、解释和利用，以解决生物学问题。
《生物信息学基础》课程教案
《生物信息学基础》课程教案生物信息学基础课程教案教案一：基本信息1. 课程名称：生物信息学基础2. 课程代码：BI50013. 学时：48学时4. 学分：3学分5. 适用专业：生物学、生物工程等相关专业教案二：课程目标本课程旨在培养学生对生物信息学的基本理论、方法和实践技能的掌握，包括生物数据库的应用、序列比对、基因预测、蛋白质结构预测等内容。
教案三：教学内容与进度安排本课程分为六个模块，每个模块包括理论讲解、案例分析和实践操作。
模块一：生物数据库的应用1. 理论讲解：介绍生物数据库的种类、分类和常用数据库的特点与应用。
2. 案例分析：分析生物数据库在基因组学、转录组学、蛋白质组学等领域的具体应用。
3. 实践操作：利用NCBI等数据库进行基本生物序列检索和分析。
模块二：序列比对1. 理论讲解：介绍序列比对的基本原理、常用算法和评估指标。
2. 案例分析：分析序列比对在物种关系分析、基因家族预测等方面的应用。
3. 实践操作：使用BLAST等工具进行序列比对和结果分析。
模块三：基因预测1. 理论讲解：讲解基因预测的原理和常用算法。
2. 案例分析：分析基因预测在基因组注释、新基因发现等方面的应用。
3. 实践操作：利用软件工具进行基因预测和基因结构分析。
模块四：蛋白质结构预测1. 理论讲解：介绍蛋白质结构预测的方法和限制。
2. 案例分析：分析蛋白质结构预测在药物研发、蛋白质功能预测等方面的应用。
3. 实践操作：利用蛋白质结构预测软件进行结构模拟和分析。
模块五：基因表达数据分析1. 理论讲解：介绍基因表达数据分析的基本方法和流程。
2. 案例分析：分析基因表达数据分析在差异基因筛选、通路富集分析等方面的应用。
3. 实践操作：利用R语言等工具进行基因表达数据分析和结果可视化。
模块六：生物信息学实践与展望1. 生物信息学实践：学生根据自己的兴趣和专业方向选择一个具体的生物信息学项目进行实践。
2. 展望与讨论：展望生物信息学在生命科学、健康医学等领域的前景和挑战，并进行深入讨论。
基因本体数据库名词解释
基因本体数据库名词解释1. 本体（Ontology）本体是一种用于描述领域内实体及其关系的概念模型。
在生物信息学中，本体被用来描述基因、蛋白质等生物分子以及它们之间的相互作用和关系。
2. 基因本体（Gene Ontology）基因本体是一个用于描述基因和基因产物特性和功能的数据库。
它包括了生物过程、分子功能和细胞组分三个主要的方面，以提供一个统一的方式来描述基因和基因产物的特性。
3. 生物过程（Biological Process）生物过程是指生物体内一系列相关生物分子参与的化学反应或信号转导通路。
在基因本体中，生物过程是描述基因或基因产物参与的生物功能的方面之一。
4. 分子功能（Molecular Function）分子功能是指生物分子在独立于其他分子的状态下所发挥的功能。
在基因本体中，分子功能是描述基因或基因产物在独立状态下的功能方面之一。
5. 细胞组分（Cellular Component）细胞组分是指细胞内部特定的区域或结构。
在基因本体中，细胞组分是描述基因或基因产物在特定细胞组分中的定位或功能的方面之一。
6. 注释（Annotation）注释是对数据的一种描述和解释。
在基因本体中，注释是用来描述基因或基因产物与本体中的概念之间的关系。
7. 数据库（Database）数据库是一种存储和管理数据的系统。
在生物信息学中，数据库通常用于存储和查询关于基因、蛋白质等生物分子的结构和功能信息。
8. 语义网络（Semantic Network）语义网络是一种用于描述概念之间关系的知识表示方法。
在生物信息学中，语义网络可以用于描述基因、蛋白质等生物分子之间的关系，以提供更深入的对于生物系统理解的工具。
生物信息学复习总结
生物信息期末总结1.生物信息学(Bioinformatics）定义:(第一章）★生物信息学是一门交叉科学，它包含了生物信息的获取、加工、存储、分配、分析、解释等在内的所有方面，它综合运用数学、计算机科学和生物学的各种工具来阐明和理解大量数据所包含的生物学意义。
（或：）生物信息学是运用计算机技术和信息技术开发新的算法和统计方法，对生物实验数据进行分析，确定数据所含的生物学意义，并开发新的数据分析工具以实现对各种信息的获取和管理的学科。
（NSFC）2。
科研机构及网络资源中心：NCBI:美国国立卫生研究院NIH下属国立生物技术信息中心；EMBnet：欧洲分子生物学网络；EMBL-EBI：欧洲分子生物学实验室下属欧洲生物信息学研究所;ExPASy:瑞士生物信息研究所SIB下属的蛋白质分析专家系统;（Expert Protein Analysis System)Bioinformatics Links Directory;PDB （Protein Data Bank）；UniProt 数据库3. 生物信息学的主要应用:1．生物信息学数据库；2．序列分析；3．比较基因组学；4．表达分析;5．蛋白质结构预测；6．系统生物学；7．计算进化生物学与生物多样性.4.什么是数据库：★1、定义：数据库是存储与管理数据的计算机文档、结构化记录形式的数据集合。
（记录record、字段field、值value)2、生物信息数据库应满足5个方面的主要需求：（1)时间性；（2)注释;（3）支撑数据;（4）数据质量;（5）集成性。
3、生物学数据库的类型：一级数据库和二级数据库。
（国际著名的一级核酸数据库有Genbank数据库、EMBL核酸库和DDBJ库等；蛋白质序列数据库有SWISS—PROT等;蛋白质结构库有PDB等。
）4、一级数据库与二级数据库的区别：★1）一级数据库：包括：a.基因组数据库--—-来自基因组作图；b.核酸和蛋白质一级结构序列数据库；c。
生物信息知识点总结高中
生物信息知识点总结高中一、生物信息学的基本概念1. 生物信息学的定义生物信息学是生物学与信息学相结合的新兴交叉学科，它主要以计算机和信息技术为工具，利用数学和统计学的方法，对生物学数据进行分析、整合和挖掘，以揭示生物学规律和发现新的生物学知识。
2. 生物信息学的研究对象生物信息学的研究对象主要包括生物学数据的获取、存储、管理、分析和可视化等方面。
生物学数据可以来自基因组、蛋白质组、代谢组和转录组等多个层面，包括基因序列、蛋白质序列、基因表达数据、代谢产物数据等。
3. 生物信息学的研究内容生物信息学的研究内容主要包括生物数据库的构建与维护、生物信息资源的开发与共享、生物数据的存储与管理、生物数据的分析与挖掘、基于生物信息学的生物学模拟与预测、以及生物信息学软件和工具的开发等。
4. 生物信息学的发展历程生物信息学的发展可以追溯到上世纪50年代，随着第一台电子计算机的出现，科学家们开始将计算机应用于生物学研究。
随着DNA测序技术的发展和生物大数据的爆发，生物信息学得到了迅猛发展，成为当今生物学研究中不可或缺的一部分。
二、生物信息学的基本方法1. 生物信息学的数据获取生物信息学的数据获取主要包括生物学实验数据、生物学数据库数据和公开共享数据等多个来源。
生物学实验数据可以通过生物学实验技术获取，如基因测序、蛋白质质谱和基因表达芯片等。
生物学数据库数据可以通过生物信息学数据库获取，如GenBank、Swiss-Prot、KEGG和GO等。
公开共享数据可以通过公共数据库和数据仓库获取，如NCBI、EBI和DDBJ等。
2. 生物信息学的数据存储与管理生物信息学的数据存储与管理主要包括生物学数据库的构建与维护、生物信息资源的开发与共享、生物数据的存储和管理等方面。
生物学数据库可以是本地数据库和网络数据库，可以使用关系型数据库、非关系型数据库和分布式数据库等技术进行存储和管理。
3. 生物信息学的数据分析与挖掘生物信息学的数据分析与挖掘主要包括生物学数据的统计学分析、生物学数据的数据挖掘与模式识别、生物学数据的生物信息学算法与工具等多个方面。
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The query species was assumed to be Homo RepeatMasker version open-3.1.5 , default mode run with blastp version 2.0MP-WashU RepBase Update 20051025, RM database version 20051025
第四章 基因组/基 因的注释
Genome/Gene Annotation
Haibo Sun Department of Bioinformatics Beijing Genomics Institute Match 04, 2007
4.1 重复序列分析
重复序列分类：
1. Tandem repeats(串联重复) Simple Sequence Repeats (SSR) + Satellites GGGGGGGGGGGGG (G)n ATATATATATATATAT (AT)n Low complexity region TTTTTTATTTTTTGTTTTTTTT (1)研究表明一些简单的重复序列与许多疾病相关，如Telomeres
RepeatMasker
（3） *.tbl重复序列的统计文件
================================================== file name: 05.suis.ranged.fa sequences: 1 total length: 2096309 bp (2096309 bp excl N/X-run GC level: 41.11 % bases masked: 9423 bp ( 0.45 %) ================================================== number of length percentage elements* occupied of sequence -------------------------------------------------SINEs: 0 0 bp 0.00 % ALUs 0 0 bp 0.00 % MIRs 0 0 bp 0.00 %
重复序列处 用“N”屏蔽 显示了
…………
RepeatMasker
（2） *.out 被比上重复序列的说明文件
…………
506 30.8 7.6 4.4 SUIS05
1 22 40.9 0.0 0.0 SUIS05 19290 19311 (2076998) + AT_rich Low_complexity 22 54.5 0.0 0.0 SUIS05 19292 19313 (2076996) + AT_rich Low_complexity 353 25.3 0.0 2.9 SUIS05 19369 19474 (2076835) + LSU-rRNA_Hsa rRNA 4 1 22 (0) 2 1 22 (0) 3 313 415 (4620)
RepeatMasker
实例
屏蔽猪链球菌（Suis05.Genome.fasta）基因组上的重复序列，基因组 长度2096309bp 命令行：/sdb/genome/RepeatMasker/RepeatMasker 05.suis.ranged.fa
结果说明
程序运行完毕，在目录下会生成如下几个文件（ls –l ）：
17740 18385 (2077924) + SSU-rRNA_Hsa rRNA
103一行为例，其代表的意义是： 506 = 比上的Smith-Waterman分值。 30.8 = % 比上区间与共有序列相比的替代率 7.6 = % 在查询序列中碱基缺失的百分率(删除碱基) 4.4 = % 在repeat库序列中碱基缺失的百分率 (插入碱基) SUIS05 = 查询序列的名称 17740 = 比上区间在查询序列中的启始位置 18385 = 比上区间在查询序列中的终止位置 (2077924) = 在查询序列中超出比上区域的碱基数 + = 比上了库中重复序列的正义链，如果是互补连用“c”表示。 SSU-rRNA_Hsa = 比上重复序列的名称 rRNA = 比上重复序列的类型 1032 = 比上区间在重复序列中的启始位置(using top-strand numbering) 1710 = 比上区间在重复序列中的终止位置 (159) = 比对区域距重复序列左端的碱基数 1 = 比对的顺序ID
（TTAGGG）。
(2)STR是存在于人类基因组DNA中的一类具有长度多态性 的DNA序列，其多态性成为法医物证检验个人识别和亲子鉴定的丰富 来源。 2. Interspersed repeats(分散重复) SINE (Short INterspersed Elements)，平均长度约300bp，平均间隔 1000bp，如：Alu家族，Hinf家族序列 •Alu：GC rich，Length: ~ 280 base pairs， •Mariner (Mariner-like) elements：Length: ~80 bp
（4） *.cat此文件内容基本同*.out
22 40.91 0.00 0.00 SUIS05 19290 19311 (2076998) C AT_rich#Low_complexity (136) 164 143 5 22 54.55 0.00 0.00 SUIS05 19292 19313 (2076996) C AT_rich#Low_complexity (141) 159 138 5
Repeats
LINE (Long INterspersed Elements) 平均长度大于1000bp，平均间隔3500-5000bp，如：rRNA，tRNA基因， 形成基因家族。 Transposable elements with Long Terminal Repeats
Length: 1.5 - 10 kbp Encode reverse transcriptase Flanked by 300 - 1000 bps terminal repeats
简介
RepeatMasker是一个屏蔽DNA序列中转座子重复序列和低复 杂度序列的程序，由Arian Smit和Robert Hubley开发，它将把输入 序列中已知的重复序列都屏蔽为Ｎ或X，并给出相应的重复序列统计 列表。一般情况下，一条人的基因组数据的大约５０％会被该程序 屏蔽。RepeatMasker可以选择cross_match或wu-blast做为比对的 搜索引擎。
RepeatMasker
使用
程序运行命令行：RepeatMasker <options> [fasta格式的序列文件] 当不带任何参数时，缺省设置是屏蔽灵长类动物所有类型的重复序 列。
重要参数
（1）-pa(rallel) [number] 并行的处理数据，当你的序列长度大于 50kb时，可以设定次参数以提高运算的速度。 （2）-nolow /-low 不屏蔽低复杂度和简单的序列。 （3）-noint /-int 只屏蔽低复杂度和简单的序列。 （4）-lib [filename] 可以用这个参数，自定义要屏蔽的序列。 （5）-species <query species> 可以指定物种信息。例如“-species human ”。现在可以指定的物种有：mammal, carnivore, rodentia, rat, cow, pig, cat, dog, chicken, fugu, danio, "ciona intestinalis" drosophila, anopheles, elegans, diatoaea, artiodactyl, arabidopsis, rice, wheat, and maize
（1） *.masked 重复序列被屏蔽后的序列文件
„„„„ TAATAGCAGAGCTTCGGGCACTGAGCATCCCTCTTTCTTTCCAAATTGCT GCATGGGCATGCAGAATGAGGAAAGCATACTTAGAGTCCATACACACATT AATCCNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTTGCTTAGTT TCTTCCAGGCTCGCTACAGCATATCCTGCTTTCCTCTCTCCCTTTGCTAC
参考文献
1.Web Site： Smit,AFA & Green,P RepeatMasker at Pearson 2.Waterston et al. (2002) Initial sequencing and comparative analysis of the mouse genome. Nature. 420(6915):5 20-62. nder E. S., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409(6822): 860-921. 4.Smit, A.F.A. (1999) Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Devel 9 (6), 657-663.
DNA Transposons
Single intron-less open reading frame Encode transposase Two short inverted repeat sequences flanking the reading frame
Repeats