生物信息学主要英文术语及释义
bandage是什么意思附录：生物信息学主要英文术语及释义
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 ud to describe structured data types formally, Within bioinformatits,it has been ud by the National Center for Biotechnology Information to encode quences, maps, taxonomic information, molecular structures, and biographical information in such a way that it can be easily accesd and exchanged by computer software.
专业在线翻译Accession number（记录号）
A unique identifier that is assigned to a single databa entry for a DNA or protein quence.推特er
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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 quence 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 quences by looking for a ries of individual characters or character patterns that are in the same order in the quences. Of the two types of alignment, local and global, a local alignment is generally the most uful. See also Local and Global alignments. Alignment score（联配/比对/联配值）
An algorithmically computed score bad on the number of matches, substitutions, inrti
qkons, 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 chon. Alignment scores are in log odds units, often bit units (log to the ba 2). Higher scores denote better alignments. See also Similarity score, Distance in quence analysis.
Alphabet（字母表）
The total number of symbols in a quence-4 for DNA quences and 20 for protein quences.
Annotation（注释）
The prediction of genes in a genome, including the location of protein-encoding genes, the quence of the encoded proteins, any significant
doablematches to other Proteins of known function, and the location of RNA-encoding genes. Predictions are bad on gene models; e.g., hidden Markov models of introns and exons
in proteins encoding genes, and models of condary structure in RNA.
Anonymous FTP（匿名FTP）
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When a FTP rvice allows anyone to log in, it is said to provide anonymous FTP r-vice. A ur can log in to an anonymous FTP rver by typing anonymous as the ur name and his E-mail address as a password. Most Web browrs now negotiate anonymous FTP logon without asking the ur for a ur name and password. See also FTP.
ASCII
The American Standard Code for Information Interchange (ASCII) encodes unaccented letters a-z, A-Z, the numbers O-9, most punctuation marks, space, and a t 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 inrt, typically 100–200 kb. Most of the large-inrt clones quenced in the project were BAC clones.
Back-propagation（反向传输）
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When training feed-forward neural networks, a back-propagation algorithm can be ud 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 ud 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 bad 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, reprenting a new prediction for A given the prior knowledge of A and the newly discovered relationships between A and B.
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