Bioinformatics Glossary


N50 length (N50长度,即覆盖50%所有核苷酸的最大序列重叠群长度)
A measure of the contig length (or scaffold length) containing a 'typical' nucleotide. Specifically, it is the maximum length L such that 50% of all nucleotides lie in contigs (or scaffolds) of size at least L.

Nats (natural logarithm)
A number expressed in units of the natural logarithm.

NCBI (美国国家生物技术信息中心)
National Center for Biotechnology Information (USA). Created by the United States Congress in 1988, to develop information systems to support thebiological research community.

Needleman-Wunsch algorithm(Needleman-Wunsch算法)
Uses dynamic programming to find global alignments between sequences.

Neighbor-joining method(邻接法)
Clusters together alike pairs within a group of related objects (e.g., genes with similar sequences) to create a tree whose branches reflect the degrees of difference among the objects.

Neural network(神经网络)
From artificial intelligence algorithms, techniques that involve a set of many simple units that hold symbolic data, which are interconnected by a network of links associated with numeric weights. Units operate only on their symbolic data and on the inputs that they receive through their connections. Most neural networks use a training algorithm (see Back-propagation) to adjust connection weights, allowing the network to learn associations between various input and output patterns. See also Feed-forward neural network.

NIH (美国国家卫生研究院)
National Institutes of Health (USA).

In sequence analysis, a small amount of randomly generated variation in sequences that is added to a model of the sequences; e.g., a hidden Markov model or scoring matrix, in order to avoid the model overfitting the sequences. See also Overfitting.

Normal distribution(正态分布)
The distribution found for many types of data such as body weight, size, and exam scores. The distribution is a bell-shaped curve that is described by a mean and standard deviation of the mean. Local sequence alignment scores between unrelated or random sequences do not follow this distribution but instead the extreme value distribution which has a much extended tail for higher scores. See also Extreme value distribution.

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