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====== N ======

== 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).

== Noise（噪音） ==
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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