DNA微阵列技术：计算序列设计方法

需积分: 5 173 浏览量更新于2024-07-18 收藏 973KB PDF 举报

"DNA微阵列科技" DNA微阵列是一种生物技术，用于同时检测大量DNA序列的表达水平或存在情况。这项技术的核心是利用DNA序列的互补性原理，通过将成千上万的小片DNA固定在固体支持物（如玻璃片、芯片）上形成微阵列，每个小片代表一个特定的DNA序列，即探针。当样本DNA与微阵列接触时，如果样本中存在与探针互补的序列，两者会通过氢键结合，形成杂交分子，从而被检测到。 SLS算法（Shortest Linear Superstring Problem）在DNA微阵列设计中扮演重要角色。这个算法主要用于构建种子序列，即较短的DNA片段，它们是构建更长序列的基础。SLS的目标是找到一系列短串，使它们能够通过重叠连接形成最长的线性串。在DNA微阵列设计中，这些种子序列用于覆盖目标基因组区域，确保能捕获所有感兴趣的序列信息。团搜索算法是另一种用于DNA序列分析的方法。在微阵列设计中，它可能用于寻找具有特定属性（如高度保守区域或特异性序列）的DNA片段集合。这种算法可以优化探针的选择，提高检测的特异性和敏感性。构建种子的方法涉及选择具有适当长度、特异性和覆盖度的DNA序列。种子序列的长度通常在15-25个碱基对之间，过短可能导致匹配不准确，过长则可能增加成本。特异性是指种子序列只应与目标序列完全匹配，避免与其他非目标序列发生交叉反应。覆盖度则确保微阵列中的探针能够覆盖整个感兴趣的基因区域。 DNA编码则是利用DNA的四种核苷酸（腺嘌呤A、胸腺嘧啶T、胞嘧啶C和鸟嘌呤G）作为信息存储和处理的介质。在DNA微阵列中，编码技术可能用于标记探针或样本，以便区分不同的序列和后续的分析步骤。例如，可以通过特定的DNA序列标签来标识不同的样本来源或实验条件。 DNA微阵列科技结合了生物信息学和计算系统生物学的方法，如SLS算法、团搜索算法和DNA编码，以高效地设计和使用微阵列进行大规模的基因表达分析或遗传变异检测。这些技术的不断发展和优化，对于理解复杂的生物过程、疾病机制以及药物研发具有重大意义。

uses a traditional approach. The criteria taken into account are specificity (use of BLAST), melting temperature and

position in the transcripts. ProMide (Rahmann, 2003) consists of a set of Perl scripts, with a C program for basic

calculations, which uses the longest common substring as a specificity measure for the oligonucleotides. It uses

complex data structures such as ‗enhanced suffix array‘ and some statistical properties of sequences. Oliz (Chen &

Sharp, 2002) is also implemented in Perl and uses a classical approach relying on BLAST for specificity testing. It

however requires additional software to function, like cap3, clustalw, EMBOSS ―prima‖ and a database in the UniGene

format. The originality of the method stems from the fact that the oligonucleotides are searched in the 3‘ untranslated

region of the mRNAs, a very specific area where sequences are largely available through expressed sequence tag

projects.

The majority of the client-server and autonomous software described above use a pipelined filtering approach for the

design of microarray probes, where at an initial stage a local alignment approach is employed for cross-hybridization

avoidance and then each ulterior filter evaluates the quality of each probe and ranks them according to well defined

scoring criteria.

DNA SEQUENCE DESIGN - PROBLEM DESCRIPTION

A single stranded DNA molecule is a long, unbranched polymer composed of only four types of subunits. These

subunits are the deoxyribonucleotides containing the bases adenine (A), cytosine (C), guanine (G) and thymine (T). The

nucleotides are linked together by chemical bonds and they are attached to a sugar-phosphate chain like four kinds of

beads strung on a necklace (Figure 1). The sugar-phosphate chain is composed by alternating the ribose and phosphate

for each nucleotide. This alternating structure gives each base and implicitly the whole strand a direction from the

ribose end (denoted by 5‘) to the phosphate end (denoted by 3‘). When the 5‘/3‘ ends are not explicitly labeled, DNA

sequences are assumed to be written in 5‘ to 3‘ direction.

Figure 1: Idealized structure of a pair of hybridized DNA sequences.

The nucleotides composing DNA bind to each other in pairs via hydrogen bonds in a process known as hybridization.

Each nucleotide pairs up with its unique complement (the Watson-Crick complement), so C pairs up with G and A with

T. C and G pair up in a more stable manner than A and T do, which is due to one extra hydrogen bond in the C-G pair.

The Watson-Crick complement of a DNA strand is the strand obtained by replacing each C nucleotide with a G and

vice versa, and each T nucleotide with an A and vice versa, and also switching the 5‘ and 3‘ ends. For example the

Watson-Crick complement of 5‘-AACTAG-3‘ is 3‘-TTGATC-5‘. Non-Watson-Crick base pairings are also possible.

For example, G-T pairs occur naturally in biological sequences (Ho et al., 1985; Pfaff et al., 2008).

Formally, these notions are captured in the following definitions:

A DNA strand w is represented by a string over the quaternary alphabet {A, C, G, T}. String w corresponds to a single-

stranded DNA molecule with the left end of the string corresponding to the 5‘-end of the DNA strand.

The complement, or Watson-Crick complement, of a DNA strand w is obtained by reversing w and then by replacing

each A with a T and vice versa, and replacing each C in the strand by G and vice versa. We denote the Watson-Crick

complement of a DNA strand w with c(w) and we will simply use the terms complementary strand, or complement in

this work. For simplicity, we simply use c

instead of c(w

Throughout the remainder of the chapter we use symbols w

, w

, ..., w

to denote unique strands and c

, c

, ..., c









denote the corresponding complements. Thus, strand w

and complement c

form a perfect match (duplex), whereas

strand w

and complement c

, and complements c

and c

, with j ≠ i, form mismatches. We will call probes the strands

attached to the surface of microarrays, while targets are the corresponding complements floating in the solution. We

adopt this terminology because it is widely used in the literature on DNA microarrays.

Finally, we formalize the optimization version of the DNA Strand Design Problem.

The DNA Strand Design (DSD) Problem is defined as follows: with respect to a fixed set C of constraints (which are

part of the input and will be detailed in the reminder of the section), given a strand length n, find the largest possible set

S with DNA strands of length n, satisfying all the constraints in

The constraints used in the design of DNA strands can be grouped into two classes: combinatorial and thermodynamic

constraints. The grouping is based on the measures that estimate the quality of one or more DNA strands upon which

the constraint is applied. Combinatorial constraints mostly rely on verifying the presence or absence of certain

nucleotides in a strand, and counting of matches and/or mismatches for pairs of DNA strands, whereas thermodynamic

constraints take into account the biochemical properties that govern DNA hybridization.

COMBINATORIAL CONSTRAINTS

Combinatorial constraints are widely used to design large sets of non-interacting DNA strands. The necessity of

designing sets of strands that avoid cross-hybridization can be superficially modeled by enforcing a high number of

mismatches between all possible pairs of strands and between pairs of strands and the complements of others. The

stability and uniformity of DNA strands can be also modeled with combinatorial constraints by either controlling the

presence or absence of undesired subsequences or counting specific bases within the same strand. Various types of

combinatorial constraints can be considered. In the following paragraphs we will formalize those of interest for our

study.

C1: Direct Mismatches. The number of mismatches in a perfect alignment of two strands (also referred to as Hamming

distance) must be above a given threshold d. Here, a perfect alignment is a pairing of bases from the two strands,

where the i

base of the first strand is paired with the i

base of the second strand, and a mismatch is a pairing of

two distinct bases. For example, G paired with A, C, or T is a mismatch whereas G paired with G is a match. For

example, the strands w=5‘-AACAA-3‘ and w

=5‘-AAGAA-3‘ have one mismatch at position i = 3, where i = 1,

2, …, 5.

C2: Complement Mismatches. The number of mismatches in a perfect alignment of a strand and a complement must

be above a given threshold d. For example, the complement of strand w=5‘-AACAA-3‘, which is c=5‘-TTGTT-

3‘ and strand w

=5‘-TTATT-3‘ have one mismatch at position i = 3, where i = 1, 2, …, 5. This constraint is also

referred to as the reverse-complement constraint.

C3: GC Content. The number of G‘s and/or C‘s in a strand must be in a given range. For example, all strands

presented in (Braich et al., 2003) have 4, 5 or 6 C‘s. One such strand is w=5‘-TTACACCAATCTCTT-3‘, which

contains five Cs.

Constraint C3 can be used to attain desired hybridization. Roughly, the stability of a strand-complement pair increases

as the content of G‘s and C‘s within strands increases. Constraint C3 is also used to ensure uniform melting

temperatures across desired strand-complement pairings. Constraints C1 and C2 can be used in various combinations to

avoid undesired hybridization. As it is widely known, the strength of hybridization between two strands (or between

bases of the same strand) depends roughly on the number of nucleotide bonds formed (longer perfectly paired regions

are more stable than shorter ones; this is modeled by C1 and C2), and on the types of nucleotides involved in bonding.

It can be observed that checking if a combinatorial constraint is satisfied can be done with computational complexity

O(n). This makes these constraints extremely tractable from a computational point of view.

THERMODYNAMIC CONSTRAINTS

While combinatorial constraints provide a good starting point for designing sets of strands for various biotechnological

剩余30页未读，继续阅读

凤凰AI

粉丝: 224
资源: 391

DNA微阵列技术：计算序列设计方法

安捷伦科技庆祝CGH微阵列芯片创新技术推出10周年.pdf

新型产后微阵列芯片快速检测遗传异常.pdf

微阵列芯片技术在儿童畸形中的应用.pdf

微阵列PCR生物芯片及其检测技术的研究.pdf

开发技术-硬件-用于疾病诊断及环境毒物检测的微阵列及微流控芯片新方法研究.zip

利用微阵列芯片技术探究基因FOXQ1与大肠癌的关系.pdf

行业-电子-利用酶检测电极微阵列上结合事件的方法的介绍分析.rar

行业-电子政务-用于测量电极微阵列上的结合事件的方法和设备.zip

开发技术-硬件-染色体微阵列技术在先天性唇腭裂畸形患者中的应用.zip

行业分类-设备装置-一种金属纳米颗粒微阵列芯片的制备装置及方法.zip

最新资源