Sequence analysis

Source: Wikipedia, the free encyclopedia. 
  Not to be confused with sequential analysis, sequence analysis of synthetic polymers, or sequence analysis in social sciences.

In

peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, and others.[1]

Since the development of methods of high-throughput production of gene and protein sequences, the rate of addition of new sequences to the databases increased very rapidly. Such a collection of sequences does not, by itself, increase the scientist's understanding of the biology of organisms. However, comparing these new sequences to those with known functions is a key way of understanding the biology of an organism from which the new sequence comes. Thus, sequence analysis can be used to assign function to genes and proteins by the study of the similarities between the compared sequences. Nowadays, there are many tools and techniques that provide the sequence comparisons (sequence alignment) and analyze the alignment product to understand its biology.

Sequence analysis in molecular biology includes a very wide range of relevant topics:

  1. The comparison of sequences in order to find similarity, often to infer if they are related (homologous)
  2. Identification of intrinsic features of the sequence such as
    regulatory elements
  3. Identification of sequence differences and variations such as
    single nucleotide polymorphism (SNP) in order to get the genetic marker
    .
  4. Revealing the evolution and genetic diversity of sequences and organisms
  5. Identification of molecular structure from sequence alone.

History

Since the very first sequences of the

secondary structure.[7] In 1970, Saul B. Needleman and Christian D. Wunsch published the first computer algorithm for aligning two sequences.[8] Over this time, developments in obtaining nucleotide sequence improved greatly, leading to the publication of the first complete genome of a bacteriophage in 1977.[9] Robert Holley and his team in Cornell University were believed to be the first to sequence an RNA molecule.[10]

Sequence alignment

Main article: Sequence_alignment § Pairwise_alignment
Main article: Multiple sequence alignment
Example multiple sequence alignment

There are millions of

Smith-Waterman algorithm
. Popular tools for sequence alignment include:

A common use for pairwise sequence alignment is to take a sequence of interest and compare it to all known sequences in a database to identify

Expectation value
.

Profile comparison

In 1987, Michael Gribskov, Andrew McLachlan, and

hidden Markov models.[12][13]
These models have become known as profile-HMMs.

In recent years,[when?] methods have been developed that allow the comparison of profiles directly to each other. These are known as profile-profile comparison methods.[14]

Sequence assembly

Sequence assembly refers to the reconstruction of a DNA sequence by aligning and merging small DNA fragments. It is an integral part of modern DNA sequencing. Since presently-available DNA sequencing technologies are ill-suited for reading long sequences, large pieces of DNA (such as genomes) are often sequenced by (1) cutting the DNA into small pieces, (2) reading the small fragments, and (3) reconstituting the original DNA by merging the information on various fragments.

Recently, sequencing multiple species at one time is one of the top research objectives. Metagenomics is the study of microbial communities directly obtained from the environment. Different from cultured microorganisms from the lab, the wild sample usually contains dozens, sometimes even thousands of types of microorganisms from their original habitats.[15] Recovering the original genomes can prove to be very challenging.

Gene prediction

Main article: Gene prediction

Gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode

Hidden markov models can be part of the solution.[16] Machine learning has played a significant role in predicting the sequence of transcription factors.[17] Traditional sequencing analysis focused on the statistical parameters of the nucleotide sequence itself (The most common programs used are listed in Table 4.1). Another method is to identify homologous sequences based on other known gene sequences (Tools see Table 4.3).[18] The two methods described here are focused on the sequence. However, the shape feature of these molecules such as DNA and protein have also been studied and proposed to have an equivalent, if not higher, influence on the behaviors of these molecules.[19]

Protein structure prediction

Main article: Threading (protein sequence)
Target protein structure (3dsm, shown in ribbons), with Calpha backbones (in gray) of 354 predicted models for it submitted in the CASP8 structure-prediction experiment.

The 3D structures of molecules are of major importance to their functions in nature. Since structural prediction of large molecules at an atomic level is a largely intractable problem, some biologists introduced ways to predict 3D structure at a primary sequence level. This includes the biochemical or statistical analysis of amino acid residues in local regions and structural the inference from homologs (or other potentially related proteins) with known 3D structures.

There have been a large number of diverse approaches to solve the structure prediction problem. In order to determine which methods were most effective, a structure prediction competition was founded called CASP (Critical Assessment of Structure Prediction).[20]

Methodology

The tasks that lie in the space of sequence analysis are often non-trivial to resolve and require the use of relatively complex approaches. Of the many types of methods used in practice, the most popular include:

See also

References

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  19. PMID 25843630
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