Sequence Comparison in Biology PDF

**What are sequences and why do we want to compare them?** Sequences in biology refer to the order of nucleotide bases (A, T, C, G) in DNA, or amino acids in proteins. These sequences hold the instructions for building proteins and controlling biological processes, so comparing them can reveal impo...

**What are sequences and why do we want to compare them?** Sequences in biology refer to the order of nucleotide bases (A, T, C, G) in DNA, or amino acids in proteins. These sequences hold the instructions for building proteins and controlling biological processes, so comparing them can reveal important insights into how organisms are related, how genes or proteins function, and how evolution has shaped various species. *[We compare sequences to:]* \- Infer evolutionary relationships: Closely related species or genes often have more similar sequences. Comparative analysis can identify homologous genes that have evolved from a common ancestor. \- Identify functional regions: Conserved sequences often indicate important functional regions in DNA or protein. For example, conserved protein domains may be essential for the protein\'s activity. \- Predict function: If we find an unknown sequence similar to a well-characterized sequence, we can infer potential functions. \- Understand genetic variations: In human genetics, comparing sequences across individuals can help identify mutations that may be linked to diseases. **2. How can we compare (align) two sequences?** Sequence alignment is the process of arranging two or more sequences to identify regions of similarity. These alignments can be done at two levels: \- Nucleotide vs nucleotide: This is done for DNA or RNA sequences. The goal is to match the corresponding bases (A, T, C, G for DNA, or A, U, C, G for RNA) between two sequences. \- Protein vs protein: Protein sequences, made of amino acids, are aligned to determine evolutionary relationships or functional similarities between different proteins. \- Nucleotide vs protein: This type of comparison typically involves translating the nucleotide sequence into its corresponding amino acid sequence (using the genetic code) before alignment. It is done when you want to compare a DNA or RNA sequence to a protein database. For nucleotide alignments, algorithms like Needleman-Wunsch (for global alignment) and Smith-Waterman (for local alignment) are commonly used. For protein alignments, the process takes into account the chemical properties of amino acids, often using substitution matrices like BLOSUM or PAM to score similarities between amino acids. **3. What is the underlying biology?** The biological foundation behind sequence alignment comes from the principles of evolution and genetic inheritance. Organisms inherit DNA from their ancestors, and over time, this DNA accumulates mutations. These mutations can lead to changes in protein sequences, but essential functions tend to be preserved, so important genes and proteins remain similar across species. \- Homology : When we compare sequences, we are often looking for homologous sequences ---genes or proteins that share a common evolutionary origin. Homologous sequences can be: \- Orthologs : Genes in different species that evolved from a common ancestral gene. \- Paralogs : Genes that arose by duplication within the same genome. In protein sequences, evolution can result in conserved regions that are crucial for the protein\'s function, as well as variable regions where mutations do not significantly affect function. **4. Which computational methods do we have?** Several computational methods exist for sequence comparison and alignment. Key methods include: \- Pairwise alignment : Methods like Needleman-Wunsch (for global alignment) and Smith-Waterman (for local alignment) compare two sequences by finding the best alignment either across their entire length (global) or in regions of high similarity (local). They use dynamic programming algorithms to optimize the alignment based on a scoring matrix. \- Multiple sequence alignment (MSA) : Aligns more than two sequences simultaneously to find conserved regions across all sequences. Algorithms include Clustal Omega , MAFFT , and MUSCLE. \- Substitution matrices : For protein alignments, matrices like BLOSUM or PAM score the likelihood of amino acid substitutions based on evolutionary data. \- Heuristic methods : Tools like BLAST (Basic Local Alignment Search Tool) use heuristic approaches to speed up sequence comparisons against large databases by breaking sequences into short \"words\" and then extending matches. \- Hidden Markov Models (HMMs) : Used by tools like HMMER to find and align sequences, particularly when comparing sequences against profile databases for conserved domains. **5. DNA versus protein alignment: when use which?** The choice between DNA and protein alignment depends on the situation: \- DNA alignment : \- Use for comparing short and highly similar sequences, such as when analyzing closely related species or individuals (e.g., human genome studies). \- Ideal when studying regions where mutations are synonymous (i.e., mutations do not change the encoded amino acid). \- Protein alignment : \- More useful when comparing distant species, as protein sequences evolve more slowly than nucleotide sequences. \- Protein alignments can handle more variability because amino acid substitutions often maintain the function of the protein if chemically similar amino acids are substituted. \- DNA can be translated to protein to simplify comparison, as 20 amino acids carry more information than 4 nucleotide bases, which leads to a higher signal-to-noise ratio. **6. How to align more than two sequences?** Multiple sequence alignment (MSA) is used to compare three or more sequences simultaneously, and it identifies conserved regions across all the sequences. Tools for MSA include: \- Clustal Omega : A widely used tool that employs a progressive alignment strategy. It first aligns the most similar pairs of sequences and then gradually incorporates more distant sequences. \- MUSCLE : Often provides better alignments than Clustal, using an iterative approach to refine the alignment. \- MAFFT : Known for being fast, especially with large datasets, and also provides iterative refinements. **7. How to search against a database?** To search for similar sequences within a large database (e.g., GenBank, UniProt), one uses algorithms like BLAST (Basic Local Alignment Search Tool). The process involves: \- Input sequence : Either DNA, RNA, or protein sequences are used as a query. \- Database selection : The query is compared against a database of known sequences (e.g., the protein database for identifying homologous proteins). \- Search algorithm : The search algorithm finds regions of local similarity between the input sequence and sequences in the database. **8. How does BLAST work and how to interpret results?** BLAST (Basic Local Alignment Search Tool) is a widely used tool for searching sequences against databases. BLAST works in the following way: 1\. Word matching : BLAST first breaks down the query sequence into small subsequences called \"words\" (usually 3 for proteins, 11 for nucleotides). 2\. Seed search : These words are then compared against the database sequences to find exact or near-exact matches. 3\. Extension : For each match, BLAST attempts to extend the alignment in both directions to find the best local alignment. 4\. Scoring : Alignments are scored using substitution matrices like BLOSUM for proteins or a nucleotide scoring matrix for DNA. Higher-scoring matches indicate more similar sequences. Interpreting results : \- E-value : The Expect value (E-value) gives the probability that a match occurred by chance. The lower the E-value, the more significant the match. An E-value of 0.01 means that there is a 1% chance that the match occurred by chance. \- Identity : The percentage of identical matches between the query and the subject sequence. \- Alignment score : Reflects the overall quality of the alignment. Results are typically listed from the highest to the lowest scoring match, with the best alignment showing the most significant sequence similarity. This comprehensive understanding of sequence alignment---from its biological basis to the computational tools available---helps in identifying evolutionary relationships, understanding protein functions, and solving various problems in bioinformatics and genetics.