nucleic: lec 7

A field that uses computer science to analyze biological data, like DNA sequences. It helps us understand patterns in these datasets.

Humans rely on context and everyday language, but struggle with large, unorganized data. Machines follow strict rules and process information quickly and accurately, but need data in specific formats.

Collections of the building blocks of DNA (Adenine, Thymine, Guanine, Cytosine) that hold genetic information. These sequences are stored in databases, which are growing rapidly.

The complete set of genetic information for an organism, including all its DNA sequences. It provides a blueprint for the organism's proteins and functions.

A standard way to store DNA and protein sequences in text files. It is simple and widely used, but less flexible than other formats.

Bioinformatics relies on large and growing datasets of biological information, like DNA sequences and protein structures. Advancements in the field are driven by the increasing amount of available data and improved algorithms.

Humans use context and natural language to process information, but struggle with large, disorganized data. Machines follow strict algorithms and process information quickly and accurately, but require standardized formats.

A complete genome sequence provides comprehensive information about an organism's proteins and the biological processes they carry out. However, not all genomes are complete or of the same quality.

Advances in sequencing technology have made it possible to generate large amounts of sequence data quickly and efficiently, leading to an exponential growth in genome data. Metagenomic sequencing, which analyzes the genetic material of entire communities, is becoming increasingly important.

The speed of discovery of new sequences outpaces the ability to study them experimentally. Automatic genome annotation is fast but can introduce errors.

A text file used to store biological sequences like DNA or protein sequences. It starts with a '>' symbol followed by the sequence identifier, and the sequence itself is on the following lines.

A unique label that identifies a biological sequence in a FASTA file. It usually includes information about the sequence's source, such as the organism, gene, or protein name.

The sequence itself is written on lines following the sequence identifier. Each line represents a string of amino acids for protein sequences or nucleotides for DNA sequences.

The '>' symbol marks the beginning of a new sequence entry in a FASTA file. It is followed by the sequence identifier.

Sometimes, the default identifier may not be informative enough. You might edit it to include additional details about the sequence, making it easier to find and understand.

A relationship between biological parts tracing back to a common ancestor, identified through statistical comparisons of sequence similarity.

Homology is a simple yes-or-no concept: two entities are either homologs (related by common ancestor) or not.

Sequences evolve through gradual changes like substitutions (one base replaced by another), insertions (adding bases), and deletions (removing bases).

Residues that remain unchanged across evolution are likely important for the sequence's function. They are 'protected' by natural selection.

While substitutions are common, insertions or deletions of bases are rarer. These occur due to various mechanisms like transposons and replication errors.

Most changes in the sequence are neutral, having little impact on the organism's survival. Changes are driven by random chance and become fixed in the population through drift.

Residues consistently present across generations are likely doing something important. This suggests that unconserved residues might be less functionally critical.

The process of identifying corresponding residues between homologous sequences, revealing evolutionary relationships.

A type of sequence alignment where residues are aligned linearly from the N-terminus to the C-terminus, with one-to-one correspondence and optional gaps.

Structural alignments reveal true residue correspondence based on 3D structure, while sequence-based alignments are more practical for large datasets but may not fully reflect true correspondence.

Residues in a sequence that remain unchanged across evolution, likely important for function and protected by natural selection.

Replacing an amino acid in a protein with another that has similar properties (like size and hydrophobicity) is less likely to disrupt the protein's function.

Replacing an amino acid with one having significantly different properties (like acidic vs. hydrophobic) can drastically alter the protein's structure and function.

Leucine and Isoleucine are both hydrophobic and mid-sized, making them often interchangeable in proteins without significant functional impact.

Aspartic acid and Glutamic acid are both acidic and mid-sized, making them often interchangeable in proteins.

The success of an amino acid substitution depends on whether the resulting protein still performs its intended function.

Matrices that quantify the likelihood of amino acid substitutions during evolution. They report log-odds ratios reflecting substitution probabilities.

Penalties applied in sequence alignments for insertions or deletions (indels). These events are rare and disruptive, so they are penalized.

BLAST algorithm identifies biologically relevant sequence matches in databases. It uses substitution scores and gap penalties for alignment scoring.

Insertions or deletions (indels) are almost as likely to be 5 amino acids long as 1 amino acid long.

BLAST uses a scoring system to evaluate the quality of a sequence alignment. It considers substitution scores, gap penalties and their lengths.

Global alignment algorithms exhaustively check all possible alignments, becoming computationally expensive for longer sequences, especially when searching a massive number of potential matches.

Sequence similarities often occur in concentrated patches, where functionally or structurally critical residues are clustered. This allows for the development of faster alignment algorithms that focus on these regions.

Residues that remain unchanged across evolution are likely functionally important. By focusing on conserved regions, alignment algorithms can prioritize the most significant similarities.

Substitution matrices quantify the likelihood of amino acid substitutions during evolution. They help assess the strength of a match by considering the probability of each substitution.

Insertions or deletions (indels) are rare evolutionary events and typically disrupt protein function, making them less likely. Gap penalties reflect this lower probability, penalizing alignments with gaps.

BLAST finds regions of similarity between two sequences, based on short, high-scoring matches. It assumes that locally conserved regions indicate evolutionary relationships.

The E-value indicates the probability that a match between two sequences is due to random chance. Lower E-values suggest a stronger evolutionary relationship.

MSAs compare multiple homologous sequences to identify conserved and non-conserved regions, highlighting evolutionary relationships. They build on pairwise alignments and can be more reliable than individual pairwise alignments.

Phylogenetic trees visualize the evolutionary relationships between sequences. Branch lengths can represent the degree of divergence between sequences.

This method uses pairwise alignment scores to calculate the distances between sequences and construct a tree. Sequences with more similar scores are considered more closely related.

BLAST uses a scoring system to evaluate alignments based on substitution scores and gap penalties. Gaps are penalized because they are rare and disruptive.

Residues that remain unchanged across evolution are likely crucial for a protein's function. They are protected by natural selection and indicate functional importance.

Substitutions involving amino acids with similar properties (size, charge, etc.) are more likely to preserve protein function. Dissimilar substitutions can disrupt function.

Substitution matrices quantify the likelihood of different amino acid changes during evolution. They help assess the strength of a match based on the probability of each substitution.

Gap penalties are used in sequence alignments to penalize insertions or deletions (indels) because they are rare and disruptive to protein structure and function.

A network representation of relationships between proteins, where nodes are proteins and connections represent similarities. They help visualize protein families and evolutionary connections.

SSNs are useful for analyzing protein superfamilies, large groups of related proteins with diverse functions. They provide a visual representation of evolutionary relationships within a family.

The crotonase superfamily exhibits a wide range of functions, which SSNs help to resolve into distinct clusters. These clusters often reflect specific biological functions.

Clusters in SSNs that don't contain proteins with known functions may indicate the presence of novel functions, providing a starting point for research and discovery.

When performing rigorous analysis with SSNs, it's crucial to remove unreliable regions in the Multiple Sequence Alignment (MSA) to ensure accurate results and meaningful insights.

Genes in different species that evolved from a common ancestor and perform the same function.

Genes within the same species that originated from a gene duplication event, often specializing in different functions.

Genes that share similar functions but have different evolutionary origins, arising from convergent evolution.

When unrelated organisms evolve similar traits due to adapting to similar environments or selective pressures.