Bioinformatics Flashcards

(45 cards)

1
Q

is the interdisciplinary science of collecting, analyzing, and interpreting
biological data using computational techniques.

A

Bioinformatics

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2
Q

It primarily deals with data derived from genome
sequences, protein structures, and gene expression.

A

Bioinformatics

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3
Q

bridges the gap between biology and computer science. It has become an
essential tool in research areas like genomics, systems biology, drug discovery, and personalized
medicine.

A

Bioinformatics

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4
Q

represents the raw or processed information obtained from experiments in
genomics, transcriptomics, proteomics, etc

A

Biological data

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5
Q

Data Type (5)

A

Nucleotide Sequences
Protein Sequences
Genomic Data
Expression Data
Structural Data

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6
Q

what type of data:

DNA/RNA sequences

A

Nucleotide
Sequences

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7
Q

what type of data:

Amino acid chains of protein

A

Protein Sequences

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8
Q

what type of data:

Whole genome sequences and variants

A

Genomic Data

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9
Q

what type of data:

Data from gene expression studies (e.g.
RNA-Seq)

A

Expression Data

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10
Q

what type of data:

3D structures of biomolecules

A

Structural Data

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11
Q

examples of Nucleotide
Sequences (2)

A

GenBank, EMBL

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12
Q

examples of Protein Sequences (2)

A

UniProt, PDB

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13
Q

examples of Genomic Data (2)

A

Ensembl, UCSC Genome
Browser

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14
Q

examples of Expression Data (2)

A

GEO, ArrayExpress

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15
Q

example of Structural Data

A

RCSB PDB

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16
Q

are organized collections of biological data that are accessible
online and used for research and analysis.

A

Bioinformatics databases

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17
Q

These databases are interconnected and often cross
reference each other to provide comprehensive biological insights

A

Bioinformatics databases

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18
Q

Types of Bioinformatics databases (3)

A

primary
secondary
tertiary

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19
Q

what type of bioinformatics database:

Contain raw data like nucleotide sequences (e.g., ____).

A

Primary databases; GenBank

20
Q

what type of bioinformatics database:

Contain analyzed and interpreted data (e.g., __for protein
families).

A

Secondary databases; Pfam

21
Q

what type of bioinformatics database:

Provide curated and integrated data (e.g., ___for human genetic
disorders).

A

Tertiary databases; OMIM

22
Q

arranges DNA, RNA, or protein sequences to identify regions of
similarity.

A

Sequence alignment

23
Q

are essential for gene annotation, identifying conserved regions, and
understanding evolutionary relationships.

24
Q

types of Sequence Alignment (2)

A

Pairwise Alignment;
Multiple Sequence Alignment (MSA):

25
what type of Sequence Alignment: Comparison between two sequences (e.g., ___,___).
Pairwise Alignment; BLAST, FASTA
26
what type of Sequence Alignment: Aligning more than two sequences simultaneously (e.g., __, __).
Multiple Sequence Alignment (MSA): Clustal Omega, MUSCLE
27
__ and ___ are used to score alignments based on evolutionary conservation.
PAM and BLOSUM matrices
28
___identifies the locations of genes in a genome, while ___involves assigning functions to these genes.
Gene prediction; annotation
29
Annotations often use ____ terms for standardization.
Gene Ontology (GO)
30
Applications of Bioinformatics 5
Drug Discovery: Personalized Medicine Comparative Genomics: Functional Genomics: Systems Biology:
31
Identification of drug targets and simulation of drug-receptor interactions
Drug Discovery:
32
Tailoring treatment based on individual genetic profiles.
Personalized Medicine:
33
Understanding evolutionary relationships.
Comparative Genomics:
34
Investigating gene functions and regulatory networks.
Functional Genomics:
35
Modeling complex interactions within biological systems.
Systems Biology:
36
helps in identifying unknown genes, assessing homology, and understanding phylogenetic relationships.
BLAST
37
Use to identify homologous genes using the ___tool.
BLAST
38
steps in using BLAST (4)
1. Obtain a DNA sequence 2. Go to NCBI BLAST 3. Paste the sequence in the BLASTN tool 4. Run the search and interpret the results
39
Current Trends in Bioinformatics (2024-2025): 6
AI & ML in Bioinformatics: Single-Cell Omics: Cloud Computing Long-Read Sequencing: Multi-Omics Integration CRISPR Analytics:
40
Used for protein structure prediction (e.g., AlphaFold), drug interaction prediction.
AI & ML in Bioinformatics:
41
Enables high-resolution understanding of cell-specific expression.
Single-Cell Omics:
42
Facilitates storage and analysis of large biological datasets.
Cloud Computing:
43
Technologies like Oxford Nanopore improve genome assemblies.
Long-Read Sequencing:
44
Combines genomics, proteomics, and metabolomics for holistic insights.
Multi-Omics Integration:
45
Bioinformatics tools assist in off-target effect prediction and guide RNA design.
CRISPR Analytics: