Introduction to Bioinformatics and Molecular Diagnostics

Introduction to Bioinformatics

• Bioinformatics applies statistics and computer science to molecular biology.
• It's used to organize biological data and extract relevant information.

Detecting Genetic Diseases

• Fetal testing:
  • Amniocentesis (16 weeks) - karyotype test
  • Chorionic villus sampling (8-10 weeks) - karyotype test
• Testing for chromosome abnormalities and defective genes:
  • Fluorescence in situ hybridization (FISH) uses probes specific to chromosomes/genes.
  • Spectral karyotype analysis analyzes chromosome structure.

Molecular Diagnostics

• Uses DNA, RNA, and proteins to detect, diagnose, subclassify, predict, and monitor disease response.
• This field improves disease detection sensitivity, specificity, and analysis speed.
• It's a cost-effective method.

Molecular Diagnosis of Sickle Cell Disease (RFLP)

• A specific hemoglobin mutation.
• RFLP (Restriction Fragment Length Polymorphism) identifies mutations.
• Uses wild type and mutant examples.
• Differs from the normal hemoglobin by an amino acid change.

Hybridization

• Single-stranded oligonucleotides create hybrids based on complementary sequences.
• Target sequences are matched by oligonucleotides.
• Probes are nucleic acids with markers for detection.

Dot Blots

• Assesses SNPs (single nucleotide polymorphisms) using PCR-amplified DNA.
• DNA is blotted onto a membrane, then unbound probes are washed.
• Bound probes are detected via radioactive or colorimetric means.

Allele-Specific Oligonucleotide (ASO) Dot Blot

• Detects sickle cell anemia via ASOs.
• Different DNA sequences are visualized as different intensities.

Reverse Dot Blot

• ASOs are bound to a membrane instead of DNA.
• Labeled target DNA hybridizes to the bound ASOs.
• Useful for analyzing genetic profiles.

Single Nucleotide Polymorphisms (SNPs)

• Common genetic variations.
• One SNP occurs approximately every 1,000-3,000 base pairs in the human genome.
• Most have no effect as they occur in non-coding regions (introns).
• SNPs contribute to pharmaceutical development (SNP Consortium).

Microarray Technology

• Compares gene expression levels in different tissues.
• Used in cancer research for comparing diseased and healthy cell gene expression.
• DNA from a patient is isolated, labeled, and hybridized to the microarray.
• Laser scanning measures the intensity of fluorescence.

Genome-Wide Association Studies (GWAS)

• Based on scoring genomic DNA from a large sample of cases and controls using single nucleotide polymorphisms (SNPs).
• Comparing frequencies of SNPs between cases and controls helps identify significant differences.
• Results are validated from independent samples.

The HapMap Project

• Public database with more than a million common variants (SNPs) in the human genome.
• Based on typing 269 DNA samples from different populations.

Basic Bioinformatics Data

• Genomics is the study of genomes.
• Genomics results from the dramatic rise in the scale of biological data collection.
• Sequencing.
• SOAP alignment.
• SNP detection and annotation.
• InDel detection and annotation.
• SV (structural variation) detection and annotation.
• CNV (copy number variation) detection and annotation.

Basic Bioinformatics Analysis

• Ancestry analysis.
• HGMD (Human Gene Mutation Database) analysis.
• Haplotypes construction.
• Demographic analysis.
• Evolution analysis.

Genome de novo Sequencing using NGS (Next-Generation Sequencing)

• Sequencing technology used for genome-wide sequencing.
• Processes include fragmenting, sequencing, and assembling DNA.

Bioinformatics Databases

Common databases for biological information.

• GeneBank/DDBJ/EMBL (nucleotide sequences)
• Ensembl (genomes of human/mammals)
• PubMed (literature references)
• Swiss-Prot (protein sequences)
• OMIM (genetic diseases)

Broad Goal of Genome-Wide Association Studies (GWAS)

• To find genetic markers associated with specific traits/diseases.
• To find markers in the genome that correlate significantly to phenotypes.

Why Study Genetic Differences?

• Prediction of adult features from a child's genetics.
• Development of gene therapies for genetic diseases.
• Creation of disease-resistant strains in agriculture.
• Understanding disease frequencies in different populations.

This quiz covers the basics of bioinformatics and its applications in detecting genetic diseases and molecular diagnostics. Explore the methodologies used in fetal testing and the molecular diagnosis of conditions such as sickle cell disease. Enhance your understanding of how statistical and computational techniques are applied to biological data.