L6 | Introduction to Bioinformatics

• Bioinformatics is a discipline for handling genetic information.
• It involves computational tools and approaches to acquire, store, visualize, and interpret medical or biological data.
• Bioinformatics requires advanced computational methods to process large datasets.
• These methods are used to form decision-support systems for complex problems in medicine, biology, and related fields.

Historical Context

• The term "bioinformatics" has been in the scientific lexicon for around two decades.
• The underlying principles of bioinformatics come from studies that combined computational and experimental approaches.

Pioneering Institutions

• Here are ten institutions that pioneered and fostered computation in biology:
  • Birkbeck College, University of London (UK)
  • Boston University (USA)
  • European Molecular Biology Laboratory (EMBL) (DE and EMBL)
  • Institute of Protein Research, Academy of Sciences (Former USSR)
  • Laboratory of Molecular Biology (LMB), MRC (UK)
  • Los Alamos National Laboratory (LANL) (USA)
  • National Biomedical Research Foundation (USA)
  • Stanford University (USA)
  • University of California San Francisco (UCSF) (USA)
  • University College, University of London (UCL) (UK)

Sequencing and Bioinformatics

• The wide availability of methods for DNA sequencing in the early 1980s led to exponential growth in molecular sequence data
• Data was stored in databases like GenBank and EMBL (European Molecular Biology Laboratory nucleotide sequence database).
• The arrival of the Internet transformed databases and access to data, publications, and information infrastructure

Bioinformatics Applications:

• CRISPR-Cas bioinformatics:
  • Bioinformatics played a key role in the discovery of the CRISPR-Cas system and identification of new functions.
  • Yoshizumi Ishino described the signature repeat-spacer architecture of CRISPR arrays in 1987.
  • Francisco Mojica, aided by bioinformatics, showed CRISPR arrays are present in Escherichia coli, most archaeal, and many bacterial genomes.

Bioinformatics tools:

• Artemis Comparison Tool (ACT):
  • ACT identifies and analyzes regions of similarity and difference between genomes.
  • It explores the conservation of synteny in the context of the entire sequences and their annotation.
  • ACT reads complete EMBL, GENBANK, and GFF entries, and FASTA or raw format sequences.

Bioinformatics in Translational Drug Discovery

• Disease-based bioinformatics approaches in translational drug discovery depend on the disease type.
• They implement different strategies to analyze cancer, genetic, and infectious diseases.
• Bioinformatics approaches help identify key drivers of cancer in patients, for personalized cancer therapy.

Bioinformatics in Clinical Genomic Sequencing

• High-throughput sequencing technologies have transformed molecular genetic pathology into an informatics-driven specialty.
• Increasing capabilities and reduced costs have made large-scale sequencing (exomes and genomes) accessible to many patients.

Sequencing Technologies

• First Generation: Sanger Sequencing (500-1000 bp fragments)
• Second Generation: 454, Solexa, Ion Torrent, Illumina (~50-500 bp fragments)
• Third Generation: PacBio, Oxford Nanopore (Tens of kb fragments on average)

Clinical Bioinformatics Process:

• Primary Analysis: Algorithms convert raw sequence reads to a string of As, Cs, Ts, and Gs.
• Secondary Analysis:
  • Mapping or aligning sequence reads onto the reference genomic sequence.
  • Variant calling identifies differences between the individual's sequence and the reference genome.
• A reference genome is a digital nucleic acid sequence database, a representative example of the set of genes.
• Variation Viewer allows viewing, searching, and navigating variations in dbSNP, dbVar, and ClinVar.
• Tertiary Analysis: Sequence variants are placed in the genomic and clinical context.
• Annotation: Variants are annotated to assess their effects on biology, physiology, and clinical relevance. This involves extracting knowledge at the gene, transcript, protein, and regulatory levels from biological databases.

Datasets and Databases for Variant Annotation

• dbSNP: Central repository for SNP/indel polymorphisms.
• Human Gene Mutation Database (HGMD): Repository for gene lesions responsible for human inherited disease.
• ClinVar: Repository of human variants related to phenotypes with supporting evidence.
• LOVD: Repository of gene-centered DNA variations, storage of patient-centered data and NGS data.
• 1000 Genomes: Project to find genetic variants with frequencies of ≥ 1%.
• gnomAD: Resource aggregating exome and genome sequencing data from large-scale sequencing projects.
• ExAC: Same as gnomAD.
• Alamut: Database that integrates NCBI, EBI, UCSC, HGVS nomenclature.
• dbNSFP: Database of human nonsynonymous SNPs and their functional predictions.

Conclusion

• Genomic sequencing is increasingly part of clinical practice.
• Understanding bioinformatics tools' purposes, outputs, and assumptions is crucial for molecular geneticists/pathologists.