Introduction to Bioinformatics
Class 11 · Biotechnology
4 ideas in Class 11 Biotechnology, explained in plain words, with games and quizzes to practise.
Stats for Life Data
Part of: Numbers Behind Biology
Null claims no link, R2 runs 0 for none to 1 for perfect with 0.91 backing rejection, p at or under 0.05 marks significance though 0.05 breeds false positives, correlation measures while regression models change, and repeats plus large random samples build trust with R open and MATLAB paid.
Why it matters. Cutoffs plus R2 reading answer data questions.
Key words
- Null hypothesis
- : Claim of no relationship.
- p-value
- : Chance result if null holds.
- R2 value
- : Fit closeness score.
- Regression
- : Model of variable change.
- Replicate
- : Independent experiment repeat.
Try it in two minutes
- State null first.
- Read R2 and p.
- Repeat samples.
Try at home. Decide significant or not for three p values.
Birth of Bioinformatics
Part of: Numbers Behind Biology
Dayhoff 1925 to 1983 published the 1965 Atlas and one-letter codes, NCBI hosts data with GenBank from 1982, the term entered literature in 1991, the genome project ran from early 1990s to draft 2001 and 2003 release with gaps in repeat-rich zones, and early-1900s protein structure work makes structural informatics older.
Why it matters. Year order plus firsts answer history questions.
Key words
- Atlas
- : First protein sequence book.
- GenBank
- : Public DNA sequence store.
- NCBI
- : US bioinformatics resource hub.
- Genome project
- : Full human DNA reading effort.
- Heterochromatin
- : Repeat-rich hard zone.
Try it in two minutes
- Date 1965 to 2003.
- Name Atlas, GenBank.
- Place structures first.
Try at home. Order five milestones by year.
Databases and Pictures
Part of: Data Stores and Tools
Libraries need catalogues because molecules cannot be shelved, GenBank keeps DNA while PDB keeps 3D shapes UniProt keeps protein function PubMed keeps papers KEGG keeps pathways and OMIM keeps disorders, SQL serves relational tables while NoSQL takes scattered huge sets, and UCSC browses genomes CIRCOS circles data IGV explores sets R plots stats.
Why it matters. Store-to-content plus tool-to-job answers matching questions.
Key words
- PDB
- : 3D structure collection.
- UniProt
- : Protein sequence collection.
- PubMed
- : Biomedical paper collection.
- SQL
- : Relational query language.
- NoSQL
- : Non-relational huge store.
Try it in two minutes
- Name six stores.
- Split SQL, NoSQL.
- Circle with CIRCOS.
Try at home. Assign each need to one store.
Genome Toolchain
Part of: Data Stores and Tools
Broad makes over 24 terabytes a day with short reads of 100 to 150 letters and long reads of kilobases, FASTA headers label sequences while FASTQ packs four lines with matching quality marks, reads align to a reference or assemble de novo with BLAST BLAT over 95 percent across 40 letters and MUSCLE beating CLUSTAL, and annotation ontology and trees link genes to function before AI assists.
Why it matters. Scale plus format plus align-or-build answers workflow questions.
Key words
- FASTA
- : Labelled sequence format.
- FASTQ
- : Sequence plus quality format.
- Contig
- : Assembled continuous stretch.
- Annotation
- : Feature labelling process.
- Phylogeny
- : Evolutionary relationship tree.
Try it in two minutes
- Size the flood.
- Pack four lines.
- Align or build.
Try at home. List four FASTQ lines in order.
What your child will practise
- Birth of Bioinformatics: Desert Puzzle (Clue Puzzle)
- Birth of Bioinformatics: Desert Quick Fire (Quick Fire)
- Databases and Pictures: Space Puzzle (Clue Puzzle)
- Genome Toolchain: Candy Quest (Flip and Match)
- Stats for Life Data: Jungle Quick Fire (Quick Fire)
- Quiz: Birth of Bioinformatics (8 questions)
- Quiz: Databases and Pictures (8 questions)
- Quiz: Genome Toolchain (8 questions)
- Quiz: Stats for Life Data (8 questions)