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06_Alignment_2022.pdf
This document discusses sequence alignment and its applications in bioinformatics. It begins by explaining the goals of learning about homology and how sequence alignment relates to function across organisms. It then describes different types of sequence alignment including global, local, Needleman-Wunsch, Smith-Waterman, and BLAST. It explains how to quantify alignment scores and perform statistical analysis of alignments. The document provides examples of alignment matrices and algorithms for finding the best alignment between sequences.
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06_Alignment_2022.pdf
- 1. Sequence Alignment MICROBIO 590BBioinformatics Lab: Bacterial Genomics Professor Kristen DeAngelis UMass Amherst Fall 2022 1
- 2. Lecture Learning Goals •Explain homology and how sequence homology is related to function or physiology across groups of organisms. • Describe global sequence alignment, and contrast global versus local sequence alignment. • Describe how to find the best alignment, including the measures that allow one to quantify alignment scores. • Needleman-Wunsch • Smith-Waterman • Basic Local Alignment via BLAST • Perform a statistical analysis of alignments. 2
- 3. The Central Dogmaof Biology • …an example of the insights we can gain from sequence alignment! 3 Crick, Nature 1970
- 4. RNA was thefirst biological molecule … 4
- 5. … with DNAevolving last 5
- 6. The replication machineryof LUCA: common origin of DNA replication and transcription • Origin of DNA replication is an enigma because the replicative DNA polymerases (DNAPs) are not homologous among the three domains of life, Bacteria, Archaea, and Eukarya. • In the RNA-protein world that predated the advent of DNA replication, maybe RNAPs and replicative DNAPs evolved from a common ancestor that functioned as an RNA-dependent RNA polymerase. 6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281927/
- 7. Phylogenies describe howorganisms are related, and more closely related organisms share more traits. 7 These blue distances describe evolutionary distance. The value of changes is described in a scale bar of the same orientation. These red lines are spacers for labels, pictures, or data. These yellow circles are hypothetical common ancestors.
- 8. Homology infers sharedancestry • Homologous characters – characters in different organisms that are similar because they were inherited from a common ancestor that also had the character 8
- 9. Homology infers sharedancestry • Analogous characters are the opposite of homologous characters 9
- 10. 10 Homologous genes have ashared ancestry. • Orthologs arise from a speciation event – multiple organisms, one gene. • Paralogs arise from a duplication event – the same organism, two different homologous genes.
- 11. The Tree ofLife • Organisms with more homolologous molecular traits also have more shared physiological traits 11 Woesian ToL: Pace NR, Science 1997
- 12. Sequence homology isused to infer shared ancestry • Conservative mutation encodes an amino acid replacement with similar biochemistry • Semi-conservative mutation encodes an amino acid replacement with somewhat different biochemistry, e.g., different charge • Non-conservative mutations encode amino acid replacements with different biochemistry 12
- 13. Sequence alignment • Thetask of finding corresponding parts in two related sequences • Biological Significance • Prediction of gene/protein function • Gene Finding • Species identification (database searching) • Evolutionary relationships (sequence divergence) • Sequence Assembly 13
- 14. Global sequence alignment •A representation of the correspondence between all the respective symbols (i.e. nucleotides or amino acids) of two sequences • If two sequences (s,t) have the same ancestor, we expect them to have many symbols and strings in common • For most symbols, we should be able to identify the corresponding homologous position in the other sequence • Represented as an “alignment” • Mutations appear as mismatches • Insertions/Deletions appear as gaps 14
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- 25. A substitution matrixcan represent scoring 25
- 26. Nucleotide substitution models •Jukes Cantor model is a one-parameter model • Two-parameter models only care about whether a substitution is a transition or transversion • Six-parameter models weighting each change differently 26
- 27. Nucleotide substitution matrixes 27 •Transitions are much more common than transversions, so these are weighted differently in deciding what distance to assign to a mismatch • Six-parameter models consider different types of transitions and transversions, weighting each change differently • Gaps are also tricky… for example, adjacent gaps are not unrelated
- 28. Amino Acid SubstitutionModels are more Complex 28 • Protein substitution matrices are significantly more complex than DNA scoring matrices • Proteins are composed of 20 amino acids with varying physico- chemical properties • Protein substitution matrix can be based on any property of amino acids: size, polarity, charge, hydrophobicity, etc
- 29. Amino Acid SubstitutionModels are more Complex 29
- 30. GC content canintroduce bias, but is NOT an indication of poor quality sequence. • Bias sequence composition of reads (ex. GC content, 5’- transcriptome) • Causes • Codon or noncoding bias • Random hexamer priming (transcriptome studies) • Library preparation (PCR bias) • Problems • Variant calling (SNPs) • Copy Number Variation (CNV) • Solution • Weighing sequence reads based on first 7 nucleotides • GC content correction • RNA-SEQC, CQN, EDA-SEQ 30
- 31. How do wefind the best alignment? 31
- 32. How do wefind the best alignment? 32
- 33. How do wefind the best alignment? 33
- 34. How do wefind the best alignment? 34
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- 68. • Like N-W,a dynamic programming algorithm • guaranteed to find the optimal local alignment with respect to the scoring system being used • Main difference with N-W is that negative scoring cells are set to zero, to highlight local alignments • Not practical for big alignment problems… 68
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- 79. Sequence Databases areEnormous! 79 ~10 Trillion Nucleotides Total! Sayers et al., Nucleic Acids Research. 2020
- 80. Sequence Databases areEnormous! NCBI SRA • Raw sequence data from high throughput sequencing 80 Terabase = 1,000 Gb 1 Gb = 1 million bases 9 890 500 490 859 nt GenBank 54 966 126 472 268 601 nt SRA Total Billion Trillion Quadrillion Million
- 81. Basic Local AlignmentSearch Tool (BLAST) 81
- 82. BLAST Output andValues 82
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- 94. Lecture Learning Goals •Explain homology and how sequence homology is related to function or physiology across groups of organisms. • Describe global sequence alignment, and contrast global versus local sequence alignment. • Describe how to find the best alignment, including the measures that allow one to quantify alignment scores. • Needleman-Wunsch • Smith-Waterman • Basic Local Alignment via BLAST • Perform a statistical analysis of alignments. 95