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1 objective functions... COFFEE (Notredame et al. 1998) measures column by column similarity between pairwise and multiple sequence alignments assumes that the pairwise alignments are optimal assumes a set of (arbitrary) costs assumes that similarity reflects history does not necessarily lead to a consistent alignment

2 ...objective functions... sum of pairs (most commonly used) sum of pairwise distance between all sequences attempts to minimize differences in the alignment does not necessarily lead to a consistent alignment

3 sum of pairs 0 A A 1 A T A 2 A C A 3 A C C A sum = 9

4 sum of pairs 0 A A 1 A T A 2 A C A 3 A C C A sum = 8

5 ...objective functions GLOCSA (Arenas Diaz et al. 2009) minimization of implied evolutionary steps additional features of the alignment are used to distinguish between otherwise equivalent alignments mean column heterogeneity distribution of indels alignment size good for phylogeny and alignment

6 ...objective functions POY (Varón et al. 2010) minimization of reconstructed evolutionary steps optimal phylogeny and alignment can violate the triangle inequality (sometimes) good for phylogeny, but not for alignment only

7 MUSCLE (Edgar 2004a,b) [0] k mer distance estimation for unaligned sequences [1] distance (UPGMA) guide tree generated [2] pairwise global alignment down tree [a] consensus (profile) constructed [b] insertions propagated up tree [3] K2P distances calculated [4] back to [1] (once) [5] pairwise global alignment down tree (like [2]) => sum of pairs used to accept/reject realignment

8 pairwise global alignment M Q T I F L H - I W L Q S - W L - S - F \ M Q T I F L H - I W Text L Q S W L - S F M Q T I F L H I W L Q S W L S F Edgar (2004)

9 α β χ δε All-to-all comparisons Unaligned sequences Distance matrix 1 α β χ δ α β γ δ β γ δ ε β γ δ ε Distance matrix 2 α α α β γ δ α β αβ β γ δ ε Group-to-group alignment Tree 1 Alignment 1 αβ γδε γδε γ γδ δε δ ε Group-to-group alignment ε β γ δ ε Tree 2 Alignment 2 α βγ δ ε α βγ δ ε B Initial alignment α β γδ ε Replace if better α β γ δ ε Group-to-group alignment α β γ α β γ δ ε Katoh and Toh (2008) Tree-dependent partitioning δ ε

10 MAFFT (Katoh and Toh 2008) (too) many different algorithms available uses variants of sum of pairs or COFFEE scoring can use local or global alignment can use structural pairwise alignments good for low similarity sequences program is really a large shell script that dispatches to a variety of special purpose programs restricts access to some algorithms by alignment size can be overridden by modifying the script

11 Nearest Alignment Space Termination (NAST) DeSantis et al. (2006), Caporaso et al. (2010) builds a multiple sequence alignment from a template for each new sequence: BLAST (etc.) to find most similar template sequence pairwise alignment of template and new sequence insert into template without introducing insertions can cause local mis alignments (or worse) primarily used for identification (DNA barcoding, etc.) other better options (i.e. identification algorithms)

12 translatorx (Abascal et al. 2010) [1] translates nucleotides to amino acids (standard tables) [2] aligns amino acids using an external program can be manually edited can be aligned using an unsupported program [3] reverse translates back to the original nucleotides removes incomplete codons from the ends has difficulty with long strings of ambiguous nucleotides useful for difficult to align coding regions

13 finding eukaryotic genes unannotated sequences novel genomes novel genes (sequenced from RNA) genes are not just transcribed (protein coding) DNA translated and untranslated regions cis regulation regions (hardest to find) can only find transcribed DNA (easily) (1) similarity to annotated sequences (2) predict based on common features

14 based on similarity... search using tools like BLAST depends on quality of the database annotation hits may not be very useful (e.g. anonymous expressed sequences) i.e. refseq is a better than all of GenBank depends on sequence similarity distant relatives of model systems do not work (well) intragenome comparisons may not work well most genes are members of large families

15 ...based on similarity... (near) exact nucleotide or amino acid match => adopt database annotation distant match => provisionally adopt database annotation complex patterns e.g. match mismatch match could be exon intron exon (or many other things) e.g. match mismatch could be exon spacer or novel gene, etc.

16 ...based on similarity (usually) need to dismember sequences for more informative matches logical portions (e.g. reads, clones, contigs, etc.) may not be biologically meaningful small (ca bp) sliding window not biologically meaningful difficult to resolve conflicting annotations open reading frames more difficult to identify than one might think

17

18 based on common features... often called ab initio (from the beginning) basically looks for long open reading frames (ORF) assumes universal (standard) genetic code unknown how non standard the code actually is identification of splice sites key to finding real ORF often GUAG (spliceosome) with many variations RNA editing makes this difficult cannot identify different exons the variation is unknown

19 ...based on common features the path of fewer assumptions: A/T vs. G/C content k mer frequency (often 6 mers) use DFA, neural networks, SVM, Markov models, etc. require a training set of known genes still cannot find different exons unsupervised training requires a training set of unannotated sequence

20 Hidden Markov Models k order Markov Models probability of {A C G T} given previous k bases k usually equals 5 i.e. basically uses frequency of 6 mers multiple models can be used simultaneously e.g. one for each codon position for speed, interpolated Markov models (IMM) are used averages several models into one (could have just used a simpler/different model)

21 software (plant biased)... non vigorous and non uniform benchmarks often programs work well on A and fail on B (i.e. they have over parameterized models) GENSCAN (Burge & Karlin 1997; Burge 1998) very model dependent (i.e. not so useful) no longer under active development executables are free for academic use

22 ...software (plant biased)... EuGène (Schiex et al. 2001) under active development (mostly for prokaryotes) additional types of models added via plugins additional features can be added via plugins open source geneid (Blanco et al. 2002) (no longer) under active development user can specify models (annoying/tedious format) open source

23 ...software (plant biased) GeneMark (Ter Hovhannisyan et al. 2008) under active development many different flavors are available GeneMark.hmm ES self training, hidden Markov models requires ca. 10 Mbp of sequence to make model very useful for non model species executables are free for academic use

24 software (plant biased) AUGUSTUS (Stanke and Waack 2003) under active development uses a Hidden Markov Model (HMM) G/C content (4 classes), intron length, splice site, and splice site adjacent positions a plant/algae/fungi trained version is available freely available BRAKER1 (Hoff et al. 2016) GeneMark ET output is used as a training set for AUGUSTUS

25 Gene Ontology (GO) a controlled vocabulary for genes (Ashburner et al. 2000) cellular/extracellular localization biochemical function type of biological processes designed to be applicable to all of life each term has a unique identifier related terms are grouped together in broader categories directed acyclic graph

26 example GO ribulose-1,5-bisphosphate carboxylase/oxygenase GO: : carbon fixation GO: : chloroplast GO: : plastid

27 GO: : carbon fixation ancestors: is_a (inferred) biological_process is_a (inferred) metabolic process is_a (inferred) single-organism metabolic process is_a organic substance metabolic process children: C4 photosynthesis is_a carbon fixation CAM photosynthesis is_a carbon fixation carbon fixation by 3-hydroxypropionate cycle is_a carbon fixation carbon fixation by acetyl-coa pathway is_a carbon fixation reductive pentose-phosphate cycle part_of carbon fixation reductive tricarboxylic acid cycle is_a carbon fixation

28 getting GO full database can be downloaded database structure is not optimized for speed modify for serious analytic use deleted unused/redundant columns/tables create indices and foreign keys as needed remove unused indices NCBI files also needed (gene_info, gene2accession) BLAST (or similar) against GenBank (full or subset) query BLAST hits against GO database

29 getting GO BLAST2GO (Conesa et al. 2005) a (JAVA) gui to BLAST and query the GO database uses the stock GO database => slow good for a small number of queries (only)

30 letting GO compare GO terms among samples shared versus unique (use vague [ancestral] terms) make candidate lists based on GO terms

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