EMP, GreenGenes - Make Local DB and BLAST

Create a reference database from my GreenGenes + 16S strains

I used the rep_seqs that were generated when I created my phyla tree as my database.
Made a new file - curto-db.fasta
*these are aligned rep_set seqs

Two ways to create your own local database:

1.   Use the BLAST command line

The sequences need to be in a specific format:

Ex.
>gnl|831711|Microbacteriaceae_Candidatus_Rhodoluna
DNA here

makeblastdb
$ makeblastdb -in curto-db.fasta -dbtype nucl -out curto.db

Find out more details HERE

2. Use Geneious

Tools -> Sequence Search

Window pops up and click "Add/Remove Databases" - select "Add Sequence Database"
Follow instructions (ie. select 'nucleotide' and 'custom BLAST')

Perform Sequence Search again, but this time Select "Database" and scroll to your new custom database!

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Next, BLAST the EMP seqs against my local database.
*The EMP seqs were generated from QIIME assign_taxonomy.py and took those who identified with Curtobacterium with greater 0.67 quality score
*The seqs are also extremely short - less than 200 bp

Export the data to a .txt file

Really strange results - EMP seqs hit rep_seqs at equal frequency

Need to look at seqs in Geneious and check alignments!

GreenGenes - Pipeline and Phylogenies

1. Download the entire GreenGenes database

Need:
gg_13_5.fasta
gg_13_5_taxonomy.txt

2. Search for taxonomy of interest - start with Microbacteriaceae
#creates a text file with IDs matching search
$ egrep "f__Microbacteriaceae" gg_13_5_taxonomy.txt | awk '{print $1}' > ./gg-microbacteriaceae.txt

3. micro-only.py 
#searches fasta file and creates a new fasta file with only IDs from gg-microbacteriaceae.txt
#found 5707 sequences

4. Combine my 16S reads
$ cat my-16S-reads.fasta gg-micro.fasta > output.fasta

5. QIIME - pick_otus.py - generates 327 OTUs
-m uclust
-s 0.97
-A #optimal search

***Swarm loses OTUs when running due to its algorithm

6. QIIME - pick_rep_set.py
-f gg-all-microbacteriaceae-with-16S.fasta
-r my-16S-reads.fasta
-m longest

6B. fasta-rename.py #renames all seqs with new names on fasta header

7. Align rep_sest sequences with SINA

8. Eliminate all OTUs with <20 seqs EXCEPT for Curtobacterium OTUs (also did <50 seqs)

9. JModel Test - Computes likelihood scores with PHYML
Base Frequencies +F
Rate Variation +I +G nCat=4
ML Optimized
Base Tree Search = NNI

Best Models:
        Models          BIC Calculation
      TlM1 + G                27589
      TrN + G                 27593
      GTR + G                 27608

10. Run TrN+G model on MEGA
-Maximum Likelihood
-Nucleotide Substitution = TrN
-Bootstrap Method = 100
-Gamma Distributed = 5
-Complete Deletion 
-NNI

11. Run GTR+G on RAxML - see RAxML manual for help
$ raxmlHPC -s input_file.phy -n output_name -m GTRGAMMA -# 100 -x 100 -p 2389 -f a -o outgroup_name

RAxML - GTG+G with OTUs > 50 seqs

 MEGA - TrN + G with OTUs > 50 seqs

GreenGenes - Phylogenetics Background

Been working on this for a few weeks, but I'll summarize:

Brief overview of Phylogenetics:

Multiple Sequence Alignment
generates a score between pairs of sequences

MUSCLE - multiple alignment software includes distance estimations using Kmer

Clustalw - takes a set of input sequences and carry out progressive alignment
   --> aligned in pairs in order to generate a distance matrix
   --> uses a Neighbor-Joining method to produced unrooted tree which serves as the guide for multiple alignment

                       INPUT DATA                         METHOD
                   2 - 100 protein seqs                      MUSCLE
          100 - 500 seqs globally aligned            
                        > 500 seqs
              small number of large seqs                Clustalw

Genetic Distance and Nucleotide Substitution Models
Genetic Distance - evolutionary distance

Rate Heterogeneity among sites - rate of nucleotide substitution can vary substantially for different positions
   --> Use Gamma Distribution - expectation 1.0 with variance 1/alpha

Phylogenetic Inference based on Distance Methods
Try to fit a tree to a matrix of genetic distances

Minimum Evolution (ME) - distance method for constructing additive trees to minimize length of tree
Neighbor-Joining - minimizes steps by finding a pair of neighboring OTUs

Phylogenetic Inference using Maximum Likelihood (ML) Methods
Highest probability of observed data under a set of parameters
Determines tree topology, branch lengths, and parameters of evolutionary model that maximizes the probability of observing the sequences in a particular arrangement

--> GOAL - to find tree among all possible tree structures that maximizes the global likelihood
However, impossible to compute all possible trees -> need to add heuristics
1. Stepwise Addition
2. Star Decomposition
3. Neighbor-Joining

PHYML - fast distance based method to quickly compute a full initial tree
RAxML - builds tree on maximum parsimony and optimizes with a variant of sub-tree
   Uses Lazy Subtree Arrangement (LSR) - assigns maximal distance between pruning and insertion point for Subtree prune and regraft (SPR) operations to restrict size of neighborhood
   Optimizes only the branch that originates at the pruning point
   Repeats using the current best tree
   Takes the 20 best trees found during LSR to reoptimize ML by adjusting branch lengths

Branch Support - all methods produce a single tree and ML values
Bootstrapping:
1. Pseudo-samples are created by randomly drawing with replacement l columns from the original l column alignment
2. From each pseudo-sample, a tree is reconstructed and a consensus tree is made
   Consensus Tree - incorporates branches that occur in the majority of trees
   Bootstrap Values used as an indicator for reliability of branches