Mini-Tutorals

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Bp-utils: sequence, alignment & tree utilities by Qiu Lab

bioseq: sequence/FASTA manipulations

  • Use accession "CP002316.1" to retrieve the Genbank file from NCBI. Save the output (in genbank format) to a file named as "cp002316.gb".
bioseq -f "CP002316.1" -o'genbank' > cp002316.gb
  • Use the above file as input, extract FASTA sequences for each genes and save the output to a new file called "cp002316.nuc". Use this file for the following questions.
bioseq -i "genbank" -F cp002316.gb > cp002316.fas
  • Count the number of sequences.
bioseq -n cp002316.fas
  • In a single command, pick the first 10 sequences and find their length
bioseq -p "order:1-10" cp002316.fas | bioseq –l
  • In a single command, pick the third and seventh sequences from the file and do the 3-frame translation. Which reading frame is the correct on both? Specify
bioseq -p "order:3,7" cp002316.fas | bioseq -t3
  • Find the base composition of the last two sequences
bioseq -p "order:25-26" cp002316.fas| bioseq –c
  • Pick the sequence with id "Bbu|D1_B11|8784|9302|1" and count the number of codons present in this sequence
bioseq -p "id:BbuJD1_B11|8784|9302|1" cp002316.fas | bioseq –C
  • Delete the last 10 sequences from the file and save the output to cp002316-v2.nuc
bioseq -d "order:17-26" cp002316.fas > cp002316-v2.nuc
  • In a single command, pick the first sequence, then get the 50-110 nucleotides and make reverse complement of the sub-sequences
bioseq -p "order:1" cp002316.fas | bioseq -s "50,110" | bioseq –r
  • In a single command, get the first 100 nucleotides of all the sequences present in the file and do 1-frame translation of all sub-sequences.
bioseq -s "1,100" cp002316.fas | bioseq -t1

bioaln: alignment/CLUSTALW manipulations

  • Go to /home/shared/LabMeetingReadings/Test-Data and find the sequence alignment file “bioaln_tutorial.aln”. Name the format of the alignment file. Use it to answer all the questions below.
CLUSTALW
  • Find the length of the alignment.
bioaln -l bioaln_tutorial.aln
  • Count the number of the sequences present in the alignment.
bioaln -n bioaln_tutorial.aln
  • How do you convert this alignment in phylip format? Save the output.
bioaln -o "phylip" bioaln_tutorial.aln > test.phy
  • Pick “seq2, seq5, seq7, seq10” from the alignment and calculate their average percent identity.
bioaln -p "seq2, seq5, seq7, seq10" bioaln_tutorial.aln | bioaln -a
  • Get an alignment slice from “50-140” and find the average identities of the slice for sliding windows of 25.
bioaln -s "50, 140" bioaln_tutorial.aln | bioaln -w "25"
  • Extract conserved blocks from the alignment.
bioaln -B bioaln_tutorial.aln
  • Find the unique sequences and list their ids.
bioaln -u bioaln_tutorial.aln | bioaln -L
  • Extract third sites from the alignment and show only variable sites in match view.
bioaln -T bioaln_tutorial.aln | bioaln -v | bioaln -m
  • Remove the gaps and show the final alignment in codon view for an alignment slice “1-100”.
 bioaln -s "1, 100" bioaln_tutorial.aln | bioaln -g | bioaln -c
  • Add a 90% consensus sequence and then show the final alignment in match plus codon view for an alignment slice “20-80”. (Hint: match view followed by codon view)
bioaln -s "20, 80" bioaln_tutorial.aln | bioaln -C "90" | bioaln -m | bioaln -c

biotree: tree/NEWICK manipulations

biopop: SNP statistics

Homology searching and clustering

BLAST+: search("google") for homologs/pariwise alignment

hmmer

cdhit

cdhit -i all.pep -o all.cdhit -c 0.5 -n 3

Options:

  • -i: input file
  • -o: output file
  • -c: percent identity (below which it is considered different families)
  • -n: word length

interproscan

../../software/interproscan/interproscan-5.13-52.0/interproscan.sh -i trep-cdhit.representatives.pep2 -o  trep-representatives.tsv -t p -goterms -pa -f tsv

Documentation page: How to run

Programs for producing multiple alignments

MUSCLE

CLUSTALW

MAFT

TCOFFEE

Programs for producing phylogeny & phylogenetic analysis

FastTree

PHYLIP

MrBayes

RaXML

PhyloNet

R packages for phylogenetics

APE

phengorn

phytools

Population genetics

ms: coalescence simulation

SFS: forward simulation

PAML: testing selection with Ka/Ks

Microbial genome databases & pipelines in Qiu Lab

borreliabase

pa2

spiro_genomes/treponema

Genome annotation pipeline

de novo variant call with cortex_var

Create binary file of fasta genome file.

Run contex_var_31_c1 (cutoff 1 used for 1 genome)

  • --se_list is the command the reads the list you want to target (ie: list-genome.txt)
  • --kmer_size is the middle size, has to be an odd integer
  • --mem_width always choose 17
  • --mem_height always choose 100
  • --dump_binary Name your file name (ie: Genome.ctx)
/home/weigang/CORTEX_release_v1.0.5.21/bin/cortex_var_31_c1 --se_list list-Evo.txt --kmer_size 31 --mem_width 17 --mem_height 100 dump_binary Evo.ctx > Evo.log

Read each binary file (.ctx) into its own individual color list (ls Evo.ctx > Evo.colorlist) Then save these lists into their own collective colorlist.txt (ls *.ctx > colorlist.txt)

Reveal genetic variation using the Bubble Caller from cortex_var.

/home/weigang/CORTEX_release_v1.0.5.21/bin/cortex_var_31_c5 --se_list colorlist.txt --kmer_size 31 --mem_width 17 --mem_height 100 dump_binary all-colors.ctx > all-colors.log

Bubble caller will detect differences between each *colored* genome by assigning

  • --multicolour_bin
  • --detect_bubbles
  • --output_bubbles