Mini-Tutorals

Running a Screen Session

use byobu

1. start a screen session by typing "byobu"
2. run commands
3. Detach by pressing "F6"
4. Reattach by typing "byobu"
5. Terminate by typing "exit"

use screen

• Start a screen session

screen

• Detach the running mission

ctrl + A + D

• Show the list of running process

screen -ls

• Reattach a running process

screen -r ProcessID

• Terminate a process

ctrl + A
:quit

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

Homology searching and clustering

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

MUGSY

• MUGSY bash

#!/bin/sh

export MUGSY_INSTALL=/home/weigang/mugsy_x86-64-v1r2.2
export PATH=$PATH:$MUGSY_INSTALL:$MUGSY_INSTALL/mapping
export PERL5LIB=$MUGSY_INSTALL/perllibs
#For testing TBA
#export PATH=$PATH:$MUGSY_INSTALL/../../multiz-tba/trunk/

• source the bash file

source mugsyenv.sh

• run mugsy

mugsy --directory /home/chongdi/Streptococcus/mugsy-output -prefix mugsy_aln mugsy-input/*.fa

Microbial genome databases & pipelines in Qiu Lab

Blast a set of genes against a bacteria genome

1. download genome
2. extract gene sequences & translate
3. Make blast database
4. Run blastp

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 save log file

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 save log file

Bubble caller will detect differences between each genome by assigning distinct colors to each genome (note that the UK spelling of color is used: colour)

• --multicolour_bin holds your all-colors.ctx binary from the Bubble Caller
• --detect_bubbles1 i/i Detects 1 variation between genomes i and i. i indicates the position number the genome is listed on the colorlist.txt file. If the genome is fourth on the colorlist.txt, for example, its corresponding i variable is 4
• --output_bubbles1 Output variant reads in fasta format (ie: Evo-RefHG.var for bubble detection between

Evolved genome and Reference HG genome)

• --print_colour_coverages necessary for output

/home/weigang/CORTEX_release_v1.0.5.21/bin/cortex_var_31_c5 
--kmer_size 31  
--mem_height 17 
--mem_width 100 
--multicolour_bin all-colors.ctx 
--detect_bubbles1 0/1 
--output_bubbles1 Evo-RefHG.var 
--print_colour_coverages  
> Evo-RefHG.log save log file

Variant call with cortex_var

Example

• Files

MS00018_S5_L001_R1_001.fastq
MS00018_S5_L001_R2_001.fastq
KU-090401_S3_L001_R1_001.fastq
KU-090401_S3_L001_R2_001.fastq
...

• File content

@M03268:52:000000000-AJFAY:1:1101:16970:1555 1:N:0:7
CCCATGAACGGCACGTTCACGATGCAGAAGGTGGTGACCAGGCCGGTGTCGGCGACCTCGACGTAATCGTCGGTGGGGGA
GCCGTCGCGCGGGTCCGCGCCGCGCGGCGGGAAGTACACCTTGCCGTCCATGCGGGCGCGGGCGCCCAGCAGACGACCCT
CCATGAGCGCATTCAGGAATTCGGCCTCCTGCGGCGCGGCGGTGTGCTTGATATTGAAGTCGACCGGGGTCACGATGCCG
GTGACCGGTT
+
11>1>111@11>1A1FGF1FC0F0A111010GB0ECBFGF0/AFECGGFHECE??EEGHE/EEEHEFHEHHGCEECE??/
<CFCGGCCGCCCCCCHCGGCCCG@G??@@?@-@BFFFFFFFFFFF;B@FBFBFF<?@;@@-=?@??@-EFFFFF@;@@@F
FFBF/BBF;@@FFFFFFFFFFFFF@FFFFFFF<@@@@@@@@@@@FBFFFFFFFFFFFFFFFFF@@@?@?@FFFFFFFBF@
?@@EFF@@<B
@M03268:52:000000000-AJFAY:1:1101:16136:1618 1:N:0:7
TCCTGGCCCGTGAAACCGCTTGCCCGGTACAGGTTCTGGACTACCGCCTGGCACCCGAGCATCCGTTCCCGGCGGCGCTC
GACGACGCCGAGGCGGCGTTCCTGGAACTGGTGGCCGCCGGCTACCGGCCCGAACGCATTGCGGTCGCGGGTGATTCGGCCGGTGGCGGGCTCTCGCTCGCGCTGGCCGAACGGCTGCGCGACCGGCACGGGCTGGTTCCGGCCGCGCTCGGGCTGATCGCGCCCTGGGC
+
11>>11C1A@A?11BDF?EEGAFGFG?ECCHBHHGFG1EGHFHHHCCGGC/GCGGGCEECEFGHGGFFGHGGGGCECCC<CCC@CCCC@CGCCCGGC?:@EBFBF/CFFFF0/CFG?=@=-9>AFF@=@@?@;@@@F--9:BF@A@@?@--;9-BFFFF@A-@99B?-9@?=EFFBAAE;9>?;@@BF@9-@-;@=-E@@@=@@;@?>@9@<?@?BBBFFF;?>;;-@@?@<?@?9-FBFF@-99-9E-9
...

Step 1

• Create matched FASTQ files (python script)

#!/usr/bin/python                                                               
  
from sys import argv
script, File1, File2 = argv

# Create a dictionary listing the sequences in the first file for reference
file1 = open(File1)
dict1 = {}
for line in file1:
    if '@M03268' in line:
        tag1 = line.rstrip()[:-9]
        tail = line.rstrip()[-9:]
        dict1[tag1] = []
    else:
        dict1[tag1].append(line.rstrip())
file1.close()

# Create two output files
f1 = open(File1.replace('.fastq', '_mat.fq'), 'w')
f2 = open(File2.replace('.fastq', '_mat.fq'), 'w')

# Match the sequence
file2 = open(File2)
for line in file2:
    if dict1.has_key(line.rstrip()[:-9]): # The has_key method
        tag1 = line.rstrip()[:-9]
        f1.write(tag1 + tail + '\n')
        for j in range(3):
            f1.write(dict1[tag1][j] + '\n')
        del dict1[tag1]
        dict2 = {} # Create a temporary dictionary for sequence in the file2
        tag2 = line.rstrip()
        dict2[tag2] = []
    else:
        dict2[tag2].append(line.rstrip())
    if len(dict2[tag2]) == 3:
        f2.write(tag2 + '\n')
        for j in range(3):
            f2.write(dict2[tag2][j] + '\n')
file2.close()

f1.close()
f2.close()

Step 2. Clean Fastq Files & Run Single-color Graph & Error Cleaning

• Create a file list showing all outcome files whose extensions need to be changed from _mat.fq to .list

bbduk.sh -Xmx1g in1=fastq_file1 in2=fastq_file2 -out1=clean1.fq -out2=clean2.fq qtrim=rl trimq=20

• Create a file list showing all outcome files whose extensions need to be changed from _mat.fq to .list

for f in *_mat.fq; 
do 
  title=$(echo $f | cut -d'_' -f2); 
  id=$(echo $f | cut -d'_' -f1); 
  echo $f > ${id}.list${title}; 
done

• Single color graph for sample

../../CORTEX_release_v1.0.5.21/bin/cortex_var_31_c1 
--pe_list MS00018_S5.list1,MS00018_S5.list2 
--kmer_size 31 
--mem_height 17 
--mem_width 100 
--dump_binary MS00018_S5.ctx 
--sample_id MS00018_S5 
--remove_pcr_duplicates 
--quality_score_threshold 20 > MS00018_S5.log

• Single color graph for reference

../../CORTEX_release_v1.0.5.21/bin/cortex_var_31_c1 
--se_list ref.filelist 
--kmer_size 31 
--mem_height 17 
--mem_width 100 
--dump_binary ref.ctx 
--sample_id ref 20 > ref.log

• Error Cleaning

../../CORTEX_release_v1.0.5.21/bin/cortex_var_31_c1 
--mem_height 18
--mem_width 100
--kmer_size 31
--multicolour_bin N18_S15.ctx
--remove_low_coverage_supernodes 10
--dump_binary N18_S15.cleaned.ctx

Step 3

• Pull the name of each .ctx file to a .list file, then create a .filelist file for all .list files.

ls file1.ctx > file1.list
ls file2.ctx > file2.list
ls ref.ctx > ref.list
ls -1 *.list > ref-sample.filelist

• Multicolour Graph

../../CORTEX_release_v1.0.5.21/bin/cortex_var_31_c3
--mem_height 20 
--mem_width 100 
--kmer_size 31 
--colour_list ref-sample.filelist 
--dump_binary ref-sample.ctx > ref-sample.log

Step 4

• Variation Discovery Using The Bubble Caller

../../CORTEX_release_v1.0.5.21/bin/cortex_var_31_c3 
--mem_height 20 
--mem_width 100 
--kmer_size 31 
--multicolour_bin ref-sample.ctx 
--detect_bubbles1 -1/-1 
--ref_colour 2 
--output_bubbles1 bubbles-in-sample.out 
--print_colour_coverages 
--experiment_type EachColourAHaploidSampleExceptTheRefColour 
--genome_size 8000000 > bubbles-in-sample.log

Step 5

• Reference Genome

(When in Cluster execute "module load stampy": doesn't work; path problem) (Run the following on wallace:)

stampy.py -G ref ref.fa
stampy.py -g ref -H ref

• Turn Into VCF with reference

perl ~/CORTEX_release_v1.0.5.21/scripts/analyse_variants/process_calls-wq.pl 
--callfile bubbles-in-samples.out2 
--callfile_log bubbles-in-samples.log 
--outvcf bubbles-in-samples 
--outdir vcfout 
--samplename_list sample.list 
--num_cols 17 
--stampy_bin ~/stampy-1.0.28/stampy.py 
--stampy_hash ref
--refcol 2
--vcftools_dir /usr/local/bin 
--caller BC 
--kmer 31 
--ploidy 1

hmmer

Annotate proteins with TIGRFAM

hmmsearch --tblout foo.hmmout   # table output for all sequences
          --domtblout foo.dmout # table output for all domains
          -E 0.01               # level of sequence significance
          --domE 0.01           # level of domain significance
          -o /dev/null          # don't show STDOUT 
          ../../TIGRFAMs-Release-15-Sep-17-2014/TIGRFAMs_15.0_HMM.LIB # HMM profile library for tiger fams 
          GCA_000583715.2_ASM58371v2_protein.faa &                    # input/query file in FASTA

PopGenome

library(PopGenome)
g = readVCF("pvt1.recode.vcf.gz", 1000, "8", 127890000, 128102000)
pops = split(sample[,1], sample[,2]) # create a list of populations
g = set.populations(g, pops, diploid = T) # set population names

# by windows
slide = sliding.window.transform(g, width = 100, jump = 20) # nsnps, not actual length
slide = F_ST.stats(slide, mode = "nucleotide")
snp.pos = slide@region.data@biallelic.sites # SNP positions
win.num = length(slide@region.names)
win.start = numeric()
for (i in 1:win.num) {win.start[i] = snp.pos[[i]][1]}
fst = slide@nuc.F_ST.vs.all
pop.names = names(slide@populations) # population names
plot(win.start, fst[,1], type ="n", las = 1, ylab = expression(F[st]), xlab = "SNP Position", ylim = c(0,0.4))
for (i in 1:length(slide@populations)) {
  lines(win.start, fst[,i], type = "l", col = pop.group[pop.names[i],4])
}
arch.coords=c(127982050, 127992931)
abline(v = arch.coords, col = "orange")
#rect(xleft = arch.coords[1], ybottom = -1, xright = arch.coords[2], ytop = 0.5, border = "transparent", col = 2)

Velvet

Cleaning

../../bbmap/bbduk.sh -Xmx1g 
           in1=WGC067462_hunhewenku_509_combined_R1.fastq # gz file works as well
           in2=WGC067462_hunhewenku_509_combined_R2.fastq # gz file works as well
           -out1=clean1.fq 
           -out2=clean2.fq 
           qtrim=rl
           trimq=20

Running Velvet Optimizer

srun ../../VelvetOptimiser-2.2.5/VelvetOptimiser.pl 
          --t 32
          --s 31 --e 31 --x 6 # kmer sizes
          -f '-shortPaired -fastq clean1.fq -shortPaired2 -fastq clean2.fq'
          -t 4 
          --optFuncKmer ‘n50’ 
          -p prefix