Single cell mutant library inspection

• Background
• Install
• Arguments
• Usage
• Results
• License

To construct a whole-genome random mutation library in an efficient, reliable and economical manner, we developed a specialized experimental design and corresponding analysis workflow.
Specifically, we utilized 24 plates, each containing 24 wells, as one batch, with each well representing an individual mutation experiment. For same plate and same well, we added unique barcode to the gRNA PCR products and then proceeded with next-generation sequencing. In parallel, we performed whole-genome sequencing for samples from each plate.
By identifying gRNAs present in both the plate and the well, we were able to accurately determine the specific gRNA for specific well and assess mutations at the targeted locations, thereby validating our results.

git clone https://github.com/gongyh/scmli.git

Python3 (3.9)
Biopython (1.79) (python package)
pandas (1.4.2) (python package)
lxml (4.9.1) (python package)
argparse (python package, only needed if python<=3.6)
fastqc (0.11.9)
trim-galore>=0.6.0 (0.6.7)
r-base (3.6.1)
r-ggplot2 (3.3.5)
bcftools (1.15)
snippy (4.6.0 modified)

The tested versions are given in parentheses.

You can install these dependencies using Conda (Miniconda3):

conda install -c bioconda fastqc trim-galore pandas biopython lxml r-base r-ggplot2 bcftools snippy

Sclmi searches reads which have target gRNAs sequence. It uses fixed sequence (all sequencing bases before gRNAs in forward reads without adapter) for filtering valid reads, then searches gene-special gRNAs sequence with gRNAs library file. The gRNAs library sequence contains universal sequence and gene-special sequence, number(a b) is used to locate gene-special sequences in gRNAs.
gRNAs_library.csv:
NO01G00240,ccgggtccgattcccggtgcctgcaGAGTGTGGTGGAATTTGCCGgttttagagctagaaatagcaagttaaaataag
NO01G00250,ccgggtccgattcccggtgcctgcaACACGATAGTCAAGACGCTGgttttagagctagaaatagcaagttaaaataag
...... , ......

required: reads(fastq file), fixed sequence(str), gRNAs library(.csv)

Call variants in the data, filtering and statistical variation information in the target region
We changed the parameters in snippy, copy lib/snippy to path/bin/snippy

required: reads(fastq file), target(bed file), reference(.gbk)

required arguments:
  -l LIB                            gRNAs library file
  -s SEQ                            All sequencing bases before gRNAs in forward reads without adapter
  -r1 READ1                         Read1 fastq file
  -r2 READ2                         Read2 fastq file

optional arguments:
  -h, --help                        Show help message and exit
  -t NUMBER                         Number of threads, default = 8
  --number NUMBER NUMBER            Start and end of the gene-special position in gRNAs,
                                    default='25 45', from the 26-th to the 45-th bases
  -n OUTPUT_NAME                    Prefix of output files, default = "my_project"
  -o OUTPUT_DIR                     Directory of output files, default = "output"
  --FASTQC_PATH                     PATH to fastqc
  --TRIM_GALORE_PATH                PATH to trim-galore

required arguments:
  -r1 READ1                         Read1 fastq file
  -r2 READ2                         Read2 fastq file
  --ref REF                         reference
  --target TARGET                   target region of variant
  --dtarget TARGET2                 selected smaller target region

optional arguments:
  -h, --help                        show this help message and exit
  -t THREADS                        Number of threads
  -n OUTNAME                        Prefix of output files, default='my_project'
  -o OUTDIR                         Directory of output files, default='output'

Script for identifying gRNA results in experimental protocols.

cd scmli
python3 scmli.py gRNA \
  -l test/NoIMET1_gRNAs.csv \
  -s GGTAGAATTGGTCGTTGCCATCGACCAGGC \
  -r1 test/test_R1.fq.gz \
  -r2 test/test_R2.fq.gz

python3 scmli.py variant \
  -r1 test/test_R1.fq.gz \
  -r2 test/test_R2.fq.gz \
  --ref test/genes.gbk \
  --target test/targets.bed \
  --dtarget test/filter.bed

file_fastqc.html/zip: Quality control results(raw data)
file_val_1/2_fastqc.html/zip: Quality control results(clean data)
file_trimming_report.txt: Trim results
my_project.counts: Raw count result
my_project.percentage: Detailed count result

my_project.stats: Statistical result

unknow.seq: List of unknow sequences
my_project.log: Process log
reads.plot: Count of different kinds of reads
frequency.plot: Frequency of all gRNAs
frequency_detected.plot: Frequency of detected gRNAs
frequency_distribution.plot: Count of different frequency of all gRNAs
frequency_distribution_detected.plot: Count of different frequency of detected gRNAs
accumulative_unknow_percentage.plot: Percentage of accumulative unknow sequences

my_project_snippy_hq.vcf: Result of variant
my_project_snippy_hq.gids: Gene id of variant
target2_variant.txt: Variation information in target region

MIT