collection of tools for the analysis and cleaning of genome assemblies
This script calculates statistics of a FASTQ file including the number of reads, the average read length, the total number of sequenced bases and the read length N50. Additionally, a read length histogram and a quality vs. read length plot can be generated.
Usage:
python3 FASTQ_stats3.py --in <FILE> | --in_dir <DIR>
mandatory:
--in STR Input FASTQ file
--in_dir STR Input folder
optional:
--rfig STR Read length histogram figure filename
--cutoff STR Read length cutoff for histogram (kb) [100]
--qfig STR Quality vs. read length figure filename
--lencut STR Upper read length cutoff (kb) [200]
--qualcut STR Upper quality cutoff (phred) [40]
--in specifies a FASTQ input file that will be analyzed. The file should be gzip compressed.
--in_dir specifies a FASTQ file containing input folder. Each (gzip compressed) FASTQ file in the folder will be analyzed. Supported file extensions: .fq, .fastq, .fq.gzip, fq.gz, fastq.gzip, .FQ, .FASTQ, .FQ.GZIP, .FASTQ.GZIP, .FQ.GZ, and .FASTQ.GZ.
--rfig specifies the filename of a read length histogram figure. Inclusion of this argument triggers the generation of this figure. Defaul: off.
--cutoff specifies the upper read length cutoff of the read length histogram figure. Default: 100 (kb).
--qfig specifies filename of a quality vs. read length figure. Inclusion of this argument triggers the generation of this figure. Defaul: off.
--lencut specifies an upper read length cutoff for the qality vs. read length figure. Default: 200 (kb).
--qualcut specifies an upper quality cutoff for the quality vs. read length figure. Default: 40.
This script calculates the statistics of a given assembly FASTA file. This includes trimming short contigs and cleaning the contig names. Statistics include the number of contigs, the N50, N90, and total assembly size. If an expression file is provided, the calculation of Ex90N50 can be performed.
Usage:
python3 contig_stats3.py --in <FILE>
--in STR Input FASTA file
optional:
--min INT Minimal contig length [1000]
--out STR Output folder
--exp STR Expression file
--in specifies a FASTA file that will be analyzed. A trimmed FASTA file and a statistics file will be placed next to the input file.
--min specifies the minimal contig length. Default: 1000 (bp).
--out specifies an output folder. The trimmed FASTA file and the statistics file will be placed in this folder. Default: off.
--exp specifies an expression file (normalized expression). Default: none.
This script cleans the header names of a given FASTA file by splitting at the first white space chracter (space or tab). Some special characters are also replaced by underscores.
Usage:
python3 clean_genomic_fasta.py --in <FILE> --out <FILE>
--in STR Input FASTA file
--out STR Output FASTA file
--in specifies a FASTA file that will be processed.
--out specifies the output FASTA file.
This script screens an assembly for contaminations. All contigs are separted into small blocks and compared against a white list and black list of sequences. This allows the identification of contamination contigs or chimeric contigs that contain contaminations.
Usage:
python3 assembly_wb_screen.py --in <FILE> --out <DIR> --white <FILE> --black <FILE>
--in STR Input FASTA folder
--out STR BAM output file
--white STR White list FASTA file
--black STR Black list FASTA file
optional:
--tmp STR Temp output folder
--in specifies an assembly FASTA file that will be screened for contaminations.
--out specifies an output folder where all result files can be placed. The folder will be created if it does not exist already.
--white specifies a FASTA file with white sequences that will be used to identify valid contigs and contig parts.
--black specifies a FASTA file with black sequences that will be used to identify invalid (contamination) contigs and contig parts.
--tmp specifies a temporary output folder. The --out folder is used for temporary files if this argument is not used.
This script analyzes the RNA-seq coverage of predicted transcripts. The results allow conclusions about the valididty of predicted transcripts.
Usage1
python3 RNAseq_cov_analysis.py --bam <FILE> --gff <FILE> --out <DIR>
Usage2
python3 RNAseq_cov_analysis.py --cov <FILE> --gff <FILE> --out <DIR>
Mandatory:
--bam STR BAM input file | --cov STR Coverage input file
--gff STR GFF input file
--out STR Output folder
Optional:
--cutoff INT Percentage of transcript covered[90]
--samtools STR Path to samtools [samtools]
--bedtools STR Path to bedtools [bedtools]
--mincov INT Minimal coverage per position [1]
--bam specifies a BAM input file. This will be converted into a coverage file (COV) to analyze the distribution of reads across transcripts. This argument can also be used to provide a comma-separated list of files for automatic processing of large batches of files.
--cov specifies a coverage file (COV) to analyze the distribution of reads across transcripts. This argument can also be used to provide a comma-separated list of files for automatic processing of large batches of files.
--gff specifies a GFF file that contains the exon positions for the RNA-seq coverage analysis.
--out specifies an output folder. If this folder does not exist, it will be created.
--cutoff specifies a minimal proportion of the transcript that needs to be covered by RNA-seq. Default value is 90.
--samtools specifies the full path to samtools. Default: samtools.
--bedtools specifies the full path to bedtools. Default: bedtools.
--mincov specifies the minimal coverage of a position in order to consider it. Default: 1.
Meckoni S. N., Nass B., Pucker B. (2023). Phylogenetic placement of Ceratophyllum submersum based on a complete plastome sequence derived from nanopore long read sequencing data. bioRxiv 2023.06.27.546741; doi: 10.1101/2023.06.27.546741.