SeqTools

Tools for NGS and third-generation sequencing (PacBio, Oxford Nanopore) data analysis.

This repository enumerates a set of basic python scripts used for sequencing analysis of NGS and third generation sequencing data like PacBio or ONT, as well as synthetic long reads (e.g. LoopSeq). Before running these scripts make sure you have all the dependencies installed! All of the tools were designed to run under UNIX with Python3 or later!

Installation

Clone or download the repository.

git clone https://github.com/moldovannorbert/seqtools.git
cd seqtools

Contents

• BulkMapper
• MotifFinder
• ReadConsensus
• ReadStatistics
• TranscriptAnalyzer

MotifFinder

MotifFinder is a tool for searching TATA, CAAT and GC-boxes as well as polyadenylation signals on a given reference genome using transcriptional start site (TSS) transcriptional end site (TES) or transcript coordinates. It will search for a specific pattern (perfect match of strings) found in motif.txt on the reference, and will report the sequence of the motif and the distance of the motif from the TSS or TES. It will also reproduce the sequence surroundings of TSS and TES positions, which can be used for initiator region (INR) or terminator region analysis.

Dependencies

Before running this script please install the following dependencies:

• pandas

Usage

You will need:

• A .fasta file containing the reference genome.
• A directory containing .gff3 file(s). The third column can contain either the text tss or tes. In these cases, the fourth and fifth columns must be the same, as they represent single nucleotide positions. The third column can also be mRNA, for which the following two columns must be different numbers. In this case the script assumes TES and TSS positions based on the fourth and fifth columns. The orientation (+/- in the seventh column) is always taken into account.

To see the help menu type -h.

./motiffinder.py –h

This line will create a directory containing the analysis.

./motiffinder.py –g /path/to/genome.fasta –i /path/to/annotation_directory/ -s /path/to/output_directory/ 

To change the number of nucleotides reproduced for the TSS use the -ts option. The default is: 5. This line will reproduce a total of 21 letters from upstream and downstream of the TSS:

./motiffinder.py –g /path/to/genome.fasta –i /path/to/annotation_directory/ -s /path/to/output_directory/ -ts 10

To change the number of nucleotides reproduced for the TES use the -te option. The default is: 50. This line will reproduce a total of 21 letters from upstream and downstream of the TES:

./motiffinder.py –g /path/to/genome.fasta –i /path/to/annotation_directory/ -s /path/to/output_directory/ -te 10

If your .gff3 file contains read counts in integer format in the sixth column, you can filter your analysis by setting a minimum read count. To filter for read count us the -mrc option. This line will output features with 10+ reads:

./motiffinder.py –g /path/to/genome.fasta –i /path/to/annotation_directory/ -s /path/to/output_directory/ -mrc 10

To exclude one contig from your analysis use the -e option followed by the contig identifier. This line will exclude lines with the contig identifier ‘ABCD1’ from the analysis:

./motiffinder.py –g /path/to/genome.fasta –i /path/to/annotation_directory/ -s /path/to/output_directory/ -e ABCD1

ReadConsensus

ReadConsensus creates a consensus sequence from reads overlapping a transcript annotation annotated by LoRTIA.

Dependencies

Before running this script please install the following dependencies:

• pandas
• pysam
• bedtools
• samtools
• minimap2

Usage

You will need:

• The folder generated by LoRTIA with three files: tr.bam, tr.gff3 and tr.tsv.
• A referece genome fasta.

To see the help menu type ‘-h’.

./ReadConsensus.py –h

This line will create a consensus sequence of the reads used by LoRTIA for annotating transcripts found in the tr.gff3 file.

./ReadConsensus.py -i /path/to/LoRTIA_output_directory/ -g /path/to/reference_genome/ -o /path/to/output_directory/

The output directory will contain the following subdirectories and files (main output files in bold):

output/
├── tmp/ -> Directory containing data used to create RefTranscriptome.fa and Transcripts.tsv.
│ ├── bam/ -> Mappings of reads to individual transcript sequences (taken from the reference genome).
│ ├── consensus/ -> The consensus sequence of each transcript as individual files.
│ ├── fa/ -> The sequence of reads per transcript taken from the .bam LoRTIA output.
│ ├── refSeq/ -> The transcript sequences taken from the reference genome.
│ └── vcf/ -> The VCF files with the variations per transcript.
├── RefTranscriptome.fa -> Fasta file containing the consensus sequence of transcripts.
├── refSeq.tsv -> Data used to create RefTranscriptome.fa and Transcripts.tsv.
└── Transcripts.tsv -> Table of transcripts containing read counts, referece and consensus sequences.

ReadStatistics

ReadStatistics is a tool for calculating and visualising descriptive statistics of mapped reads in bulk.

Dependencies

Before running this script please install the following dependencies:

• pandas
• seaborn

Usage

You will need:

• A directory containing .sam files.

To see the help menu type ‘-h’.

./readstats.py –h

This line will create directories containing data files, the statistical analysis and figures:

./readstats.py /path/to/sam_directory/ /path/to/output_directory/

To exclude reads of one or more contigs from the analysis use the ‘-e’ option followed by a list of contigs separated by a coma. This line will exclude reads mapping to contigs ABCD1, ABCD2 and ABCD3 from the analysis:

./readstats.py /path/to/sam_directory/ /path/to/output_directory/ -e ABCD1,ABCD2,ABCD3

If you already have the data files produced by the script and you want to rerun the statistics and plotting use the ‘-st’ option and replace the first path with the path to the folder containing the data files. This line will use the DATA files in the specified directory to produce the output:

./readstats.py /path/to/DATA_directory/ /path/to/output_directory/ -st

TranscriptAnalyzer

TranscriptAnalyzer is a script for calculating the transcript length and exon count of transcripts from a ‘.gff3’ files and to determine the isoforms of transcripts. It can be used to determine differential transcript usage.

Dependencies

Before running this script please install the following dependencies:

• pandas

Usage

You will need:

• A directory with one or more.gff3 files containing only mRNA and exon lines. The files can rerpesent control data or experimental datasets. The control datasets have the _control suffix in their file name.

To see the help menu type ‘-h’.

./tranalyzer.py –h

This line will create directories containing per-transcript-per-sample reand lengths and exon counts and a statistical summary calculated from these:

./tranalyzer.py -i /path/to/input_directory/ -o /path/to/output_directory/

To determine differential transcript usage, use the -c option. You will need at least one contorl file and one experimental file for this in your input directory! This line will output additional information about the type of a transcript in the experimental file compared to the control:

./tranalyzer.py -i /path/to/input_directory/ -o /path/to/output_directory/ -c

To change the interval in which TSSs and TESs are considered the same as in the control use the -w option followed by an integer. The default value is 0 bp. : This line will consider a TSS or TES the same as the control if the TSS or TES is in a ±10 bp interval from the control:

./tranalyzer.py -i /path/to/input_directory/ -o /path/to/output_directory/ -w 10

To change the default suffix for the control file use the -s option. The default is _control. This line will change the default suffix of the control files for which the script is looking for in the input directory:

./tranalyzer.py -i /path/to/input_directory/ -o /path/to/output_directory/ -s _suffix