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https://github.com/adamtaranto/graphtagger
Small script to compute coverage of contigs from reads
https://github.com/adamtaranto/graphtagger
Last synced: 3 days ago
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Small script to compute coverage of contigs from reads
- Host: GitHub
- URL: https://github.com/adamtaranto/graphtagger
- Owner: Adamtaranto
- License: gpl-3.0
- Created: 2024-01-13T04:22:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-03T06:34:30.000Z (3 months ago)
- Last Synced: 2024-11-21T02:33:41.144Z (2 months ago)
- Language: Python
- Size: 91.8 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GraphTagger
Collection of python tools to edit tags in assembly graph (.gfa) files.
Sequences in GFA files are called Segments and may have meta-data stored in tags with the format `TAG:TYPE:VALUE`. See the [GFA-Spec](https://gfa-spec.github.io/GFA-spec/).
For example, mean read depth of 100 stored as a float would be: `DP:f:100`. You can use `csv2tag` to add or update arbritrary tags.
**New features**
Got an tag related task in need of automation? [Suggest a feature](https://github.com/Adamtaranto/GraphTagger/issues).
## Installation
GraphTagger requires Python >= v3.8
Install directly from this git repository.
```bash
pip install git+https://github.com/Adamtaranto/GraphTagger.git
```
Or clone and install locally.
```bash
git clone https://github.com/Adamtaranto/GraphTagger.git && cd GraphTagger
pip install -e .
```
## Tools
### Tools for updating segment tags
**csv2tag**: Adds or updates gfa segment tags from a CSV.
CSV lines have the format `Name`,`Tag`,`Type`,`Value`.
**map2tag**: Uses Minimap2 to map reads to the assembly and approximates the coverage of each contig. Updates the `DP` depth tag.
### Working with Paths
[DEV] **gaf2path**: Generate GFA format Path lines from GraphAligner GAF output.
### Feature annotation tools
**tel2bed**: Quick annotation of telomeric repeat runs in FASTA file, outputs BED format. This is useful for visualising potential chromosome ends in [Bandage-NG](https://github.com/asl/BandageNG).
### Converting between GFA and FASTA
#### Converting from FASTA to GFA
**fa2gfa**: Convert FASTA to GFA format. Produces Segment records for each FASTA record. Adds `LN` tags.
#### Converting from GFA to FASTA
To convert an assembly graph into a FASTA of contigs you can use one of these `awk` one-liners.
If you want to preserve the GFA segment tags (i.e. segment length `LN`, or depth `DP`) in the FASTA description, option #2 will append them to the FASTA headers.
```bash
# Convert GFA to FASTA
# Supports sequence names longer than the 80 char sequence wrap length.
awk '/^S/{print ">"$2; printf "%s", $3 | "fold -w 80"; close("fold -w 80"); print ""}' in.gfa > out.fa
# Include GFA tags in FASTA header line.
awk '/^S/{header=">"$2; for(i=4; i<=NF; i++) {header=header" "$i}; print header; printf "%s", $3 | "fold -w 80"; close("fold -w 80"); print ""}' in.gfa > out.fa
```
## Usage
### csv2tag
Add tags to GFA from csv file.
INPUT_GFA: Path to the input GFA file (can be gzipped).
INPUT_CSV: Must have format = [NAME,TAG,TYPE,VALUE]
OUTPUT_GFA: Write updated GFA to this file.
Options:
- --preserve_tags:
If set, preserve pre-existing tags from gfa file.
- --calc_len:
If set, calculate new LN tags from length of sequence.
- --calc_hash:
If set, calculate new SH tag as sha256 hash of sequence.
- --strict
If set, only return tags that comply with the GFA 1.0 spec
```bash
csv2tag -i input.gfa -c new_tags.csv -o output.gfa
```
### map2tag
Approximate coverage from mapped long reads.
```bash
map2tag -i input.gfa -r nanopore_reads.fq.gz -p outdir/tagged_graph -t 4 -x map-ont
# Output: outdir/tagged_graph.gfa
```
### gaf2path
Convert graph-alignments (GAF) to GFA path-lines. Only reports paths that have at least 2 segments.
INPUT_GAF: GAF file containing path information. Produced by [GraphAligner](https://github.com/maickrau/GraphAligner).
OUTPUT_GFA: GFA formatted path-lines. Can be manually appended to the end of the original GFA graph used to produce the GAF file.
```bash
# Align long-reads to GFA
GraphAligner --threads 8 --multimap-score-fraction 1 -x vg -f reads.fq -g input_graph.gfa -a longreads_aligned_on_gfa.gaf
# Convert GAF to GFA path-lines
gaf2path -g longreads_aligned_on_gfa.gaf -o pathlines.gfa
# Append pathlines to end of original graph
cat pathlines.gfa >> input_graph.gfa
```
### fa2gfa
Convert a FASTA file to GFA format.
INPUT_FASTA: Path to the input FASTA file (can be gzipped).
OUTPUT_GFA: Path to the output GFA file. If not provided, the output will be same as input but with the '.gfa' extension.
```bash
fa2gfa -i assembly.fa.gz -o output assembly.gfa
```
### tel2bed
Quick annotation of telomeric repeat runs in fasta file.
INPUT_FASTA: Path to the input FASTA file (can be gzipped).
Options:
- -o,--output_bed:
Path to the output BED file. If not provided, the output file will have the same basename as the input with the '.bed' extension.
- -m,--motif:
Telomeric motif to annotate in genome. By default tel2bed will search in fwd and rev orientations and will allow +/- 1bp flexibility in the pattern at any polynucleotide run.
- -r,--min_repeats:
Minimum number of sequential pattern matches required for a hit to be reported. Default: 3
```bash
tel2bed -i contigs.fa -m TTAGGG -r 3
```
## Example: Add depth tags from MosDepth.
Convert [MosDepth](https://github.com/brentp/mosdepth) summary.txt file into CSV and use csv2tag to update `DP` tags in a corresponding `GFA`.
```bash
MINIASM_FASTA="path/to/miniasm_assembly.fa"
TEMP=/tmp
# Index asm
bwa-mem2 index -p minasm_idx $MINIASM_FASTA
# Map illumina data to asm
# Use smart pairing option -p to pass paired and unpaired reads
(seqtk mergepe paired_R?.fastq.gz; zcat unpaired.fastq.gz) | bwa-mem2 mem -p -t 8 -o aligned_reads.sam minasm_idx -
# Sort and write to bam
samtools view -u aligned_reads.sam | samtools sort -@ 8 -T $TEMP -o aligned_reads.sorted.bam
# Delete sam
rm aligned_reads.sam
# Index the sorted bam
samtools index -@ 8 aligned_reads.sorted.bam
# Calc median depth
mkdir mosdepth
mosdepth -t 8 --use-median --no-per-base -F 1796 --by 500 mosdepth/miniasm-asm aligned_reads.sorted.bam
```
Inspect the mosdepth summary file.
```bash
cat mosdepth/miniasm-asm.mosdepth.summary.txt
```
output:
```
chrom length bases mean min max
utg000001l 1258785 128682030 102.23 0 422
utg000001l_region 1258785 128682030 102.23 0 422
utg000002l 1176747 122097652 103.76 0 314
utg000002l_region 1176747 122097652 103.76 0 314
utg000003l 872965 88294465 101.14 0 434
utg000003l_region 872965 88294465 101.14 0 434
utg000004c 81755 115821276 1416.69 0 3204
utg000004c_region 81755 115821276 1416.69 0 3204
...
total 34741114 3752939904 108.03 0 15782
total_region 34741114 3752939904 108.03 0 15782
```
Convert the `*.mosdepth.summary.txt` file to CSV
```bash
# Exclude lines that begin with "chrom length" or "total"
# Exclude lines that contain "region"
# Write NAME,TAG,TYPE,VALUE in comma delimited format
awk -F'\t' 'BEGIN {OFS=","} !/^chrom\tlength|^total|region/ {print $1, "DP", "f", $4}' mosdepth/miniasm-asm.mosdepth.summary.txt > new_tags.csv
```
Update tags with csv2tag
```bash
csv2tag -i miniasm_assembly.gfa -c new_tags.csv -o miniasm_assembly_with_tags.gfa
```