Hapo-G (pronounced like apogee) is a tool that aims to improve the quality of genome assemblies by polishing the consensus with accurate reads.
Publication : link
In case of troubles when using or installing the software, please open up an issue by clicking here.
Hapo-G depends on some software and libraries:
- GCC and G++ (Hapo-G has been tested with GCC 4.9.2 and GCC 7.3.0)
- Autoconf with minimum 2.69 version (to build HTSlib)
- Python3 (minimum version 3.6)
- HTSlib
- BioPython
- BWA
- Samtools
conda create -n hapog
conda activate hapog
conda install -c bioconda hapog
First, clone this repository:
git clone https://github.com/institut-de-genomique/HAPO-G hapog
If htslib is already installed on your system, go to the next point Build with existing htslib. If you want Hapo-G to download and install htslib for you, go to the point Build with a new htslib install
Building with an existing htslib ensures that Hapo-G and Samtools are using the same version of the library and should reduce compatibility issues. To build with an existing htslib, do:
cd hapog
bash build.sh -l path_to_htslib
If samtools is already installed on your system at /home/user/samtools, htslib is probably installed at /home/user/samtools/htslib.
Hapo-G can download and compile htslib for you, to do so, please run:
cd hapog
bash build.sh
If everything went as expected, a binary of Hapo-G was created in build/ and a symlink was created in the bin/ folder
Before running Hapo-G, you should make sure that BWA and Samtools are in your $PATH:
which bwa
which samtools
You can launch Hapo-G by using the Python3 script in its root directory:
python3 HAPOG_ROOT/hapog.py \
--genome assembly.fasta \ # Fasta file of the genome to polish
--pe1 R1.fastq.gz \ # Illumina R1 reads in .fastq or .fastq.gz, can be given multiple times
--pe2 R2.fastq.gz \ # Illumina R2 reads in .fastq or .fastq.gz, can be given multiple times
-o polishing \ # Output directory
-t 36 \ # Number of threads to use
-u # Include unpolished sequences in the output
NOTE: If you installed Hapo-G using conda, you can invoke it by directly running hapog.
The mapping step can be skipped if a sorted BAM file is provided via the -b switch. Please verify that your fasta headers don't contain any non-alphanumerical characters (-and _are accepted) before launching Hapo-G.
IMPORTANT: The BAM file should not contain secondary alignments, these could lead to non-ACTG characters being introduced in the consensus sequence. You can use Minimap2's option secondary=no to produce a SAM file with no secondary alignments.
A typical command line with a bam file would look like this:
python3 HAPOG_ROOT/hapog.py \
--genome assembly.fasta \ # Fasta file of the genome to polish
-b mapping.sorted.bam # Sorted BAM file
-o polishing \ # Output directory
-t 36 \ # Number of threads to use
-u # Include unpolished sequences in the output
The corrected sequences. Hapo-G will parse the read alignments to the genome and focus on phasing errors (i.e the assembly switched from one haplotype to the other) and base errors (insertions, deletions, mismatches) that may be related or not to phasing errors. Remember to include the -u flag to tell Hapo-G to output sequences with no reads mapped and thus could not be changed.
Hapo-G will not add any new contigs or scaffolds to the assembly if, as an example, one of the haplotype is missing in the input assembly file. Instead, it will correct the haplotype that is present in the input file and output a corrected version of the sequence that is phased as best as we could with the data at hand.
As an example, let’s consider the following heterozygous genome:
maternal hap.: ACCGTTA
paternal hap.: ATCGTGA
If the assembler outputted a version with one phasing error (the C in 2nd position is associated with a G in 6th position) and one deletion in 4th position:
assembly: ACC-TGA
Then, if Hapo-G was able to correct all the errors, the hapog.fasta file will contain:
hapog.fasta: ACCGTTA
This file is a tabulated file that gives more information on what Hapo-G did to the input assembly. It has eight columns that show:
- Name of the input sequence where the change was made
- Position in the input sequence where the change was made
- Nucleotide(s) at the position in column 2
- Nucleotide(s) that will replace the nucleotide(s) shown in column 3
- Name of the read that is used as the current template. In the Hapo-G algorithm, we try to follow a read for as long as possible to not switch haplotypes. If an error is found in the template read, we switch to a different read of the same haplotype
heteroif the change is only present in one of the two possible haplotypes (i.e a phasing error).homoif the change is present in both haplotypes - Ratio of reads from the same haplotypes as the template read that validate the changes
- Ratio of reads from both haplotypes that validate the changes
Here is an example:
Contig_1 1000 ref=A read=TA readname=read_2 hetero ratio1=0.7419 ratio2=0.4237
Contig_1 2000 ref=T read=G readname=read_2 homo ratio1=0.8142 ratio2=0.8323
We can see that on the contig Contig_1, Hapo-G found a phasing error (hetero on the first line) and replaced a A at position 1000 by TA. This change was validated by 74.19% of reads of the same haplotype as the template read (ratio1) and by 42.37% of reads if we do not discriminate on which haplotype they belong to. It also found a mismatch at position 2000 (homo) and replaced a T by a G. This change was validated by 83% of reads of no matter which haplotype (ratio2).
Some Cmake files have been taken and/or modified from several projects. We would like to thank:
- panguangze for their
FindHTSLIB.cmakelibrary - L. Kärkkäinen for their
LibFindMacros.cmakelibrary
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