Currently, there are three carnivorous bat species, namely Ia io, Nyctalus lasiopterus, and Nyctalus aviator, are known to actively prey on seasonal migratory birds (hereinafter referred to as “avivorous bats”). However, the absence of reference genomes impedes a thorough comprehension of the molecular adaptations of avivorous bat species. Herein, we present the high-quality chromosome-scale reference genome of N. aviator based on PacBio subread, DNBSeq short-read and Hi-C sequenceing data. The genome assembly size of N. aviator is 1.77 Gb, with a scaffold N50 of 102 Mb, of which 99.8% assembly was anchored into 20 pseudo-chromosomes. After masking 514.3 Mb repetitive sequences, a total of 19,703 protein-coding genes were identified, of which 96.2% were functionally annotated. The genome assembly and gene prediction reached 95.6% and 96.5% completeness of Benchmarking Universal Single-Copy Orthologs (BUSCO), respectively. This chromosome-level reference genome of N. aviator fills a gap in the existing information on the genomes of carnivorous bats, especially avivorous ones, and will be valuable for mechanism of adaptations to dietary niche expansion in bat species.
Detailed information about the parameters and custom scripts utilized in this artical can be obtained from this repository
Python >= 3.8.10
Dependence:
pandas
Biopython
rpy2
tqdm
gffutils
Software:
fastp
Prior to usage, verify the proper installation of all dependencies and software. As the scripts underwent testing solely in a local environment, adjusting the software paths within the script may be required based on the user's environment.
The script 01.quality.py utilizes fastp to filter sequences containing over 20% low-quality bases or more than 10% 'N' bases.
usage: 01.quality.py [-h] --flist FLIST [--in INPUT] [--ou OUPUT] [--na NAME]
optional arguments:
-h, --help show this help message and exit
--flist FLIST The file contains names of all input files. The file contains two columns, the delimter is tab. The first column is the name of first file of PE paried, the second is the second of PE paired. The file should not contain the paths of any input files, the path of input file can be set uing parameter `--in`
--in INPUT The path of directory, which contains all input files
--ou OUPUT The ouput path, If the output path is not set, it defaults to the input path.
--na NAME The prefix of output files
The script 02.genome-survey.py estimates the genome size from Illumina short reads using jellyfish with a progressive k-mer approach.
usage: 02.genome-survey.py [-h] [--flist FLIST] [--in INPUT] [--ou OUTPUT] [--na NAME] [--range KRANGE] [--jpath JPATH]
optional arguments:
-h, --help show this help message and exit
--flist FLIST The file contains all input, each row was divided two columns by 'tab', the first column was the first file of a paired-end fastq file, and the second row is the second
--in INPUT the path of the directory, which contains all fastq files
--ou OUTPUT the path of output directory of resutls
--na NAME the prefix of results files
--range KRANGE The program iterates through a range of k-mer sizes specified as start:end:step, such as 10:21:1 The default k-mer range spans from 10 to 21 with a step size of 1.
--jpath JPATH The absoult path of jellyfish programe
The script 03.genome-assembly.py assembles PacBio subreads into contig-level assemblies using nextDenovo and wtdbg2. This script follows this process: 1) acquiring consensus reads with nextDenovo; 2) obtaining paired alignments with kbm2 in wtdbg2; 3) executing wtdbg2 with paired alignments using progressive parameters; 4) selecting the output with the highest contigN50 and genome size values as the optimal result.
usage: 03.genome-assembly.py [-h] [--in INPUT] [--ou OUTPUT] [--gsize GSIZE] [--step STEP]
optional arguments:
-h, --help show this help message and exit
--in INPUT the path of input directory, which contains all input files
--ou OUTPUT the path of output directory
--gsize GSIZE the prediceted size of genome, a string ended with g, for example, 2g
--step STEP the different step in process, 1: nextDenovo; 2: kbm2 in wtdbg2; 3: wtdbg in wtdbg2; 4 produce the best assembly
Users can modify the parameters of the wtdbg2 assembly by editing the script file (line: 113-116).
110 '''
111 Modified parameters used in wtdbg2 assembly process
112 '''
113 p_nodelen = [1536, 2048, 2304, 2560, 1024]
114 p_s = [0.5]
115 p_alndovetail = [-1, 4608, 9216, 256]
116 p_L = [5000, 0]
The script 04.genome-polish.py performs polishing on the contig-level assembly derived from Illumina short reads and PacBio subreads using nextPolish.
usage: 04.genome-polish.py [-h] [--initial INITIAL] [--inl LGS_PATH] [--ins SGS_PATH] [--ou OU_PATH]
optional arguments:
-h, --help show this help message and exit
--initial INITIAL the initial assembly fasta from wtdbg2
--inl LGS_PATH the input path for directory, which contains PacBio subreads
--ins SGS_PATH the input path for directory, which contains Illumina short reads
--ou OU_PATH the output directory of nextPolish
The script 05.genome-scaffold.py scaffolds the contig-level assembly into scaffold-level using YaHS. In this script, you can select which tools be used, 3D-DNA or YaHS
usage: 05.genome-scaffold.py [-h] [--genome GENOME] [--hic_path HICPATH] [--ou_path OUPATH] [--step STEP] [--review REVIEW]
optional arguments:
-h, --help show this help message and exit
--genome GENOME The path of polished fasta file
--hic_path HICPATH The path of directory, which contains all hic fastq files
--ou_path OUPATH The path of output directory
--step STEP Each number corresponds to a specific step, 1:chromap index; 2:chromap alignment; 3:yash scaffold; 4:yash2juicer; 5:manual curation by juicer; 6: review; 7: generate the hic concat map file
--review REVIEW The review results of juicer, only be used in second round of 3D-DNA
The script 06.genome-completeness.py evaluates genome completeness utilizing busco.
usage: 06.genome-completeness.py [-h] [--genome GENOME] [--ou OU_PATH] [--protein PROTEIN] [--step STEP]
optional arguments:
-h, --help show this help message and exit
--genome GENOME The finally assembly genome fasta file
--ou OU_PATH The path of output directory
--protein PROTEIN The protein fasta of annotation, only be used in annotaion completes analysis
--step STEP Each number corresponds to a specific step: 1:busco with metaeuk; 2: busco with augustus; 3: annotation completes using busco
The script 07.genome-repeat.py annotates repeat sequences by using EDTA.
usage: 07.genome-repeat.py [-h] [--genome GENOME] [-o OU_PATH] [--cds CDS] [--curatedlib CURATED] [--step STEP]
optional arguments:
-h, --help show this help message and exit
--genome GENOME The finally genome assembly
-o OU_PATH The path of output directory
--cds CDS The cds sequences of closely-related specie
--curatedlib CURATED The curated lib file
--step STEP Each number corresponds to a specific step, 1:EDTA; 2:softmask;
Various annotation tools were utilized in 08.genome-annotation.py to annotate the genome structures of the assembly. The tools employed include hisat2, stringtie, TransDecoder, PASA, Trinity, braker3, miniprot, and EVM.
usage: 08.genome-annotation.py [-h] [-g GENOME] [-p PROTEIN] [-o OU_PATH] [-n PREFIX] [--rna RNA] [--SS {false,RF,FR}] [--dbname DBNAME] [--gff GFF] [-s STEP] [--verbose VERBOSE] [--threads THREADS]
optional arguments:
-h, --help show this help message and exit
-g GENOME, --genome GENOME
The path of genome input (softmasked)
-p PROTEIN, --protein PROTEIN
The path of protein fasta file of closely-related species, this protein file will be used in miniprot and braker3
-o OU_PATH, --ou_path OU_PATH
The path of output directory
-n PREFIX, --name PREFIX
The prefix of miniprot output file
--rna RNA The path of directory, which contains RNA fastq files
--SS {false,RF,FR} The BOOL value of `SS_lib_type` parameter in Trintiy
--dbname DBNAME The SQL name used in pasa, if not gived, the pasa db will named with 5 random letters
--gff GFF The GFF annotation of PASA update, this option only used in step 8.
-s STEP, --step STEP The step in annotation process. 1:miniprot; 2:hisat2; 3:stringtie; 4:trinity+pasa; 5:braker; 6:EvidenceModeler; 7:PASA upate; 8:functional annotation
--verbose VERBOSE Print the verbpse information (True) or not (Flase), default is False
--threads THREADS The number of threads
The script 09.genome-systeny.py generates pairwise genome alignments with LASTZ. To expedite the alignment, the target and query genome files were partitioned based on scaffolds using the R package CNEr.
It is recommended to use
make_lastz_chainsto accelerate computation.
usage: 09.genome-systeny.py [-h] [--target TARGET] [--query QUERY] [--output OU_PATH] [--tprefix TPREFIX] [--qprefix QPREFIX] [--step STEP] [--verbose {True,False}] [--threads THREADS]
optional arguments:
-h, --help show this help message and exit
--target TARGET, -t TARGET
The softmasked target genome used to perform lastz alignment. The genome files should be 2bit format
--query QUERY, -q QUERY
The softmasked query genome, 2bit format
--output OU_PATH, -o OU_PATH
The path of output directory
--tprefix TPREFIX The name of target
--qprefix QPREFIX The name of query
--verbose {True,False}
Print all (True, default) information of programe or not (False)
--threads THREADS The nubmer of threads
The script 10.evolution-obtain_the_longest_sequence_from_NCBI.py is designed to retrieve the longest isoform for every coding gene. The script is capable of trimming the CDS sequences extracted from the gff3 file based on correspond protein sequence to ensure that all CDS sequence lengths are multiples of three. CDS sequences with a length shorter than three times the corresponding protein sequence will be removed.
usage: 10.evolution-obtain_the_longest_sequence_from_NCBI.py [-h] [-g GFF] [-f FNA] [--prefix PREFIX]
optional arguments:
-h, --help show this help message and exit
-g GFF, --gff GFF The NCBI gff3 file
-f FNA, --fna FNA The geneome file
--prefix PREFIX The prefix of the longest files, such as: prefix.gff3, prefix.cds, prefix.pep
The script 11.evolution-single_copy_gene_inparanoid.py is designed to identified single-copy orthologous genes by using inparanoid
usage: 11.evolution-single_copy_gene_inparanoid.py [-h] [-i INPUT] [-o OUTPUT] [-m MRNA] [--step STEP]
optional arguments:
-h, --help show this help message and exit
-i INPUT The input directory
-o OUTPUT The output directory
-m MRNA The corresponding mRNA directory
--step STEP Each number corresponds to a specific step: 1:inparanoid; 2: parse the results of inparanoid
The script 12.evolution-species_tree.py is designed to reconstruct phylogenetic tree (species tree) based on the process: 1) MSA using prank; 2) pal2nal; 3) iqtree2 on every orthologous gene; 4) ASTRALIII ; 5) estimated branch length with supergene matrix (concatenated orthologous genes) by using iqtree2
usage: 12.evolution-species_tree.py [-h] [-i INPUT] [-o OUTPUT] [--threads THREADS] [--step STEP] [-m MRNA] [-t TREE]
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
The input directory
-o OUTPUT, --output OUTPUT
The output directory
--threads THREADS The number of threads
--step STEP Each number corresponds to a specific step, 1) prank MSA; 2) pal2nal; 3) iqtree with every gene; 4) ASTRALIII; 5) estimated branch length with concatenated genes
-m MRNA The directory contains corresponding mRNA fasta files
-t TREE The labeled tree file used in mcmctree