DOI: 10.5061/dryad.18931zd25
title: De novo assembly of SNPs in VCF format for 112 individualss of Campylorhynchus in western Ecuador

description: Climate variability has a significant impact on the evolution of
 biodiversity, which results in genetic and phenotypic diversity within
 species. A balance between gene flow and selection maintains changes in
 the frequency of genetic and phenotypic variants that occur along an
 environmental gradient. Here, we investigate a hybrid zone in western
 Ecuador involving C. zonatus, C. fasciatus, and admixed populations. We
 hypothesized that different ecological preferences and geographical
 distances result in limited dispersal between populations along the
 precipitation gradient in western Ecuador. In the context of testing IBE
 and IBD shaping distributions of C. zonatus, C. fasciatus, and potential
 hybrids, we asked (1) Is there evidence of genetic admixture and
 introgression between these taxa in Western Ecuador? And (2) What is the
 relative contribution of IBE and IBD on patterns of genetic
 differentiation and admixture patterns? We analyzed 4409 SNPs from the
 blood of 112 individuals sequenced using ddRadSeq. The most likely
 clusters ranged from K=2-4, corresponding to categories defined by
 geographic origins, known phylogenetics, and physical or ecological
 constraints. Evidence for IBE was weak but stronger for IBD. We observed
 gradual changes in genetic admixture between C. f. pallescens and C.
 zonatus along the environmental gradient. Genetic differentiation of the
 two populations of C. f. pallescens could be driven by a previously
 undescribed potential physical barrier near the center of western Ecuador.
 Lowland habitats in this region may be limited due to the proximity of the
 Andes to the coastline, limiting dispersal and gene flow, particularly
 among dry-habitat specialists.
description: We collected blood samples from the brachial vein of 112
 individuals of Campylorhynchus along Western Ecuador. Most samples were
 collected between July and December 2018. We also obtained 27 tissue
 samples of <em>C. fasciatus</em> from Peru at the Florida
 Museum of Natural History (FLMNH). DNA for all samples was extracted using
 Qiagen DNeasy blood & tissue kits (Qiagen, Maryland USA). We
 followed the ddRAD-seq protocol by Peterson et al. (2012) and modified by
 Thrasher et al. (2018). We trimmed all sequences to 147bp using
 fastX_trimmer (FASTX-Toolkit) to exclude low-quality calls near the 3’ of
 the reads. We removed reads containing at least a single base with a Phred
 quality score of less than 10 (using fastq_quality_filter). We removed
 sequences if more than 5% of the bases had a Phred quality score of less
 than 20. Using the process_radtags module from the STACKS version 2.3
 (Catchen <em>et al.</em>, 2013), we demultiplexed the reads to
 obtain files with specific sequences for each individual.
description: # De novo assembly of SNPs in VCF format for 112 individuals of
 Campylorhynchus in Western Ecuador. --- We have submitted the resulting
 populations.snps.vcf (Variant Call Format) file from the de novo assembly
 performed using the STACK software. This file is the primary dataset
 utilized for the various analyses described in our article,
 "Unraveling the Genomic Landscape of Campylorhynchus Wrens along
 Western Ecuador's Precipitation Gradient: Insights into
 Hybridization, Isolation by Distance, and Isolation by Environment."
 The article investigates the population structure and hybridization
 patterns between Campylorhynchus zonatus and C. fasciatus across western
 Ecuador. Additionally, we have included the file DeNovoAssemblyStack.bash,
 which contains the Unix code used to perform the de novo assembly with
 STACK, this code uses as input the raw FASTA files deposited at NCBI
 Sequence Read Archive under Bioproject accession number PRJNA925654. ##
 Description of the data and file structure #### Dataset Overview This
 Variant Call Format (VCF) file contains Single Nucleotide Polymorphism
 (SNP) data from 112 individuals across 16 sampling locations. It comprises
 4409 SNPs with an average coverage of 35.4x per individual (min=12.8x,
 max=77.9x) and an average genotype call rate of 93.5%. ####
 populations.snps.vcf #### File Details * **File Name**:
 populations.snps.txt * **File Format**: VCF (Variant Call Format) Version
 4.2 * **Date**: September 6, 2020 * **Source Software**: Stacks v2.41 ####
 Data Description The VCF file includes the following columns standard to
 the format: * `#CHROM`: Chromosome number * `POS`: Position of the SNP on
 the chromosome * `ID`: Identifier of the SNP * `REF`: Reference base *
 `ALT`: Alternate base(s) * `QUAL`: Quality score of the SNP * `FILTER`:
 Filter status * `INFO`: Additional information (e.g., allele frequency,
 number of samples) * `FORMAT`: Data format * Sample columns: One per
 individual, containing genotype information #### Specific Fields in `INFO`
 * `NS`: Number of samples with data * `AF`: Allele frequency * `DP`: Total
 depth of reads #### Specific Fields in `FORMAT` * `GT`: Genotype * `AD`:
 Allele depth * `DP`: Read depth * `GQ`: Genotype quality * `GL`: Genotype
 likelihood #### How to Open and Analyze the Data The VCF file can be
 opened and analyzed using various bioinformatics tools and software, some
 of the most common include: * **IGV (Integrative Genomics Viewer)**:
 Useful for visualizing genomic data. * **BCFtools**: Command-line tool for
 working with VCF/BCF files, including filtering, viewing, and converting.
 * **GATK (Genome Analysis Toolkit)**: Provides tools to analyze
 high-throughput sequencing data with a focus on variant discovery. *
 **vcftools**: Command-line program designed to work specifically with VCF
 files to perform various types of analyses. ## Sharing/Access information
 All sequencing data from this study has been deposited at NCBI Sequence
 Read Archive under Bioproject accession number PRJNA925654. All scripts
 for the analyses derived below can be accessed
 at https://github.com/ldmontalvo/Landscape-Genomic-Wrens ####
 DeNovoAssemblyStack.bash ### Content Description 1. **SLURM Directives**:
 The script begins with SLURM directives for job scheduling on a
 high-performance computing (HPC) cluster: * `--ntasks=1`: Requests one
 task. * `--cpus-per-task=8`: Allocates eight CPUs per task. *
 `--mem-per-cpu=1gb`: Allocates 1 GB of memory per CPU. *
 `--time=05:00:00`: Sets the maximum job run time as five hours. *
 `--job-name=check_test`: Names the job "check_test". *
 `--mail-type=END,FAIL`: Configures SLURM to send an email when the job
 ends or fails. * `--mail-user=ldmontalvo@ufl.edu`: Specifies the email
 address to receive job notifications. * `--output=check_test_%j.log`:
 Directs the output to a log file named with the job ID. 2. **Environment
 Setup**: The script prints the current directory, hostname, and date to
 the output file, providing a log of where and when the job was executed.
 3. **Module Loading**: It loads the STACKS software module, preparing the
 environment for executing STACKS-related commands. 4. **Directory and File
 Preparation**: * Creates a new directory `./denovo_n5nb` to store the
 output of the de novo assembly. * Runs `denovo_map.pl`, a script from
 STACKS, with various parameters like minimum stack depth (`-m 3`),
 mismatches between loci within individuals (`-M 5`), mismatches between
 loci in the catalog (`-n 5`), using 15 threads (`-T 15`), and specifies
 input and output directories. 5. **Population Analysis**: * Executes the
 `populations` program from STACKS with parameters to specify the input
 directory, population map, minimum percentage of individuals in a
 population required to process a locus (`-r 0.8`), minimum number of
 populations a locus must be present in to process it (`-p 1`), and output
 format flags for STRUCTURE, VCF, and Genepop files. 6. **Background
 Execution**: * Runs the script `Wren_denovo_n5nb.sh` in the background
 using `nohup` to prevent it from stopping if the session disconnects and
 appends the output to `nohup.out`. ### How to Run the Script To run this
 script on a Unix-like system (assuming you have access to a cluster using
 SLURM workload manager): 1. **Transfer the Script**: Upload the
 `DeNovoAssemblyStack.bash` script to your cluster's user directory.
 2. **Permission**: Change the permission of the file to make it
 executable: ``` bash ``` Copy code `chmod +x DeNovoAssemblyStack.txt` 3.
 **Submission**: Submit the script to the SLURM scheduler: ``` Copy code
 ``` `sbatch DeNovoAssemblyStack.bash` ## Methods #### Data Collection We
 collected blood samples from the brachial vein of 112 individuals of
 Campylorhynchus along Western Ecuador. Most samples were collected between
 July and December 2018. We also obtained 27 tissue samples of C. fasciatus
 from Peru at the Florida Museum of Natural History (FLMNH). DNA for all
 samples was extracted using Qiagen DNeasy blood & tissue kits (Qiagen,
 Maryland USA). We followed the ddRAD-seq protocol by Peterson et al.
 (2012) and modified by Thrasher et al. (2018). We trimmed all sequences to
 147bp using fastX_trimmer (FASTX-Toolkit) to exclude low-quality calls
 near the 3 of the reads. We removed reads containing at least a single
 base with a Phred quality score of less than 10 (using
 fastq_quality_filter). We removed sequences if more than 5% of the bases
 had a Phred quality score of less than 20. Using the process_radtags
 module from the STACKS version 2.3 (Catchen et al., 2013), we
 demultiplexed the reads to obtain files with specific sequences for each
 individual. We assembled the sequences de novo using the STACKS pipeline
 (Catchen et al., 2013). First, we selected 12 samples with the highest
 number of reads and ran denovo_map.pl testing values from one to nine for
 -M (number of mismatches allowed between stacks within individuals) and n
 (number of mismatches permitted between stacks between individuals)
 following the n=M rule (Paris et al., 2017) while keeping m=3 (stack
 depth/number of identical reads required to initiate a new allele). We
 kept r=0.8 (the minimum percentage of individuals in a population required
 to process a locus for that population). It has been shown that at least
 80% of the population should present a specific locus to be included,
 known as the 80% rule or r80 loci (Paris et al., 2017). We set all samples
 to the same population (p=1) for the parameter testing assembly. We
 plotted the number of SNPs called against the M parameters to find the
 optimum M, after which no additional SNP calling was observed. We found an
 optimum value for n=m=5 for the final de novo assemblies. After the
 parameters testing assembly, we performed de novo assembly with all the
 samples and the parameters described above and set p=1. When a RAD locus
 had more than one SNP, the data were restricted to the first
 (--write_single_snp) to avoid including SNPs in high linkage
 disequilibrium (LD). We required a minor allele frequency of at least 0.05
 to process a nucleotide site (--min_maf). #### Analyses Performed using
 the Data Set In our study, we analyzed the population structure and
 admixture patterns of Campylorhynchus wrens using a combination of spatial
 principal component analysis (sPCA) and Bayesian clustering. The sPCA,
 conducted through the Adegenet package in R (Jombart, 2008), helped
 visualize genetic structures by building a distance-based connection
 network based on the maximum dispersal distances observed in Cactus Wrens.
 This analysis differentiated global structures (clines and broad genetic
 structures) from local structures (sharp genetic divergences between
 neighbors), and its significance was tested via a Monte Carlo procedure
 with extensive permutations. Complementarily, STRUCTURE software version
 2.3.4; (Pritchard et al., 2000) was used to estimate individual membership
 coefficients across potentially mixed populations, incorporating prior
 geographical information and allowing for admixture. The optimal number of
 genetic clusters was determined through an analysis in STRUCTURE
 HARVESTER (Earl & Vonholdt, 2012) and further refined by discriminant
 analysis of principal components (DAPC), enhancing our understanding of
 the genetic grouping and population structure. Further, we delved into the
 genetic diversity from the R package dartR (Gruber et al., 2018) within
 these populations by employing DAPC to ascertain the most probable number
 of genetic clusters, which was corroborated by STRUCTURE when considering
 K=4. This analysis not only allowed a detailed classification within the
 population but also aligned the genetic clusters with previously
 recognized biogeographical regions, indicating potential barriers within
 Western Ecuador. By leveraging allele frequencies and inbreeding
 coefficients, we explored the genetic health and diversity of these
 clusters, ensuring robustness in our estimates by adjusting for potential
 biases from related individuals. This comprehensive genetic profiling was
 bolstered by an analysis of molecular variance (AMOVA) to quantify genetic
 differentiation, providing a nuanced understanding of the genetic
 landscape that influences these bird populations across varied ecological
 zones. #### References Earl, D. A., & Vonholdt, B. M. (2012).
 STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE
 output and implementing the EVANNO method. *Conservation Genetics
 Resources*, *4*(2), 359–361. https://doi.org/10.1007/s12686-011-9548-7
 Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A., & Cresko, W.
 A. (2013). Stacks: An analysis tool set for population genomics.
 *Molecular Ecology*, *22*(11), 3124–3140.
 https://doi.org/10.1111/mec.12354 Gruber, B., Unmack, P. J., Berry, O. F.,
 & Georges, A. (2018). dartR: An r package to facilitate analysis of
 SNP data generated from reduced representation genome sequencing.
 *Molecular Ecology Resources*, *18*(3), 691–699.
 https://doi.org/10.1111/1755-0998.12745 Jombart, T. (2008). adegenet: A R
 package for the multivariate analysis of genetic markers.
 *Bioinformatics*, *24*(11), 1403–1405.
 https://doi.org/10.1093/bioinformatics/btn129 Peterson, B. K., Weber, J.
 N., Kay, E. H., Fisher, H. S., & Hoekstra, H. E. (2012). Double Digest
 RADseq: An Inexpensive method for De Novo SNP discovery and genotyping in
 model and non-model species. *PLoS ONE*, *7*(5), e37135. Pritchard, J. K.,
 Stephens, M., & Donnelly, P. (2000). Inference of population structure
 using multilocus genotype data. *Genetics*, *155*(2), 945–959.