Identifying Ateles geoffroyi Individuals Noninvasively Using Third-Generation Sequencing Technologies

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Participants

  • Michael Bliss; Master’s Student, Kent State University, USA
  • Brandy Nelson; PhD Student, Kent State University, USA
  • Salan Ghaju; PhD Candidate, Kent State University, USA
  • Prashant Ghimire; Graduate Student, Kent State University, USA
  • Abbigail Swiney; Undergraduate Student, Kent State University, USA
  • Roy Heath; Research Engineer, Kent State University, USA
  • Sangeet Lamichhaney; Associate Professor, Kent State University, USA
  • Wilson Chung; Associate Professor, Kent State University, USA
  • Rafaela Takeshita; Associate Professor, Kent State University, USA

Summary

Genotyping animals is critical for field-based applications that require accurate individual identification. Fecal samples, traditionally considered low-quality DNA sources, are increasingly being used in noninvasive studies due to advances in molecular techniques. In this project, we sequenced whole genomes from Ateles geoffroyi feces using the portable MinION platform. Our objective was to establish a standardized workflow for developing custom SNP panels for genotyping by leveraging existing software and informatics suites, enabling easy reproducibility across species. While this study did not produce a fully informative SNP panel, it establishes a foundation for future noninvasive genotyping and demonstrates the potential of third-generation sequencing for SNP discovery.

Techniques

Fecal sample collection, DNA extraction optimized for low-quality biological material, library preparation, whole-genome sequencing using MinION, BWA-GATK bioinformatic workflow.

Future Directions

Future work will focus on validating the most informative SNPs by using robust sequencing technologies like NGS Illumina to confirm they are true genetic markers. We will explore enrichment and targeting strategies, including multiplex PCR, FecalSeq enrichment, and adaptive sampling, to improve host-specific DNA yields and SNP coverage. Once the SNP panel is validated, we plan to test its utility in the field and implement it for research and monitoring purposes. These steps will strengthen the utility of noninvasive genotyping tools for population-level research and conservation applications.

Future Directions

Future work will focus on validating the most informative SNPs by using robust sequencing technologies like NGS Illumina to confirm they are true genetic markers. We will explore enrichment and targeting strategies, including multiplex PCR, FecalSeq enrichment, and adaptive sampling, to improve host-specific DNA yields and SNP coverage. Once the SNP panel is validated, we plan to test its utility in the field and implement it for research and monitoring purposes. These steps will strengthen the utility of noninvasive genotyping tools for population-level research and conservation applications.

Why This Matters

Noninvasive genotyping will enhance the efficacy of monitoring endangered species. Using fecal samples instead of capturing animals reduces stress, avoids handling risks, and simplifies the permitting process, particularly under regulations such as CITES, which governs the transport and study of endangered species. Fecal samples are easier to obtain, safe for the animal, and allow repeated sampling in populations where invasive methods are challenging. They also provide various endocrine data, allowing simultaneous monitoring of stress and other physiological indicators alongside genotyping. These approaches enable more effective, ethical, and feasible conservation monitoring of cryptic or hard-to-identify endangered species.

Code

We provide the full workflow we used for variant calling to ensure reproducibility for other researchers. See below.

BWA-GATK Variant Calling Workflow Code

Merge FASTQ Files:
cat * > your_file.fastq.gz

Burrows-Wheeler Aligner Index:
bwa index your _file.fasta

Picard SequenceDictionary:
java-jar picard.jar CreateSequenceDictionary \"
R=your_file.fasta
O=your_file.dict

Burrows-Wheeler Aligner:
bwa mem -t 28-R'@RG\tID:your_ID \tSM:your_sample your _file.fasta your _file.fastqgz >your_file.sam

Samtools View:
samtools view-s -b your_file.sam > your_file.bam

Samtools Sorted:
samtools sort -o your_file_sorted bam your_file.bam

Picard MarkDuplicates:
java-jar picard.jar MarkDuplicates \
-I your _file_sorted.bam -O your_file_sorted_markduplicates.bam -M
your file_sorted _markduplicates_metrics.txt

Samtools Index:
samtools index your _file_sorted_markduplicates.bam

GATK HaplotypeCaller:
./gatk-4.3.0.0/gatk HaplotypeCaller \
-R GCA_023783555.1/GCA_023783555.1_ASM2378355v1_genomic.fna \
-I your_file _sorted_markduplicates.bam \
-O your_file.g.vcf\

Gatk CombineGVCFs:
./gatk-4.3.0.0/gatk CombineGVCFs \
-R your_file.fasta \
-V your _file1.g.vcf\
-V your _file2.g.vc \
-O your_file_combined.g.vcf

GATK GenotypeGVCFs:
./gatk-4.3.0.0/gatk GenotypeGVCFs \
-R your _file.fasta\
-V your_file_combined.g.vcf \
- O your_file.vcf

GATK SelectVariants:.
/gatk-4.3.0.0/gatk SelectVariants \
-R your_file.fasta \
-V your_file.vcf \
--select-type-to-include SNP \
-O your_file_snps.vcf

GATK VariantFilatration:.
/gatk-4.3.0.0/gatk VariantFiltration -V your_file_snps.vef
-filter-expression "QD < 2.0" -filter-name "QD2" \
-filter-expression "QUAL < 30.0" --filter-name "QUAL30" \
-filter-expression "SOR > 3.0" -filter-name "SOR3" \
-filter-expression "FS > 60.0" --filter-name "FS60" \
-filter-expression "MQ < 30.0" -filter-name "MQ30" \
-filter-expression "MQRankSum < -12.5" --filter-name "MQRankSum-12.5" \
-filter-expression "ReadPosRankSum < -8.0" --filter-name "ReadPosRankSum-8" \
-O your_file_snps_filtered.vcf

GATK VariantsToTable:
/gatk-4.3.0.0/gatk VariantsToTable \
-V your_file_sps_filtered.vef \
-F CHROM -F POS -F TYPE -F REF -F ALT -F QUAL \
-GF GT -GF PL -GF AD \
-O your_file_snps_filtered.table

Figure 1. Sample preparation, loading, and sequencing with the MinION

Figure 2. Genotyping with the BWA-GATK variant calling workflow

Figure 3. Optimized phenol chloroform extraction method protocol.

Poster