Abstract
Parelaphostrongylus tenuis causes ungulate morbidity and mortality in eastern and central North America, but no reference genome sequence exists to facilitate research. Here, we present a P. tenuis genome assembly and annotation, generated with PacBio and Illumina technologies. The assembly is 491 Mbp, with 7285 scaffolds and 185 kb N50.
Keywords: Annotation, assembly, brainworm, genome, genomics, meningeal worm, Parelaphostrongylus tenuis, reference
Announcement
Climate change is predicted to increase the geographic range of Protostrongylid nematodes, which cause morbidity and mortality in many wild and domestic ungulate species (Carreno and Hoberg, 1999; Kutz et al., 2005). In North America, Parelaphostrongylus tenuis has already expanded its range northward in the last half-century, affecting the persistence and management of wildlife and domestic species (Lankester, 2001; Pickles et al., 2013). P. tenuis is a driver of moose population decline in the north-central United States and south-central Canada (Lankester, 2010; Carstensen et al., 2017) and impedes translocations and reintroductions of caribou (Vors and Boyce, 2009), mule deer (Oates et al., 2000), and elk (Samuel et al., 1992). It also causes neurological symptoms and mortality in a variety of domestic species (Keane et al., 2022). Currently, there is no publicly available reference genome sequence for P. tenuis, or any other member of the Protostrongylidae family. Hence, the generation of a P. tenuis reference genome and annotation is a significant advance in the molecular study of both P. tenuis and Protostrongylids. Here, we present a high-quality de novo genome assembly and annotation of P. tenuis. This sequence will aid wildlife conservation and domestic animal husbandry by facilitating future studies of Protostrongylid transmission and evolution.
On October 23, 2019, we extracted two adult P. tenuis nematodes using methods described in Slomke et al. (1995) from a single hunter-harvested white-tailed deer doe head near Rochester, Minnesota. We determined the nematodes to be female based on their large size relative to males (Lankester, 2001). We then flash-froze the specimens in nitrogen and stored them at −80°C until DNA extraction two weeks later. We combined the two worms for a single DNA extraction, which the University of Minnesota Genomics Center (UMGC; St. Paul, MN) performed using the Gentra Puregene (Qiagen, Hilden, Germany) Tissue kit. UMGC then used the Genomic DNA ScreenTape System and Tapestation (Agilent Technologies, Santa Clara, CA) software to confirm sufficient DNA mass and quality for downstream applications. The DNA yield from the nematodes was 3.86 μg, with absorbance ratios of 1.4 at 260/230 nm and 1.85 at 260/280 nm. The DNA integrity number (scale of 1 to 10, with 1 being highly degraded and 10 being highly intact) was 8.9, and 93.11% of the fragments were between 12,198 and >60,000 bp. UMGC performed library preparation on this DNA using the PacBio SMRTbell Express Template Prep Kit 2.0 (Pacific Bioscience of California Inc., Menlo Park, CA) and carried out sequencing on a PacBio Sequel using 3 1M v3 SMRT cells.
We used PacBio SMRT® Tools to create circular consensus sequences from the raw reads and perform quality filtering. We removed reads that had a minimum predicted accuracy of lower than 80%, a consensus read length of below 100 bp, or a consensus read length of above 745,000 bp. Among the approximately 1.4M reads that passed filters, the average Phred quality score was 8, and the mean length was 8,000 bp. We de novo assembled consensus reads with Flye (v2.7; Kolmogorov et al., 2019). Using QUAST (Gurevich et al., 2013), we generated quality statistics for our assembly. Our final assembly was 491 Mbp, with a coverage of 23X. Our assembly contained 7,285 scaffolds and had an N50 of 185 kbp (Table 1). Based on these statistics, P. tenuis has one of the larger genomes in the order Strongylidae (Table 2). Our assembly quality is also in the top half of publicly available genomes in Strongylidae based on N50 and number of scaffolds.
Table 1:
Quality measures and descriptive statistics for our genome assembly and annotation of Parelaphostrongylus tenuis.
| Assembly/Annotation Features | P. tenuis Genome |
|---|---|
| Scaffolds (no.) | 7,285 |
| N50 (bp) | 158,736 |
| N75 (bp) | 73,872 |
| Total length (bp) | 491,304,140 |
| Longest scaffold (bp) | 1,543,493 |
| GC content (%) | 40.07 |
| Complete Genome BUSCOs (no./%) | 2,818/90 |
| Complete Genome single-copy BUSCOs (no./%) | 2724/87 |
| Complete and duplicated Genome BUSCOs (no./%) | 94/3 |
| Fragmented Genome BUSCOs (no./%) | 54/1 |
| Missing Genome BUSCOs (no./%) | 259/8.3 |
| Protein-coding genes (no.) | 29,657 |
| Non-coding tRNA genes (no.) | 9,623 |
| Complete Proteome BUSCOs (no./%) | 2,224/71 |
| Fragmented Proteome BUSCOs (no./%) | 54/7.1 |
| Missing Proteome BUSCOs (no./%) | 259/21.9 |
Table 2:
A comparison of descriptive statistics of the Stronglyid family genomes available in WormBase ParaSite.
| Species | Author/Source | Accession no. | Family | Number of Contigs or Scaffolds | Genome Size | N50 | Largest Contig or Scaffold | GC content (%) |
|---|---|---|---|---|---|---|---|---|
| Parelaphostronglus tenuis | Our genome (University of Minnesota) | GCA_019055375.1 | Protostrongylidae | 7,414 | 491.3 Mb | 158.7 kb | 1.5 Mb | 40.1 |
| Ancyclostoma caninum | McDonnell Genome Institute | GCA_003336725.1 | Ancylostomatidae | 25,335 | 465.7 Mb | 256.7 kb | 5.5 Mb | 42.5 |
| Ancylostoma ceylanicum | Cornell University | GCA_000688135.1 | Ancylostomatidae | 1,736 | 313.1 Mb | 668.4 kb | 4.8 Mb | 43.4 |
| Ancylostoma duodenale | McDonnell Genome Institute | GCA_000816745.1 | Ancylostomatidae | 100,268 | 332.9 Mb | 10.1 kb | 0.3 Mb | 42.5 |
| Angiostrongylus cantonensis | Sun Yat-sen University | GCA_009735665.1 | Angiostrongylidae | 35,378 | 293.3 Mb | 860.8 kb | 4.7 Mb | 41.5 |
| Angiostrongylus costaricensis | Wellcome Sanger Institute | GCA_900624975.1 | Angiostrongylidae | 6,384 | 262.8 Mb | 112.5 kb | 0.8 Mb | 41.2 |
| Angiostrongylus vasorum | University of Zurich | GCA_018806985.1 | Angiostrongylidae | 468 | 279.9 Mb | 1,700 kb | 7.3 Mb | 41.6 |
| Cylicostephanus goldi | Wellcome Sanger Institute | GCA_900617965.1 | Cyathostominae | 154,509 | 173.4 Mb | 1.2 kb | 0.02 Mb | 40.2 |
| Dictyocaulus viviparus | The Genome Institute | GCA_000816705.1 | Dictyocaulidae | 7,157 | 161 Mb | 225.7 kb | 2.2 Mb | 34.5 |
| Haemonchus contortus | Wellcome Sanger Institute | GCA_000469685.2 | Trichostrongylidae | 191 | 283.4 Mb | 47,400 kb | 51.8 Mb | 43.1 |
| Haemonchus placei | Wellcome Sanger Institute | GCA_900617895.1 | Trichostrongylidae | 24,923 | 259.1 Mb | 37.6 kb | 0.3 Mb | 42.8 |
| Heligmosomoides polygyrus | Wellcome Sanger Institute | GCA_900618505.1 | Heligmosomidae | 44,726 | 560.7 Mb | 35.8 kb | 0.4 Mb | 42.0 |
| Heterorhabditis bacteriophora | McDonnell Genome Institute | GCA_000223415.1 | Rhabditidae | 1,240 | 77 Mb | 102 kb | 2.2 Mb | 33.3 |
| Necator americanus | McDonnell Research Institute | GCF_000507365.1 | Ancylostomatidae | 11,864 | 244.1 Mb | 211.9 kb | 1.9 Mb | 40.2 |
| Nippostrongylus brasiliensis | Wellcome Sanger Institute | GCA_900618405.1 | Heligosomidae | 44,362 | 294.4 Mb | 33.5 kb | 0.4 Mb | 42.7 |
| Oesophagostomum dentatum | McDonnell Genome Institue | GCA_00079755.1 | Chabertiidae | 64,255 | 443 Mb | 26.5 kb | 1.6 Mb | 41.4 |
| Strongylus vulgaris | Wellcome Sanger Institute | GCA_900624965.1 | Strongylidae | 167,310 | 291.1 Mb | 2.4 kb | 0.09 Mb | 37.9 |
| Teladorsagia circumcincta | McDonnell Genome Institute | GCA_002352805.1 | Trichostrongylidae | 81,730 | 700.6 Mb | 47.1 kb | 1.5 Mb | 44.5 |
Using BUSCO (v5.2.2; Simão et al., 2015) with the Augustus gene predictor v3.4.0 (Stanke et al., 2006), we searched our assembly for ortholog sequences from the nematode_odb10 lineage to assess genome assembly completeness. We detected 2,818 of the 3,131 BUSCO genes in our assembly, indicating that it is 90% complete, with 3% of the BUSCOs duplicated (94 BUSCO genes). Another 54 (1.7%) of BUSCO genes were fragmented, and 259 (8.3%) were missing.
To identify and annotate repetitive elements in the genome assembly, we built a custom repeat library from our assembly with RepeatModeler (v2.0.1; Smit and Hubley, 2019). We used RepeatMasker (v4.0.5; Smit et al. 2015) to combine this custom library with a standard RepeatMasker library and then ran the program in sensitive mode to find repeats in our assembly. The assembly contained 7.17% repetitive content (160,749 repeat elements, 35,213,890 bp), comprised largely of long interspersed nuclear elements (LINEs; 31,166 elements, 4.5% of genome sequence), DNA transposons (42,041 elements, 1.12% of genome sequence), long terminal repeats (LTRs; 11,592 elements, 0.16% of genome sequence), and short interspersed nuclear elements (SINEs; 3590 elements, 0.05% of genome sequence). These repetitive elements were labeled and masked with RepeatMasker so as not to interfere with later annotation steps. This amount of repeat content is low relative to other large nematode genomes, and it is possible that undermasking explains the large number of protein-coding genes predicted.
We used RNA sequencing data to inform the gene annotation process. The RNA libraries for this step were prepared from a single, whole, adult, female P. tenuis worm from hunter-harvested white-tailed deer from Oak Ridge, Tennessee. The nematode was collected and stored in RNAlater (Thermo Fisher Scientific Inc, Waltham, MA) at −20° C. RNA was enriched using the MasterPure (Illumina Inc, San Diego, CA) RNA Purification kit and associated protocol. After RNA extraction and purification, a transcriptomic library was prepared using the Illumina Tru-seq RNA-seq protocol. RNA was converted into cDNA using RT-PCR. Sequencing was performed with an Illumina MiSeq (Illumina Inc, San Diego, CA) at the University of Tennessee Genomics Core (Knoxville, TN). Purified RNA was loaded at 6 picomolar with 5% 6 picomolar phiX as a control on a version 3 flow cell reading 250 bases, paired end. The 20,116,257 RNA-seq reads that passed quality control were trimmed with Trimmomatric (v0.39, Bolger et al., 2014) (settings used: ILLUMINACLIP:${ADAPTERS}:4:15:7:2:true LEADING:0 TRAILING:0 SLIDINGWINDOW:4:15 MINLEN:75) and then aligned to the P. tenuis whole genome assembly with the splice read aligner STAR (v2.7.1a, Dobin et al., 2013) using the following settings: --alignIntronMin 10 --alignIntronMax 10000 --outFilterMultimapNmax 20. We had 16,594,862 (82.5%) reads that mapped uniquely to the genome, 1,022,828 (5.0%) reads that mapped to multiple locations, and 2,471,022 (12.3%) that were too short to map to the assembly.
Using the funannotate pipeline (v1.8.1; Palmer, 2016), we trained the ab initio gene prediction algorithms and annotated genes. Specifically, we performed the funnanotate train command on the RNA-seq data and genome assembly, which created a genome-guided Trinity (Grabherr et al., 2011) RNA-seq assembly and PASA (Haas et al., 2003) assembly resulting in a BAM file (Trinity), Trinity transcript file (Trinity), and a GFF3 file (PASA). These files were used in conjunction with the STAR alignments and 1,100 predicted P. tenuis proteins harvested from the RNA-seq data to train the ab initio gene predictors Augustus v3.3.3 (Stanke et al., 2006) and Genemark v4.61 (Lomsadze et al., 2014) for gene prediction with the predict command (additional options: -max_intronlen 15000). Gene models were then compared against the RNA-seq data to add untranslated regions and fix gene models that disagreed with the RNA-seq data using EvidenceModeler and PASA (Haas et al., 2008) in the funannotate update step. The output of that command is NCBI-compatible gene models, which were then used to assign functional annotations to the protein-coding gene models with the annotate command. The following databases and software were used for functional annotation: hmmer with Pfam-A database (Eddy, 2011; Mistry et al., 2020), Diamond v2.0.4.142 with UniProt DB version 2020_05 (Buchfink et al., 2021), EggNOG-mapper v1.0.3-diamond-2.0.4 with EggNOG database v.4.5 (Hyatt et al., 2010; Huerta-Cepas et al., 2016; Steinegger and Söding, 2017; Rawlings et al. 2018; Huerta-Cepas et al., 2019), SignalP v4.1 (Peterson et al., 2011), MEROPS (Rawlings et al. 2018), and CAZYme (Drula et al., 2022).
The funannotate pipeline predicted 38,371 gene models and identified 29,657 protein-coding genes (Table 1). The average gene length was 4,088 bp, with a maximum length of 128,078 bp. Using the predicted protein sequences from gene models, we assessed the proteome completeness with BUSCO and the nematode_odb10 lineage dataset. We found 71% (2,224) of the BUSCO proteins being complete and 7.1% (223) of the BUSCO proteins fragmented in our annotation. We did not detect 21.9% (684) of the BUSCO proteins.
We anticipate this de novo genome assembly will facilitate a broad range of studies aimed at investigating the evolution and biology of P. tenuis and other Protostrongylids. For example, our team has leveraged the assembly as a reference for reduced-representation methods facilitating population-level insights into the transmission of P. tenuis. Additionally, the annotation opens the door for genome-wide association studies, which may identify a genetic basis for pathogenicity in brainworm. This information might also be used to design vaccines or treatments to reduce morbidity and mortality in moose and other aberrant hosts.
Database Submission
Nucleotide accession numbers associated with this announcement are SRR15359507 (BioSample SAMN20601477) for the RNA sequencing, PRJNA729714 for the whole genome assembly, and JAHQIW000000000 for the annotation.
Acknowledgements
Funding for the sequencing and bioinformatics for this project was provided by the Northeast Wildlife Disease Cooperative, the National Center for Veterinary Parasitology, the Van Sloun Foundation, and many generous private donations obtained through crowdfunding. We thank M. Schwabenlander, the Minnesota Center for Prion Research and Outreach, and the Minnesota Department of Natural Resources for facilitating sample collection. Additional thanks to R.J. Kania for computer programming support.
Literature Cited
- Bolger A. M., Lohse M., Usadel B.. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120. doi: 10.1093/bioinformatics/btu170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Buchfink B., Reuter K., Drost H. G.. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods. 2021;18:366–368. doi: 10.1038/s41592-021-01101-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carreno R. A., Hoberg E. P.. Evolutionary relationships among the Protostrongylidae (Nematoda: Metastrongyloidea) as inferred from morphological characters, with consideration of parasite-host coevolution. Journal of Parasitology. 1999;85:638–348. [PubMed] [Google Scholar]
- Carstensen M., Hildebrand E. C., Plattner D., Dexter M., St-Louis V., Jennelle C., Wright R. G. Determining cause-specific mortality of adult moose in northeast Minnesota, February 2013–July 2017. St. Paul, MN: Minnesota Department of Natural Resources; 2017. [Google Scholar]
- Dobin A., Davis C. A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T. R.. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Drula E., Garron M.-L., Dogan S., Lombard V., Henrissat B., Terrapon N.. The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Research. 2022;50:D571–D577. doi: 10.1093/nar/gkab1045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Eddy S. R.. Accelerated Profile HMM Searches. PLoS Computational Biology. 2011;7:e1002195. doi: 10.1371/journal.pcbi.1002195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Grabherr M. G., Haas B. J., Yassour M., Levin J. Z., Thompson D. A., Amit I.. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nature Biotechnology. 2011;29:644–652. doi: 10.1038/nbt.1883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gurevich A., Saveliev V., Vyahhi N., Tesler G.. QUAST: Quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–1075. doi: 10.1093/bioinformatics/btt086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haas B. J., Delcher A. L., Mount S. M., Wortman J. R., Smith R. K., Hannick L. I., Maiti R.. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Research. 2003;31:5654–5666. doi: 10.1093/nar/gkg770. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haas B. J., Salzberg S. L., Zhu W., Pertea M., Allen J. E., Orvis J., White O., Buell C. R., Wortman J. R.. Automated eukaryotic gene structure annotation using EvidenceModeler and the Program to Assemble Spliced Alignments. Genome Biology. 2008;9:R7. doi: 10.1186/gb-2008-9-1-r7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huerta-Cepas J., Szklarczyk D., Forslund K., Cook H., Heller D., Walter M. C., Rattei T.. et al. eggNOG 4.5 - a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Research. 2016;44:D286–D293. doi: 10.1093/nar/gkv1248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huerta-Cepas J., Szklarczyk D., Heller D., Hernández-Plaza A., Forslund S. K., Cook H., Mende D. R.. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research. 2019;47:D309–D314. doi: 10.1093/nar/gky1085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hyatt D., Chen G. L., Locascio P. F., Land M. L., Larimer F. W., Hauser L. J.. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119. doi: 10.1186/1471-2105-11-119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Keane C., Marchetto K. M., Oliveira-Santos L. G. R., Wünschmann A., Wolf T. M.. Epidemiological investigation of meningeal worm-induced mortalities in small ruminants and camelids over a 19 year period. Frontiers in Veterinary Science. 2022;9:1–8. doi: 10.3389/fvets.2022.859028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kolmogorov M., Yuan J., Lin Y., Pevzner P. A.. Assembly of long, error-prone reads using repeat graphs. Nature Biotechnology. 2019;37:540–546. doi: 10.1038/s41587-019-0072-8. [DOI] [PubMed] [Google Scholar]
- Kutz S. J., Hoberg E. P., Polley L., Jenkins E. J.. Global warming is changing the dynamics of Arctic host-parasite systems. Proceedings of the Royal Society B: Biological Sciences. 2005;272:2571–2576. doi: 10.1098/rspb.2005.3285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lankester M. W. Samuel W. M., Pybus M. J., Kocan A. A. Parasitic diseases of wild mammals. ed. 2. Ames: Iowa State University Press; 2001. Extrapulmonary lungworms of cervids; pp. 228–253. , ed. [Google Scholar]
- Lankester M. W.. Understanding the impact of meningeal worm, Parelaphostrongylus tenuis, on moose populations. Alces. 2010;46:53–70. [Google Scholar]
- Lomsadze A., Burns P. D., Borodovsky M.. Integration of mapped RNA-seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Research. 2014;42:e119. doi: 10.1093/nar/gku557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mistry J., Chuguransky S., Williams L., Qureshi M., Salazar G. A., Sonnhammer E. L.. et al. Pfam: The protein families database in 2021. Nucleic Acids Research. 2021;49:D412–D419. doi: 10.1093/nar/gkaa913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Oates D. W., Sterner M. C., Boyd E.. Meningeal worm in deer from western Nebraska. Journal of Wildlife Diseases. 2000;36:370–373. doi: 10.7589/0090-3558-36.2.370. [DOI] [PubMed] [Google Scholar]
- Palmer J. M. Funannotate: Pipeline for genome annotation. Available at https://funannotate.readthedocs.io/en/latest/ (accessed September 29, 2022).
- Peterson T. N., Brunak S., von Heijne G., Nielsen H.. SignalP4.0: Distriminating signal peptides from transmembrane regions. Nature Methods. 2011;8:785–786. doi: 10.1038/nmeth.1701. [DOI] [PubMed] [Google Scholar]
- Pickles R. S. A., Thornton D., Feldman R., Marques A., Murray D. L.. Predicting shifts in parasite distribution with climate change: A multitrophic level approach. Global Change Biology. 2013;19:2645–2654. doi: 10.1111/gcb.12255. [DOI] [PubMed] [Google Scholar]
- Rawlings N. D., Barrett A. J., Thomas P. D., Huang X., Bateman A., Finn R. D.. The MEROPS database of proteoly tic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Research. 2018;46:D624–D632. doi: 10.1093/nar/gkx1134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Samuel W. M., Pybus M. J., Welch D. A., Wilke C. J.. Elk as a potential host for meningeal worm: Implications for translocation. Journal of Wildlife Management. 1992;56:629–639. [Google Scholar]
- Simão F. A., Waterhouse R. M., Ioannidis P., Kriventseva E. V., Zdobnov E. M.. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–3212. doi: 10.1093/bioinformatics/btv351. [DOI] [PubMed] [Google Scholar]
- Slomke A. M., Lankester M. W., Peterson W. J.. Infrapopulation dynamics of Parelaphostrongylus tenuis in white-tailed deer. Journal of Wildlife Diseases. 1995;31:125–135. doi: 10.7589/0090-3558-31.2.125. [DOI] [PubMed] [Google Scholar]
- Smit A. F. A., Hubley R. RepeatModeler Open-2.0. Institute for Systems Biology; Seattle: 2019. Available at http://www.repeatmasker.org. (accessed September 29, 2022). [Google Scholar]
- Smit A. F. A., Hubley R., Green P. Repeat Masker Open-4.0. Institute for Systems Biology; Seattle: 2015. Available at http://www.repeatmasker.org. (accessed September 29, 2022). [Google Scholar]
- Stanke M., Keller O., Gunduz I., Hayes A., Waack S., Morgenstern B.. AUGUSTUS: Ab initio prediction of alternative transcripts. Nucleic Acids Research. 2006;34:W435–W439. doi: 10.1093/nar/gkl200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Steinegger M., Söding J.. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. 2017;35:1026–1028. doi: 10.1038/nbt.3988. [DOI] [PubMed] [Google Scholar]
- Vors L. S., Boyce M. S.. Global declines of caribou and reindeer. Global Change Biology. 2009;15:2626–2633. [Google Scholar]
