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opencc-zeroDec 2017View details →

Data from: Exploring the phylogeography of a hexaploid freshwater fish by RAD sequencing

The KwaZulu-Natal yellowfish (Labeobarbus natalensis) is an abundant cyprinid, endemic to KwaZulu-Natal Province, South Africa. In this study we developed a Single Nucleotide Polymorphism (SNP) dataset from double-digest Restriction-site Associated DNA (ddRAD) sequencing of samples across the distribution. We addressed several hidden challenges, primarily focussing on proper filtering of RAD data and selecting optimal parameters for data processing in polyploid lineages. We used the resulting high-quality SNP dataset to investigate the population genetic structure of L. natalensis. A small number of mitochondrial markers present in these data had disproportionate influence on the recovered genetic structure. The presence of singleton SNPs also confounded genetic structure. We found a well-supported division into northern and southern lineages, with further subdivision into five populations, one of which reflects north-south admixture. Approximate Bayesian Computation scenario testing supported a scenario where an ancestral population diverged into northern and southern lineages, which then diverged to yield the current five populations. All river systems showed similar levels of genetic diversity, which appears unrelated to drainage system size. Nucleotide diversity was highest in the smallest river system, the Mbokodweni, which, together with adjacent small coastal systems, should be considered as a key catchment for conservation.

FIGURE 12 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 12. Ranges of the species based on existing information. A—H. spatzi, B—H. nitidulus, C—H. dintelmanni, D—H. sp. 1, E—H. tuberculatus, F—H. bangwae, G—H. parallelus, H—H. marmoratus, I—H. marginatus, J—H. noblei, K—H. goetzei, L—H. glandicolor, M—H. mariae, N—H. viridiflavus.

FIGURE 11 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 11. Illustrations of typical colour patterns in the Hyperolius viridiflavus complex: A—H. viridiflavus, from an illustration of 1845 by J. Vignaud; B—H. marmoratus, Storms River, South Africa, photo A. Channing; C—H. marginatus, North Luangwa National Park, Zambia, photo A. Channing; D—H. parallelus, PEM A12451, Cuanavale Source, Angola, photo W. Conradie; E—H. nitidulus, Lamto, Côte d'Ivoire, photo M.-O. Rödel; F—H. glandicolor, Kinangop, Kenya, photo A. Channing; G—H. tuberculatus, ZMB 91677, Dja Reserve, Cameroon, photo M.-O. Rödel; H—H. mariae, Kiloza, Tanzania, photo A. Channing; I—H. noblei, PEM A10608, Quinonga, Mozambique, photo W. Conradie; J—H. goetzei, Iringa, Tanzania, photo A. Channing; K—H. spatzi, Senegal, photo A. Hillers; L—H. dintelmanni, CAS 254137, Edib Hills, Cameroon, photo D. Portik.

FIGURE 10 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 10. Localities of sequences (black circles), type localities (stars) and location of junior synonyms of Hyperolius noblei (A), H. goetzei (B), H. spatzi (C) and H. dintelmanni (D). Locality codes are explained in Table 3.

FIGURE 8 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 8. Localities of sequences (black circles), type localities (stars) and location of junior synonyms of Hyperolius nitidulus (A), H. mariae (B) and H. sp 1 (C). Locality codes are explained in Table 3.

FIGURE 9 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 9. Localities of sequences (black circles), type localities (stars) and location of junior synonyms of Hyperolius bangwae (A), H. tuberculatus (B), and H. glandicolor (C). Locality codes are explained in Table 3.

FIGURE 6 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 6. Localities of sequences (black circles), type locality (star) and location of junior synonyms of Hyperolius marginatus. Locality codes are explained in Table 3.

FIGURE 7 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 7. Localities of sequences (black circles), type locality (star) and location of junior synonyms of Hyperolius parallelus. Locality codes are explained in Table 3.

FIGURE 5 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 5. Localities of sequences (black circles), type locality (star) and location of junior synonyms of Hyperolius marmoratus. Locality codes are explained in Table 3.

FIGURE 1 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 1. Localities from where sequences were obtained (black circles), and from where available names were described (red triangles). The dashed lines indicate the range of the Hyperolius viridiflavus species complex.

FIGURE 4 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 4. Localities of sequences (black circles), type locality (star) and location of junior synonyms of Hyperolius viridiflavus. Locality codes are explained in Table 3.

FIGURE 3 in Colour patterns to sequences: a perspective on the systematics of the Hyperolius viridiflavus group (Anura: Hyperoliidae) using mitochondrial DNA

FIGURE 3. Colour patterns of some of the variation recognised in taxa now included in H. viridiflavus. After Laurent (1983): 1–9, 11–13 Hyperolius karissimbiensis françoisi; 10, 14, 16–18 Hyperolius viridiflavus xanthogrammus; 15, 19–26 Hyperolius viridiflavus hybridus. After Ahl (1931): 27–31 Hyperolius variabilis. Ahl's illustrations were taken from Tornier (1897).

openbsd-licenseMar 2022View details →

Convolutions are competitive with transformers for protein sequence pretraining

- Pretrained models of protein sequences. See https://github.com/microsoft/protein-sequence-models for instructions on how to load models. - IDR datasets used for evaluation.  - March 2020 version of UniRef50 with splits used for training.

Genome wide mRNA sequencing data in macrophages without and with CX-5461 treatment

CX-5461, a novel selective RNA polymerase I inhibitor, shows potential anti-inflammatory and immunosuppressive activities. However, the molecular mechanisms underlying the inhibitory effects of CX-5461 on macrophage-mediated inflammation remain to be clarified. In the present study, we attempted to identify the systemic biological processes which were modulated by CX-5461 in inflammatory macrophages. Primary peritoneal macrophages were isolated from normal Sprague Dawley rats, and primed with lipopolysaccharide or interferon-gamma. Genome-wide RNA sequencing was performed. The study suggests that limiting cell proliferation predominates in the inhibitory effects of CX-5461 on macrophage-mediated inflammation.

Amplicon_sorter: a tool for reference-free amplicon sorting based on sequence similarity and for building consensus sequences

Oxford Nanopore Technologies (ONT) is a third-generation sequencing technology that is gaining popularity in ecological research for its portable and low-cost sequencing possibilities. Although the technology excels at long-read sequencing, it can also be applied to sequence amplicons. The downside of ONT is the low quality of the raw reads. Hence, generating a high-quality consensus sequence is still a challenge. We present Amplicon_sorter, a tool for reference-free sorting of ONT sequenced amplicons based on their similarity in sequence and length and for building solid consensus sequences.

Constructing the hierarchy of predictive auditory sequences in the marmoset brain

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up g and top-down b oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

A method for determining the origin of crude drugs derived from animals using MinION, a compact next-generation sequencer

We evaluated whether MinION, an inexpensive, portable sequencer, can be applied for identifying the origin of crude drugs derived from animals. Standard and nonstandard crude drugs with different species of origin were examined. In addition, the standards mixed with nonstandard samples were used. As a target gene, cytochrome c oxidase I was amplified and sequenced. The Fast mode results had a slightly lower match ratio than High-accuracy mode, but the animals of origin were correctly determined by BLAST for all samples. For antler velvet derived from Rangifer tarandus, even the sequences were aligned based on Cervus elaphus, the animal of origin was determined correctly. Minor contents could be detected from mixtures of two animals, if the mixtures contained at least 19:1 mtDNA when the coverage allele-fraction threshold was 0.05. By contrast, in Fast mode, two sequences could not be separated due to the low accuracy of the base-calling in each read. For field work, the species of origin of crude drugs could be identified, by only simple DNA extraction and library preparation. Therefore, MinION appears to be a convenient tool for identifying the origin of crude drugs derived from animals.

opencc-by-4.0Jun 2022View details →

Chr18 sequences of 37 soy lines

chromosome 18 sequences of 37 soy lines: - 2 reference sequences: WM82 v2.1 and ZH13 v2.0 - 27 assemblies from Liu et al., Pan-Genome of Wild and Cultivated Soybeans, Cell (2020), https://doi.org/10.1016/ j.cell.2020.05.023 - 8 assemblies from Chu, J.SC., Peng, B., Tang, K. et al. Eight soybean reference genome resources from varying latitudes and agronomic traits. Sci Data 8, 164 (2021). https://doi.org/10.1038/s41597-021-00947-2

opencc-zeroJun 2022View details →

Aligned DNA sequences of Vanilla

Premise Although vanilla is one of the best-known spices, there is a limited understanding of its biology and genetics within Mexico, where its cultivation originated and where phenotypic variability is high. This study aims to augment our understanding of vanilla's genetic resources by assessing species delimitation and genetic, geographic, and climatic variability within Mexican cultivated vanilla. Methods Nuclear and plastid DNA sequence data from 58 Mexican samples collected from three regions and 133 ex-situ accessions were used to assess species monophyly using phylogenetic analyses and genetic distances. Intra-specific genetic variation was summarized through the identification of haplotypes. Within the primarily cultivated species, V. planifolia, haplotype relationships were further verified using plastome and rRNA gene sequences. Climatic niche and haplotype composition were assessed across the landscape. Key Results Three species (Vanilla planifolia, V. pompona, and V. insignis) and 13 haplotypes were identified among Mexican vanilla. Within V. planifolia haplotypes, hard phylogenetic incongruences between plastid and nuclear sequences suggest past hybridization events. Eight haplotypes exclusively consisted of Mexican samples. The dominant V. planifolia haplotype occurred throughout all three regions as well as outside of its country of origin. Haplotype richness was found to be highest in regions around Papantla and La Chinantla. Conclusions Long histories of regional cultivation support the consideration of endemic haplotypes as landraces shaped by adaptation to local conditions and/or hybridization. Results may aid further genomic investigations of vanilla's genetic resources and ultimately support the preservation of genetic diversity within the economically important crop.