To identify orangutan lncRNAs, we used the spliced read aligner TopHat (14 (link)) (version V1.3.1) to map all sequenced reads to the orangutan genome (ponAbe2) with the following parameters: min-anchor = 5, min-isoform-fraction = 0 and the rest set as default. We then aligned reads of each tissue from TopHat and assembled them into transcriptome separately by Cufflinks (15 (link)) (version 1.0.3) with default parameters (and ‘min-frags-per-transfrag = 0’). After that, we constructed the transcripts from six tissues and merged them together to constitute the final transcript set of orangutan and then compared them with known genes annotated by Ensembl database (v69). Novel long transcripts (>200 bp) that do not overlap with any known annotation and are localized in intronic, antisense or intergenic region were filtered by CNCI and added to the lncRNA catalog of orangutan.
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Living Beings
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Mammal
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Pongo pygmaeus
Pongo pygmaeus
Pongo pygmaeus, also known as the Bornean orangutan, is a critically endangered great ape species native to the rainforests of Borneo.
These arboreal primates are known for their distinctive reddish-brown fur, long arms, and thoughtful, solitary nature.
Pongo pygmaeus plays a crucial role in the ecosystem as a seed disperser and plays a significant part in the cultural heritage of indigenous communities.
Ongoing threats to their habitat, such as deforestation and palm oil production, have led to a decline in their population.
Researchers studying this specis can utilize PubCompare.ai's innovative platform to streamline their work, locate relevant protocols, and optimize their Pongo pygmaeus research for improved reproducibility and impact.
These arboreal primates are known for their distinctive reddish-brown fur, long arms, and thoughtful, solitary nature.
Pongo pygmaeus plays a crucial role in the ecosystem as a seed disperser and plays a significant part in the cultural heritage of indigenous communities.
Ongoing threats to their habitat, such as deforestation and palm oil production, have led to a decline in their population.
Researchers studying this specis can utilize PubCompare.ai's innovative platform to streamline their work, locate relevant protocols, and optimize their Pongo pygmaeus research for improved reproducibility and impact.
Most cited protocols related to «Pongo pygmaeus»
Genes
Genome
Intergenic Region
Introns
Pongo pygmaeus
Protein Isoforms
RNA, Long Untranslated
Tissues
Transcriptome
We sequenced to a mean coverage of 25X (Illumina HiSeq 2000) a total of 79 great ape individuals, representing 10 subspecies and four genera of great apes from a variety of populations across the African continent and Southeast Asia. SNPs were called using GATK12 (link) after BWA28 (link) mapping to the human genome (NCBI Build 36) using relaxed mapping parameters. Samples combined by species were realigned around putative indels. SNP calling was then performed on the combined individuals for each species. For indels, we used the GATK Unified Genotyper to produce an initial set of indel candidates applying several quality filters and removing variants overlapping segmental duplications and tandem repeats. We also removed groups of indels clustering within 10 bp to eliminate possible artifacts in problematic regions. Conservative allelic imbalance filters were used to eliminate false heterozygotes that may affect demographic analyses, some of which are sensitive to low levels of contamination. We estimate that the application of this filter resulted in a 14% false negative rate for heterozygotes. Our multispecies study design facilitated this assessment of contamination, which may remain undetected in studies focused on assessing diversity within a single species. The amount of cross-species contamination was estimated from the amount of non-endogenous mitochondrial sequence present in an individual. Because we wished to compare patterns of variation between and within species, we report all variants with respect to coordinates of the human genome reference. For FRAPPE analyses, we used MAF0.06 (human, orangutan, and bonobo) and 0.05 (chimpanzee and gorilla) to remove singletons. For most of the analyses, we only used autosomal markers, except in the X/A analysis. To determine the amount of inbreeding, we calculated the heterozygosity genome-wide in windows of 1 Mbp with 200 kbp sliding windows. We then clustered together the neighboring regions to account for runs of homozygosity. For the PSMC analyses, we called the consensus bases using SAMtools29 (link). Underlying raw sequence data is available through the SRA (PRJNA189439/SRP018689). Data generated in this work are available from http://biologiaevolutiva.org/greatape/ . A complete description of the material and methods is provided in the Supplementary Note .
Allelic Imbalance
Genome
Genome, Human
Gorilla gorilla
Heterozygote
HIVEP1 protein, human
Homo sapiens
Homozygote
INDEL Mutation
Mitochondria
Negroid Races
Pan paniscus
Pan troglodytes
Pongidae
Pongo pygmaeus
Segmental Duplications, Genomic
Tandem Repeat Sequences
For human training sets, protein-coding genes were collected from RefSeq database and long non-coding genes were collected from Gencode (v11) (9 (link)). For mouse testing sets, both protein-coding and non-coding genes were collected from Ensembl (v65) (10 (link)) database. As other testing sets, gene annotation of other vertebrates or plants was downloaded from Ensembl (v69) and EnsemblPlants (v16) (10 (link)) databases, respectively. LincRNA catalog was obtained from human body map (11 (link)). Whole-transcriptome sequencing data of the six organs of orangutan were obtained from the study of David Brawand et al. (3 (link)) and downloaded from Gene Expression Omnibus under accession code GSE30352. All the data were summarized in Supplementary Table S1 , and the length distributions of the human and mouse transcript collections are depicted in Supplementary Table S2 .
Gene Annotation
Gene Expression
Gene Products, Protein
Genes
Homo sapiens
Human Body
Long Intergenic Non-Protein Coding RNA
Mice, Laboratory
Plants
Pongo pygmaeus
Proteins
Vertebrates
African People
Alleles
East African People
Europeans
Genome
Gorilla gorilla
Neanderthals
Negroid Races
Pan troglodytes
Pongo pygmaeus
CAT annotation is improved when species-specific RNA-seq data are provided. These data are used as hints for AugustusTMR and AugustusCGP. In AugustusTMR, RNA-seq helps fill in missing information in the alignment and resolve evolutionary changes. In AugustusCGP, RNA-seq additionally helps prevent false positives inherent in ab initio gene finding. For these reasons, RNA-seq was obtained from SRA for all species annotated in this paper. All RNA-seq were aligned to their respective genomes with STAR (Dobin et al. 2013 (link)), and the resulting BAM files were passed to CAT to construct the extrinsic hints database. See Supplemental Table S2 for accessions and tissue types of RNA-seq data used for annotation. In addition, for the PacBio great ape annotation, RNA-seq data were generated using iPSC lines for human, chimpanzee, gorilla, and orangutan derived from cells from the same individuals as the assemblies (Kronenberg et al. 2018 (link)). For all expression analyses, Kallisto (Bray et al. 2016 (link)) was used.
Biological Evolution
Cells
Genes
Genome
Gorilla gorilla
Histocompatibility Testing
Homo sapiens
Induced Pluripotent Stem Cells
Pan troglodytes
Pongidae
Pongo pygmaeus
RNA-Seq
Most recents protocols related to «Pongo pygmaeus»
We used 128 S. fuelleborni genotypes compiled as part of previous work (Barratt et al., 2019a (link); Barratt and Sapp, 2020 (link)), plus 18 from an undefined Strongyloides species identified in Bornean slow lorises (Frias et al., 2018 (link)). Our dataset included 24 cox1 sequences and HVR-IV sequences from Thai isolates of S. fuelleborni: one from a human and 23 from pig-tailed macaques (Macaca nemestrina) (Janwan et al., 2020 (link)). We also included 39 S. fuelleborni cox1 sequences generated by Ko et al. (2023) (link) from a range of Asian non-human primates including rhesus macaques from Myanmar (Macaca mulatta), Japanese macaques (Macaca fuscata), and sequences from four primate species housed in zoological parks in Japan; siamang (Symphalangus syndactylus), red-shanked douc (Pygathrix nemaeus), Francois’ langur (Trachypithecus francoisi), and Bornean Orangutan (Pongo pygmaeus) (Ko et al., 2023 (link)). To serve as an outgroup for our phylogenetic analysis, we sequenced our S. stercoralis PCR positive control amplicons in the same Illumina library as the vervet samples, and included 28 previously published S. stercoralis genotypes (Barratt and Sapp, 2020 (link)), plus a cox1 sequence extracted from the mitochondrial genome of S. stercoralis reference strain PV001 (GenBank [GB] - accession: NC_028624.1). A detailed overview of all specimens within this dataset, including information on hosts, genotype, and GenBank accession numbers, is provided in File S1, Tab A.
Asian Persons
DNA Library
Genome, Mitochondrial
Genotype
Homo sapiens
Japanese Monkeys
Macaca mulatta
Macaca nemestrina
Nycticebus
Pongo pygmaeus
Primates
PTGS1 protein, human
Pygathrix
Siamang
Strains
Strongyloides
Thai
Trachypithecus francoisi
GH loci sequences from the human (Homo sapiens), Neanderthal (Homo sapiens neanderthalensis), chimpanzee (Pan troglodytes), gorilla (Gorilla gorilla), orangutan (Pongo abelli), gibbon (Nomascus leucogenys), and wild boar (S. scrofa) were retrieved from GenBank (Table 1 ). We also relied on the chimpanzee, gorilla, and orangutan sequences obtained before from the BACs containing the GH locus. These BACs were sequenced by NGS using the Roche 454 platform, as previously described [25 (link),26 (link)].
Gibbons
Gorilla gorilla
Homo sapiens
Neanderthals
Nomascus leucogenys
Pan troglodytes
Pongo
Pongo pygmaeus
Sus scrofa
Amino acid motifs at positions 76–83, encoded by exon 2, within MHC class I molecules are recognized by Killer-cell immunoglobulin-like receptors (KIR). For hominid MHC-B, two alternative KIR epitopes, Bw4 and C1, are carried by some of the allotypes32 (link),64 (Fig. 3b , Fig. 2 , Supplementary Table 9 ). Bw4 is defined primarily by an arginine at position 83, while C1 is defined primarily by a valine at position 76 and an asparagine at position 80. Among bonobos examined to date, these two epitopes are found exclusively among Papa-B, however these epitopes are also observed among other MHC-A or MHC-C class I allotypes in other extant hominids (i.e., humans, gorillas, chimpanzees, and orangutans)32 (link),64 .
Arginine
Asparagine
Epitopes
Exons
Familial recurrent arthritis
Genes, MHC Class I
Gorilla gorilla
Histocompatibility Antigens Class I
Hominidae
Homo
Killer Cell Immunoglobulin-Like Receptors
Pan paniscus
Pan troglodytes
Pongo pygmaeus
Valine
To compare landscapes of diversity and divergence along chromosomes, we computed the Spearman correlation between the landscapes across windows within a chromosome. Because of computational constraints, we focus on chromosome 12. Chromosome 12 is one of the smallest chromosomes in the great apes, there are no major inversions, and it has good variation in exon density and recombination rate. The choice was made blindly before looking at the data, but we found it behaves similarly to other chromosomes (see Figure S6 through Figure S27 ).
We expected landscapes of two closely related species to be more correlated than the landscapes of two distantly related species. Thus, the correlation between any two landscapes of diversity and divergence is expected to depend on distances between them in the phylogenetic tree. We decided to plot our correlations against distance (in generations) between the most common recent ancestor (MRCA) of each landscape. In comparing two landscapes of diversity, this amounts to the total distance between the two tips in the species tree. For instance, the phylogenetic distance between diversity in humans and diversity in bonobos is the sum of the lengths of the human, pan and bonobo branches in the species tree (Figure 1 ). In comparing a landscape of diversity to a landscape of divergence, this amounts to the distance between the species of the landscape of diversity and the MRCA of the two species involved in the divergence. For example, for the landscapes of diversity in humans and divergence between Sumatran orangutans and eastern gorillas would be the distance between the humans tip and the great apes internal node. for the landscapes of divergence between the orangutans and divergence between the gorillas would be the distance between the orangutan and gorilla internal nodes. Some divergences may share branches in the tree, but these are excluded from our main figures; see subsection 4.1 and Figure S2 .
We expected landscapes of two closely related species to be more correlated than the landscapes of two distantly related species. Thus, the correlation between any two landscapes of diversity and divergence is expected to depend on distances between them in the phylogenetic tree. We decided to plot our correlations against distance (in generations) between the most common recent ancestor (MRCA) of each landscape. In comparing two landscapes of diversity, this amounts to the total distance between the two tips in the species tree. For instance, the phylogenetic distance between diversity in humans and diversity in bonobos is the sum of the lengths of the human, pan and bonobo branches in the species tree (
Chromosomes
Chromosomes, Human, Pair 12
Exons
Gorilla gorilla
Homo
Homo sapiens
Inversion, Chromosome
Pan paniscus
Pongidae
Pongo abelii
Pongo pygmaeus
Recombination, Genetic
Trees
We generated eight cases in which different lineages of primates were under accelerated evolution: (1) only human, (2) subtree of all the hominini(human, chimp), (3) subtree of human, chimp, gorilla, (4) subtree of all the apes(human, chimp, gorilla, orangutan), (5) only chimp, (6) only groilla, (7) only orangutan, (8) only macaque. For each case, folds of increase in substitution rates of accelerated lineage span from 1.2 to 5. We generated 10,000 ten-sequence alignments based on the reference model plus those assumptions. Each alignment is 200 bp long, which is the median length in TFBS data. We then compared the performance of our GroupAcc methods and traditional element-level LRT methods in detecting lineage-specific acceleration. Group-level LRT method and Phylogenetics-based mixture model were described in the former two sections. Traditional element-level LRT was implemented via the R program for the group-level LRT followed with Bonferroni correction to the p-values.
In scenario 1, we generated 10,000 200 bp alignments upon reference model and a scaled tree with increased branch length in lineages of each case. First, we applied the group-level LRT and phylogenetics-based mixture model to the simulated alignments, taking the accelerated lineage listed in each case (1–8) as foreground lineage, respectively. We compared the estimated fold of increase in substitution rate in foreground lineage with the scaling factor of the phylogenetic tree in simulation setting. We also compared the estimated number of elements under accelerated evolution from phylogenetics-based mixture model and element-level LRT methods. Second, the same methods were used with human as foreground lineage for all the cases. Cases 1–4 were designed to test the sensitivity of the methods to identify accelerated evolution when the foreground lineage (human) is truly under accelerated evolution. Cases 5–8 were designed to test the specificity of the methods when the foreground lineage (human) is mis-specified and not under accelerated evolution. We then compared the estimated fold of increase in substitution rate in foreground lineage with the scaling factor of phylogenetic tree in simulation setting. We also compared the estimated number of elements under accelerated evolution from phylogenetics-based mixture model and element-level LRT methods.
The second scenario considered heterogeneity of evolutionary dynamics in each binding site: only parts of each binding site (L: portion of each binding site under accelerated evolution) were under accelerated evolution. We simulated 10,000 ten-sequence alignments representing 10,000 binding sites in one group, each binding site is 200 bp long (200 × L bp generated from a scaled tree with X-fold increase in branch length of the lineage shown in the cases, 200 − 200 × L bp generated from unscaled tree). We analyzed the data with our mixture model to see if our method could estimate the proportion of binding sites with accelerated evolution accurately. In addition, we tested with group-level LRT method to see if our methods could detect group-level signals and estimate the fold of increase in substitution rates when the acceleration only happens in specific positions or motifs.
The third scenario considered heterogeneity in groups of binding sites: only certain numbers of binding sites (M: proportion of binding sites in a group under accelerated evolution) in one group have accelerated evolution in a specific lineage, while the other binding sites do not have accelerated evolution. We simulated 10,000 elements in a group, 10,000 × M elements from a scaled tree, while 10,000 − 10,000 × M from unscaled tree. Each element is 200 bp long. We analyzed the data with our mixture model to see if our method can estimate the number of binding sites with accelerated evolution accurately. In addition, we tested with group-level LRT method to see if our methods can detect group-level signals and estimate the fold of increase in substitution rates when the acceleration only happens in parts of the binding sites in a group.
In scenario 1, we generated 10,000 200 bp alignments upon reference model and a scaled tree with increased branch length in lineages of each case. First, we applied the group-level LRT and phylogenetics-based mixture model to the simulated alignments, taking the accelerated lineage listed in each case (1–8) as foreground lineage, respectively. We compared the estimated fold of increase in substitution rate in foreground lineage with the scaling factor of the phylogenetic tree in simulation setting. We also compared the estimated number of elements under accelerated evolution from phylogenetics-based mixture model and element-level LRT methods. Second, the same methods were used with human as foreground lineage for all the cases. Cases 1–4 were designed to test the sensitivity of the methods to identify accelerated evolution when the foreground lineage (human) is truly under accelerated evolution. Cases 5–8 were designed to test the specificity of the methods when the foreground lineage (human) is mis-specified and not under accelerated evolution. We then compared the estimated fold of increase in substitution rate in foreground lineage with the scaling factor of phylogenetic tree in simulation setting. We also compared the estimated number of elements under accelerated evolution from phylogenetics-based mixture model and element-level LRT methods.
The second scenario considered heterogeneity of evolutionary dynamics in each binding site: only parts of each binding site (L: portion of each binding site under accelerated evolution) were under accelerated evolution. We simulated 10,000 ten-sequence alignments representing 10,000 binding sites in one group, each binding site is 200 bp long (200 × L bp generated from a scaled tree with X-fold increase in branch length of the lineage shown in the cases, 200 − 200 × L bp generated from unscaled tree). We analyzed the data with our mixture model to see if our method could estimate the proportion of binding sites with accelerated evolution accurately. In addition, we tested with group-level LRT method to see if our methods could detect group-level signals and estimate the fold of increase in substitution rates when the acceleration only happens in specific positions or motifs.
The third scenario considered heterogeneity in groups of binding sites: only certain numbers of binding sites (M: proportion of binding sites in a group under accelerated evolution) in one group have accelerated evolution in a specific lineage, while the other binding sites do not have accelerated evolution. We simulated 10,000 elements in a group, 10,000 × M elements from a scaled tree, while 10,000 − 10,000 × M from unscaled tree. Each element is 200 bp long. We analyzed the data with our mixture model to see if our method can estimate the number of binding sites with accelerated evolution accurately. In addition, we tested with group-level LRT method to see if our methods can detect group-level signals and estimate the fold of increase in substitution rates when the acceleration only happens in parts of the binding sites in a group.
Acceleration
Binding Sites
Biological Evolution
Genetic Heterogeneity
Gorilla gorilla
Hominini
Homo sapiens
Hypersensitivity
Macaca
Pan troglodytes
Paraproteins
Pongidae
Pongo pygmaeus
Primates
Sequence Alignment
Trees
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More about "Pongo pygmaeus"
Bornean orangutan, Pongo pygmaeus, is a critically endangered great ape species native to the rainforests of Borneo.
These arboreal primates are known for their distinctive reddish-brown fur, long arms, and thoughtful, solitary nature.
The Bornean orangutan plays a crucial role in the ecosystem as a seed disperser and holds significant cultural heritage for indgenous communities.
Ongoing threats to their habitat, such as deforestation and palm oil production, have led to a decline in their population.
Researchers studying this species can utilize innovative tools like PubCompare.ai's platform to streamline their work.
The platform can help locate relevant protocols from literature, pre-prints, and patents, and provide intelligent comparisons to identify the best protocols and products for Pongo pygmaeus studies.
This can optimize the research process and improve reproducibility.
When conducting Porno pygmaeus research, scientists may also leverage common lab reagents and tools, such as Penicillin/streptomycin, AmnioMax C-100 Supplement, AmnioMax C-100 Basal Medium, PCR SuperMix High Fidelity, HiSeq 2000, L-glutamine, Cy3-dUTP, Cy5-dUTP, Fluorescein-dUTP, and RPMI 1640 with L-glutamine.
These can help support various experimental methods and analyses.
By utilizing the right tools and resources, researchers can streamline their Pongo pygmaeus studies, enhance reproducibility, and ultimately contribute to the conservation and understanding of this critically endangered great ape species.
These arboreal primates are known for their distinctive reddish-brown fur, long arms, and thoughtful, solitary nature.
The Bornean orangutan plays a crucial role in the ecosystem as a seed disperser and holds significant cultural heritage for indgenous communities.
Ongoing threats to their habitat, such as deforestation and palm oil production, have led to a decline in their population.
Researchers studying this species can utilize innovative tools like PubCompare.ai's platform to streamline their work.
The platform can help locate relevant protocols from literature, pre-prints, and patents, and provide intelligent comparisons to identify the best protocols and products for Pongo pygmaeus studies.
This can optimize the research process and improve reproducibility.
When conducting Porno pygmaeus research, scientists may also leverage common lab reagents and tools, such as Penicillin/streptomycin, AmnioMax C-100 Supplement, AmnioMax C-100 Basal Medium, PCR SuperMix High Fidelity, HiSeq 2000, L-glutamine, Cy3-dUTP, Cy5-dUTP, Fluorescein-dUTP, and RPMI 1640 with L-glutamine.
These can help support various experimental methods and analyses.
By utilizing the right tools and resources, researchers can streamline their Pongo pygmaeus studies, enhance reproducibility, and ultimately contribute to the conservation and understanding of this critically endangered great ape species.