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7 results for “nuclear protein- coding gene”

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zenodo40/100

Fig. 3 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes

Fig. 3. Mitochondrial and nuclear genes (ND4, Cyt b, COI, 12S rRNA, 16S rRNA, C-mos, RAG-1, and NT3) Bayesian phylogeny for Common Krait (Bungarus caeruleus).

opencc-by-4.0Dec 2019View details →
zenodo40/100

Fig. 2 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes

Fig. 2. Mitochondrial and nuclear genes (ND4, Cyt. b, COI, 12S rRNA, 16S rRNA, C-mos, RAG-1, NT3, and BDNF) based Maximum Likelihood phylogeny for Common Krait (Bungarus caeruleus).

opencc-by-4.0Dec 2019View details →
zenodo36/100

Fig. 1 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes

Fig. 1. Sample collection sites in Pakistan for Common Krait (Bungarus caeruleus).

opencc-by-4.0Dec 2019View details →
dryad28/100

Data from: Successful recovery of nuclear protein-coding genes from small insects in museums using illumina sequencing

In this paper we explore high-throughput Illumina sequencing of nuclear protein-coding, ribosomal, and mitochondrial genes in small, dried insects stored in natural history collections. We sequenced one tenebrionid beetle and 12 carabid beetles ranging in size from 3.7 to 9.7 mm in length that have been stored in various museums for 4 to 84 years. Although we chose a number of old, small specimens for which we expected low sequence recovery, we successfully recovered at least some low-copy nuclear protein-coding genes from all specimens. For example, in one 56-year-old beetle, 4.4 mm in length, our de novo assembly recovered about 63% of approximately 41,900 nucleotides in a target suite of 67 nuclear protein-coding gene fragments, and 70% using a reference-based assembly. Even in the least successfully sequenced carabid specimen, reference-based assembly yielded fragments that were at least 50% of the target length for 34 of 67 nuclear protein-coding gene fragments. Exploration of alternative references for reference-based assembly revealed few signs of bias created by the reference. For all specimens we recovered almost complete copies of ribosomal and mitochondrial genes. We verified the general accuracy of the sequences through comparisons with sequences obtained from PCR and Sanger sequencing, including of conspecific, fresh specimens, and through phylogenetic analysis that tested the placement of sequences in predicted regions. A few possible inaccuracies in the sequences were detected, but these rarely affected the phylogenetic placement of the samples. Although our sample sizes are low, an exploratory regression study suggests that the dominant factor in predicting success at recovering nuclear protein-coding genes is a high number of Illumina reads, with success at PCR of COI and killing by immersion in ethanol being secondary factors; in analyses of only high-read samples, the primary significant explanatory variable was body length, with small beetles being more successfully sequenced.

opencc-zeroDec 2015View details →
dryad28/100

Data from: Successful recovery of nuclear protein-coding genes from small insects in museums using illumina sequencing

Open the record for dataset details and reuse information.

publicNov 2016View details →
geo24/100

Arabidopsis AtMORC4 and AtMORC7 form Nuclear Bodies and Repress a Large Number of Protein-Coding Genes

GEO Series GSE78836. Arabidopsis thaliana. 32 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.

openGEO-OpenMay 2016View details →
geo20/100

RNA-seq gene expression profiling of protein coding and lncRNA genes in nuclear RNA from livers of male and female mice, with or without exposure to TCPOBOP, a CAR (Nr1i3) agonist ligand [G175]

GEO Series GSE160716. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2021View details →

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