Additional file 1 of Genomic divergence and demographic history of Quercus aliena populations

Additional file 1: Figure S1. Distribution range of Q. aliena in China, based on specimens from NSII ( http://www.nsii.org.cn/ )

Data_Sheet_1_A chromosome-level genome assembly of the Chinese cork oak (Quercus variabilis).docx

Quercus variabilis (Fagaceae) is an ecologically and economically important deciduous broadleaved tree species native to and widespread in East Asia. It is a valuable woody species and an indicator of local forest health, and occupies a dominant position in forest ecosystems in East Asia. However, genomic resources from Q. variabilis are still lacking. Here, we present a high-quality Q. variabilis genome generated by PacBio HiFi and Hi-C sequencing. The assembled genome size is 787 Mb, with a contig N50 of 26.04 Mb and scaffold N50 of 64.86 Mb, comprising 12 pseudo-chromosomes. The repetitive sequences constitute 67.6% of the genome, of which the majority are long terminal repeats, accounting for 46.62% of the genome. We used ab initio, RNA sequence-based and homology-based predictions to identify protein-coding genes. A total of 32,466 protein-coding genes were identified, of which 95.11% could be functionally annotated. Evolutionary analysis showed that Q. variabilis was more closely related to Q. suber than to Q. lobata or Q. robur. We found no evidence for species-specific whole genome duplications in Quercus after the species had diverged. This study provides the first genome assembly and the first gene annotation data for Q. variabilis. These resources will inform the design of further breeding strategies, and will be valuable in the study of genome editing and comparative genomics in oak species.</p

Data_Sheet_2_A chromosome-level genome assembly of the Chinese cork oak (Quercus variabilis).xlsx

Quercus variabilis (Fagaceae) is an ecologically and economically important deciduous broadleaved tree species native to and widespread in East Asia. It is a valuable woody species and an indicator of local forest health, and occupies a dominant position in forest ecosystems in East Asia. However, genomic resources from Q. variabilis are still lacking. Here, we present a high-quality Q. variabilis genome generated by PacBio HiFi and Hi-C sequencing. The assembled genome size is 787 Mb, with a contig N50 of 26.04 Mb and scaffold N50 of 64.86 Mb, comprising 12 pseudo-chromosomes. The repetitive sequences constitute 67.6% of the genome, of which the majority are long terminal repeats, accounting for 46.62% of the genome. We used ab initio, RNA sequence-based and homology-based predictions to identify protein-coding genes. A total of 32,466 protein-coding genes were identified, of which 95.11% could be functionally annotated. Evolutionary analysis showed that Q. variabilis was more closely related to Q. suber than to Q. lobata or Q. robur. We found no evidence for species-specific whole genome duplications in Quercus after the species had diverged. This study provides the first genome assembly and the first gene annotation data for Q. variabilis. These resources will inform the design of further breeding strategies, and will be valuable in the study of genome editing and comparative genomics in oak species.</p

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMS^E applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The “core” sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMS^E is influenced by several factors, with some proteins being measured in all participants’ samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMS^E applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The “core” sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMS^E is influenced by several factors, with some proteins being measured in all participants’ samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMS^E applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The “core” sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMS^E is influenced by several factors, with some proteins being measured in all participants’ samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance