Viral metagenomics: Analysis of begomoviruses by illumina high-throughput sequencing
Piatek, Marek J.
Brown, Judith K.
KAUST DepartmentBioscience Core Lab
Biological and Environmental Sciences and Engineering (BESE) Division
Desert Agriculture Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/325365
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AbstractTraditional DNA sequencing methods are inefficient, lack the ability to discern the least abundant viral sequences, and ineffective for determining the extent of variability in viral populations. Here, populations of single-stranded DNA plant begomoviral genomes and their associated beta- and alpha-satellite molecules (virus-satellite complexes) (genus, Begomovirus; family, Geminiviridae) were enriched from total nucleic acids isolated from symptomatic, field-infected plants, using rolling circle amplification (RCA). Enriched virus-satellite complexes were subjected to Illumina-Next Generation Sequencing (NGS). CASAVA and SeqMan NGen programs were implemented, respectively, for quality control and for de novo and reference-guided contig assembly of viral-satellite sequences. The authenticity of the begomoviral sequences, and the reproducibility of the Illumina-NGS approach for begomoviral deep sequencing projects, were validated by comparing NGS results with those obtained using traditional molecular cloning and Sanger sequencing of viral components and satellite DNAs, also enriched by RCA or amplified by polymerase chain reaction. As the use of NGS approaches, together with advances in software development, make possible deep sequence coverage at a lower cost; the approach described herein will streamline the exploration of begomovirus diversity and population structure from naturally infected plants, irrespective of viral abundance. This is the first report of the implementation of Illumina-NGS to explore the diversity and identify begomoviral-satellite SNPs directly from plants naturally-infected with begomoviruses under field conditions. 2014 by the authors; licensee MDPI, Basel, Switzerland.
CitationIdris A, Al-Saleh M, Piatek M, Al-Shahwan I, Ali S, et al. (2014) Viral Metagenomics: Analysis of Begomoviruses by Illumina High-Throughput Sequencing. Viruses 6: 1219-1236. doi:10.3390/v6031219.
PubMed Central IDPMC3970147
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Data for : Poly(A) Dataset for PAS sequences and pseudo-PAS sequences Classification (fasta format)Albalawi, Fahad; Chahid, Abderrazak; Guo, Xingang; Albaradei, Somayah; Magana-Mora, Arturo; Jankovic, Boris R.; Uludag, Mahmut; Van Neste, Christophe; Essack, Magbubah; Laleg-Kirati, Taous-Meriem; Bajic, Vladimir B. (2018-11-15) [Dataset]This Dataset contains DNA sequences of the human genome hg38 from GENCODE folder at EBI ftp server (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/GRCh38.primary_assembly.genome.fa.gz) A-Positive set (PAS sequences) Using GENCODE annotation for poly(A) (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.polyAs.gff3.gz) We selected poly(A) signal annotation. Using bedtools-slop option, we found regions extended 300 bp upstream and 300 bp downstream of the poly(A) hexamer. With the bedtools-getfasta option, we extracted 606 bp fasta sequences from these regions. After eliminating duplicates, we obtained 37’516 presumed true functional poly(A) signal (PAS) sequences. Sequences from this set will be denoted as positive. B- Negative set (pseudo-PAS sequences) For the negative set, we looked for regions extended outside the region covering 1’000 bp upstream and downstream of the positive poly(A) hexamer signal using bedtools-complement. Homer tool was used to find matches for the 12 most frequent human poly(A) variants. Since the number of matches was huge, sampling was used to select 37’516 pseudo-PAS sequences. Sampling was done from each chromosome proportionally to the lengths of the chromosomes and also to the expected frequency of the poly(A) variants. Out of these predictions, for each PAS hexamer, we selected the same number of pseudo-PAS sequences as in the positive set. Training and testing sets We selected randomly from each of the positive and negative datasets 20% of sequences for the independent test data. The testing set thus consisted of 15’020 sequences. The remaining data represented the training set that consisted of 60’012 sequences. Both datasets are balanced relative to the true PAS and pseudo-PAS sequences.
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