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22 protocols using smrtlink

1

Whole-Genome Sequencing Using PacBio Sequel II

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We performed whole-genome sequencing by using the Pacific Biosciences Sequel II system (https://www.pacb.com). We performed genome assembly by using the Genome Assembly tool in PacBio SMRTLink version 10.2 and 150 Mb of the HiFi reads >5 kb (Appendix).
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2

Transcriptome Analysis of Rhododendron Species

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Total RNA was extracted from flowers and leaves of R. kiyosumense using the Plant Total RNA Purification Mini Kit (Favorgen, Ping-Tung, Taiwan). The isolated total RNA was converted into cDNA with NEBNext Single Cell/Low Input cDNA Synthesis & Amplification Module (New England BioLabs, Ipswich, MA, USA). A sequence library for isoform sequencing (Iso-Seq) was prepared using the Iso-Seq Express Template Preparation Kit (PacBio) and sequenced on the PacBio Sequel system (PacBio). Transcript isoforms for each sample were generated with the Iso-Seq3 pipeline (PacBio) implemented in SMRTlink (PacBio). The transcript sequences were aligned to the pseudomolecule sequences of R. ripense with Minmap2.
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3

Protein Sequence Analysis of PacBio Data

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The data obtained from the PacBio machine were checked for quality using SMRT® Link (v6.0.0.47841) [56 ]. The subreads bam files produced by SMRT® Link were then converted to CCS (Circular Consensus Sequences) FASTA files using ccs (v3.3.0) from PacBio tools. The ccs FASTA files were then collapsed and counted using the Biostrings package from R [57 ]. The unique DNA sequences were then queried for potential ORFs and translated to protein sequences using the getorf tool (getorf -minsize 2100 -find 1 -table 0) from EMBOSS [58 (link)]. The protein sequences were further collapsed and counted to obtain the final protein frequencies (Counts/Total). Additional filters were applied, in order to remove possible contaminants, prior to the selection of the top clones. The relevant plots were made using various R packages [59 ].
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4

Transcriptome Analysis and SSR Identification

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After sequencing, high-quality transcriptome data was obtained by filtering and the clean data was processed using PacBio SMRT Link version 5.1. To obtain annotation information of the transcripts, the non-redundant transcript sequences obtained were aligned to the NR, SwissProt, GO, COG, KOG and KEGG databases using BLAST software (version 2.2.26)31. The full-length consensus sequence was used for subsequent analysis. Potential SSRs included in transcript sequences were searched and analyzed using MISA32 with default parameters. The acquired SSRs were containing basic motifs with mono-, di-, tri-, tetra-, penta-, and hexanucleotide repeats.
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5

Analyzing PacBio Circular Consensus Sequences

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We analyzed SMRT sequence data using SMRTLink, a web-based end-to-end workflow manager for PacBio Sequel Systems, and SMRT Tools, a suite of command-line tools included with SMRTLink. We chose only those circular consensus sequence (CCS) reads that resulted from three complete passes of DNA, and that had a predicted accuracy of 99 percent. We also discarded any CCS reads that were shorter than 850 base pairs to exclude partial GFP sequences. We then de-multiplexed the CCS reads with a barcode score of 80 or higher. To this end, we used the 384 Sequel barcodes provided by PacBio and searched for all possible barcode combinations in our sequence data. We could accurately identify the 136 barcode combinations that we had used, out of more than seventy thousand possible combinations. We mapped the de-multiplexed reads using the SMRT tool ‘pbalign’, which uses the ‘blasr’ algorithm60 (link). We restricted the minimum mapping length to at least 900 base pairs, the maximum possible divergence from the ancestral sequence to 75%, and the minimum mapping accuracy to 90%. Through this procedure, we recovered 819 to 4241 reads for each population.
We then used SAMtools to convert the mapped sam files to bam format61 (link). We used custom Python scripts and MEGA software (Molecular Evolutionary Genetics Analysis, v10.0.5)62 (link) for all further data analysis.
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6

Long-Read Sequencing of Barcoded PCR Amplicons

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Purified amplicon pools were sequenced at the Australian Genome Research Facility (AGRF) at The University of Queensland (UQ), QLD, Australia. SMRTbell adapters were ligated onto barcoded PCR products and the libraries were sequenced using Pacific Biosciences single molecule real-time (SMRT) hi-fidelity (HiFi) sequencing on a single SMRT cell using the PacBio Sequel II System. Raw data were processed at AGRF-UQ using PacBio SMRTLink to generate demultiplexed .fastq files.
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7

Analyzing PacBio Circular Consensus Sequences

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We analyzed SMRT sequence data using SMRTLink, a web-based end-to-end workflow manager for PacBio Sequel Systems, and SMRT Tools, a suite of command-line tools included with SMRTLink. We chose only those circular consensus sequence (CCS) reads that resulted from three complete passes of DNA, and that had a predicted accuracy of 99 percent. We also discarded any CCS reads that were shorter than 850 base pairs to exclude partial GFP sequences. We then de-multiplexed the CCS reads with a barcode score of 80 or higher. To this end, we used the 384 Sequel barcodes provided by PacBio and searched for all possible barcode combinations in our sequence data. We could accurately identify the 136 barcode combinations that we had used, out of more than seventy thousand possible combinations. We mapped the de-multiplexed reads using the SMRT tool ‘pbalign’, which uses the ‘blasr’ algorithm60 (link). We restricted the minimum mapping length to at least 900 base pairs, the maximum possible divergence from the ancestral sequence to 75%, and the minimum mapping accuracy to 90%. Through this procedure, we recovered 819 to 4241 reads for each population.
We then used SAMtools to convert the mapped sam files to bam format61 (link). We used custom Python scripts and MEGA software (Molecular Evolutionary Genetics Analysis, v10.0.5)62 (link) for all further data analysis.
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8

HiFi Assembly Polishing Pipeline

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We polished the CLR-based assemblies using the Arrow algorithm in the gcpp tool from PacBio’s SMRT Link v8.0 stack. First, we aligned the raw CLR data against the initial assembly using pbmm2 v1.1.0, which is a version of Minimap2 [67 (link)] adapted to PacBio’s native format. The alignment is then used for consensus calling and polishing using gcpp v1.0.0. We repeated the process for two additional polishing cycles whereby we feed the polished assembly from the previous cycle as the alignment reference in the next cycle. The HiFi-based assemblies do not require additional polishing to the highly accurate starting CCS sequences [44 (link)].
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9

PacBio Sequel II SMRT Sequencing

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Purified amplicon pools were sequenced at the Australian Genome Research Facility (AGRF) at The University of Queensland, QLD, Australia. SMRTbell adapters were ligated onto the barcoded PCR products and the libraries were sequenced by Pacific Biosciences single molecule real-time (SMRT) high-fidelity (HiFi) sequencing on two SMRT cells using the PacBio Sequel II System. Raw data were processed using PacBio SMRTLink to generate demultiplexed .fastq files.
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10

PacBio Sequencing Pipeline for Isoform Detection

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Primary data was analyzed with PacBio SMRT Link v.11.1.0.166339, [58 ] generating demultiplexed circular consensus sequence (CCS) reports and output files required for use in the following SKERA application [59 ]. SKERA was used in split mode with default parameters to trim MAS-Seq adapters (Additional file 15) and to deconcatenate reads into individual segmented reads (s-reads). The s-reads were processed using lima [60 ] which removes Iso-Seq specific primers that were added during cDNA synthesis to generate full-length reads. The lima software was run in specialized –isoseq mode and the parameter –peek-guess to accommodate more than one pair of 5’ to 3’ primer sequences. The resulting full-length reads were then used as input into the IsoSeq3 refine application [61 ] to trim the poly(A) tail and to remove any remaining concatemer reads. Reads that remain are classified as full-length non-concatemer (FLNC) reads. Next, the IsoSeq3 cluster tool [61 ] collapses two or more FLNC reads that differ by < 100 bp on the 5’ end, and < 30 bp on the 3’ end, and has no internal gaps that exceeds ten bp. The resulting collapsed FLNC are defined as High Quality Isoforms (HQ_Isoforms).
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