Manual
User Manual:
Open the PDF directly: View PDF 
.
Page Count: 41
| Download | |
| Open PDF In Browser | View PDF |
User Manual LRSDAY: Long-read Sequencing Data Analysis for Yeasts Release v1.1.0 (2018-07-11) Author Contact: Jia-Xing Yue (岳家兴) Email: yuejiaxing@gmail.com GitHub: yjx1217 Twitter: iAmphioxus Website: http://www.iamphioxus.org Table of Contents INTRODUCTION .......................................................................................................................... 1 Background...................................................................................................................................................................................... 1 Overview of the LRSDAY workflow ....................................................................................................................................... 2 Comparison with other methods ............................................................................................................................................ 3 Experimental Design.................................................................................................................................................................... 4 Limitations and potential adaptation ................................................................................................................................. 8 Expected improvements ............................................................................................................................................................. 8 CITATIONS .................................................................................................................................. 9 MATERIALS ................................................................................................................................. 9 Hardware, operating system and network........................................................................................................................ 9 Software or library requirements.......................................................................................................................................... 9 Input data ...................................................................................................................................................................................... 10 Example data ............................................................................................................................................................................... 10 PROCEDURE.............................................................................................................................. 12 Download, install and configure LRSDAY ● TIMING <1 h ................................................................................... 12 Run LRSDAY with the testing example ● TIMING 50 h ........................................................................................... 14 ? TROUBLESHOOTING .............................................................................................................. 25 ● TIMING ................................................................................................................................ 28 ANTICIPATED RESULTS ............................................................................................................. 29 REFERENCES ............................................................................................................................. 33 APPENDIX ................................................................................................................................. 36 Appendix 1: Pre-shipped supporting data for LRSDAY............................................................................................. 36 Appendix 2: Tips for adapting LRSDAY to other eukaryotic organisms ........................................................... 38 INTRODUCTION Background Twenty years ago, the genome sequence of the budding yeast Saccharomyces cerevisiae was published1. As the first complete eukaryotic genome ever sequenced, this marked a major scientific milestone in biology. Since then, the genomes of many model and non-model organisms have been sequenced, with this process accelerating after the emergence of next-generation sequencing (NGS) technologies. Despite the notably improved throughputs, NGS technologies suffer from the limitation of short reads and usually result in highly fragmented genome assemblies containing numerous gaps and local mis-assemblies. The recently developed longread sequencing technologies represented by Pacific Biosciences (PacBio) and Oxford Nanopore offer compelling alternatives to overcome such hurdles, producing high-quality genome assemblies with substantially improved continuity and accuracy. Although initially tested in microbial genome sequencing, their recent applications in complex mammalian and plant genomes also achieved high-quality results2–6. With such new sequencing technologies, challenging genomic regions with enriched repetitive elements, strongly biased GC-content, or complex structural variants can often be correctly resolved. It is therefore anticipated that genome sequencing projects will routinely adopt long-read-based sequencing technologies in the coming years to gain insight in these complex genomic regions. Yeast is a leading model organism with great importance in both basic biomedical research and biotechnological applications. Its small genome size makes it particularly suitable for long-readbased high-coverage genome sequencing. The resulting complete genome assembly with fullyresolved subtelomere structure can in turn illuminate the genetic basis of many complex phenotypic traits with unprecedented resolution. Recently, we used the long-read sequencing technologies to generate the first panel of population-level end-to-end reference genomes of 12 yeast strains representing major subpopulations of the partially domesticated S. cerevisiae and its sister species Saccharomyces paradoxus7. In addition, there have been a number of other studies carrying out long-read sequencing for many S. cerevisiae strains8–11. Given the vast genomic and phenotypic diversity of S. cerevisiae, we expect the incoming collection of long-readbased high-quality genome assemblies of strains from widespread geographic locations and ecological niches will substantially deepen our understanding in the S. cerevisiae natural genetic variation and its associated biotechnological values. 1 Overview of the LRSDAY workflow Here we present a highly organized and modular computational framework named Long-Read Sequencing Data Analysis for Yeasts (LRSDAY), which enables automated high-quality yeast genome assembly and annotation production from raw long-read sequencing data. The prototype of LRSDAY has been developed to generate the yeast population reference panel (YPRP) in our previous study7. Under the hood, LRSDAY contains a series of task-specific modules handling long-read-based de novo genome assembly, short-read-based assembly polishing, referenceguided assembly scaffolding, as well as comprehensive genomic feature annotations. These tasks can be run individually, selectively or coordinately depending on users’ needs. LRSDAY supports both leading long-read sequencing technologies: PacBio and Oxford Nanopore. Running the full LRSDAY workflow, the final output is a chromosome-level genome assembly with high-quality annotations of centromeres, protein-coding genes, tRNAs, transposable elements (TEs; Ty1-Ty5 for S. cerevisiae and S. paradoxus), and telomere associated core X and Y’ elements. LRSDAY is shipped with various auxiliary scripts, configuration files and supporting data that enable its semi-automatic installation, configuration, and execution with minimal manual intervention (Appendix 1). This design concept greatly alleviates the technical barrier for bench biologists with limited bioinformatics experiences. In addition, a real case example and its final outputs are also provided for users’ test and comparison. All these task-specific modules, auxiliary files, installed tools, sample outputs, together with the user-created project directories for the testing example and users’ own data are hosted under the same home directory ($LRSDAY_HOME) in a self-contained fashion (Fig. 1). This design makes LRSDAY well-isolated from the rest of the system and therefore greatly improves its portability. To sum up, LRSDAY is a highly transparent, automated and powerful computational framework that handles both genome assembly and annotation, which suits the needs of the yeast community in performing long-read-based genome sequencing projects. In the PROCEDURE section of this article, we provide a step-by-step walkthrough on how to install, configure, and run LRSDAY with our prepared testing example. 2 Figure 1. Overview of the LRSDAY directory system. All the top-level directories (boxes, solid lines) and individual files of LRSDAY are listed and briefly described. Additional directories and files will be generated during the installation or execution of LRSDAY (boxes, dashed lines). Comparison with other methods Genome assembly is a rapidly moving field, co-evolving with the fast-paced development of sequencing technologies. In recent years, both hybrid (i.e. using both long and short reads) and 3 native (i.e. using long reads only) assemblers that support long-read sequencing data have been developed and tested on many different organisms8,12–17. As for gene annotation, there is also a wide range of choices that perform gene model prediction in an ab initio fashion or based on additional evidences (e.g. mRNA transcripts, protein-sequence alignment)18–20. Specifically for yeasts, a web-based gene annotation tool has been developed that combines both approaches21. However, there is currently no integrated solution that handles both genome assembly and annotation in a seamless way. To fill this gap, we revamped our original workflow for deriving the yeast population-level reference genomes7 into a self-contained package to considerably streamline this process with modular design and automated implementation. Moreover, rather than simply combining the existing tools for genome assembly and gene annotation, LRSDAY assembled a well-integrated workflow with many other functionalities (e.g. reference-guided scaffolding, gene orthology identification, and additional genomic feature annotation) built in, which makes it a unique one-stop solution for high-quality genome assembly and annotation production from long-read sequencing data. Experimental Design Genome assembly and annotation are complex computational processes with many intermediate steps and inputs/outputs involved. With LRSDAY, we designed a highly structured project directory system to help users to run the whole workflow in an organized and modular way (Fig. 2). Within such project directory system, the three subdirectories holding the pre-shipped reference genome as well as the user-supplied long (PacBio or Oxford Nanopore) and short (Illumina) reads are numbered as “00” and the task-specific subdirectories are numbered sequentially from “01” to “13” according to their execution orders. For each subdirectory, a selfexplained name is attached after the number index to help users to navigate through the workflow. To run each task, users only need to edit (i.e. to specify the input and output file paths and certain task-specific parameters) and execute the task-specific pipeline scripts pre-placed in these subdirectories. These pipeline scripts will automatically set environment variables, process the data, and formulate the results. All computationally intensive tasks can be processed using multiple threads to substantially save computation time. Although LRSDAY is mainly designed for yeasts, most of these tasks can be further adapted for the analysis on any eukaryotic organisms (See Appendix 2 for details). Below we briefly describe the computational processes executed by each task-specific module in LRSDAY with the corresponding PROCEDURE Step labeled in parentheses. 4 01. Long-read-based_Genome_Assembly (Step 13): Long reads generated from PacBio or Oxford Nanopore technologies are used to perform de novo genome assembly using an overlap-layout-consensus (OLC) algorithm. 02. Illumina-read-based_Assembly_Polishing (Step 15; optional): Illumina reads are first clipped and trimmed to remove potential sequencing adapters and low-quality regions. The cleaned reads are subsequently mapped to the raw long-read-based genome assembly. The resulting BAM file is further processed for alignment sorting, mate information and read group fixing, duplicates removal as well as local realignment. Finally, the processed bam file is used for correcting base-level errors of the long-read-based assembly. 03. Reference-guided_Assembly_Scaffolding (Step 16): The contigs from the polished genome assembly are first aligned to the reference genome to identify their shared sequence homology, based on which reference-guided assembly scaffolding is subsequently performed. The chromosomal identity of each scaffold is labeled accordingly. Structural rearrangements captured in the contigs will remain untouched during the scaffolding. 04. Centromere_Identity_Profiling (Step 18): The pre-shipped S. cerevisiae centromere sequences are searched against the scaffolded assembly for chromosome-specific centromere identity profiling. 05. Mitochondrial_Genome_Assembly_Improvement (Step 19): The polished contigs corresponding to the mitochondrial genome are re-collected from the scaffolded assembly. The mitochondrial contigs spanning over the designated starting point (the ATP6 gene by default) are broken into subsegments to prevent assembly problems caused by the circular organization of the mitochondrial genome. The resulting contigs are then reassembled into a single linear sequence, which is further circularized by the designated ATP6 starting point. The nuclear scaffolds and the circularized mitochondrial sequence together form the improved genome assembly. 06. Supervised_Final_Assembly (Steps 20-22): A modification list containing the ordering, orientation and naming information of each sequence from the improved genome assembly is generated for users to review and to make manual adjustment when needed. The final genome assembly is further generated based on the user-edited modification list. 07. Centromere_Annotation (Step 23): The pre-shipped S. cerevisiae centromere sequences are searched against the final genome assembly for centromere annotation. 5 08. Gene_Annotation (Step 25): De novo protein-coding and tRNA gene annotations are performed for the final genome assembly, which are further leveraged by the mRNA transcripts and protein sequences alignments. 09. TE_Annotation (Step 28): The pre-shipped curated TE library (containing the long terminal repeats (LTRs) and internal sequences of the S. cerevisiae and S. paradoxus Ty1-Ty5 by default) is searched against the final genome assembly to identify TEs. The identified TEs are further curated and classified into the full-length, truncated, and soloLTRs of Ty1-Ty5. 10. Core_X_Element_Annotation (Step 30): The pre-shipped curated hidden Markov model (HMM) of the S. cerevisiae core X elements is searched against the final genome assembly to annotate core X elements. 11. Y_Prime_Element_Annotation (Step 32): The pre-shipped representative S. cerevisiae Y’ element sequence is searched against the final genome assembly to annotate Y’ elements. Note that Y’ elements can have long, short or degenerated forms22, and we used a representative long-form Y’ element as the query to maximize detection power. 12. Gene_Orthology_Identification (Step 34): The annotated protein-coding genes are compared with the reference protein-coding genes based on both sequence homology and gene order conservation to identify gene orthology relationship between these two sets. Based on such gene orthology relationships, the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) systematic names are assigned to the annotated protein-coding genes. 13. Annotation_Integration (Step 35): The annotations of centromeres, TEs, protein-coding genes, tRNAs, as well as core X and Y’ elements are combined and sorted to form a final integrated multi-feature annotation. 6 Figure 2. The workflow of LRSDAY. Each box represents an individual module. These modules are numbered according to the tasks described in Experimental Design, with the corresponding protocol step numbers also indicated. Modules that can be adapted for other eukaryotes are colored in light blue while those yeast-specific are colored in orange. 7 Limitations and potential adaptation In its distributed form, LRSDAY is tailored for the model budding yeast S. cerevisiae and its closely related sister species S. paradoxus with a number of pre-shipped auxiliary data files configured accordingly. However, given its modular design, the backbone of LRSDAY can be adapted for virtually any eukaryotic organisms to perform genome assembly, sequence polishing, referenceguided scaffolding, protein-coding genes and tRNA annotations, gene orthology identification, and annotation integration. In Appendix 2, we provide some tips with regard to such adaptation. Moreover, those assembly polishing, scaffolding and various annotation modules (See Experimental Design) can also be used to analyze existing genome assemblies derived from any or any combination of sequencing technologies. Such flexibility makes LRSDAY very useful for expanded use cases and therefore suits the needs of a broader audience. Expected improvements As thousands of yeast strains have been or are currently under sequencing23–26, our knowledge of the overall genome content diversity27 of this important model organism is expanding rapidly, revealing a whole new picture of the pan-genome diversity of S. cerevisiae. For example, our lab is currently working on characterizing the pan-genome of >1,000 S. cerevisiae isolates across the globe (The 1002 Yeast Genomes Project26; http://1002genomes.u-strasbg.fr/). Future developments of LRSDAY will incorporate such pan-genome dataset to provide additional annotation information for those non-reference genes, especially with regard to their evolutionary origin, population prevalence, and putative functions. Such information will greatly help users to dissect and interpret complex genotype-phenotype interactions in diverse ecological and biotechnological settings. An additional potential future direction of our research is the direct integration with the downstream synteny analysis tools (e.g. CHROnicle28, MCScanX29, etc) to perform automatic large-scale structural variants discovery, which exploits one of the major benefits of having a high-quality genome assembly derived from long reads. Finally, we envision developing a dedicated web-based tool to implement such database and tool integration at a larger scale towards fully automated genomics analysis for the yeast community in the long run. 8 CITATIONS Jia-Xing Yue & Gianni Liti. (2018) Long-read sequencing data analysis for yeasts. Nature Protocols, 13:1213–1231. Jia-Xing Yue, Jing Li, Louise Aigrain, Johan Hallin, Karl Persson, Karen Oliver, Anders Bergström, Paul Coupland, Jonas Warringer, Marco Cosentino Lagomarsino, Gilles Fischer, Richard Durbin, Gianni Liti. (2017) Contrasting evolutionary genome dynamics between domesticated and wild yeasts. Nature Genetics, 49:913-924. MATERIALS Hardware, operating system and network This protocol is designed for a desktop or computing server running an x86-64-bit Linux operating system. Multi-threaded processors are preferred to speed up the process since many steps can be configured to use multiple threads in parallel. For assembling and analyzing the budding yeast genomes (genome size = ~12 Mb), at least 16 Gb of RAM and 100 Gb of free disk space are recommended. When adapted for other eukaryotic organisms with larger genome sizes, the RAM and disk space consumption will scale up, majorly during de novo genome assembly (performed by Canu17). Please refer to Canu’s manual (http://canu.readthedocs.io/en/latest/) for suggested RAM and disk space consumption for assembling large genomes. Stable Internet connection is required for the installation and configuration of LRSDAY as well as for retrieving the testing data. Software or library requirements ● Bash (https://www.gnu.org/software/bash/) ● Bzip2 (http://www.bzip.org/) ● Cmake (https://cmake.org/) ● GCC and G++ v4.7 or newer (https://gcc.gnu.org/) ● Ghostscript (https://www.ghostscript.com) ● Git (https://git-scm.com/) ● GNU make (https://www.gnu.org/software/make/) ● Gzip (https://www.gnu.org/software/gzip/) ● Java runtime environment (JRE) v1.8.0 or newer (https://www.java.com) 9 ● Perl v5.12 or newer (https://www.perl.org/) ● Python v2.7.9 or newer (https://www.python.org/) ● Python v3.6 or newer (https://www.python.org/) ● Tar (https://www.gnu.org/software/tar/) ● Unzip (http://infozip.sourceforge.net/UnZip.html) ● Virtualenv v15.1.0 or newer (https://virtualenv.pypa.io) ● Wget (https://www.gnu.org/software/wget/) ● Zlib (https://zlib.net/) Input data ● Long reads: A single FASTQ file containing PacBio or Oxford Nanopore reads is needed, which will be used for long-read based de novo genome assembly (Task 01). ● Short reads: Short reads are optional for LRSDAY but LRSDAY could take advantage of short reads when such data is available to perform additional assembly polishing (Task 02). If paired-end Illumina sequencing is performed, two FASTQ files containing the forward and reverse Illumina reads respectively are needed. If only single-end Illumina sequencing data is available, one FASTQ file containing the single-end reads is needed. ● Reference genome: For the budding yeast S. cerevisiae, we pre-shipped two reference genome files (one original assembly and one with hard-masked subtelomeres and chromosome-ends based on our previous study7). The masked version is used for chromosomal scaffolding to minimize the confounding effect due to interchromosomal subtelomeric rearrangemnets. When working with organisms of which the subtelomeric regions are undefined, users can just use a single raw reference genome instead. The reference genome file(s) will be used for reference-guided scaffolding, mitochondrial genome assembly improvement and supervised final genome assembly (Task 03, 05 and 06 respectively). ● A number of S. cerevisiae-specific auxiliary data have been pre-shipped with LRSDAY for genomic feature annotation and gene orthology identification (Task 07-12). Example data ● The S. cerevisiae reference genome pre-shipped with LRSDAY is taken from our previous study7 with the Genbank accession number GCA_002057635.1 (https://www.ncbi.nlm.nih.gov/assembly/GCA_002057635.1/). The sequencing reads 10 used for the testing example come from the same study, which consists of both PacBio and Illumina reads produced from the S. cerevisiae strain SK1. The PacBio reads can be retrieved with the ENA analysis accession number ERZ448251 (https://www.ebi.ac.uk/ena/data/view/ERZ448251). The Illumina reads can be retrieved with the SRA sequencing run accession number SRR4074258 (https://www.ncbi.nlm.nih.gov/sra/?term=SRR4074258). In LRSDAY, we have provided bash scripts to automatically download and setup these data for the testing example. 11 PROCEDURE Download, install and configure LRSDAY ● TIMING <1 h 1) Download the latest LRSDAY release (current version: v1.1.0) by entering the following commands in a terminal window: $ wget https://github.com/yjx1217/LRSDAY/releases/download/v1.1.0/LRSDAY-v1.1.0.tar.gz $ tar xvzf LRSDAY-v1.1.0.tar.gz $ cd LRSDAY-v1.1.0 $ bash install_dependencies.sh ▲CRITICAL STEP Make sure you have a fast and stable Internet connection when running this step since many tools will be downloaded here. Check that all the prerequisites (see Software or library requirements in EQUIPMENT or the prerequisite.txt file in the downloaded LRSDAY directory) have been installed on your system. ▲CRITICAL STEP Upon the successful completion of executing the bash script, you should see a confirmation message prompted out: “LRSDAY message: This bash script has been successfully processed! :)”. Otherwise, it means an error has occurred during the execution of the bash script, which interrupted the automatic installation process. This also applies to all the bash scripts used in Steps 8-35. Whenever an error is encountered, please check the error message and refer to the troubleshooting section when available. When the cause of the error is fixed, re-run this step to initiate a new installation. The installer will prompt for the confirmation of deleting the build directory generated by the old run, always answer “yes” to authorize such action so that the new installation can start. ▲CRITICAL STEP Pay attention to the final message prompted by the installation script for a number of notes with regard to the additional manual setup steps as well as the licensing information for commercial users. ? TROUBLESHOOTING 2) Load the environment settings for LRSDAY by entering: $ source env.sh After loading the pre-configured environment settings, the current directory should be assigned to the environment variable $LRSDAY_HOME. You can check to see if the full path to your current directory is displayed after entering: $ echo $LRSDAY_HOME 12 ▲CRITICAL STEP Although most required tools have been automatically installed and configured, manual downloading and/or configuration are needed for GATK30 and RepeatMasker31 due to license restriction of these tools or of their dependent libraries. Make sure to run this command to load the pre-configured environment settings before such manual setup (described in Steps 3-7). If you exited your terminal session before or in the middle of your manual setup, you need to re-load the environment settings before proceeding. These environment settings will be automatically loaded each time the taskspecific bash pipelines of LRSDAY are executed. ? TROUBLESHOOTING 3) Download and set up GATK3. Go to the official website of GATK (https://software.broadinstitute.org/gatk/download/archive) and download GATK v3.8 (the recently released GATK4 will not work). Registration might be needed for unregistered GATK users. Place the downloaded GATK package (file name: GenomeAnalysisTK-3.81- gf15c1c3ef.tar.bz2) in the GATK installation directory and set up GATK by entering the following command in terminal: $ mv GenomeAnalysisTK-3.8-1- gf15c1c3ef.tar.bz2 $LRSDAY_HOME/build/GATK3 $ cd $LRSDAY_HOME/build/GATK3 $ tar xjf GenomeAnalysisTK-3.8-1.tar.bz2 $ mv ./GenomeAnalysisTK-3.8-1*/GenomeAnalysisTK.jar . $ chmod 755 GenomeAnalysisTK.jar 4) Set up TRF for RepeatMasker. Go to the TRF website (http://tandem.bu.edu/trf/trf.download.html) to download the TRF v4.09 executable built for 64-bit Linux. Place the downloaded file (file name: trf409.linux64) in the RepeatMasker installation directory and set up TRF by entering: $ mv trf409.linux64 $LRSDAY_HOME/build/RepeatMasker/ $ cd $LRSDAY_HOME/build/RepeatMasker/ $ chmod 755 trf409.linux64 $ ln -s trf409.linux64 trf 5) Set up the RepBase library for RepeatMasker. Go to the RepBase website (http://www.girinst.org/repbase/) and register a user account (using a non-profit institutional email address, e.g. the “.com” extension will not work) to download the RepeatMasker edition of the RepBase library (version: 20170127). Place the downloaded file (file name: RepBaseRepeatMaskerEdition-20170127.tar.gz) in the RepeatMasker installation directory and uncompress it by entering: $ mv RepBaseRepeatMaskerEdition-20170127.tar.gz $LRSDAY_HOME/build/RepeatMasker/ 13 $ cd $LRSDAY_HOME/build/RepeatMasker/ $ tar xzf RepBaseRepeatMaskerEdition-20170127.tar.gz ? TROUBLESHOOTING 6) Get the installation path of TRF and rmblastn/makeblastdb by entering: $ echo $trf_dir $ echo $rmblast_dir Remember these two paths since they will be used for the RepeatMasker configuration in Step 7. ? TROUBLESHOOTING 7) Run the configuration script for RepeatMasker by entering: $ perl ./configure This configuration script will prompt for several questions. Please do the following to answer these questions. Enter “env” for the question about the installation path of Perl. Just press enter for the question about the installation path of RepeatMasker. Enter the first path that you obtained in Step 6 for the question about the installation path of TRF. Enter “2” for the question about selecting a search engine. Then enter the second path that you obtained in Step 6 for the question about the installation path of rmblastn and makeblastdb. Just press enter for the question about the default search engine. And finally enter “5” to complete the configuration. Run LRSDAY with the testing example ● TIMING 50 h 8) Create the project directory. When running LRSDAY with your own data, it is recommended to make a copy of our Project_Template directory to create your own project directory such as Project_abc, where “abc” can be any string containing letters, numbers, or underscores. For this testing example, we make a copy of the Project_Template directory and name it as Project_Example by entering: $ cd $LRSDAY_HOME $ cp -r Project_Template Project_Example ▲CRITICAL STEP Before proceeding to your own project, it is advised to first run our prepared testing example to check if LRSDAY is working properly as well as to get acquainted with the logic and workflow of LRSDAY. 9) Prepare the reference genome files. When running LRSDAY with your own data, you can directly put the reference genome (in FASTA format without compression) in the 00.Ref_Genome subdirectory of your project directory (e.g. Project_abc). If your 14 sequenced organism is S. cerevisiae or S. paradoxus, you can use the reference genome pre-shipped with LRSDAY. Here we prepare the pre-shipped reference genome for the testing example by entering: $ cd ./Project_Example/00.Ref_Genome $ bash LRSDAY.00.Prepare_Sc_Ref_Genome.sh 10) Prepare the long reads. When running LRSDAY with your own data, you can directly put the long reads in the 00.Long_Reads subdirectory of your project directory (e.g. Project_abc). The reads file should be in FASTQ or FASTA format. Compressed files with file extensions of “.gz”, “.bz2”, and “.xz” are also supported. For this testing example, you can download the sample long reads by entering: $ cd ./../00.Long_Reads $ bash LRSDAY.00.Retrieve_Sample_PacBio_Reads.sh 11) (Optional) If your long reads are generated from the PacBio Sequel platform, your reads are likely to be in BAM format. In this case, convert it to our supported format (FASTA or FASTQ) using the following commands: $ source ./../../env.sh $ $bedtools_dir/bedtools bamtofastq -i long_reads.bam -fq long_reads.fastq $ gzip long_reads.fastq 12) (Optional) Prepare the Illumina reads. Like for the long reads, directly put your Illumina reads in the 00.Illumina_Reads subdirectory of your project directory (e.g. Project_abc) when running LRSDAY for your own data. The reads file should be in FASTQ format with “gzip” comprehension (identified by the “.gz” extension). For this testing example, you can download the Illumina reads by entering: $ cd ./../00.Illumina_Reads $ bash LRSDAY.00.Retrieve_Sample_Illumina_Reads.sh ? TROUBLESHOOTING 13) Perform long-read-based de novo genome assembly by running the following commands: $ cd ./../01. Long-read-based_Genome_Assembly $ bash LRSDAY.01.Long-read-based_Genome_Assembly.sh Upon the completion of this step, a summary file (SK1.canu.stats.txt for this testing example) will be generated to report some basic summary statistics (e.g. total assembly size, N50 (i.e. the contig length such that 50% of the total assembly size is contained in contigs of at least this size), L50 (i.e. the number of longest contigs such that 50% of the total assembly size is contained), GC-content, etc) to assist gauging the genome assembly quality (Table 1). Two VCF files (SK1.canu.filter.mummer2vcf.SNP.vcf and 15 SK1.canu.filter.mummer2vcf.INDEL.vcf for the testing example) will also be generated to report base-level differences between the raw genome assembly and the reference genome for their uniquely alignable regions, which could also help for assessing the genome assembly quality. ▲CRITICAL STEP This step can be run with multiple threads to speed up the process. Depending on the CPU configuration of your Linux server/desktop, you can edit the “threads=” option in the bash script LRSDAY.01.Long-read-based_Genome_Assembly.sh to enable multi-threading. You can do the same for all the following tasks whenever the “threads=” option is provided in the corresponding task-specific bash script. Simple text editors such as emacs, vim, gedit or pico are highly recommended for such editing. Rich text editors might not work ▲CRITICAL STEP Note this step will take long to finish (see TIMING), so we recommend running this step and all the other time-consuming steps with “nohup” (https://en.wikipedia.org/wiki/Nohup), which allows the process to continue running after you exit the terminal or logout from the server. As an example, you can run the bash script using nohup as follows: $ nohup bash LRSDAY.01.Long-read-based_Genome_Assembly.sh >run_log.txt 2>&1 & ▲CRITICAL STEP Starting from v1.1.0, LRSDAY added the "customized_canu_parameters” option to support customized parameter settings for the Canu assembler. In addition, two additional assemblers, Flye and smartdenovo, were further supported. Based on our test, Flye32 (https://github.com/fenderglass/Flye) and smartdenovo (https://github.com/ruanjue/smartdenovo) run much faster than Canu but also came with understandable tradeoff in assembly precision, as reflected by the higher base-level error rate of their resulting assemblies. Therefore, when assembling with Flye or smartdenovo, post-assembly polishing is strongly recommended. ▲CRITICAL STEP When running LRSDAY with your own data, modify the bash script to specify the input reads and reference genome, the input reads type (e.g. “pacbio_raw”, “pacbio_corrected”, “nanopore_raw” or “nanopore_corrected”), the estimated genome size for the assembled genome, as well as the prefix for the output data. Remember to do similar project-specific adjustment for all the following steps. ? TROUBLESHOOTING 14) (Optional) Polishing genome assembly with long-reads. When running LRSDAY for your own data, if you performed PacBio sequencing and also have access to a locally installed PacBio SMRT Analysis software package (http://www.pacb.com/products-and- 16 services/analytical-software/smrt-analysis/), we recommend running the first-pass polishing for the assembly generated in Step 13 based on raw PacBio reads by using PacBio’s own Quiver/Arrow pipeline13. If you performed Oxford Nanopore sequencing, we recommend using nanopolish (https://github.com/jts/nanopolish) or equivalent tools to perform the assembly polishing based on raw Nanopore reads in this step. We expect to directly pack nanopolish into LRSDAY in our next release. We will also try to the same for PacBio’s Quiver/Arrow pipeline but it seems quite challenging. Finally, we also tested Racon (https://github.com/isovic/racon) for polishing but its performance was not satisfying. 15) (Optional) Polishing genome assembly with Illumina reads. When Illumina reads are available, we recommend running this additional polishing step either for the raw assembly generated in Step 13 (when Step 14 is skipped as in our testing example) or for the longread-polished assembly generated in Step 14. Use the following commands to perform Illumina-read-based assembly polishing. This step can be run with multiple threads. $ cd ./../02.Illumina-read-based_Assembly_Polishing $ bash LRSDAY.02.Illumina-read-based_Assembly_Polishing.sh TABLE 1 | Assembly statistics for the genome of S. cerevisiae strain SK1 assembled in the testing example. Assembly statistics Raw assembly Scaffolded assembly Final assembly Total sequence count 35 34 33 Total sequence length (bp) 12505999 12511058 12489291 Min sequence length (bp) 1248 1248 1248 Max sequence length (bp) 1480196 1480203 1480203 Mean sequence length (bp) 357314.26 367972.29 378463.36 Median sequence length (bp) 103703.00 74951.00 84648.00 N50 (bp) 818058 923633 923633 L50 6 6 6 N90 (bp) 341459 341463 341463 L90 15 14 14 GC% 38.27 38.25 38.29 17 N% 0.00 0.08 0.04 Note N50: the contig length such that 50% of the total assembly size is contained in contigs of at least this size. L50: the number of longest contigs such that 50% of the total assembly size is contained. N90: the contig length such that 90% of the total assembly size is contained in contigs of at least this size. L90: the number of longest contigs such that 90% of the total assembly size is contained. GC%: the percentage of guanine (G) and cytosine (C) bases in the nucleotide sequences. N%: the percentage of the N bases in the nucleotide sequence. In genome assembly, the N bases are usually used to represent scaffolding gaps. Figure 3. Genome-wide dotplots of the S. cerevisiae SK1 genome assembly generated in the LRSDAY testing example. Both the raw scaffolded assembly (panel a; generated in Step 16) and the final assembly (panel b, generated in Step 22) are analyzed. The forward and reverse sequence matches are depicted in red and blue respectively, while the zoomed-in views of the mitochondrial genome (chrMT) comparison are shown in insets. In addition to the sixteen nuclear chromosomes and the mitochondrial genome, the scaffolded and final assemblies also contain some short contigs (named as tig*_pilon) that are derived from highly repetitive regions. 16) Perform chromosome-level scaffolding for the long-read-based assembly. Run the following commands: $ cd ./../03.Reference-guided_Assembly_Scaffolding $ bash LRSDAY.03.Reference-guided_Assembly_Scaffolding.sh 18 This step can be run with multiple threads. Upon completion, a list of summary statistics (SK1.ragout.stats.txt for this testing example) will be generated for the scaffolded assembly (Table 1). ▲CRITICAL STEP Please check the generated genome-wide dotplot (SK1.ragout.filter.pdf for the testing example) (Fig. 3a) to verify the correctness of chromosomal identity assignment performed by Ragout33 and apply manual adjustment in Step 21 when necessary. When running LRSDAY with your own data, you might see a single scaffold corresponds to more than one reference chromosomes, which could be due to shared sequence homology between duplicated regions or interchromosomal rearrangements. Both types of events can be correctly interpreted based on the genomewide dotplot generated in this Step. In either case, LRSDAY can correctly assign chromosomal identity of the corresponding scaffold based on its encompassed centromere identity as annotated in Step 18. Check the generated AGP file (SK1.ragout.agp for the testing example) for the details of reference-based scaffolding. ▲CRITICAL STEP Due to the high AT and repeat contents and the circular conformation of the mitochondrial genome, multiple contigs corresponding to the mitochondrial genome are often obtained from the raw genome assembly, as shown in the generated mitochondrial genome dotplot (SK1.ragout.chrMT.filter.pdf for the testing example) (Fig. 3a, inset). A list of such mitochondrial contigs will also be generated (SK1.mt_contig.list for the testing example), which will be used in Step 19 for improving mitochondrial genome assembly. 17) (Optional) When running LRSDAY for your own data, if you have strong evidence for misscaffolding based on prior knowledge or other experimental data (e.g. mate-pair libraries or chromosomal contact data), break the corresponding ragout scaffolds back to contigs and re-joined them with corrected order using the pre-shipped Perl scripts break_scaffolds_by_N.pl, join_contigs_by_N.pl and extract_region_from_genome.pl in the $LRSDAY_HOME/scripts directory by running the following commands: perl $LRSDAY_HOME/scripts/break_scaffolds_by_N.pl -i-o < the output FASTA file containing the scaffolds after the breaking> -g perl $LRSDAY_HOME/scripts/join_contigs_by_N.pl -i -o –g -t 19 perl $LRSDAY_HOME/scripts/extract_region_from_genome.pl -i -o -q -f A scenario for such use case is when the breakpoints of structural rearrangements are also the breakpoints of the genome assembly. In this case, the reference-based scaffolding will arrange contigs according to the reference genome configuration and therefore un-do the genome rearrangement. 18) Perform centromere profiling for the scaffolded genome assembly by running the following commands: $ cd ./../04.Centromere_Identity_Profiling $ bash LRSDAY.04.Centromere_Identity_Profiling.sh ▲CRITICAL STEP The chromosome-specific centromere identities profiled here will be used as another layer of information for the final chromosomal identity assignment in Step 21. The profiled centromere identities usually agree well with the chromosomal identities labeled in Step 16, so that chrI will have the CEN1 centromere and chrII will have the CEN2 centromere, etc. Exception can occur when interchromosomal rearrangements are involved in your sequenced genome. In such case, we recommend naming those rearranged chromosomes according to their encompassed centromeres in Step 21. 19) Perform mitochondrial genome assembly improvement by running the following commands: $ cd ./../05.Mitochondrial_Genome_Assembly_Improvement $ bash LRSDAY.05.Mitochondrial_Genome_Assembly_Improvement.sh ▲CRITICAL STEP Check the generated final mitochondrial genome dotplot (SK1.mt_improved.chrMT.filter.pdf for the testing example) and compare it with the mitochondrial genome dotplot generated in Step 16 to see how the mitochondrial genome assembly has been improved when aligning with the reference mitochondrial genome (Fig. 3b, inset). When running this step for your own data, the degree of such improvement may vary because it depends on both the complexity of the assembled mitochondrial genome and the quality of library preparation and sequencing experiments. ? TROUBLESHOOTING 20) Generate the assembly modification list file for performing the final chromosome assignment by running the following commands: 20 $ cd ./../06.Supervised_Final_Assembly $ bash LRSDAY.06.Supervised_Final_Assembly.1.sh 21) Edit the generated assembly modification list file (SK1.modification.list for the testing example) based on the genome-wide dotplot generated in Step 16 and the centromere profiles generated in Step 18. The modification list file consists of three comma-separated columns, which correspond to the original sequence name, sequence orientation, and new sequence name respectively. With this file, you can do three types of editing: If you need to change the current sequence order, you can move the corresponding rows upward or downward to reflect the correct order. If you need to invert the orientation of a given sequence, you can change its orientation from “+” to “-” in column 2. If you need to rename a given sequence, you can specify the new name in the third column. For this testing example here, we need to move the row “chrIX,+,chrIX” downward to place it after the row “chrVIII,+,chrVIII”, so that chrIX will be placed after chrVIII in the final assembly. Also, we need to change the row “chrMT_Contig1,+,chrMT_Contig1” to “chrMT_Contig1,+,chrMT” for renaming the assembled sequence corresponding to the mitochondrial genome. 22) Once all the modifications have been specified, run the following bash script to generate the final genome assembly as well as the associated genome-wide dotplot (Fig. 3b), assembly statistics (Table 1), and VCF files: $ bash LRSDAY.06.Supervised_Final_Assembly.2.sh 23) Re-run centromere annotation for the final genome assembly using the following commands: $ cd ./../07.Centromere_Annotation $ bash LRSDAY.07.Centromere_Annotation.sh 24) (Optional) Customize the configuration file for gene annotation. When running LRSDAY with your own data, edit the configuration file $LRSDAY_HOME/misc/maker_opts.customized.ctl if your sequenced organisms is neither S. cerevisiae nor S. paradoxus (See Appendix 2 for the details). If your sequenced organism is S. cerevisiae or S. paradoxus, no customization is needed unless you have native transcriptome or expressed sequence tag (EST) data for the strain that you sequenced. In this case, you can edit the line 16 of this file to provide the full path of the native transcriptome or EST assembly for your sequenced strain. 21 25) Annotate protein-coding genes and tRNAs for the final genome assembly, using the following commands. This step can be run with multiple threads. $ cd ./../08.Gene_Annotation $ bash LRSDAY.08.Gene_Annotation.sh 26) (Optional) A manual checklist file (SK1.EVM.manual_check.list for the testing example) containing a list of genes with suspicious annotations will be generated in Step 25. As labeled in this file, these annotated gene models could be fragmented, frameshifted or containing internal stop codons. Potentially, these genes could be good candidates for pseudogenes. Manually inspect the annotated gene models of these genes by loading the annotation result (SK1.EVM.gff3 for the testing example) together with the protein/ESTalignment evidence files generated during the annotation in Step 25 (SK1.protein_evidence.gff3 and SK1.est_evidence.gff3 for the testing example) into IGV34 to check how well these suspicious gene models are supported by the corresponding protein/EST-alignment evidence and to tag or remove those truly problematic ones in your downstream analysis. 27) (Optional) Perform dedicated mitochondrial protein-coding and RNA annotation. If you are interested in studying mitochondrial genomes, we highly recommend running dedicated mitochondrial feature annotation with specialized software such as (http://megasun.bch.umontreal.ca/cgi-bin/mfannot/mfannotInterface.pl). MFannot Although MFannot does not support local installation so far, you can run MFannot conveniently via its web portal. Be sure to select the correct genetic code table (e.g. “3 Yeast Mitochondrial” for annotating yeast mitochondrial genomes) for your analysis. 28) Annotate transposable elements (TEs) for the final genome assembly. This step can be run with multiple threads using the following commands: $ cd ./../09.TE_Annotation $ bash LRSDAY.09.TE_Annotation.sh 29) (Optional) TE activity can be highly dynamic in the genome with many complex cases such as fragmentation and nested insertion. In LRSDAY, we used REannotate35 to automatically resolve these complex cases, which works well for most cases but it could occasionally misjoin two adjacent TEs when they are closely spaced. Further inspect and curate the LRSDAY TE annotation output (SK1.TE.gff3 for the testing example) by visualizing it in IGV34 together with the raw REannotate annotation (SK1.REannotate.gff for the testing example). For each TE found in the LRSDAY TE annotation output, examine its corresponding LTR and internal region structure based on the raw REannotate 22 annotation to check for misjoinings. If needed, you can manually edit the corresponding TE annotation output file to decouple the misjoinings. 30) Annotate yeast telomere-associated core X elements for the final genome assembly, using the following commands: $ cd ./../10.Core_X_Element_Annotation $ bash LRSDAY.10.Core_X_Element_Annotation.sh 31) (Optional) Inspect the alignment file for annotated core X element. In LRSDAY, we label the identified core X elements to be “partial” if they are shorter than 300 bp. This should work for most cases but we recommend inspecting the generated alignment file (SK1.X_element.aln.fa for the testing example) for further curation. Upon the curation, manually adjust the “partial” labeling in the annotation file (SK1.X_element.gff3 for the testing example) when needed. 32) Annotate yeast telomere-associated Y’ elements for the final genome assembly by using the following commands: $ cd ./../11.Y_Prime_Element_Annotation $ bash LRSDAY.11.Y_Prime_Element_Annotation.sh 33) (Optional) Inspect the alignment file for annotated Y’ element. Like for the core X element annotation, we used a hard length cutoff (3500 bp) to label if the identified Y’ elements are “partial”. We recommend manually inspecting the generated alignment file (SK1.Y_prime_element.aln.fa for the testing example) to check if the “partial” labeling is needed and edit the annotation file (SK1.Y_prime_element.gff3 for the testing example) accordingly. 34) Perform orthology identification for protein coding genes by using the following commands: $ cd ./../12.Gene_Orthology_Identification $ bash LRSDAY12.Gene_Orthology_Identification.sh In this step, a gene orthology relationship list is created between the annotated proteome and the SGD S. cerevisiae reference proteome based on both sequence similarity and synteny conservation. Based on this list, we further attach SGD systematic names to our gene annotation as shown in the “Name=” field of the generated GFF3 file (SK1.updated.gff3 for the testing example). For a given annotated gene, when more than one orthologous gene can be found in the SGD reference proteome, we will label all of its co-orthologs in the “Name=” filed with “/” between the alternative SGD systematic names (e.g. “YAR071W/YHR215W”), whereas when no orthologous gene can be found, we will label its gene name as “Name=NA”. This step can be run with multiple threads. 23 35) Integrate the annotation of different genomic features into a unified GFF3 file by using the following commands: $ cd ./../13.Annotation_Integration $ bash LRSDAY.13.Annotation_Integration.sh 24 ? TROUBLESHOOTING Troubleshooting advice can be found in Table 2. TABLE 2 | Troubleshooting table. Step Problem Possible reason Solution 1 Downloading errors or Unstable internet Stabilizing the internet connection unresponsive remote connection or temporary and give another try. servers problems of the remote servers where the tools for downloading are hosted. 1 Compilation or Prerequisites not satisfied If missing prerequisites are found installation error for or corner cases due to (see software and library specific tools. your specific system requirements in EQUIPMENT), settings. install the missing prerequisites and give another try as described above. Otherwise, record the error message and email it to the developers of the problematic tools or the authors of this protocol for further problem diagnosis. 2 Cannot find the file Step 1 has failed. “env.sh”. Check the error message that you got when running Step 1 and refer to the troubleshooting for Step 1. 5 Rejected by RepBase A personal or commercial Use a non-commercial institutional for the license email address (e.g. with a email address for the registration registration. .com extension) was used or purchase the commercial for the registration. license if you work in a commercial entity. Also, the installation of RepBase can be potentially skipped although not recommended by RepeatMasker. Users can still follow the remaining steps of this protocol even if they skip the RepBase installation here. 25 6 “echo $rmblast_dir” Step 2 has failed or your Re-load environment settings in returns nothing. terminal session got Step 2 and then re-try this step. interrupted before this step. 12 Downloading Temporary SRA server Directly download the sample warnings/errors problems. Illumina reads for the testing encountered. example by running the following commands: $ wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SR R407/008/SRR4074258/SRR4074 258_1.fastq.gz $ ln -s SRR4074258_1.fastq.gz SRR4074258_pass_1.fastq.gz $ wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SR R407/008/SRR4074258/SRR4074 258_2.fastq.gz $ ln -s SRR4074258_2.fastq.gz SRR4074258_pass_2.fastq.gz 13 The total assembly Insufficient sequencing Obtain more reads. We size is much smaller depth of coverage. recommend a minimal sequencing than the expected depth of 20X. value or the assembly is too fragmented. 13 Assembly runs too This likely will happen for slow. Canu with high-depth nanopore reads. Three options are available: 1) Use the pre-shipped perl script subsampling_seqeunces.p l in the $LRSDAY_HOME/scripts directory to downsample (either randomly or preferably sampling the 26 longer ones) the reads. Example: perl subsampling_sequences.p l -i input.fq(.gz) -f fastq -s 0.1 -m random -p output # randomly sampling the 10% sequences or perl subsampling_sequences.p l -i input.fq(.gz) -f fastq -s 0.1 -m longest -p output # sampling the 10% longest sequences 2) Use the customized parameters as suggested in the bash script LRSDAY.01.Long-readbased_Genome_Assembly .sh 3) Use alternative assemblers such as Flye or smartdenovo for this step. 19 Only partial assembly High AT and repeat If the sequencing is done with is obtained for the content of the PacBio, obtaining more reads mitochondrial genome. mitochondrial genome is should solve the problem. If challenging for de novo sequencing is done with Oxford genome assembly. Nanopore data, currently there is no satisfying solution. Future improvement of the Oxford Nanopore sequencing technology will likely solve this problem given the rapid development of this technology. 27 ● TIMING The following timing information was measured on a Linux computing server with an Intel Xeon CPU E5-2630L v3 (1.80GHz) using 4 threads. Enabling multithreading can substantially decrease the processing time. All optional steps except Steps 12 and 15 were not processed when measuring the computation time. Steps 1-7, set up LRSDAY: < 1 h. Step 8, prepare the project directory for the testing example: 5 s. Step 9, prepare the reference genomes for the testing example: 1 s. Step 10, prepare the long reads for the testing example: 22 min. Step 12 (optional), prepare the Illumina reads for the testing example: 20 min. Step 13, de novo assembly using long-reads: 26 h. Step 15 (optional), assembly polishing using Illumina reads: 1h. Step 16, reference-guided scaffolding for the raw assembly: 12 min. Step 18, centromere identity profiling for the scaffolded assembly: 1 s. Step 19, mitochondrial genome assembly improvement: 4 min. Step 20, generate the assembly modification list file: 1s. Step 21, manual editing the assembly modification list file: 30 s. Step 22, finalize genome assembly based on the assembly modification list file: 20 s. Step 23, centromere annotation for the final assembly: 1s. Step 25, protein-coding gene and tRNA annotation for the final assembly: 19 h. Step 28, TE annotation for the final assembly: 5 min. Step 30, core X element annotation for the final assembly: 2 min. Step 32, Y’ element annotation for the final assembly: 2.5 min. Step 34, gene orthology identification for the annotated protein-coding genes: 20 min. Step 35, final annotation integration: 1 min. 28 ANTICIPATED RESULTS Upon the completion of the LRSDAY workflow described above, users can expect to obtain a chromosome-level genome assembly with comprehensive genomic feature annotation, which will lay a solid foundation for all kinds of downstream genomic and functional analyses. As demonstrated in Fig. 3b, the final genome assembly is highly continuous, with each chromosome assembled in an end-to-end fashion. Both genome-wide dotplots (like those shown in Fig. 3) and summary statistics (like those listed in Table 1) will be generated to help users to evaluate the genome assembly quality both graphically and quantitatively. As for the annotation, LRSDAY profiles a full spectrum of genomic features for the assembled yeast genome, which include centromeres, protein-coding genes, tRNAs, Ty1-Ty5 transposable elements, as well as the telomere-associated core X and Y’ elements. The availability of such rich information can be very valuable for users working on diverse biological questions. In Table 3, we further summarized the major outputs of the testing example. The final genome assembly and annotation outputs generated with this testing example are also provided in the directory Example_Outputs for users to make direct comparison with their own results. TABLE 3 | Major LRSDAY outputs from the testing example. Task Step Output file or directory Explanation for the output 01 13 SK1.canu.fa SK1.canu.stats.txt SK1.canu.filter.pdf SK1.canu.filter.mummer2vcf.SNP .vcf The long-read-based de novo genome assembly containing all the contigs assembled by Canu17. The summary table reporting basic assembly statistics, such as the number of the assembled sequences, the total length of the assembled sequences, the minimal, maximal, mean and median lengths of the assembled sequences, the N50, L50, N90, and L90 of the assembled sequences, as well as the base composition (A%, T%, G%, C%, AT%, GC% and N%) of the assembled sequences. Users can compare this file with the similar file generated in Step 16 and Step 22. We also summarized such comparison in Table 1. The genome-wide dotplot for the comparison between the raw genome assembly and the reference genome. The VCF file showing SNP differences between the raw genome assembly and the reference genome. This file can be used for assessing the raw assembly quality. 29 SK1.canu.filter.mummer2vcf.IND EL.vcf SK1_canu_out 02 15 SK1.pilon.fa SK1.pilon.vcf SK1.pilon.changes SK1.realn.bam 03 16 SK1.ragout.fa SK1.ragout.stats.txt SK1.ragout.agp SK1_ragout_out SK1.ragout.filter.pdf SK1.mt_contig.list SK1.mt_contig.fa SK1.ragout.chrMT.filter.pdf 04 18 SK1.centromere.gff3 05 19 SK1.mt_improved.fa SK1.mt_improved.chrMT.filter.pdf The VCF file showing INDEL differences between the raw genome assembly and the reference genome. This file can be used for assessing the raw assembly quality. the directory containing all the output files of Canu The polished genome assembly generated by Pilon36. The VCF file reporting the variants identified by Pilon based on short reads mapping against the input genome assembly. The space-delimited record of all the changes that Pilon made during the assembly polishing. The four columns are: the original sequence coordinate, the new sequence coordinate after the correction, the original base, the new base after the correction. The BAM file of short reads mapping against the input genome assembly. The scaffolded genome assembly based on the reference genome. The summary table reporting basic assembly statistics of the scaffolded genome assembly. Users can compare this file with the similar file generated in Step 13 and Step 22. We also summarized such comparison in Table 1. the AGP file reporting the order and orientation of each input contig used during scaffolding. The directory containing all the output files of Ragout33. The genome-wide dotplot for the comparison between the scaffolded assembly and the reference genome. The list of assembled contigs corresponding to the mitochondrial genome. This file will be used for Step 19. The assembled contig sequences corresponding to the mitochondrial genome. The dotplot for the comparison between the scaffolded mitochondrial genome assembly and the reference mitochondrial genome. The profiled centromere identities for the scaffolded genome assembly. The improved genome assembly with better processing (re-assembling and circularization) of the mitochondrial genome. The dotplot for the comparison between the improved mitochondrial genome 30 06 20-22 SK1.modification.list SK1.final.fa SK1.final.filter.pdf SK1.final.stats.txt SK1.final.filter.mummer2vcf.SNP. vcf SK1.final.filter.mummer2vcf.INDE L.vcf 07 23 SK1.centromere.gff3 08 25 SK1.maker.raw.gff3 SK1.EVM.gff3 SK1.protein_evidence.gff3 SK1.est_evidence.gff3 SK1.EVM.trimmed_cds.fa SK1.EVM.trimmed_cds.log SK1.EVM.pep.fa SK1.EVM.manual_check.list SK1.EVM.PoFF.gff assembly and the reference mitochondrial genome. You should see improved collinearity in this plot than the similar plot generated in Step 16. The assembly modification list file for manual editing to guide the final genome assembly. The final genome assembly generated by LRSDAY. The genome-wide dotplot for the comparison between the final genome assembly and the reference genome. The summary table reporting basic assembly statistics of the final genome assembly. Users can compare this file with the similar file generated in Step 13 and Step 16. We also summarized such comparison in Table 1. The VCF file showing SNP differences between the final genome assembly and the reference genome. This file can be used for assessing the final assembly quality. The VCF file showing INDEL differences between the final genome assembly and the reference genome. This file can be used for assessing the final assembly quality. The centromere annotation for the final genome assembly. The raw MAKER37 annotation for proteincoding genes and tRNA genes. The final EVM38 annotation for proteincoding genes and tRNA genes with systematically assigned gene IDs. The protein-to-genome alignment evidences generated by MAKER. This file can be used for manual curation of those suspicious annotations. The EST-to-genome alignment evidences generated by MAKER. This file can be used for manual curation of those suspicious annotations. The CDSs of the final EVM protein-coding annotation with the out-of-frame parts trimmed. The log file of the CDS trimming for the final EVM protein-coding gene annotation. The translated protein sequences of the trimmed CDSs derived from the final EVM protein-coding gene annotation. The list of suspicious gene annotations for manual curation. The gene synteny information derived from SK1.EVM.gff3, which will be used for Task 12 (Step 34). 31 SK1.EVM.PoFF.ffn SK1.EVM.PoFF.ffa 09 28 SK1.REannotate.gff 10 30 SK1.TE.gff3 SK1.X_element.gff3 SK1.X_element.fa SK1.X_element.aln.fa 11 32 SK1.Y_prime_element.gff3 SK1.Y_prime_element.fa SK1.Y_prime_element.aln.fa 12 34 SK1.proteinortho SK1.poff SK1.updated.gff3 13 35 SK1.final.gff3 SK1.final.trimmed_cds.fa SK1.final.trimmed_cds.log SK1.final.pep.fa SK1.final.manual_check.list SK1.final.fa Same as SK1.EVM.trimmed_cds.fa but with simpler sequence IDs, which could be used for Task 12 (Step 34). Same as SK1.EVM.pep.fa but with simpler sequence IDs, which will be used for Task 12 (Step 34). The raw TE annotation from REannotate. This file can be used for further curating TE annotation. The final TE annotation from LRSDAY. The final core X element annotation from LRSDAY. The sequences of all the annotated core X elements. The sequence alignment of all the annotated core X elements for further checking whether the annotated feature is complete or partial as well as whether this is consistent with the labeling in the annotation file SK1.X_element.gff3. The final Y’ element annotation from LRSDAY. The sequences of all the annotated Y’ elements. The sequence alignment of all the annotated Y’ elements for further checking whether the annotated feature is complete or partial as well as whether this is consistent with the labeling in the annotation file SK1.Y_prime_element.gff3. The gene orthology mapping between the annotated genes and the reference gene sets based only on sequence similarity. The gene orthology mapping between the annotated genes and the reference gene sets based on both sequence similarity and synteny conservation. The updated gene annotation with reference-based gene name labeling. The final integrated annotation from LRSDAY. The CDSs of the final protein-coding gene annotation with the out-of-frame parts trimmed. SK1.final.trimmed_cds.log: the log file of the CDS trimming for the final proteincoding gene annotation. The translated protein sequences of the trimmed CDSs derived from the final protein-coding gene annotation. The list of suspicious gene annotations for manual curation. A copy of the final genome assembly generated in Task 06 (Step 22). 32 REFERENCES 1. Goffeau, A. et al. Life with 6000 Genes. Science 274, 546–567 (1996). 2. Gordon, D. et al. Long-read sequence assembly of the gorilla genome. Science 352, aae0344 (2016). 3. VanBuren, R. et al. Single-molecule sequencing of the desiccation-tolerant grass Oropetium thomaeum. Nature 527, 508–11 (2015). 4. Bickhart, D. M. et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat. Genet. 49, 643– 650 (2017). 5. Badouin, H. et al. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature 546, 148–152 (2017). 6. Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338–345 (2018). 7. Yue, J.-X. et al. Contrasting evolutionary genome dynamics between domesticated and wild yeasts. Nat. Genet. 49, 913–924 (2017). 8. Goodwin, S. et al. Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome. Genome Res. 25, 1750–1756 (2015). 9. McIlwain, S. J. et al. Genome sequence and analysis of a stress-tolerant, wild-derived strain of Saccharomyces cerevisiae used in biofuels research. G3 (Bethesda). 6, 1757– 66 (2016). 10. Istace, B. et al. de novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer. Gigascience 6, 1–13 (2017). 11. Giordano, F. et al. De novo yeast genome assemblies from MinION, PacBio and MiSeq platforms. Sci. Rep. 7, 3935 (2017). 12. Koren, S. et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat. Biotechnol. 30, 693–700 (2012). 13. Chin, C.-S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–9 (2013). 14. Berlin, K. et al. Assembling large genomes with single-molecule sequencing and localitysensitive hashing. Nat. Biotechnol. 33, 623–630 (2015). 15. Chin, C.-S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016). 16. Li, H. Minimap and miniasm: Fast mapping and de novo assembly for noisy long 33 sequences. Bioinformatics 32, 2103–2110 (2016). 17. Koren, S. et al. Canu: Scalable and accurate long-read assembly via adaptive κ-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017). 18. Salamov, A. A. & Solovyev, V. V. Ab initio gene finding in Drosophila genomic DNA. Genome Res. 10, 516–522 (2000). 19. Stanke, M., Steinkamp, R., Waack, S. & Morgenstern, B. AUGUSTUS: A web server for gene finding in eukaryotes. Nucleic Acids Res. 32, W309–W312 (2004). 20. Besemer, J. & Borodovsky, M. GeneMark: Web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 33, (2005). 21. Proux-Wéra, E., Armisén, D., Byrne, K. P. & Wolfe, K. H. A pipeline for automated annotation of yeast genome sequences by a conserved-synteny approach. BMC Bioinformatics 13, 237 (2012). 22. Louis, E. J. & Haber, J. E. The structure and evolution of subtelomeric Y’ repeats in Saccharomyces cerevisiae. Genetics 131, 559–574 (1992). 23. Strope, P. K. et al. The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen. Genome Res. 25, 762–774 (2015). 24. Almeida, P. et al. A population genomics insight into the Mediterranean origins of wine yeast domestication. Mol. Ecol. 24, 5412–5427 (2015). 25. Gallone, B. et al. Domestication and divergence of Saccharomyces cerevisiae beer yeasts. Cell 166, 1397–1410.e16 (2016). 26. Peter, J. et al. Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature 556, 339–344 (2018). 27. Bergström, A. et al. A high-definition view of functional genetic variation from natural yeast genomes. Mol. Biol. Evol. 31, 872–888 (2014). 28. Drillon, G., Carbone, A. & Fischer, G. SynChro: A fast and easy tool to reconstruct and visualize synteny blocks along eukaryotic chromosomes. PLoS One 9, (2014). 29. Wang, Y. et al. MCScanX: A toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, (2012). 30. McKenna, A. et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010). 31. Smit, A., Hubley, R. & Green, P. RepeatMasker Open-4.0. 2013-2015 . http://www.repeatmasker.org (2013). 32. Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. Assembly of Long Error-Prone Reads 34 Using Repeat Graphs. bioRxiv 247148 (2018). doi:10.1101/247148 33. Kolmogorov, M., Raney, B., Paten, B. & Pham, S. Ragout - A reference-assisted assembly tool for bacterial genomes. Bioinformatics 30, (2014). 34. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). 35. Pereira, V. Automated paleontology of repetitive DNA with REANNOTATE. BMC Genomics 9, 614 (2008). 36. Walker, B. J. et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, (2014). 37. Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics 12, 491 (2011). 38. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008). 39. Lowe, T. M. & Eddy, S. R. A computational screen for methylation guide snoRNAs in yeast. Science 283, 1168–71 (1999). 40. Minkin, I., Patel, A., Kolmogorov, M., Vyahhi, N. & Pham, S. Sibelia: A scalable and comprehensive synteny block generation tool for closely related microbial genomes. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8126 LNBI, 215–229 (2013). 35 APPENDIX Appendix 1: Pre-shipped supporting data for LRSDAY With LRSDAY, we pre-ship the following supporting datasets for the automatic execution of LRSDAY. Unless labeled otherwise, most of these pre-shipped datasets were either described or generated in our previous study7. 1) ATP6.cds.fa # The coding sequence (CDS) of the S. cerevisiae S288C ATP6 gene. 2) fuzzy_defragmentation.txt # the fuzzy defragmentation file for REannotate35. 3) Proteome_DB_for_annotation.CDhit_I95.fa # our curated proteome dataset for S. cerevisiae and other closely related yeast sensu stricto species. 4) query.Y_prime_element.long.fa # the sequence of a representative S. cerevisiae Y’ element. 5) S288C.ASM205763v1.fa.gz # the S. cerevisiae S288C genome assembly. 6) S288C.ASM205763v1.noncore_masked.fa.gz # the S. cerevisiae S288C genome assembly with subtelomeres and chromosome-ends hard-masked. 7) S288C.centromere.fa # the centromere sequence of S. cerevisiae S288C. 8) S288C.gene.hmm # the hidden Markov model (HMM) for de novo gene annotation based on S. cerevisiae S288C. 9) S288C.X_element.hmm # the hidden Markov model (HMM) for the core X element annotation based on S. cerevisiae S288C. 10) Sc-meth.sites # the S. cerevisiae methylation sites (shipped with snoScan39). 11) Sc-rRNA.fa # the S. cerevisiae rRNA sequences (shipped with snoScan39). 12) SGDref.PoFF.faa # the proteinortho proteome file generated for the SGD reference genome. 13) SGDref.PoFF.ffn # the proteinortho CDS file generated for the SGD reference genome. 14) SGDref.PoFF.gff # the proteinortho gene order gff file generated for the SGD reference genome. 15) te_proteins.fasta # protein sequences for genes encoded within TEs (shipped with MAKER37). 16) TY2_specific_region.fa # the sequence of a Ty2 specific regions for differentiating Ty1 and Ty2. 17) TY_lib.Yue_et_al_2017_NG.fa # a custom RepeatMasker library for Ty annotation in S. cerevisiae and S. paradoxus. 36 18) TY_lib.Yue_et_al_2017_NG.LTRonly.fa # representative Ty LTR sequences of S. cerevisiae and S. paradoxus. 37 Appendix 2: Tips for adapting LRSDAY to other eukaryotic organisms The backbone modules of LRSDAY can be easily adapted for other eukaryotic organisms. Here are some tips with regard to this: 1) For Task 01 (long-read-based genome assembly; Step 13), be sure to adjust the genome size parameter in line 13 of the bash script LRSDAY.01.Long-read- based_Genome_Assembly.sh based on the estimated genome size of the organism that you sequenced. 2) For Task 03 (reference-guided assembly scaffolding; Step 16), be sure to modify the bash script LRSDAY.03.Reference-guided_Assembly_Scaffolding.sh to provide the reference genome file of your sequenced organisms to guide the scaffolding and chromosome assignment. It is very likely that the chromosomal cores and subtelomeres of your reference genome have not been clearly defined. In such case, you can provide the same reference genome file for both the “ref_genome_raw=” and “ref_genome_noncore_masked=” parameters. The “chrMT_tag=” and “gap_size=” parameters should also be adjusted for your own project. 3) By default, Task 03 (reference-guided assembly scaffolding; Step 16) performs referenceguided scaffolding using the whole genome alignment constructed by Sibelia40. Sibelia is designed for processing small genomes (<100 Mb). For organisms with large genomes, you should install Progressive Cactus (https://github.com/glennhickey/progressiveCactus) and use it to build the whole genome alignment in HAL format and feed it directly to Ragout33. Please refer to Ragout’s user manual (http://fenderglass.github.io/Ragout/usage.html) for this advanced usage. We have preshipped the HAL tools (https://github.com/ComparativeGenomicsToolkit/hal) to enable Ragout to process the HAL file generated by Progressive Cactus. 4) For Task 05 (mitochondrial genome assembly improvement; Step 19), be sure to modify the “gene_start=”, “ref_genome_raw=”, and “chrMT_tag=” parameters in the bash script LRSDAY.05.Mitochondrial_Genome_Assembly_Improvement.sh based on your own project. 5) While many of the genomic feature annotation tasks are S. cerevisiae and S. paradoxus specific, Task 08 (protein-coding genes and tRNA annotation; Step 25) can be adapted for any eukaryotic organism. In general, you will need to edit the lines 16, 22, 34, 36, 44, 45, and 68-71 in the $LRSDAY_HOME/misc/maker_opts.customized.ctl file to feed organism-specific parameters into MAKER. Also, please refer to MAKER’s own Wiki page (http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Main_Page) and protocols 38 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4286374/) for technical details and advanced usage. Similarly, you can learn more about EVM from its website (http://evidencemodeler.github.io/). 6) While Task 09 (TE annotation; Step 28) is heavily tuned for yeasts, the same tools that we used here (RepeatMasker31 and REannotate35) can be used for any eukaryotic organism. We recommend reading their respective manuals (distributed with the corresponding software) for adapting these tools in your own study. 7) For Task 12 (gene orthology identification; Step 34), you need to edit the “ref_PoFF_faa=” and “ref_PoFF_gff=” parameters based on the reference gene annotation that you used. Please check ProteinOrtho’s manual (https://www.bioinf.uni- leipzig.de/Software/proteinortho/manual.html) for more details on required file formats. The pre-shipped Perl script prepare_PoFFfaa_simple.pl and prepare_PoFFgff_simple.pl in the $LRSDAY_HOME/scripts directory should help for this. You can run these two scripts as follows: $ source ./../../env.sh $ perl $LRSDAY_HOME/scripts/prepare_PoFFffn_simple.pl -i prefix.pep.fa -o prefix.PoFF.faa $ perl $LRSDAY_HOME/scripts/prepare_PoFFgff_simple.pl -i prefix.raw.gff -o prefix.PoFF.gff 39
Source Exif Data:
File Type : PDF File Type Extension : pdf MIME Type : application/pdf PDF Version : 1.3 Linearized : No Page Count : 41 Title : Microsoft Word - Manual_20180711.docx Producer : Mac OS X 10.13.5 Quartz PDFContext Creator : Word Create Date : 2018:07:11 13:20:38Z Modify Date : 2018:07:11 13:20:38ZEXIF Metadata provided by EXIF.tools