Where does sra toolkit download sra files

The official installation instruction from Ubuntu provides a step-by-step instruction with screenshots for first-time users. Note only Ubuntu Starting from version 1. Note the software setup instruction here is mainly for Ubuntu users. If you are a Mac user, please refer to the macOS section for more download and setup instructions.

This will create a new directory called seqtools-dl. This new directory contains the following files:. Although it is optional, it is recommended to open a terminal and run.

Note that running install. To launch the GUI application, you can do one of the following: Open the File Manager and go to the directory seqtools-dl. Double click SeqTools file to launch. Open a terminal and go to the directory containing SeqTools. Type in.

Don't forget the dot and the forward slash when you run an executable from the current directory! The software can be downloaded by clicking the "Automatic Setup" button to run an installation script. Alternatively, the user can choose to manually download the required software applications, instead of running the script. The "Automatic Setup" method is recommended for users with limited experience with Linux.

The instructions for downloading GATK can be found on here. The "Automatic Setup" method provides an easy way to install the required software with no license restrictions.

Upon clicking the "Automatic Setup" button, a new terminal will show up. The user will then be prompted to enter a sudo password, and the application will start to download an installation script from the internet which is kept updated and install the required software. Note that if you do not have the sudo password, you need to ask your system administrator for help. Check it out here in FAQ on how to allow an existing user to have root privileges. After the installation is finished, the user will be asked to press the ENTER key to close the terminal.

If the required software has been previously installed, you can click the Browse button to select the directory for each software package. It is assumed HTSeq is available under a global environment so there is no entry for it. Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Log In. Log In Sign Up About. How to download oxford nanopore sequencing fast5 files from SRA. Entering edit mode. Downloading authorized data from dbGAP leads to a "looped" message. Mapping SRA files without unpacking? Server denied on SRA download. Bulk download w prefetch but unable to 'zip' plus more Splitting fastq files. Library id - SRA submission. SRA splitting for each metagenome-assembled genome. Confused with 2 SRA runs for one sample. Download multiple SRA samples and convert into fasta format at once.

SRA fastq-dump Toolkit. Is there is a way to know that the sra data I'm interested in has a bad fastqc from sra database on ncbi before downloading it or before making fastq….

Is the number of spots in SRA equal to the number of reads. Much of the forum, wiki and community content was migrated to the IBM Support forums. The search field on the IBM Support forum will help you find the migrated content.

For more information about the Support Transformation initiative, please follow the IBM Support Insider blog to learn more and to stay up to date. Sign In. Search Options. Skip to main content Press Enter. Skip auxiliary navigation Press Enter. IBM Community Home. The duration of a whole assembly run is affected by the Word size in various ways Fig. Generally, with unlimited number of rounds, there are two significant but antagonistic effects shaping the curve of the duration against the Word size or in the form of WSR.

In the performance test Fig. In empirical studies, the minimum number of rounds required is unknown. Using the pre-grouping algorithm with a sufficient value i. The optimal Word size is dictated by read length, read quality, total number of reads, percentage of organelle DNA content, heterogeneity of organelle base coverage, and other factors. The automatically estimated Word size tends to be small to enhance the success rate, but possibly at the cost of increasing the computational burden Fig.

Importantly, though, the automatically estimated Word size does not guarantee the best performance in assembly results see eight plastome samples with customized parameters in our tests nor in computational costs. GetOrganelle uses SPAdes as the core de novo assembler, which allows the user to use a k -mer gradient for assembly.

One advantage of this is that SPAdes combines the assemblies from multiple k- mers. Someone might draw a specious conclusion that the base coverage is usually high enough to use the largest k -mer for assembling the complete plastome or mitogenome from WGS data.

However, except for those with low base coverage for the plastome e. For example, if only one large k- mer value was used for each run i. Another advantage of using a k -mer gradient is that GetOrganelle could iteratively attempt to disentangle the assembly graph of each k- mer from the largest to the smallest, then find a larger one with the organelle-sufficient graph. A larger k- mer value is preferable when there are longer repeats and coverage is sufficiently high. However, they only used the default options for evaluation, which are designed to have high chloroplast genome completion rate, rather than high computational efficiency.

However, we do not recommend these options. Firstly, in the current version of GetOrganelle, the complete circular assemblies are better than incomplete results, justifying a reasonable tradeoff of assembly accuracy against computation speed.

Secondly, extremely high Word size may generate higher error rate in assemblies with two cases in the 50 plant datasets i. GetOrganelle is a fast and versatile toolkit for de novo assembly of complete and accurate organelle genomes using low coverage WGS data. Our evaluations show that the GetOrganelle toolkit can efficiently and accurately assemble different types of organelle genomes from a broad range of organisms.

In general, compared with NOVOPlasty, GetOrganelle has far better success rates for assembling plastomes while consuming similar or even less computational resources.

Additionally, GetOrganelle-reassembled plastomes generally have much higher accuracy than those reassembled by NOVOPlasty or published ones that were assembled by various tools in accordance with the read mapping evaluation. GetOrganelle can also generate all possible configurations when plastomes or mitogenomes have flip-flop configurations or other isomers mediated by repeats.

Potential applications of GetOrganelle include quickly extracting organelle genomes from whole genome assemblies and evaluating organelle genome quality. Assembling organelle genomes from metagenomic data would also be possible by using a customized database and scheme. The maximum extending length option enables rough control of the length of the target assembly, which could be used to quickly assemble interesting loci or genes from the metagenomic and transcriptomic data.

Additionally, the Python Classes and Functions defined in GetOrganelleLib could be used to manipulate and disentangle non-organelle assembly graphs. Currently, GetOrganelle exports all possible configurations without using library information of the paired-end reads. However, the long insert size library or long-read sequencing data can be used for repeat resolution and configuration verification. A function that could use this information and estimate the proportion of all the potential isomers configurations is expected in a future version of GetOrganelle.

Improvements in the seed databases and the label databases are also expected, which should result in better parameter estimation and higher success rates in assembling mitogenomes.

GetOrganelle v1. In this extension process, the key comparison method for determining overlaps is classic substring hashing. Substring s are referred as Word s here to distinguish them from k -mers, a similar concept in the assembly process.

The uniform length of the Words is thus named as Word size. Pre-grouping is an algorithm for speeding up target-read recruitment.

This algorithm is based on the idea that it would be more efficient to firstly compare reads that are more likely to be target-associated. Given that the organelle genomes usually have more copies, and hence higher base coverage than most non-organelle chromosomes, the duplicated reads are more likely to be organelle-associated than non-duplicated reads. Any groups, including those with only a single read, will have a hash table storing Words chopped from all reads of this group.

Any two groups sharing at least a single Word in their hash table will be merged. During the following extension iterations, once a read is accepted as a target-associated read, all other reads ids in the same group will be marked as acceptable.

As mentioned above, all other read ids in the same group will be treated as accepted. The best Word size for extension is affected by read length, read quality, total number of reads, percentage of organelle genome reads, heterogeneity of organelle base coverage, and other factors. The recruited target-associated reads will be then automatically assembled using SPAdes Fig.

Both paired and unpaired reads will be used. The outputs of each k- mer of SPAdes include an assembly graph FASTG format , which records the connections of contigs as a graph with some allelic polymorphism and assembly uncertainty. Other assemblers that are able to generate the assembly graph, such as Velvet, may be used for completing this step, but are not yet implemented in GetOrganelle.

Because sequences are often shared among plastomes, mitogenomes, and nuclear genomes, the accepted reads from step 2 sometimes unavoidably include non-target reads. As a consequence, the output assembly graph might also include non-target contigs. However, previously reported tools did not account for or adequately addressed this concern. GetOrganelle searches for the target-like contigs from the original assembly graph file by jointly using the contig label table, contig connections, and contig coverages Fig.

For GetOrganelle, the default label database of a certain organelle is made from the coding regions of that organelle genome. We created six default label databases that correspond to the six types of organelle genome in the seed databases. Any contig that is directly or indirectly connecting to that target-hit-contig is called a target-associated-contig.

Here, we define a group of interconnected contigs as a connected component of the assembly graph. GetOrganelle by default retains all connected components with target-hit-contig s. Generally, this roughly filtering step is designed to be conserved and avoiding removing true target contigs.

GetOrganelle then uses the simplified assembly graph file and the contig label table to 5a further accurately identify narrow down to target organelle contigs, 5b estimate multiplicities copy number of contigs in an organelle-only graph, and 5c export all possible distinctive path s [stored as FASTA file s ] from the organelle assembly graph stored as a cognominal GFA format file Fig.

Each path represents a possible configuration of the target organelle genome. In case of organelle genome with a large number of repeats, GetOrganelle sets up an option for limiting the calculation time of disentangling to avoid generating inexhaustible combinations.

GetOrganelle requires three assumptions to disentangle the assembly and declare the result as a complete circular organelle:. Assumption 1 : All configurations, if there are more than two, of the target organelle genome are compositionally identical. This assumption limits the multiplicities of contigs to be the same among different configurations. In other words, polymers are found in real plastid DNA molecules [ 55 ], whereas GetOrganelle can only export the monomer form; potential sub-genomic configurations are currently not implemented in the current version.

If there are parallel contigs caused by nucleotide polymorphism, all subgraphs composed of any of those polymorphisms will be disentangled independently. Therefore, all configurations of each subgraph will be compositionally identical. Assumption 2 : The topology of each organelle genome will be represented as a single circular molecule. This assumption holds when the real organelle genome is a circular molecule or organized in polymers most plastomes, and type I and type II mitogenomes and the assembly graph is an organelle-sufficient graph.

If this assumption is violated, GetOrganelle only exports the target contigs. Assumption 3 : The coverage values of contigs of the same organelle genome are generally proportional to their multiplicities copy numbers.

Therefore, coverage values of contigs with the same multiplicity of the same organelle genome generally are consistent. Some contigs, including mitochondrial contigs that have short sequence of plastome origin or target-like shallow-depth contaminant contigs, would be labeled incorrectly as target-hit-contigs false positive. On the other hand, some sequences might be true target contigs but are too short or divergent from sequences in the label database to be labeled as target-hit-contigs false negative.

Therefore, we used additional information to improve the identification of target contigs, such as the assembly graph characters Assumptions 1 and 2 and contig coverage values Assumption 3.

GetOrganelle uses an integrated strategy that iteratively uses all or part of the following modules to approach this task until no more changes are going to be made to the assembly graph. For records representing the same gene in the local BLAST database, only the record with the best HW is kept as the only valid hitting record for that gene. Given our experience that the false positive hits generally correspond to shorter length and shallower depth contigs, HW can be a criterion for excluding the false positive hit records.

For example, the plastid CW for a contig is defined as the sum of the HWs of all plastid gene hit records of that contig, while the mitochondrial CW for the same contig is defined as the sum of the HWs of all mitochondrial gene hit records of the same contig. For a contig, if the target CW is much larger default factor: 3 times than the non-target organelle CW, this contig would be labeled as a target-anchor contig very likely to be a true target contig , and vice versa. Adding more target labels to some target contigs that do not hit the label database according to assembly graph characters.

Based on Assumption 2, any configuration of the target organelle genome is a single circular molecule. As a result, in an organelle-sufficient graph, both ends of any true target contig should be connected to at least one true target contig.

If the tail end of a true target contig, Contig A marked as A tail has only one edge that connects A tail and the head end of another unknown contig, Contig B B head , then Contig B should be a true target contig.

However, in a real assembly graph with missing contigs incomplete organelle genome , Contig B may be missing and the unknown contig connected to A tail may be a non-target contig. Using coverage values of contigs to remove contigs with coverage value that significantly deviates from the target-anchor contigs.

Based on Assumption 3, GetOrganelle uses the Gaussian mixture distribution to approximate the coverage values of all contigs in the simplified assembly graph, which is a mixture of different organelle contigs and nuclear contigs. In most cases of empirical plant genome skimming data, the plastome has significantly higher coverage than the mitogenome, the coverage of which in turn is higher than the nuclear genome except for highly repeated regions. Therefore, in a plant WGS dataset, the coverage values of plastid and mitochondrial and nuclear contigs are expected to be classified into different Gaussian components of the Gaussian mixture distribution.

GetOrganelle could thus delete the contigs with coverage value far from the target coverage distribution. Specifically, GetOrganelle applies an EM Expectation-Maximization algorithm with the semi-supervised learning and the weighted Gaussian mixture model to cluster the coverage values of all candidate contigs. Here, the semi-supervised learning means that the coverage values of the target-anchor contigs the labeled data are not updated during EM iterations.

The coverage value of a contig in the Gaussian mixture model is weighted by the length of the contig. Removing contigs isolated from the main target connected component that includes the target-anchor contigs.

Based on Assumption 2, true target contigs in an organelle-sufficient assembly graph should occur in one connected component. Thus, for a real organelle-sufficient assembly graph, GetOrganelle retains the connected component with the most target-anchor contigs and deletes other such connected components.