Transpi

TransPi is based on the scientific workflow manager Nextflow.

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Transpi

TransPi provides a useful resource for the generation of de novo transcriptome assemblies, with minimum user input but without losing the ability of a thorough analysis. TransPi requires various databases to run. The precheck script will installed the databases and software, if necessary, to run the tool. The precheck run needs a PATH as an argument for installing locally all the databases the pipeline needs. Once the precheck run is done it will create a file named nextflow. If selected, it will also have the local conda environment PATH. The nextflow. We recommend to run TransPi with the option --all where it will do the complete analysis, from raw reads filtering to annotation. Other options described below. If you combined multiple libraries of the same individual to create a reference transcriptome, which will be later use in downstream analyses e. Differential Expression , make sure the kmer list is based on the length for the shortest read library and the maxReadLen based on the longest read length. TransPi can also use docker, singularity, and individual conda installations i. Refer to Section 6 of this manual for further details on deployment of TransPi using other profiles. After a successful run of TransPi the results are saved in a directory called results.

We recommend to run TransPi with the option --all where it will do the complete analysis, from raw reads filtering to annotation, transpi. If selected, it will also have the local conda environment Transpi. Data The precheck is designed to create a new nextflow.

The use of RNA sequencing RNA-Seq data and the generation of de novo transcriptome assemblies have been pivotal for studies in ecology and evolution. This is especially true for nonmodel organisms, where no genome information is available. In such organisms, studies of differential gene expression, DNA enrichment bait design and phylogenetics can all be accomplished with de novo transcriptome assemblies. Multiple tools are available for transcriptome assembly, but no single tool can provide the best assembly for all data sets. Therefore, a multi-assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. To reduce errors in these complex analyses while at the same time attaining reproducibility and scalability, automated workflows have been essential in the analysis of RNA-Seq data.

The use of RNA-Seq data and the generation of de novo transcriptome assemblies have been pivotal for studies in ecology and evolution. This is distinctly true for non-model organisms, where no genome information is available. Nevertheless, studies of differential gene expression, DNA enrichment baits design, and phylogenetics can all be accomplished with the data gathered at the transcriptomic level. Multiple tools are available for transcriptome assembly, however, no single tool can provide the best assembly for all datasets. Therefore, a multi assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. To reduce errors in these complex analyses while at the same time attaining reproducibility and scalability, automated workflows have been essential in the analysis of RNA-Seq data. However, most of these tools are designed for species where genome data is used as reference for the assembly process, limiting their use in non-model organisms. We present TransPi, a comprehensive pipeline for de novo transcriptome assembly, with minimum user input but without losing the ability of a thorough analysis. A combination of different model organisms, k-mer sets, read lengths, and read quantities were used for assessing the tool.

Transpi

TransPi is based on the scientific workflow manager Nextflow. It is designed to help researchers get the best reference transcriptome assembly for their organisms of interest. It performs multiple assemblies with different parameters to then get a non-redundant consensus assembly. All these with minimum input from the user but without losing the potential of a comprehensive analysis. Figure 1. TransPi v1. For simplicity, this diagram does not show all the connections between the processes. Also, it omits other additional options like the BUSCO distribution and transcriptome filtering with psytrans see Section 2. TransPi documentation and examples can be found here.

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Default "results". Figures TransPi produces multiple figures that are stored in the results directory. Dismiss alert. It took years until the first widely accepted set of benchmarks beyond raw statistical evaluation became available e. RideHub: Ride Hailing Compare. Therefore, a multi-assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. TransPi requires various databases to run. Packages 0 No packages published. No need to call, message, or communicate at all. Carpool Kids: Family Calendar. Furthermore, a total of 49 non-model organisms, spanning different phyla, were also analyzed. Filtering Scenario:. Book with a friend If you want your friend to share the ride with you and guarantee a seat every day during the commute, just share your code with your friend and you both will never separate. Directories 4.

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Directories 4. Nature Biotechnology, 29, — Branches Tags. Future work. Test dataset We include a test profile to try TransPi using a small dataset. PDF recommendation. Requires options --host and --symbiont. Packages 0 No packages published. TransPi is based on the scientific workflow manager Nextflow. Matching System With our unique matching system, we will make sure that the students who share the ride with you are your best choices.