transcriptassembler
Science Score: 57.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README -
○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (13.6%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: EPegueroS
- License: mit
- Language: Nextflow
- Default Branch: dev
- Size: 22.1 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Introduction
transcriptassembler is a bioinformatics pipeline that performs de novo transcriptome assembly and extensive transcript annotation.
The pipeline is still under development. The plan is to incorporate the latest machine learning tools to annotate and predict the structure/properties of the resulting RNAs after assembly and the predicted proteins/peptides.
Pipeline diagram
mermaid
graph TD;
Input_reads-->FASTQC;
FASTQC-->UMITOOLS;
UMITOOLS-->FASTP;
FASTP-->FASTQC_TRIM;
FASTQC_TRIM-->TRINITY;
TRINITY-->BUSCO;
TRINITY-->TRANSDECODER;
TRANSDECODER-->DEEPSIG;
DIAMOND_MAKEDB-->DIAMOND_BLASTP;
STAR_GENOMEGENERATE-->STAR_ALIGN;
FASTP-->STAR_ALIGN;
Usage
[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with
-profile testbefore running the workflow on actual data:
bash
nextflow run transcriptassembler \
-profile test,docker \
--outdir <OUTDIR>
The previous command is equivalent to:
bash
nextflow run transcriptassembler \
-profile docker \
--input ./assets/samplesheet_test.csv \
--outdir <OUTDIR>
(The pipeline comes with a minimal test dataset defined in assets/samplesheet_test.csv)
Next, prepare a samplesheet with your input data that looks as follows:
samplesheet.csv:
csv
sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
RUN2,READS1.fastq.gz,READS2.fastq.gz
Each row represents a fastq file (single-end) or a pair of fastq files (paired end).
Now, you can run the pipeline using:
bash
nextflow run transcriptassembler \
-profile docker \
--input samplesheet.csv \
--outdir <OUTDIR>
[!WARNING] Please provide pipeline parameters via Nextflow
-params-fileoption. Custom config files including those provided by the-cNextflow option can be used to provide any configuration except for parameters; see docs.
For more details and further functionality, please refer to the usage documentation and the parameter documentation.
Pipeline output
Credits
transcriptassembler was originally written by Esteban Peguero-Sanchez, Moises Sotelo Rodrigues and Teresa Romero-Gutierrez.
Citations
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
This pipeline is based on the nf-core framework standards:
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Owner
- Name: Esteban Peguero-Sanchez
- Login: EPegueroS
- Kind: user
- Repositories: 1
- Profile: https://github.com/EPegueroS
Citation (CITATIONS.md)
# nf-core/transcriptassembler: Citations ## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/) > Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031. ## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/) > Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311. ## Pipeline tools - [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. - [fastp](https://www.ncbi.nlm.nih.gov/pubmed/30423086/) > Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1;34(17):i884-i890. doi: 10.1093/bioinformatics/bty560. PubMed PMID: 30423086; PubMed Central PMCID: PMC6129281. - [Trinity](https://pmc.ncbi.nlm.nih.gov/articles/PMC3571712/) > Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011 May 15;29(7):644-52. doi: 10.1038/nbt.1883. PMID: 21572440; PMCID: PMC3571712. - [BUSCO](https://academic.oup.com/bioinformatics/article/31/19/3210/211866) > GSeppey M, Manni M, Zdobnov EM. BUSCO: Assessing Genome Assembly and Annotation Completeness. Methods Mol Biol. 2019;1962:227-245. doi: 10.1007/978-1-4939-9173-0_14. PMID: 31020564. - [Transdecoder](https://github.com/TransDecoder/TransDecoder) > Haas, BJ. https://github.com/TransDecoder/TransDecoder - [DeepSig](https://pmc.ncbi.nlm.nih.gov/articles/PMC5946842/) > Savojardo C, Martelli PL, Fariselli P, Casadio R. DeepSig: deep learning improves signal peptide detection in proteins. Bioinformatics. 2018 May 15;34(10):1690-1696. doi: 10.1093/bioinformatics/btx818. PMID: 29280997; PMCID: PMC5946842. - [DIAMOND](https://pubmed.ncbi.nlm.nih.gov/25402007/) > Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015 Jan;12(1):59-60. doi: 10.1038/nmeth.3176. Epub 2014 Nov 17. PMID: 25402007. - [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/) > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924. ## Software packaging/containerisation tools - [Anaconda](https://anaconda.com) > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web. - [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/) > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506. - [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/) > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671. - [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241) > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241. - [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/) > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
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