rnaseq

rnaseq

全面分析RNA测序数据流程

nf-core/rnaseq是一个用于分析RNA测序数据的开源生物信息学流程。它接收样本表和FASTQ文件作为输入,执行质量控制、修剪和比对,生成基因表达矩阵和质量报告。该流程支持多种比对和定量方法,提供全面的质量控制功能,包括读取、比对、基因类型、样本相似性和链特异性分析。适用于有参考基因组和注释的RNA-seq数据处理。

nf-core/rnaseqRNA测序生物信息学Nextflow基因表达Github开源项目

nf-core/rnaseq nf-core/rnaseq

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo

Nextflow run with conda run with docker run with singularity Launch on Nextflow Tower

Get help on SlackFollow on TwitterFollow on MastodonWatch on YouTube

Introduction

nf-core/rnaseq is a bioinformatics pipeline that can be used to analyse RNA sequencing data obtained from organisms with a reference genome and annotation. It takes a samplesheet and FASTQ files as input, performs quality control (QC), trimming and (pseudo-)alignment, and produces a gene expression matrix and extensive QC report.

nf-core/rnaseq metro map

  1. Merge re-sequenced FastQ files (cat)
  2. Sub-sample FastQ files and auto-infer strandedness (fq, Salmon)
  3. Read QC (FastQC)
  4. UMI extraction (UMI-tools)
  5. Adapter and quality trimming (Trim Galore!)
  6. Removal of genome contaminants (BBSplit)
  7. Removal of ribosomal RNA (SortMeRNA)
  8. Choice of multiple alignment and quantification routes:
    1. STAR -> Salmon
    2. STAR -> RSEM
    3. HiSAT2 -> NO QUANTIFICATION
  9. Sort and index alignments (SAMtools)
  10. UMI-based deduplication (UMI-tools)
  11. Duplicate read marking (picard MarkDuplicates)
  12. Transcript assembly and quantification (StringTie)
  13. Create bigWig coverage files (BEDTools, bedGraphToBigWig)
  14. Extensive quality control:
    1. RSeQC
    2. Qualimap
    3. dupRadar
    4. Preseq
    5. DESeq2
  15. Pseudoalignment and quantification (Salmon or 'Kallisto'; optional)
  16. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (MultiQC, R)

Note The SRA download functionality has been removed from the pipeline (>=3.2) and ported to an independent workflow called nf-core/fetchngs. You can provide --nf_core_pipeline rnaseq when running nf-core/fetchngs to download and auto-create a samplesheet containing publicly available samples that can be accepted directly as input by this pipeline.

Warning Quantification isn't performed if using --aligner hisat2 due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments. However, you can use this route if you have a preference for the alignment, QC and other types of downstream analysis compatible with the output of HISAT2.

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 test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1,fastq_2,strandedness CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,auto CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,auto CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,auto

Each row represents a fastq file (single-end) or a pair of fastq files (paired end). Rows with the same sample identifier are considered technical replicates and merged automatically. The strandedness refers to the library preparation and will be automatically inferred if set to auto.

Warning: Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Now, you can run the pipeline using:

nextflow run nf-core/rnaseq \ --input samplesheet.csv \ --outdir <OUTDIR> \ --genome GRCh37 \ -profile <docker/singularity/.../institute>

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow 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

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

This pipeline quantifies RNA-sequenced reads relative to genes/transcripts in the genome and normalizes the resulting data. It does not compare the samples statistically in order to assign significance in the form of FDR or P-values. For downstream analyses, the output files from this pipeline can be analysed directly in statistical environments like R, Julia or via the nf-core/differentialabundance pipeline.

Online videos

A short talk about the history, current status and functionality on offer in this pipeline was given by Harshil Patel (@drpatelh) on 8th February 2022 as part of the nf-core/bytesize series.

You can find numerous talks on the nf-core events page from various topics including writing pipelines/modules in Nextflow DSL2, using nf-core tooling, running nf-core pipelines as well as more generic content like contributing to Github. Please check them out!

Credits

These scripts were originally written for use at the National Genomics Infrastructure, part of SciLifeLab in Stockholm, Sweden, by Phil Ewels (@ewels) and Rickard Hammarén (@Hammarn).

The pipeline was re-written in Nextflow DSL2 and is primarily maintained by Harshil Patel (@drpatelh) from Seqera Labs, Spain.

The pipeline workflow diagram was initially designed by Sarah Guinchard (@G-Sarah) and James Fellows Yates (@jfy133), further modifications where made by Harshil Patel (@drpatelh) and Maxime Garcia (@maxulysse).

Many thanks to other who have helped out along the way too, including (but not limited to):

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #rnaseq channel (you can join with this invite).

Citations

If you use nf-core/rnaseq for your analysis, please cite it using the following doi: 10.5281/zenodo.1400710

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

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:

编辑推荐精选

讯飞智文

讯飞智文

一键生成PPT和Word,让学习生活更轻松

讯飞智文是一个利用 AI 技术的项目,能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定,还是学生群体的职业生涯规划、实习避坑指南,亦或是活动策划、旅游攻略等内容,它都能提供支持,帮助用户精准表达,轻松呈现各种信息。

AI办公办公工具AI工具讯飞智文AI在线生成PPTAI撰写助手多语种文档生成AI自动配图热门
讯飞星火

讯飞星火

深度推理能力全新升级,全面对标OpenAI o1

科大讯飞的星火大模型,支持语言理解、知识问答和文本创作等多功能,适用于多种文件和业务场景,提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台,涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能,能为不同需求的用户提供便捷高效的帮助,助力用户轻松获取信息、解决问题,满足多样化使用场景。

热门AI开发模型训练AI工具讯飞星火大模型智能问答内容创作多语种支持智慧生活
Spark-TTS

Spark-TTS

一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型

Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目,由多个知名机构联合参与。该项目提供了高效的 LLM(大语言模型)驱动的语音合成方案,支持语音克隆和语音创建功能,可通过命令行界面(CLI)和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数,生成高质量的语音。该项目适用于多种场景,如有声读物制作、智能语音助手开发等。

Trae

Trae

字节跳动发布的AI编程神器IDE

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

AI工具TraeAI IDE协作生产力转型热门
咔片PPT

咔片PPT

AI助力,做PPT更简单!

咔片是一款轻量化在线演示设计工具,借助 AI 技术,实现从内容生成到智能设计的一站式 PPT 制作服务。支持多种文档格式导入生成 PPT,提供海量模板、智能美化、素材替换等功能,适用于销售、教师、学生等各类人群,能高效制作出高品质 PPT,满足不同场景演示需求。

讯飞绘文

讯飞绘文

选题、配图、成文,一站式创作,让内容运营更高效

讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。

热门AI辅助写作AI工具讯飞绘文内容运营AI创作个性化文章多平台分发AI助手
材料星

材料星

专业的AI公文写作平台,公文写作神器

AI 材料星,专业的 AI 公文写作辅助平台,为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能,支持 30 + 文稿类型生成,助力快速完成领导讲话、工作总结、述职报告等材料,提升办公效率,是体制打工人的得力写作神器。

openai-agents-python

openai-agents-python

OpenAI Agents SDK,助力开发者便捷使用 OpenAI 相关功能。

openai-agents-python 是 OpenAI 推出的一款强大 Python SDK,它为开发者提供了与 OpenAI 模型交互的高效工具,支持工具调用、结果处理、追踪等功能,涵盖多种应用场景,如研究助手、财务研究等,能显著提升开发效率,让开发者更轻松地利用 OpenAI 的技术优势。

Hunyuan3D-2

Hunyuan3D-2

高分辨率纹理 3D 资产生成

Hunyuan3D-2 是腾讯开发的用于 3D 资产生成的强大工具,支持从文本描述、单张图片或多视角图片生成 3D 模型,具备快速形状生成能力,可生成带纹理的高质量 3D 模型,适用于多个领域,为 3D 创作提供了高效解决方案。

3FS

3FS

一个具备存储、管理和客户端操作等多种功能的分布式文件系统相关项目。

3FS 是一个功能强大的分布式文件系统项目,涵盖了存储引擎、元数据管理、客户端工具等多个模块。它支持多种文件操作,如创建文件和目录、设置布局等,同时具备高效的事件循环、节点选择和协程池管理等特性。适用于需要大规模数据存储和管理的场景,能够提高系统的性能和可靠性,是分布式存储领域的优质解决方案。

下拉加载更多