Scallop paper has been published at Nature Biotechnology. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at scalloptest.

Please also checkout the podcast about Scallop (thanks Roman Cheplyaka for the interview). It is available at both the bioinformatics chat and iTunes.

https://github.com/Kingsford-Group/scallop

Address of the bookmark: https://github.com/Kingsford-Group/scallop

Address of the bookmark: https://github.com/knausb/vcfR

CPAT - https://github.com/liguowang/cpat, http://lilab.research.bcm.edu/ (web server)

CPC2 - https://github.com/gao-lab/CPC2_standalone, http://cpc2.gao-lab.org/ (web server)

Infernal - http://eddylab.org/infernal/, https://github.com/EddyRivasLab/infernal

NCBI RefSeq - https://www.ncbi.nlm.nih.gov/refseq/

Rfam - http://rfam.xfam.org/, https://docs.rfam.org/en/latest/index.html

SILVA - https://www.arb-silva.de/

RNAmmer - http://www.cbs.dtu.dk/services/RNAmmer/ (web server, standalone download link)

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

• FASTQC: For quality control of raw RNA-seq reads.
• Trimmomatic: For trimming and filtering RNA-seq reads.
• HISAT2/STAR: High-performance aligners for RNA-seq reads.
• FeatureCounts: For quantifying gene expression.
• DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

• Trinity: For de novo transcriptome assembly.
• StringTie: For transcript assembly and quantification from RNA-seq alignments.
• TransDecoder: To predict coding regions within assembled transcripts.
• TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

• Infernal: For identifying ncRNA sequences based on covariance models.
• Rfam: Database and tools for ncRNA families.
• miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

• RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
• RNAstructure: Tools for RNA secondary structure prediction and analysis.
• MEME Suite: For identifying motifs in RNA sequences.
• IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

• REDItools: For RNA editing analysis.
• m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

• FLAIR: For isoform-level analysis of long-read RNA-seq data.
• NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

• RBPmap: For identifying RNA-binding protein motifs.
• PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

• getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
• ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

• IGV: Genome browser for visualizing RNA-seq alignments.
• Circos: Circular visualization of RNA-seq data.
• DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.

• BigDataScript – A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ paper-2014 | web ]
• Bpipe – A small language for defining pipeline stages and linking them together to make pipelines. [ web ]
• Common Workflow Language – a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ web ]
• Cromwell – A Workflow Management System geared towards scientific workflows. [ web ]
• Galaxy – a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ paper-2018 | web ]
• Nextflow (recommended) – A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ paper-2018 | web ]
• Ruffus – Computation Pipeline library for python widely used in science and bioinformatics. [ paper-2010 | web ]
• SeqWare – Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ paper-2010 | web ]
• Snakemake – A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ paper-2018 | web ]
• Workflow Descriptor Language – Workflow standard developed by the Broad. [ web ]

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/IONiseR/inst/doc/IONiseR.html

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

It installs in any computer even if you don't have administrator rights!

Address of the bookmark: http://www.dnabaser.com/download/DNA-Counter/index.html

https://function.princeton.edu/