Address of the bookmark: http://www.sthda.com/english/wiki/rna-seq-differential-expression-work-flow-using-deseq2

What Are Kallisto and Salmon?

At their core, both Kallisto and Salmon are tools for quantifying transcript abundance from RNA-Seq reads. They bypass traditional alignment-based methods, replacing them with pseudoalignment or quasi-mapping, which drastically speeds up the process.

• Kallisto was developed by Lior Pachter’s lab and introduced the concept of pseudoalignment using a de Bruijn graph.
• Salmon, developed by Rob Patro’s group, builds on this idea with quasi-mapping and offers additional features like advanced bias correction.

Head-to-Head Comparison

1. Algorithm

• Kallisto uses pseudoalignment, focusing on matching k-mers from reads to a transcriptome index.
• Salmon uses quasi-mapping, which adds more flexibility and can also work with aligned reads (BAM files).

2. Input and Flexibility

• Kallisto works with raw FASTQ reads and requires a custom transcriptome index.
• Salmon accepts FASTQ or pre-aligned BAM files, giving you more workflow options.

3. Bias Correction

One of Salmon’s major advantages is its sophisticated bias correction system. It corrects for:

• Sequence-specific bias
• Positional bias
• GC-content bias

Kallisto offers basic sequence bias correction but lacks the comprehensive models found in Salmon.

4. Speed and Resources

• Kallisto is blazing fast and slightly more memory-efficient.
• Salmon is still very fast, but the added features can come at a small computational cost.

5. Output and Downstream Analysis

• Both tools provide transcript-level quantifications and support bootstrapping for variance estimation.
• Salmon can also summarize counts at the gene level if provided with a mapping file (--geneMap).
• Kallisto integrates seamlessly with Sleuth for differential expression analysis.
• Salmon works well with tximport, DESeq2, edgeR, and other Bioconductor tools.

Choosing the Right Tool

Example Command Lines

Kallisto (paired-end):

kallisto quant -i transcriptome.idx -o output -b 100 sample_R1.fastq sample_R2.fastq

Salmon (paired-end, bias correction):

salmon quant -i salmon_index -l A -1 sample_R1.fastq -2 sample_R2.fastq \
  -p 8 --validateMappings --seqBias --gcBias -o output

Conclusion

Both Kallisto and Salmon are exceptional tools that have transformed RNA-Seq analysis. Your choice largely depends on your priorities—whether it's speed, accuracy, flexibility, or compatibility with downstream tools.

For many users, Salmon offers a more complete and flexible solution, especially when bias correction and gene-level outputs are essential. However, Kallisto remains a favorite for quick, accurate quantification, especially when paired with the Sleuth pipeline.

Analysis of regulatory sequences (RSAT project)
Classification and analysis of mobile genetic elements (ACLAME project).
Analysis of molecular interaction networks (NeAT project)
Inference of metabolic pathways from genomic and post-genomic data
(metabolic pathfinding, see also metabolic pathfinding in NeAT)
Critical assesment of protein interactions (CAPRI)

Lab Page http://www.bigre.ulb.ac.be/

• Assembly of nanopore reads using Canu.
• Polish canu contigs using racon (optional).
• Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
• Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

• dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en

• You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

Using AlignGraph

AlignGraph --read1 reads_1.fa --read2 reads_2.fa --contig contigs.fa --genome genome.fa --distanceLow distanceLow --distanceHigh distancehigh --extendedContig extendedContigs.fa --remainingContig remainingContigs.fa [--kMer k --insertVariation insertVariation --coverage coverage --part p --fastMap --ratioCheck --iterativeMap --misassemblyRemoval --resume]

Address of the bookmark: https://github.com/baoe/AlignGraph

The tool should be compatible with most UNIX flavors and has been successfully tested on the following operating systems:

• Mac OS X 10.11.1
• Mac OS X 10.10.3
• Ubuntu 14.04 LTS

Address of the bookmark: https://github.com/mculinovic/EAGLER