Address of the bookmark: https://ccb.jhu.edu/software/stringtie/

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.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

The new features include

(i) building gene-based and supermatrix-based phylogenies using a single command,

(ii) testing and visualizing evolutionary models,

(iii) calculating distances between trees of different size or including duplications, and

(iv) providing seamless integration with the NCBI taxonomy database.

ETE is freely available at http://etetoolkit.org

Address of the bookmark: http://etetoolkit.org

Address of the bookmark: https://github.com/d3/d3/wiki/Gallery

Address of the bookmark: https://github.com/SysBioChalmers/RAVEN

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz. 

Address of the bookmark: https://clauswilke.com/dataviz/

Location where with post of Research Associate / Senior Research Fellow is available:
Bioinformatics Centre, Bose Institute in Kolkata, West Bengal

Title of the Project: "Setting up of National Facility on Interactive Graphics Computer System".

No. of the Post: 01 One

Salary: Rs. 22000/- per month plus admissible HRA and Medical Benefits for RA and Rs. 20000/- per month plus admissible HRA and Medical Benefits for SRF (Extended).

Age Limit: Max. 35 years for RA and Max. 33 years for SRF (Extended)

Required Job Profile:

Candidate must possess PhD degree in biological or chemical sciences with in depth understanding of protein structure and function; experience on protein crystallization and various modeling software will be advantages and candidate who have submitted PhD thesis can be considered as extended SRF.

The project would involve cloning, expression, crustallization of vibrio cholera toxin, Ace and modeling of its admissible HRA and medical benefits for SRF (Extended).

How to apply:

Eligible and interested candidates should need to appear before the selection committee along with typed application addressed to the above mentioned address along with biodata with complete details and attested copies of certificates on 02.03.2016 at 11:30 am. No TA or DA will be paid for attending the interview. No TA or DA will be paid for attending the interview.

Refer to http://www.boseinst.ernet.in/ADVT/15/p_30.pdf

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693