minimap -t 24 assembly.fasta long_reads.fastq.gz | racon -t 24 long_reads.fastq.gz - assembly.fasta racon_assembly.fasta
nucmer -p nucmer assembly.fasta racon_assembly.fasta
show-snps -C -T -r nucmer.delta

This reports Racon's changes in a table. You can exclude indels with the -I option in show-snps. 

This process (Racon -> MUMmer -> SNP table) solves the problem I originally raised in this issue. So as far as I'm concerned, you can close this issue (or keep it open if you still want to implement some kind of variant table).

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features. 

Reference: 

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.

Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

Address of the bookmark: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

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.

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

Source: Life technologies

Address of the bookmark: http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264