Data Science & Machine Learning

Kallisto vs. STAR: Alignment and Quantification of Bulk RNA-seq Data

Kriti Srivastava
May 29, 2023

The accuracy of alignment and quantification methods for bulk RNA-seq data processing can impact downstream analysis, such as differential expression analysis, functional annotation, and pathway analysis. Alignment refers to mapping the sequence reads to a reference genome or transcriptome. In contrast, quantification refers to estimating the abundance of transcripts or genes based on the aligned reads. Inaccurate alignment or quantification can lead to false positives or false negatives in downstream analyses, resulting in incorrect conclusions.

Therefore, it is crucial to use alignment and quantification methods that are both accurate and efficient to ensure reliable analysis of bulk RNA sequencing data.

Tools for Bulk RNA-seq Data Alignment

Several methods are available for alignment and quantification, such as BWA, Salmon, Kallisto, and STAR, which have been developed to address the challenges posed by the high-throughput sequencing data generated by bulk RNA sequencing. These methods employ different algorithms to align and quantify RNA-seq reads. Each has advantages and limitations, depending on the experimental design and data quality of the analyzed RNA-seq data. This blog explores two popular tools, Kallisto and STAR, shedding light on their features and functionalities.

What is Kallisto?

  • Kallisto is a lightweight and fast tool for RNA sequencing analysis that uses a pseudoalignment algorithm to determine the abundance of transcripts in a sample.
  • The final output of Kallisto generates both transcripts per million (TPM) and estimated counts.

What is STAR?

  • STAR, on the other hand, is a more traditional alignment-based tool that maps RNA-seq reads to a reference genome or transcriptome using an alignment algorithm.
  • The final output of STAR is a table of read counts for each gene in the sample.

Kallisto Vs. Star - Feature-wise Comparison

Kallisto and STAR are two popular tools for analyzing bulk RNA-seq data, but they have different features and are better suited for different types of analyses. Here is a detailed comparison of their characteristics:

Features STAR Kallisto
Alignment Approach Traditional alignment approach. Can align reads to both reference genome and transcriptome. The pseudoalignment approach, which is faster and requires less memory than alignment, may not be as accurate for some applications.
Speed and Memory Usage 1. Amongst the complete aligners, STAR is an ultrafast aligner. However it is slower than Kallisto for quantification due to difference in alignment mechanisms.

2. STAR uses a more memory-intensive approach based on the Burrows-Wheeler transform
1. Much faster than STAR and requires lesser memory.

2. Kallisto can process tens of millions of reads in minutes.

3. It uses a memory-efficient data structure called a de Bruijn graph
Quantification Accuracy 1. STAR uses a straight forward counting approach unlike Kallisto which uses estimation and because of this way of counting, STAR will always be more accurate than Kallisto.

2. Can quantify gene expression levels based on read counts.

3. Suitable for analyzing samples with low expression levels.
1. Less accurate than STAR for quantifying gene expression levels.

2. It uses a maximum likelihood approach to estimate the number of reads originating from each transcript.

3. Can estimate transcript-level expression, i.e., may be better suited for analyzing samples with high levels of isoform diversity.
Compatibility with Splice Junctions 1. Better suited for analyzing data from samples with novel splice junctions or alternative splicing events.

2. Can detect and quantify splicing events that are not present in the reference genome
1. Kallisto is not splicing aware.

2. Kallisto relies on transcriptome annotation to quantify gene expression.
Compatibility with Other Downstream Tools Better suited for downstream analyses that rely on the alignment, such as variant calling or fusion gene detection and DEGs analysis. Better suited for quantification on the transcript (isoform) level.

Impact of Experimental Design and Data Quality on the Choice of Alignment and Quantification Method

Experimental design and data quality can significantly impact the alignment and quantification method choice between Kallisto and STAR for bulk RNA-seq data analysis. Let's take a detailed look at this.

Experimental Design:

  • Sample Size: Kallisto's fast and memory-efficient pseudoalignment approach is well-suited for large-scale studies with many samples. However, if computational resources are not a concern and the study involves a small number of samples, STAR's more accurate alignment and quantification may be preferred.
  • Transcriptome Completeness: If the transcriptome is well annotated and complete, Kallisto's pseudoalignment approach can quickly and accurately quantify gene expression levels. However, if the transcriptome is incomplete or contains many novel splice junctions, STAR's traditional alignment approach may be more suitable.

Data Quality:

  • Read Length: Kallisto performs well with short read lengths, while STAR may be more suitable for longer read lengths. This is because longer reads can help identify novel splice junctions and improve alignment accuracy.
  • Library Complexity: Libraries with high complexity may require more accurate alignment and quantification methods like STAR, while less complex libraries may be well-suited for Kallisto's pseudoalignment approach.
  • Sequencing Depth: Kallisto's pseudoalignment approach is less sensitive to sequencing depth than STAR's alignment-based approach. This means that Kallisto may be more suitable for analyzing samples with low sequencing depth, while STAR may be better suited for samples with high sequencing depth.


The choice between Kallisto and STAR relies on the experimental design and data quality of the RNA-seq data. Each tool has strengths and weaknesses, with the selection hinging on the analysis objectives. Kallisto is an excellent choice for swift and precise quantification of gene expression levels in bulk RNA-seq data. On the other hand, if the aim is to uncover novel splice junctions or detect fusion genes, STAR emerges as the superior option.

Elucidata's biomedical data platform, Polly, hosts the world's most extensive collection of highly curated, ML-ready bulk RNA seq data processed consistently using Kallisto. Our curation pipelines, high-quality, accurately annotated data, standard workflows, and scientific expertise are used by industries and academia across the globe to accelerate their drug discovery process.

Reach out to us to learn more about how to accelerate your research!

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