Alignment-free isoform quantification software tools | RNA sequencing data analysis
Traditional quantification algorithms that use alignments of sequencing reads to the genome or transcriptome require substantial computational resources and do not scale well with the rate at which data are produced. Sailfish achieved an order-of-magnitude improvement in speed by replacing traditional read alignment with the allocation of exact k-mers to transcripts. Kallisto achieves similar speed improvements and further reduces the gap in accuracy with traditional alignment based methods by replacing the k-mer-counting approach with a procedure called pseudoalignment, which is capable of rapidly determining the set of transcripts that are compatible with a given sequenced fragment.
Allows users to quantify abundances of transcripts from RNA-Seq data and target sequences using high-throughput sequencing (HTS) reads. kallisto is based on pseudo-alignment concept to determine the compatibility of reads with targets. In test, this tool is able to treat over 30 million human reads using the read sequences and a transcriptome index.
Avoids mapping reads entirely, resulting in large savings in time and space, and substantially reducing parametric complexity. Sailfish is a software for isoform quantification from RNA-seq data. It is based on the philosophy of lightweight algorithms, which make frugal use of data, respect constant factors and effectively use concurrent hardware by working with small units of data where possible.
A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.
Offers a way to manage pipelines. Toil supports arbitrary worker and leader failure, with strong check-pointing that allows resumption. It can be employed to run scientific workflows on a large scale in cloud or high-performance computing (HPC) environments. This tool was used to compute gene- and isoform- level expression values for 19 952 samples from four studies.
Estimates the relative abundance of circular RNA transcripts from high-throughput RNA-seq data. Sailfish-cir transforms circular transcripts to pseudo-linear transcripts. Comparing to count-based tools, it is superior in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNAdepleted) RNA-seq datasets. Several factors, such as gene length, amount of expression, and the ratio of circular to linear transcripts, had impacts on quantification performance of the tool.
Determines the transcript abundance levels in RNA-Seq data. RNA-Skim separates transcripts into clusters and employs bloom filters to identify all sig-mers for each transcript cluster, from which a small yet informative subset of sig-mers is selected to be used in the quantification stage. It selects sig-mer with uniform coverage. This tool does not support bias correction but produces results comparable with the state-of-the-art methods with bias correction on real data.
An agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI.