Samtools macos homebrew9/6/2023 ![]() sra2fastq(), which converts SRA files into FASTQ format.sra_download(), which can download RNA-seq data from public repositories.These tools can allow users to run the exact tools that suit their needs. Using BP4RNAseq as a toolbox for RNA-seq analyses and customizing gene expression quantification to improve the sensitivity and accuracy of RNA-seq analyses.īP4RNAseq offers individual tools to users. Users should place all the fastq files in the work directory. It needs two nontechnical parameters at a minimum, i.e., taxa as explained above and pairwhich specifies the sequencing type with single for single-end (SE) reads or paired for paired-end (PE) reads. However, an option is given to the users to provide the adapter sequence if they know it.įastq2quan works with local RNA-seq data in fastq formats. During the quality control procedure, if the contamination of the adapter exists, the program will automatically detect the adapter sequence to trim. Will download the public RNA-seq data of two ‘BioSample’ with accession id “SRR11486115” and “SRR11486114”, respectively, and the latest reference genome, transcript and annotation data of Drosophila melanogaster, do the quality control (filter out the poor-quality reads and contaminations), reads alignments and gene expression quantification based on both alignment-free and alignment-based workflows in the work directory. ![]() Last but not least, the package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses based on the Alevin algorithm. Users can also use the package as a toolbox to run the exact tools that suit their needs. This can allow users to inspect intermediate outputs and thus to further improve the accuracy and sensitivity of RNA-seq analyses. Third, it offers individual tools to provide users full control to fine tune precisely how individual steps are optimized. Second, it improves the accuracy and sensitivity of RNA-seq analyses by using an optimized pipeline. It can take only two nontechnical parameters and output six formatted gene expression quantification at gene and transcript levels. First, the package is a highly automated tool. The package offers several benefits to researchers. ![]() The goal of BP4RNAseq is to make the RNA-seq analysis smooth and easy and to minimize efforts from researchers. These steps and the details that they involve are even tedious for bioinformatic scientist. For example, when working with public RNA-seq data, researchers need to download the RNA-seq data, convert data to FASTQ format, check the sequencing type (i.e., single-end or pair-end), do the quality control (when needed, trim adapters and poor quality reads), download the reference genome, transcript and annotation file, align reads to the reference genome or transcript and quantify gene expression, etc. However, processing raw reads of RNA-seq data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Retrospectively analyzing these data or conducting a brand new RNA-seq study is fundamentally important for researchers. An enormous volume of RNA-seq data have been accumulating and deposited in public data repositories, such as the Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA). ![]() RNA-sequencing (RNA-seq) now is the routine to assess the genome wide gene expression due to its high speed, accuracy and reproducibility, and low cost. The assessment of gene expression is central to uncovering the functions of the genome, understanding the regulation of development and investigating the molecular mechanisms that underlie cancer and other diseases. ![]()
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