alignment-and-mapping

Automating read alignment involves processing paired-end FASTQ files against a reference genome to produce sorted, indexed BAM/CRAM artifacts. This pipeline handles end-to-end mapping, sorting, and indexing to ensure outputs are ready for downstream analysis.

Preparing RNA-Seq data for downstream analysis requires mapping raw reads to a reference transcriptome using tools like STAR or hisat2. The workflow generates sorted, indexed alignment files accompanied by mapping metrics and QC-ready artifacts.

This read alignment workflow supports both DNA and RNA sequencing analyses. It flexibly maps reads to reference genomes or transcriptomes using common tools like bwa, bowtie2, hisat2, and STAR, outputting standardized alignment artifacts.

Yes, you can use bwa and bowtie2 for DNA mapping within this flexible workflow. It recommends multiple alignment tools and applies post-processing for sorting, indexing, and generating QC-ready artifacts to ensure full reproducibility.

Yes, you need reference genomes and their corresponding indexes as required inputs alongside FASTQ files. The pipeline uses these to generate sorted, indexed CRAM files and accompanying mapping statistics for quality control.

Read alignment pipelines need post-processing with samtools or pysam to sort and index raw alignments. This step converts mapped reads into ready-to-use BAM/CRAM files and generates basic QC reports for downstream reliability.