seqtk
seqkit
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seqtk
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Illumina adapters and quality trimming
seqtk: A lightweight and versatile tool for processing FASTQ and FASTA files. https://github.com/lh3/seqtk
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looking for a tool to filter non-coding regions/excise ORFs from a draft assembly
Perhaps seqtk could be helpful https://github.com/lh3/seqtk
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Help with understanding awk code
You could also check out tools specialized for FASTA processing like https://github.com/shenwei356/seqkit and https://github.com/lh3/seqtk
- !help
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Doubts with my first ever mRNA-seq QC analysis
If I were to analyze I would use a random fastq sampler like Seqtk and bring all your samples to a lowest read depth of your 27 libraries although I wouldn't analyze a library with less than 2mil reads. 5 mil is fine for differential, you can obviously get more reads and probably received more information but increasing read depth may plateau.
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Reverse sequencing of fastq file
It's a little toolkit written by one of the Illuminati of the Bioinformatics world: seqtk on GH
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[Help] Copying head of fastq file into a .txt file named .fastq, doesn't include the header resolving in an error when converting to .bam file.
I recommend installing seqtk, which makes this easy. Of course sed/awk/perl are theoretically entirely sufficient but why make life more difficult than necessary?
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Looking for small SRA Data Sets
Most SRA files are grouped by projects. On a basic level for something common like RNA-seq you will have replicates of the control and treatment/diseased samples. Each file (i.e. sample) contains raw sequencing reads, usually millions per sample. You could randomly subsample the sequencing reads very easily using many tools (common choice is https://github.com/lh3/seqtk). There is no way you are going to assemble an animal genome with MB file sizes (for example the human genome itself is already over 3GB in size). You should probably look for bacterial or viral DNA samples and subset those to an appropriate size.
seqkit
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A look at the Mojo language for bioinformatics
I've been thinking to learn Rust for these use cases, but always get frustrated with the complexity.
I find Go is a great middle-ground though! And now there starts to be a few more bio-related tools and toolkits out there, including:
- https://github.com/vertgenlab/gonomics
- https://github.com/biogo/biogo
- https://github.com/shenwei356/bio
... except from there being some really popular bio tools written in Go, like:
- https://github.com/shenwei356/seqkit
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Help with understanding awk code
You could also check out tools specialized for FASTA processing like https://github.com/shenwei356/seqkit and https://github.com/lh3/seqtk
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What are some good examples of well-engineered bioinformatics pipelines?
Seqkit - thoroughly maintained with extensive tutorials and benchmarking info - https://github.com/shenwei356/seqkit
What are some alternatives?
samtools - [Moved to: https://github.com/ingolia/SamTools]
sage - Proteomics search & quantification so fast that it feels like magic
htslib - C library for high-throughput sequencing data formats
rush - A cross-platform command-line tool for executing jobs in parallel
samtools - Tools (written in C using htslib) for manipulating next-generation sequencing data
rnaseq - RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
minimap2 - A versatile pairwise aligner for genomic and spliced nucleotide sequences
juicer - A One-Click System for Analyzing Loop-Resolution Hi-C Experiments
bam-filter - Use simple expressions to filter a BAM/CRAM file
spades - SPAdes Genome Assembler
MMseqs2 - MMseqs2: ultra fast and sensitive search and clustering suite
fasql - DuckDB Extension for reading and writing FASTA and FASTQ Files