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Seqtk Alternatives
Similar projects and alternatives to seqtk
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fastp
An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Synthetic_In_Silico_Genome_Generator
A library for generating and modifying genomes for the purposes of testing Bioinformatics software and methods.
seqtk reviews and mentions
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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
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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.
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
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lh3/seqtk is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of seqtk is C.
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