BOL

github.com - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC...

github.com - The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible...

github.com - Run a pipeline processing fast5s to a consensus in a single command. Recommended fixed "standard" and "fast" pipelines. Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath. Seemless...

github.com - ClinCNV detects CNVs in germline and somatic context in NGS data (targeted and whole-genome). We work in cohorts, so it makes sense to try ClinCNV if you have more than 10 samples (recommended amount - 40 since we estimate variances from...

github.com - Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a...

benjjneb.github.io - The DADA2 tutorial goes through a typical workflow for paired end Illumina Miseq data: raw amplicon sequencing data is processed into the table of exact amplicon sequence variants (ASVs) present in each sample. The DADA2...

github.com - Ktrim is written in C++ for GNU Linux/Unix platforms. After uncompressing the source package, you can find an executable file ktrim under bin/ directory compiled using g++ v4.8.5 and linked with libz...

github.com - gget is a free, open-source command-line tool and Python package that enables efficient querying of genomic databases. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of...

biokit.readthedocs.io - BioKit is a set of tools dedicated to bioinformatics, data visualisation (biokit.viz), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis...

For a beginner this can be is the hardest part, it is also the most important to get right. It is possible to create a vector by typing data directly into R using the combine function ‘c’ x same as x creates the vector x...