- Default Values
- Nested Workflows
|Identifies non-coding RNAs using Rfams covariance models|
|Aligns DNA query sequences against a protein reference database||
DIAMOND is a sequence aligner for protein and translated DNA searches,
designed for high performance analysis of big sequence data.
|Normalizes input sequences to FASTA using esl-reformat||
Normalizes input sequences to FASTA with fixed number of sequence characters per line using esl-reformat from https://github.com/EddyRivasLab/easel
|TransDecoder 2 step workflow, running TransDecoder.LongOrfs (step 1) followed by TransDecoder.Predict (step2)|
|Assesses genome assembly and annotation completeness with single-copy orthologs||
BUSCO v3 provides quantitative measures for the assessment of genome assembly, gene set, and transcriptome completeness, based on evolutionarily-informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB v9. BUSCO assessments are implemented in open-source software, with a large selection of lineage-specific sets of Benchmarking Universal Single-Copy Orthologs. These conserved orthologs are ideal candidates for large-scale phylogenomics studies, and the annotated BUSCO gene models built during genome assessments provide a comprehensive gene predictor training set for use as part of genome annotation pipelines. Please visit http://busco.ezlab.org/ for full documentation. The BUSCO assessment software distribution is available from the public GitLab project: https://gitlab.com/ezlab/busco where it can be downloaded or cloned using a git client (git clone https://gitlab.com/ezlab/busco.git). We encourage users to opt for the git client option in order to facilitate future updates. BUSCO is written for Python 3.x and Python 2.7+. It runs with the standard packages. We recommend using Python3 when available.
|Search a single protein sequence against a protein sequence database. (BLASTP-like)||
The phmmer and jackhmmer programs search a single protein sequence against a protein sequence database, akin to BLASTP and PSIBLAST, respectively. (Internally, they just produce a profile HMM from the query sequence, then run HMM searches.) Please visit https://github.com/EddyRivasLab/hmmer for full documentation. Releases can be downloaded from https://github.com/EddyRivasLab/hmmer/releases
|Runs InterProScan on batches of sequences to retrieve functional annotations.|