| Name | Version | Summary | date |
| transit1 |
3.3.12 |
TRANSIT is a tool for the analysis of Tn-Seq data. It provides an easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality in a single condition as well as between conditions. |
2024-12-13 22:14:11 |
| kitikiplot |
0.1.2 |
A Python library for visualizing sequential and time-series categorical Sliding Window data. |
2024-12-07 09:36:56 |
| blobtk |
0.5.9 |
Core utilities for BlobToolKit. |
2024-11-26 10:52:53 |
| genome_info |
1.0.7 |
Python package genome annotations |
2024-11-20 22:30:52 |
| transit2 |
1.1.6 |
TRANSIT2 is a tool for the analysis of Tn-Seq data. It provides an easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality in a single condition as well as between conditions. |
2024-11-17 23:05:02 |
| gfftk |
24.10.30 |
GFFtk: genome annotation GFF3 tool kit |
2024-11-04 06:55:41 |
| buscolite |
24.11.3 |
busco analysis for gene predictions |
2024-11-04 06:26:38 |
| pyrodigal |
3.6.3 |
Cython bindings and Python interface to Prodigal, an ORF finder for genomes and metagenomes. |
2024-11-04 01:51:47 |
| tnseq-transit |
3.3.8 |
TRANSIT is a tool for the analysis of Tn-Seq data. It provides an easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality in a single condition as well as between conditions. |
2024-10-26 13:22:20 |
| baumeva |
0.7.1 |
Library for the solution of optimization problems with evolution algorithms |
2024-09-26 14:02:22 |
| grumps |
1.0.3 |
Genomic distance based Rapid Uncovering of Microbial Population Structures |
2024-09-16 17:07:14 |
| testgrumps |
1.0.0 |
Genomic distance based Rapid Uncovering of Microbial Population Structures |
2024-09-03 23:44:26 |
| pyrodigal-gv |
0.3.2 |
A Pyrodigal extension to predict genes in giant viruses and viruses with alternative genetic code. |
2024-08-15 14:43:43 |
| edugenome |
0.2.8 |
It consists of three genetic algorithms that are simply implemented with Python code for genetic algorithm training: creating a number sum of 20, creating (4, 4) images, and implementing linear regression. |
2024-08-06 08:40:24 |
| CrossMap |
0.7.3 |
CrossMap -- Lift over genomics coordinates between assemblies. |
2024-07-18 03:28:51 |
| cocopye |
0.5.0 |
Feature-based prediction of genome quality indices |
2024-07-17 22:59:24 |