Ubuntu
rockhopper package

rockhopper 2.0.3+dfsg2-3 source package in Ubuntu

Changelog

rockhopper (2.0.3+dfsg2-3) unstable; urgency=medium

  * Team upload.
  * Add example data as multi-orig tarball
  * New upstream version 2.0.3+dfsg2
  * Add autopkgtests
  * Add script to fetch test data
  * routine-update: Packaging update
  * routine-update: Standards-Version: 4.6.1
  * routine-update: Ready to upload to unstable

 -- Mohammed Bilal <email address hidden>  Thu, 14 Jul 2022 20:22:27 +0530

Upload details

Uploaded by:
Debian Med
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Mantic release universe misc
Lunar release universe misc

Builds

Kinetic: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
rockhopper_2.0.3+dfsg2-3.dsc 2.4 KiB e7b7cd292321dc99053eab25697821e7d58254683e07b2eaa7550deeab68f843
rockhopper_2.0.3+dfsg2.orig-debian-tests-data.tar.xz 30.8 MiB 479e267acfd79924201dc34ddad3b65ac57f716abf70306e1ea193b037d22d57
rockhopper_2.0.3+dfsg2.orig.tar.xz 439.2 KiB 6fd62d6c7ef7b1455a05093bf9701b2e4510e3ca4f986b2265834f29ea49fe3e
rockhopper_2.0.3+dfsg2-3.debian.tar.xz 10.4 KiB fd439a4023d147f6fe8eaae1de74e5a556ecf57fef34b5d8f4392849252cb2a2

Available diffs

No changes file available.

Binary packages built by this source

rockhopper: system for analyzing bacterial RNA-seq data

 Rockhopper is a comprehensive and user-friendly system for
 computational analysis of bacterial RNA-seq data. As input, Rockhopper
 takes RNA sequencing reads output by high-throughput sequencing
 technology (FASTQ, QSEQ, FASTA, SAM, or BAM files). Rockhopper supports
 the following tasks:
 .
  * Reference based transcript assembly (when one or more reference
    genomes are available)
    - Aligning reads to genomes
    - Assembling transcripts
    - Identifying transcript boundaries and novel transcripts such as
      small RNAs
  * De novo transcript assembly (when reference genomes are unavailable)
  * Normalizing data from different experiments
  * Quantifying transcript abundance
  * Testing for differential gene expression
  * Characterizing operon structures
  * Visualizing results in a genome browser