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[ bullseye ]
[ 原始碼： r-bioc-bitseq ]
套件：r-bioc-bitseq（1.34.0+dfsg-1）
r-bioc-bitseq 的相關連結
Debian 的資源：
下載原始碼套件 r-bioc-bitseq：
- [r-bioc-bitseq_1.34.0+dfsg-1.dsc]
- [r-bioc-bitseq_1.34.0+dfsg.orig.tar.xz]
- [r-bioc-bitseq_1.34.0+dfsg-1.debian.tar.xz]
維護小組：
外部的資源：
- 主頁 [bioconductor.org]
相似套件：
transcript expression inference and analysis for RNA-seq data
The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression.
其他與 r-bioc-bitseq 有關的套件
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- dep: libc6 (>= 2.29)
- GNU C 函式庫：共用函式庫
同時作為一個虛擬套件由這些套件填實： libc6-udeb
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- dep: libcurl4 (>= 7.18.0)
- easy-to-use client-side URL transfer library (OpenSSL flavour)
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- dep: libgcc-s1 (>= 3.4.4)
- GCC 支援函式庫
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- dep: libgomp1 (>= 6)
- GCC OpenMP (GOMP) support library
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- dep: libstdc++6 (>= 5.2)
- GNU Standard C++ Library v3
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- dep: r-api-4.0
- 本虛擬套件由這些套件填實： r-base-core
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- dep: r-api-bioc-3.12
- 本虛擬套件由這些套件填實： r-bioc-biocgenerics
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- dep: r-base-core (>= 4.0.3-1)
- GNU R core of statistical computation and graphics system
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- dep: r-bioc-iranges
- GNU R low-level containers for storing sets of integer ranges
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- dep: r-bioc-rhtslib (>= 1.15.5)
- HTSlib high-throughput sequencing library as GNU R package
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- dep: r-bioc-rsamtools (>= 1.99.3)
- GNU R binary alignment (BAM), variant call (BCF), or tabix file import
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- dep: r-bioc-s4vectors
- BioConductor S4 implementation of vectors and lists
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- sug: r-bioc-biocstyle
- standard styles for vignettes and other Bioconductor documents
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- sug: r-bioc-deseq
- GNU R differential gene expression analysis
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- sug: r-bioc-edger
- Empirical analysis of digital gene expression data in R