简约反应包合与转录分布（激流）
riptide的Python项目详细描述
涨潮
reactioninclusion by parsimony和transcriptd分布
对细菌的转录组学分析有助于我们了解它们对环境变化的反应。虽然传统的分析已经提供了信息，但是在基因组规模的代谢网络重建中利用这些数据集可以为代谢调控的变化提供路径利用和下游/上游分支的变化的更好的背景。以前的成绩单集成技术主要集中在与输入数据集建立最大一致性。然而，这些方法在代谢预测方面总体上表现不佳，甚至与转录不可知的通量最小化方法相比，后者在给定生长限制的情况下确定了最有效的代谢模式。我们的新方法，激流，结合了这些概念，并利用整体最小流量结合转录组分析，以确定最节能的途径，以实现增长，包括更高转录酶。涨潮需要低水平的人工干预，从而减少预测偏差。
使用时请注明：
Jenior ML, Moutinho TJ, and Papin JA. (2019). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. bioRxiv 637124; doi: https://doi.org/10.1101/637124
利用gapsplit流量采样器的python实现。请同时引用：
Keaty TC and Jensen PA (2019). gapsplit: Efficient random sampling for non-convex constraint-based models. bioRxiv 652917; doi: https://doi.org/10.1101/652917
依赖关系
>=python-3.6.4
>=cobra-0.15.3
>=pandas-0.24.1
>=symengine-0.4.0
>=scipy-1.3.0
安装
安装是：
$ pip install riptide
或来自github:
$ pip install git+https://github.com/mjenior/riptide
用法
fromriptideimport*my_model=cobra.io.read_sbml_model('examples/genre.sbml')transcript_abundances_1=riptide.read_transcription_file('examples/transcriptome1.tsv')transcript_abundances_2=riptide.read_transcription_file('examples/transcriptome2.tsv',replicates=True)riptide_object_1_a=riptide.contextualize(model=my_model,transcriptome=transcript_abundances_1)riptide_object_1_b=riptide.contextualize(model=my_model,transcriptome=transcript_abundances_1,include=['rxn1'],exclude=['rxn2','rxn3'])riptide_object_2=riptide.contextualize(model=my_model,transcriptome=transcript_abundances_2)
主要涨潮函数的附加参数：
riptide.read_transcription_file（）
file : string
User-provided file name which contains gene IDs and associated transcription values
header : boolean
Defines if read abundance file has a header that needs to be ignored
default is no header
replicates : boolean
Defines if read abundances contains replicates and medians require calculation
default is no replicates (False)
sep: string
Defines what character separates entries on each line
defaults to tab (.tsv)
riptide.contextualize（）
model : cobra.Model
The model to be contextualized (REQUIRED)
transcriptome : dictionary
Dictionary of transcript abundances, output of read_transcription_file (REQUIRED)
samples : int
Number of flux samples to collect, default is 500
norm : bool
Normalize transcript abundances using RPM calculation
Performed by default
fraction : float
Minimum percent of optimal objective value during FBA steps
Default is 0.8
minimum : float
Minimum linear coefficient allowed during weight calculation for pFBA
Default is None
conservative : bool
Conservatively remove inactive reactions based on genes
Default is False
bound : bool
Bounds each reaction based on transcriptomic constraints
Default is False
objective : bool
Sets previous objective function as a constraint with minimum flux equal to user input fraction
Default is True
set_bounds : bool
Uses flax variability analysis results from constrained model to set new bounds for all reactions
Default is True
include : list
List of reaction ID strings for forced inclusion in final model
exclude : list
List of reaction ID strings for forced exclusion from final model
标准输出报告示例：
Initializing model and integrating transcriptomic data...
Pruning zero flux subnetworks...
Analyzing context-specific flux distributions...
Reactions pruned to 285 from 1129 (74.76% change)
Metabolites pruned to 285 from 1132 (74.82% change)
Flux through the objective DECREASED to ~54.71 from ~65.43 (16.38% change)
Contextualized GENRE is concordant with the transcriptome (p=0.003)
RIPTiDe completed in 15 seconds
产生的激流物（类）属性：
- 模型-背景化基因组规模代谢网络重建
- 转录组-用户提供的转录组丰度
- 最小化系数-通量和最小化期间使用的线性系数
- flux样本-来自约束模型的流量样本
- 通量变化-来自约束模型的通量变化分析
- fraction_of_optimum-上下文化期间最佳目标通量的最小指定百分比
- 用户定义的-包含或排除在2元素词典中的用户定义的反应
- 一致性-抽样的线性系数和中值通量之间的spearman相关结果
其他信息
感谢您对激流的兴趣，如有其他问题，请发邮件至mljenior@virginia.edu。
如果您遇到任何问题，请file an issue连同详细说明。
“Riptide”是根据MIT许可证的条款发布的免费开源软件
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